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EFFECTS OF IONIZING RADIATION
United Nations Scientific Committee on the
Effects of Atomic Radiation
UNSCEAR 2006
Report to the General Assembly,
with Scientific Annexes

VOLUME I

UNITED NATIONSNew York, 2008

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NOTE
The report of the Committee without its annexes appears as Official Records of the General
Assembly, Sixty-first Session, Supplement No. 46 and corrigendum (A/61/46 and Corr. 1).
The report reproduced here includes the corrections of the corrigendum.
The designation employed and the presentation of material in this publication do not imply theexpression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area, or of its authorities, or concerning
the delimitation of its frontiers or boundaries.
The country names used in this document are, in most cases, those that were in use at the timethe data were collected or the text prepared. In other cases, however, the names have been updated,
where this was possible and appropriate, to reflect political changes.

UNITED NATIONS PUBLICATION
Sales No. E.08.IX.6
ISBN 978-92-1-142263-4

Corrigendum
Ref.: Sales No. E.08.IX.6
1 July 2009
ViennaEffects of Ionizing Radiation: United Nations ScientificCommittee on the Effects of Atomic Radiation 2006 Report
Volume 1
Corrigendum
Annex A
1.

Paragraph 541, line 9

For the existing model substituteEAR( a, y, s, D, e) = h( a, y, s, D, e) − h( a, y, s, 0, e)2.

				(7)

Paragraph 549, line 8

For the existing model substituteh0 (a, e, c, s) ⋅ 1 + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ1 ⋅1s= female + κ2 ⋅ ln[ a − e] + κ3 ⋅ ln[ a ]]

07-82087(14)

Corr. to Sales No. E.08.IX.6

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ANNEX A
Epidemiological studies of radiation and cancer
Contents
Page

INTRODUCTION............................................................................

17I.

FEATURES OF EPIDEMIOLOGICAL STUDIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A. Criteria for good-quality epidemiological studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B. Impact of dose level on statistical power and sample size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C. Impact of dose levels on the precision of risk estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
D. Impact of dose measurement error and other uncertainties on study associations . . . . . . . . . . . . . . .
E. Use of biodosimetry for epidemiological studies of radiation risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
F. Problem of multiple comparisons in epidemiological studies of radiation risk . . . . . . . . . . . . . . . . . . .
G. Measures of radiation risk, including lifetime risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
H. Transfer of radiation risk estimates between populations, and interactions of carcinogens . . . . . . . .
I. Impact of human genetic susceptibility on radiation risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
J. Effects of dose protraction or fractionation and radiation quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
K. Thresholds and other departures from linear–quadratic curvature . . . . . . . . . . . . . . . . . . . . . . . . . . . .
L. Effect of age at exposure, latency and time since exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19
19
21
24
25
26
28
29
29
32
33
37
40

II.

NEW OR UPDATED STUDIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A. Survivors of the atomic bombings in Japan (LSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B. Mayak worker study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C. Techa River study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
D. Semipalatinsk weapons test site fallout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
E. International worker study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
F
United States medical radiologic technologists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
G. Chinese radiologists and technologists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
H. Studies of aircrew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
I. Patients treated with radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
J. Worker and public exposure to uranium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45
45
47
48
49
49
50
51
51
52
53

III.

SITE-SPECIFIC CANCERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A. Total solid cancers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B. Salivary gland cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C. Oesophageal cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55
56
56
56
56
56
57
58
58
58

13

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D.

E.

F.

G.

H.

I.

J.

K.

L.

3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Stomach cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Transfer of risk estimates across populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cancer of the small intestine, including the duodenum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Colon cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rectal cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Liver cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pancreatic cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cancers of the trachea, bronchus and lung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. External low-LET exposures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. Internal low-LET exposures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Internal high-LET exposures (plutonium) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5. Internal high-LET exposures (Thorotrast and radium) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6. Internal high-LET exposures (radon) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7. Transfer of risk estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Malignant tumours of the bone and connective tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cutaneous malignant melanoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58
59
59
59
59
60
61
61
62
62
62
62
62
62
63
63
64
64
64
65
66
66
66
66
66
68
68
68
68
69
70
70
70
70
73
73
74
74
80
80
81
81
81
81
83
83
83
83
84
84

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

15

Page

M. Non-melanoma skin cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
N. Breast cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
O. Uterine cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
P. Ovarian cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
Q. Prostate cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
R. Cancer of the urinary bladder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
S. Kidney cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
T. Brain and central nervous system tumours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
U. Thyroid cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
V. Non-Hodgkin’s lymphoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

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IV.

W. Hodgkin’s disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
X. Multiple myeloma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Y. Leukaemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. General background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Summary of UNSCEAR 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. New or updated studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

110
110
111
111
111
111
111
112
112
113
113
113
114
114
120

LIFETIME RISK FOR TOTAL CANCER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A. Methods and assumptions of calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Risk models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Low-dose response, fractionation and dose-rate effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. Projection methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Populations, mortality rates and cancer incidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5. Transfer of risk estimates between populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B. Lifetime risk estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

123
123
123
126
128
129
129
129

CONCLUSIONS...........................................................................

137

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

139

TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

141

APPENDIX A.
SCORE TESTS AND STATISTICAL POWER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

259

APPENDIX B.
MEASURES OF RADIATION RISK, INCLUDING LIFETIME RISK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

262

APPENDIX C.
MODELLING OF DOSIMETRIC ERROR FOR THE DATA ON THE ATOMIC BOMBING SURVIVORS . . . . . . . .

264

APPENDIX D.
RISK MODELS FITTED TO THE ATOMIC BOMBING SURVIVOR DATA BY CLASSICAL,
LIKELIHOOD-BASED METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

265

APPENDIX E.
RISK MODELS FITTED TO THE ATOMIC BOMBING SURVIVOR DATA BY BAYESIAN MARKOVCHAIN MONTE CARLO METHODS, AND THEIR USE TO OBTAIN UNCERTAINTY BOUNDS ON
POPULATION RISK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

286

REFERENCES

290...........................................................................

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INTRODUCTION
1. Epidemiological studies of cancer risks associated withinternal and external exposure to ionizing radiation werereviewed extensively in the UNSCEAR 1994 and 2000
Reports [U4, U2]. The UNSCEAR 2000 Report assesseddata on cancer incidence and mortality up to 1990 among
the Life Span Study (LSS) cohort of survivors of the atomic
bombings in Japan [P1, P4, T1], as well as many studies
relating to other persons exposed occupationally, therapeutically or diagnostically.

3. Although resolving inconsistencies in the dosimetry forthe survivors of the atomic bombings has reduced one
source of uncertainty in estimating cancer risks to a population from low doses of radiation, a considerable numberof other sources of uncertainty remain. A major one relates
to extrapolating risks from the moderate-dose but highdose-rate exposures received by survivors of the atomic
bombings to low doses and dose rates. This is also true forinterpreting data on many therapeutically exposed groups.
The topic has long been controversial, and was discussed inannex G, “Biological effects at low radiation doses”, of the
UNSCEAR 2000 Report [U2]. There is also uncertainty
related to extrapolating cancer risk to the end of lifetime.
In particular, about half of the LSS cohort is at present still
alive [P10]. In estimating lifetime risk factors from the dataon this cohort, it is vital to determine the pattern between
radiation dose and expression of cancer risk for those who
were exposed in childhood and who are now reaching theage at which larger numbers of cancers would be expected
to arise spontaneously. Another source of uncertainty relates
to the transfer of radiation-induced cancer risk estimates
between populations with different underlying rates ofcancer. For example, the rates of lung and breast cancer for
the Japanese population tend to be lower than for many
North American and Western European populations,whereas rates of stomach cancer tend to be much higher[P19]. The available evidence, most recently reviewed in the
UNSCEAR 1994 Report [U4], did not suggest that there isan easy resolution of this problem.

2. The UNSCEAR 2000 Report presented cancer risk
estimates based on the LSS data and using the set of survivor dose estimates produced in the mid-1980s, the “DS86dosimetry” [R20]. For some time it was thought that theDS86 neutron doses for the survivors of the Hiroshimabombing were systematically underestimated, particularly
for survivors beyond 1,000 m from the hypocentre [R20,
S39]. This perception was largely based on the results ofmeasurements of thermal neutron activation products insamples taken from the city [S39]. The DS86 estimates forthe gamma doses at both Hiroshima and Nagasaki, as wellas the estimates for the neutron doses at Nagasaki [S40],
were thought to be more reliable than the estimates for theneutron doses at Hiroshima [R20]. Recent analysis of all
the information, including that on fast-neutron activation
products, suggests that there are no appreciable systematic
errors in the DS86 neutron dose estimates for Hiroshima[C13, R12, S41]. The latest set of dose estimates for thesurvivors of the atomic bombings, the “DS02 dosimetry”,
differs slightly from the DS86 system, for both neutronand gamma doses. The difference is generally no more than
20% for distances of up to 1,500 m from the two hypocentres, where the doses were greatest [C13, R12]. The DS02
estimates of colon doses due to neutrons were lower forboth cities but by no more than about 20% compared with
the DS86 estimates. The DS02 estimates were progressively lower relative to the DS86 estimates with increasing distance from the hypocentre; this was particularlymarked for Nagasaki [P10]. For Hiroshima survivors, theDS02 estimates for colon dose due to gamma radiation
were lower by about 10% compared with the DS86 estimates at all distances; for Nagasaki survivors, the estimatesfor colon dose within 1,800 m from the hypocentre wereabout 10% higher, but were somewhat less than 10%
higher for greater distances [P10]. Analyses of the LSS
epidemiological data using the DS02 dosimetry indicatethat cancer risk factors might be lower by about 8% as aresult, but with no appreciable change in the shape of thedose response or in the patterns of excess risk with age ortime [P10].

4. This annex presents the Committee’s reassessment of the
LSS data for the estimation of the risks of cancer and cancer
mortality due to radiation exposure, wherever possible
making use of the latest DS02 dosimetry and follow-up. Thisannex also contains assessments of all the evidence from studies of groups exposed therapeutically, diagnostically or occupationally. The Committee has made assessments of the risks
for cancer in a variety of organs, including the salivary gland,
oesophagus, stomach, small intestine (including duodenum),colon, rectum, liver, pancreas, lung, bone and connective
tissue, female breast, uterus, ovary, prostate, urinary bladder,kidney, brain and central nervous system, and thyroid, andfor cutaneous melanoma, non-melanoma skin cancer, nonHodgkin’s lymphoma, Hodgkin’s disease, multiple myeloma
and leukaemia. This somewhat extends the list of organ sites
from those considered in the UNSCEAR 2000 Report [U2].
The Committee has attempted to consider separately theuncertainties associated with estimation of cancer risks
arising from the sources listed above. As for the UNSCEAR
2000 Report, the Committee has assessed separately the risks
17

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arising from internal and external exposure, and from lowand high-linear-energy-transfer (LET) radiation. It has madeestimates of the population-averaged risks of cancer andcancer mortality for a variety of current populations. Theseestimates have been made using risk models fitted to the latest
mortality and cancer incidence data from the follow-up of
the survivors of the atomic bombings [P10, P48]; both setsof data use the latest DS02 dosimetry. The term incidence
has two uses in this annex: in a general sense, often tocontrast cancer incidence with cancer mortality, and in a
specific sense, where the incidence of a disease is the numberof cases of the disease that occur during a specified periodof time (usually a year). The incidence rate is this number
divided by a specified unit of population.

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I. FEATURES OF EPIDEMIOLOGICAL STUDIES
detailed histories of radiation exposure and other information(e.g. history of smoking), which may be difficult to collect
for a cohort, can be collected relatively easily for the specificcases and controls. The International Radiation Study of
Cervical Cancer Patients (IRSCCP) is an example of a seriesof nested case-control studies of the occurrence of a second
primary cancer in a cohort of women followed after treatment
for a first primary cancer of the cervix [B5, B7, B8]. Anotherform of study, not so frequently used, is the “case-cohort” or“case-base” study [P13], in which information is collected on
all cases with a certain disease status (e.g. cancer) as well as
on a sample of persons from the underlying cohort, sampled
without regard to their disease status. This type of study isparticularly useful when one is interested in a number ofdifferent end points, because one can reuse the cohort sample
for each disease end point under consideration. This study
design was used in an early analysis of the IRSCCP [H31].
Other, more novel designs, which generalize the above, haverecently been proposed [L38]. An RCT, if the randomizationis conducted properly, should not be subject to any bias, andis generally regarded as the epidemiological “gold standard”.
The case-control study is prone to more biases (e.g. recall
bias and investigation bias—see below) than the cohort study,
and for this reason cohort studies are regarded as the next
most reliable type of study after the RCT.

A. Criteria for good-quality epidemiological studies
5. Epidemiology is the study of the distribution and determinants of disease in human populations [M26]. It is by its
nature observational rather than experimental. In contrast to
randomized controlled trials (which are largely experimental in design), in epidemiological studies there is always thepossibility that biases or confounding factors of various sorts
may give rise to spurious results, as discussed in more detailbelow. A well-designed study should attempt to minimize
these. A good investigator will design a study to have adequate statistical power, and this is discussed in greater detailin section I.B below. Epidemiological studies are commonlyof two types: the cohort study and the case-control study. In
a “cohort study”, a defined population (preferably with awide range of radiation exposures) is followed forward intime to examine the occurrence of many possible health end
points. Such a study can be performed either prospectively,by following a current cohort into the future, or retrospectively, by using registers to construct a cohort of persons
alive at some time in the past, and then following it forward, possibly to the current time and beyond. The LSS of
the survivors of the atomic bombings in Japan is an example of a cohort study, partly retrospective and partly prospective in nature. The LSS data were assembled in the late
1950s using questions posed in the Japanese national census
of October 1950 to ascertain those persons who were ineither Hiroshima or Nagasaki at the time of the atomic
bombings. This cohort and other related cohorts were thenfollowed forward in time, and are still being followed up,
for mortality due to all causes [P1, P9, P10], cancer incidence [P4, P48, T1] and various other end points [O3, O4,
W17, Y3]. A “correlation study” is a particular type ofcohort study that is based on data averaged over groups, andin particular uses data grouped on exposure. In a “randomized controlled trial (RCT)”, people are assigned at random
to various groups before planned exposure to radiation (e.g.
radiotherapy treatment [F10]), and these groups are then followed up to assess their response to the treatment over somedefined period. An RCT may be regarded as a special formof cohort study; however, its essentially experimental design,
as opposed to the more observational design of most cohort
studies, should be noted. In a “case-control study”, data on
persons with some specified disease (e.g. some class of cancers) are assembled (the “cases”) together with data on asuitably matched (e.g. by age and sex) set of persons
otherwise similar to these cases but without the disease(the “controls”). These two groups are then compared toassess differences in the distribution of a number of exposure
variables. The advantage of a case-control study is that

6. “Bias” in a study may be defined as any process at any
stage in the conduct of the study that tends to produce results
or conclusions that differ systematically from the truth [S34].
One sort of bias is “follow-up bias”, which arises when there
is a lack of follow-up information, for example if persons
have, unknown to the investigator, migrated outside of thestudy area, so that their health status cannot be reported. In
this instance, they still apparently contribute to the numberof person-years (PY) of follow-up in the study, but in reality there is no chance of observing any detrimental effect totheir health, making them appear “effectively immortal”.
Unless corrected for, by censoring members of the study
cohort (i.e. stopping their contribution to the total number of
person-years) when they are lost to follow-up, estimates of
disease risks will generally be biased downwards and therefore be underestimates of the true risk. This form of bias
applies equally to cohort studies and case-control studies. It
is sometimes supposed that case-control studies are immune
to this bias, but this is not so; case and control selection will
be biased if certain members of the full cohort are not available to be selected. Related to follow-up bias is “ascertainment bias”, also sometimes known as “selection bias”, whicharises when there is variation in ascertainment of diseasestatus, perhaps correlated with exposure variables. For this19

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reason, much the strongest studies are those that rely on independently maintained registers of disease and health status,
e.g. the mortality and cancer incidence registers maintainedin many developed countries. As an example, certain
tumours, such as those of the thyroid, are notoriously difficult to detect, so that the recorded incidence of thyroid
cancer in a cohort will very much depend on the diligencewith which clinical examinations have been conducted in theunderlying cohort. In the LSS cohort of the survivors of theatomic bombings, the detection of thyroid tumours is betterin the higher-dose groups, because many people in thesegroups are subject to biennial screening [T1], as they arealso members of the Adult Health Study (AHS), a subcohort of the LSS. Unless corrected for, this ascertainment bias,
which is correlated with dose, would bias the slope of thedose–response curve upwards; however, in this case theascertainment bias can be corrected for by stratification of
the cohort according to membership in the AHS, and conducting a suitably adjusted analysis [T1]. Another example
of such bias occurred in a study of workers involved in therecovery from the Chernobyl accident, for whom a statistically significant increase in the incidence rate of leukaemia
was reported compared with the incidence rate for the general population [I5]. However, the workers received frequent
medical examinations, so that the accuracy and completeness of their leukaemia diagnoses are likely to differ fromthose for the general population. Indications that ascertainment biases may have produced this result come from a casecontrol study nested within the Chernobyl recovery operation
worker cohort, which found no evidence of an increase in
the incidence of leukaemia [I6]. Again, it should be pointed
out that ascertainment bias applies equally to both cohortand case-control studies. In the context of case-control studies, ascertainment bias can arise if the selection of cases orcontrols is influenced by exposure status. In such studies itis therefore important that there be comparable ascertainment
for cases and controls, and in particular that ascertainment
be as complete as possible for both groups. For example,
when it is necessary to approach potential study subjects, ortheir relatives, for interviews, it is important that the refusal
rate for both cases and controls be as low as possible.
7. It is sometimes necessary to approach cohort members,
or their relatives, to recall exposures. This is very likely tobe the situation when studies, in particular case-control studies, are organized retrospectively. “Recall bias” arises when
information, for example on exposure, is collected retrospectively, and patients, or their relatives, are subject to differential recall of this information, depending on theirdisease status. For this reason, much the strongest studies
are those that rely on independently maintained registers of
exposure, for example the registers of radiation dose thatare maintained for regulatory purposes for many cohorts ofnuclear workers [M12]. Related to recall bias is “investigation bias”, which results if investigators scrutinize exposures more thoroughly for cases than for controls. Althoughregister-based studies are not prone to recall or investigation bias, they are subject to errors due, for example, toinaccurate diagnostic information. To the extent that suchstudies should not be biased by knowledge of radiation
exposures, one would expect that random misclassification
due to inaccurate diagnosis would not affect values of theratio of the excess disease rate to the underlying disease
rate in the absence of radiation exposure, that is to say theexcess relative risk (ERR), although values of the excess
disease rate itself, or excess absolute risk (EAR), might bebiased, either positively or negatively.
8. A “confounding factor” is one that is correlated bothwith the disease under study and with an exposure of interest. Confounding factors can lead to bias. In many studies
there is no reason to expect correlations between mostfactors and the radiation exposure, so that confounding
ought not to be a problem. In studies of medical exposures,confounding may arise if the clinical indications that leadto the exposures are related to a subsequent diagnosis of
the relevant disease; this is sometimes referred to as“confounding by indication”. For example, in a study ofpatients administered 131I for diagnostic purposes, a slightly
elevated risk of thyroid cancer was observed [H14].
However, this risk was not related to dose and was concentrated among patients referred because of a suspected
thyroid cancer [H14], indicating that the apparent elevatedrisk was probably due to the underlying condition. There
are known to be correlations between smoking rate and theDS86 radiation dose among female survivors of the atomic
bombing of Hiroshima, although there are no such correlations for the male survivors in this city, or for either males
or females in Nagasaki [P14]. This may be connected withthe (statistically non-significant) indications that theradiation-associated excess relative risk (ERR) of lungcancer increases with increasing age at exposure and attained
age in this data set [L39], findings at odds with the customary reduction of ERR with increasing values of thesevariables [U2, U4]. Cigarette smoking is one of the mostserious confounding factors that have to be dealt with in epidemiological studies. As shown in table 1 (reproduced fromreference [P17]), the ratio of the disease rate to the underlying disease rate in the absence of the relevant exposure(in this case to cigarette smoke), i.e. the relative risk (RR),of lung cancer associated with cigarette smoking (which formoderate to heavy smokers generally exceeds 10 [P8, P17])
is much greater than the RR associated with exposure tohigh doses of radiation (which rarely exceeds 2). Therefore
even slight confounding by factors related to cigarettesmoking can seriously bias studies of lung cancer or othersmoking-related cancers. Confounding factors can usually be
dealt with at the analysis stage, either by incorporation ofsuch factors into the regression model, or by stratifying thedata according to levels of the confounding factor.
9. RCTs, cohort and case-control studies all use individual-related data, in particular data on individual
exposures. By contrast, correlation studies are based ondata averaged over groups, as noted above. A particularform of this type of study is the “geographical correlation
study” (often referred to as an “ecological study”), in
which disease rates based on data aggregated over

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geographical areas are compared with aggregated data onlevels of exposure, for example to natural radiation or to
man-made increases in environmental radiation levels. The
possibilities for bias in such studies are well known. The
principal cause of bias (sometimes termed “ecologicalbias”) is the failure to take account of correlations withineach area between multiple risk factors (e.g. radiation andsmoking) [G13, P15]. Examples of such studies include
ones of leukaemia [H32] and lung cancer [C14] in relation to environmental radon daughter exposure. The possibilities for bias in such studies are illustrated by a studyof lung cancer in relation to environmental radon daughter exposure in Sweden, which when analysed as a casecontrol study yielded a positive slope for lung cancer risk
versus radon daughter concentration, but when analysed asa correlation study, with grouped exposure estimates,
yielded a negative slope [L40].

B. Impact of dose level on statistical powerand sample size
10. The concept of statistical power and various factorsthat affect it were summarized in the UNSCEAR 2000
Report [U2] and have also been addressed in a recent report[B26]. However, a few points merit further elaboration and21

illustration, especially in relation to the dose levels in astudy. Under an assumption of a linear association between
radiation dose and the probability of cancer induction, thesample size required to detect a radiation effect with adequate statistical power (e.g. 80% power) is approximately
proportional to the inverse of the dose squared, or approximately proportional to the inverse square of the ERR coefficient (see appendix A). For example, if the dose
distribution is that among the survivors of the atomic bombings (table A1) and the anticipated ERR is 4.0 Sv–1 (similar to that observed for leukaemia mortality from the latest
follow-up of the LSS data [P10]), about 34 cancer deaths
would be needed in order for the probability of observing
a statistically significant (1-sided p = 0.05) excess risk to
be at least 80% (figure I). However, if the ERR is assumed
to be 0.4 Sv–1 (similar to that observed for solid cancer
mortality from the latest follow-up of the LSS data [P10]),765 cancer deaths would be needed for the excess to beobserved with the same probability (figure I). If the ERR
is assumed to be 0.04 Sv–1, about 50,000 cancer deaths
would be needed for the excess to be observed with thesame probability. Further calculations along these lines are
given in reference [B26]. If the dose–response relationshipwere instead linear–quadratic with an upward curvature,then the number of cancer deaths or cases needed to detect
radiation effects for the aforementioned low-dose studies
would be even larger.

Figure I. Influence of the ERR on the number of cancer deaths or cases required by a study to detect an increasing trendof risk with dose
The curves are for 80% power of detecting a statistically significant (1-sided p = 0.05) increasing trend of risk with dose. The assumed
distributions for colon and bone marrow doses are as in the latest LSS data (see table A1 in appendix A)

NUMBER OF DEATHS/CASES REQUIRED

100 000

DS02 colon dose distribution

10 000DS02 red bone marrow dose distribution

1 000

100

10
0

1

2

3

EXCESS RELATIVE RISK (Sv-1)

4

5

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Figure II. Influence of the average dose on the number of cancer deaths or cases required by a study to detect an increasing trend of risk with dose
The curves are for 80% power of detecting a statistically significant (1-sided p = 0.05) increasing trend of risk with dose. The assumed
distributions for colon and bone marrow doses are some multiple of those in the latest LSS data (see table A1 in appendix A)
100 000

NUMBER OF DEATHS/CASES REQUIRED

10 000DS02 colon dose distribution, solid cancer ERR = 0.467 Sv -1
DS02 red bone marrow dose distribution, leukaemia ERR = 3.968 Sv -1

1 000

100

10

1
0.0

0.5

1.0

1.5

2.0

AVERAGE DOSE (Sv)

11. A corollary of the large sample sizes needed at low
doses is that, for a given sample size, the statistical powerof a study is affected dramatically by the dose levels of the
exposed group. In this regard, most low-dose studies
reported in the literature have inadequate statistical power.
Figure II shows the influence of the average dose in a study
on the number of cancer deaths or cases needed to detectan excess risk. For example, at an average dose of 0.1 Sv(about that of the LSS, for both colon and bone marrowdose), and assuming an ERR for solid cancers of 0.467 Sv–1,
and for leukaemias of 3.968 Sv–1 (as observed from the LSS
data [P10]), about 700 solid cancer deaths or cases would
be needed to have an 80% power of observing a significant
excess (figure II), whereas only 43 leukaemia deaths would
be needed for this purpose. If the average dose is 1.0 Sv,
only 37 solid cancers and 9 leukaemias would be needed(figure II). If on the other hand the average dose is only
0.01 Sv, then the numbers needed increase to about 45,700
solid cancers and 910 leukaemias (figure II).
12. The duration of follow-up is often the crucial determinant of how many cases will be observed in a cohort,and therefore of the statistical power. Cancer rates generally increase substantially with age [D44]. This means thatin many cohorts the cancer deaths and cases are concentrated in the final years of follow-up. For example, in the
LSS, about 25% of all solid cancer deaths have occurred in
the last 10 years of follow-up (1991–2000) [P10]. Figure
III illustrates how the statistical power to detect a positive

trend with dose varies with the duration of follow-up. It is
assumed that the cohort accumulates cancers over time inaccordance with the distribution observed for solid cancers
in the LSS [P10]. Four different values for the total numbers of cancers within 50 years after exposure (500, 1,000,
1,500 and 2,000) are considered. The figure demonstrates
that even if a total of 2,000 cancers were to arise within 50
years after exposure, a statistical power of 80% or more isachieved only after about 20–25 years of follow-up.
13. Another factor that may complicate statistical power ispossible heterogeneity of risk expression within the cohort.
However, as can be seen from figure IV, in practice thismay not greatly affect calculations of statistical power, evenwhen the ERR varies by nearly 20-fold within the cohort.
Statistical power is slightly lower in the group with heterogeneous ERR (comprised of three equal subgroups of cases
arising from ERR = 0.1 Sv–1, 1.0 Sv–1 and 1.9 Sv–1) compared with a group with homogeneous ERR (= 1.0 Sv–1).
However, the difference is no more than a few per cent.
14. To the degree that a given sample of exposed people
has variation in individual dose levels, there can be amodest improvement in the statistical power when adose–response analysis is performed, providing the estimated individual doses are reasonably accurate and there issome spread among them [S6]. However, the mean dose isstill an important limiting factor in determining the degreeof statistical power achievable.

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23

Figure III. Influence of the duration of follow-up on the power of a study to detect an increasing trend of risk with dose
The curves are for various numbers of total deaths after 50 years. The power illustrated is to detect a statistically significant (1-sided
p = 0.05) increasing trend of risk with dose. The assumed distributions for colon dose are as in the latest LSS data (see table A1 inappendix A), assuming ERR = 0.467 Sv–1 (as observed for solid cancers in reference [P10])

1.0

STATISTICAL POWER

0.8

0.6

0.4
M = 500 deaths
M = 1 000 deaths
M = 1 500 deaths

0.2

M = 2 000 deaths

0.0
5

10

15

20

25

30

35

40

45

50

TIME AFTER EXPOSURE (a)Figure IV. Influence of the heterogeneity of ERR in a cohort on the power of a study to detect an increasing trend of risk
with dose
Two curves are presented: one for a cohort with assumed homogeneous ERR (1.0 Sv–1) and one for a cohort with assumed heterogeneous
ERR (three equal strata with ERR = 0.1 Sv–1, 1.0 Sv–1 and 1.9 Sv–1). The power illustrated is to detect a statistically significant (1-sided
p = 0.05) increasing trend of risk with dose. The assumed distributions for colon doses are as in the latest LSS data [P10] (see table A1
in appendix A)
1.0

0.8
Heterogeneous ERRSTATISTICAL POWER

Homogeneous ERR

0.6

0.4

0.2

0.0
0

50

100

150

NUMBER OF DEATHS/CASES

200

250

300

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15. When the dose levels are low, two other phenomena
affect the study results. The first occurs because epidemiological studies are based on natural human populations with
their extraneous variability in genetic make-up, diet,lifestyle and other exposures, rather than having tightly controlled experimental conditions. This means there may be
subtle differences between exposed and unexposed groups
in some unmeasured factors that affect cancer risk. For ahigh-dose study with a large expected radiation effect, suchvariations are fairly inconsequential, but for a low-dosestudy with a small expected radiation effect, the magnitudeof such extraneous variations may equal or surpass the sizeof the expected radiation effect. Hence for a low-dose study
there is great potential for a false negative or false positive
result, with little way of even knowing whether such aneffect has occurred; this reduces the credibility of theresults. Assessment of the pattern of results in low-dosestudies may sometimes provide indications of artefactual
findings. For example, on the basis of an analysis of results
for non-malignant respiratory diseases related to smoking,which exhibited negative trends with radiation dose,
Muirhead et al. [M12] suggested that smoking may confound the radiation dose–response relationship for somesmoking-related cancers, e.g. lung cancer.
16. Secondly, for a low-dose study with small numbers ofcases or deaths expected and therefore with inadequate
statistical power, if any result for RR is found to be“statistically significant”, its magnitude is in all likelihood
a substantial overestimate of the “true” risk. For instance,
Land [L3] showed that if women received a 10 mGy doseto both breasts at age 35 and were followed up for 20 years
thereafter, the prediction from high-dose studies may be thatabout 60 excess breast cancers per million exposed women
could be expected between years 10 and 20 of follow-up,
compared with 19,100 spontaneous breast cancers duringthat same period. If the study were on a cohort of a million such women, the statistical power would still be only
a little above 5%. (Adequate statistical power is usuallytaken as at least 80%.) If such a study were to be repeatednumerous times, for the occasions when there was a nominal “statistically significant” excess, the RR estimateswould be about nine times greater on average than the“true” relative risk. However, in a single given study, theauthors will usually derive the best estimate of the “true”
risk from their own central estimate, which is likely to bea substantial overestimate.

C. Impact of dose levels on the precisionof risk estimates
17. The precision of a risk estimate is normally definedby the width of the confidence interval (CI) around thecentral estimate of the risk. Risk estimates with narrowconfidence intervals are more informative than those withwide confidence intervals. Technically, a 95% confidence
interval implies that there is a 95% chance that theconfidence interval includes the “true” value of the parameter (e.g. a relative risk) under investigation. One can also
think of the confidence interval as indicating the possible
values the ‘true’ risk may have that are consistent with theobserved data.
18. The width of the confidence interval for the observed
RR is largely dependent on the number of cancers observedin that study, and the width of this confidence interval
would be approximately equal (on a logarithmic scale) fora low-dose and a high-dose study if the two studies involved
equal numbers of observed cancers. However, the kinds ofrisk estimate useful for radiation risk assessment are typically expressed per unit dose (with units of, for example,
Gy–1), and the RR estimate and its confidence interval are
explicitly divided by the mean dose for the exposed group(or else a similar division by dose occurs implicitly indose–response analyses that directly estimate the ERR per
unit dose). As an example, suppose the underlying ERR at
1 Gy for some cancer of interest was 1.0 (i.e. the RR at
1 Gy was 2.0), and a study was performed of people incurring a 1 Gy dose and an unexposed group with an equal
number of persons and length of follow-up. Suppose that
800 cancers of this type were found in total, distributed
between the exposed and the unexposed group (see scenario
E in table 2). A calculation of the estimated ERR would
yield 1.00 Gy–1 with a 95% likelihood-based confidence
interval of (0.73, 1.32) Gy–1. This is a fairly narrow confidence interval that would be useful information to help
define risk estimates. Suppose, however, that the samegroup of people had received only 0.05 Gy instead. Scenario
J in table 2 shows the expected result. The ERR per unit
dose is similar (1.03 Gy–1), but now the confidence interval is very wide: (–1.70, 4.16) Gy–1. In fact, to achieve confidence intervals for ERR per unit dose as narrow as thatshown in scenario E with a dose of 0.05 Gy would requirea study large enough to have over 70,000 cancers of thetype of interest. As with any study in which such small RRs
are being assessed, the influence of any uncontrolled confounding factors would be appreciable. If now one assumes
that a dose of 1 Gy is given to the exposed groups, but that
this represents only 10% of the total cohort in terms of numbers of persons and length of follow-up (scenario O), thenthe estimated ERR is much the same (0.99 Gy–1), with an
only slightly wider confidence interval for the ERR (0.66,
1.38) Gy–1 than in the base case. This shows that the lossof statistical power occasioned by an uneven distribution ofdose within a cohort need not be very marked.
19. The conclusion from this discussion is that exceptionally large studies are required to provide bounds on therisk estimate at low doses that will be informative anduseful. In addition, the probable influence of confounding
factors becomes increasingly important at low doses. For
example, heavy cigarette smoking is associated with a riskof lung cancer that is more than 20 times higher than thatfor never smoking [P8]. Therefore even a slight imprecision in knowledge of smoking habits could easily produce
artificial elevations (or mask true elevations) in estimates

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of the lung cancer risk anticipated from very low doses ofradiation. These are important considerations to bear inmind when proposing or evaluating low-dose studies.

D. Impact of dose measurement error and otheruncertainties on study associations
20. In recent years there has been much development ofmethods for evaluating the impact of uncertainties in individual dose estimates upon the associations between doseand cancer risk [C12, F9]. A primary distinction is between
random errors and systematic ones. Systematic errors indose measurement could result, for example, from incorrect
calibration of a dosimetry badge reader or from incorrect
assumptions or coefficients in an algorithm to reconstructdoses. Such errors would be specific to a particular caseand might bias the dose–response association in a positiveor negative direction, depending on the particular error.
Systematic and random errors are either differential, when
they are statistically dependent on the disease end point
being considered, or non-differential, when the errors are
statistically independent of the disease. More precisely, if
the “true” (unobserved) dose is D, the “nominal” or measured dose is d and outcome for the disease end points measured by the binary variable Y, then the measurement errors
are non-differential if P[d | D,Y = P[d | D,Y = 1], or equivalently if P[Y | d,D] = P[Y | D]; otherwise they are differential. Differential measurement errors can arise, forexample, if a pathologist codes a death certificate being
aware of the subject’s exposure history. These errors canintroduce serious and unpredictable bias into the analysis ofa study [T17]. Fortunately such errors can usually be eliminated by careful study design, for example by a blind
assessment of the study variables.
21. However, even when the errors are non-differential,
random measurement error affects virtually all quantitative
radiation epidemiological studies to one degree or another,and can introduce bias. Two types of measurement error
model have been customarily assumed, classical andBerksonian. Classical measurement error arises when the“nominal” (assigned) dose, d, is assumed to vary around the“true” (usually unknown) dose, D. For example, in the data
for the survivors of the atomic bombings in Japan, the errors
are assumed to be of classical form [J3]. This is because
the “nominal” dose, derived as a result of survivors’ recall,a few years after the event, of where they were and theirorientation with respect to the bomb detonation, will contain errors, probably random and non-differential. There areother (random, non-differential) errors associated withshielding uncertainties, and with the radiation energy spectrum and magnitude of the source term, which result in thelogarithm of the “nominal” dose, 1n[d], being distributed
approximately normally around the logarithm of the “true”
dose, 1n[D] [J3, R20]. Berkson error, on the other hand,arises when classification of individuals into groups resultsin the distribution of individual “true” doses, D, around the25“nominal” mean dose, d. A Berkson error structure is often
assumed for occupational studies, because the classificationof individuals into groups results in the distribution of individual “true” doses around a “nominal” film badge meandose [T17]. There may be a variety of sources and types ofrandom measurement error in a given study. When the dose
measurement error in a study is Berksonian, and a linear
model is fitted, failing to account for it means the varianceof the slope of a linear dose–response regression line will
be underestimated but the slope itself will be unaffected (i.e.
the risk estimate will be unbiased). However, this may not
be the case for non-linear models [T17]. When classicalmeasurement error occurs, failure to take it into accountgenerally means that not only will the variance of the slope
be underestimated but the slope estimate itself will also bebiased towards the null (i.e. closer to zero than it shouldbe). The direction of the slope, however, would not be
expected to change [A1]. Error models combining classicaland Berkson error have been developed [R19].
22. Classical measurement error generally reduces thestatistical power of a study because it increases the varianceof the risk estimate while simultaneously biasing the estimate itself towards the null [M7]. This can be understood
intuitively: random measurement error will tend to blur thedose differences among people. This reduces the correlation with the “true” doses (where ideally the correlation
should be 1.0) and thereby tends to reduce any correlation
between the nominal doses and a disease outcome.
23. There are typically other uncertainties in evaluating
the association between radiation exposure and cancer risk.
To name a few, there may be uncertainties associated withthe completeness of cancer case or mortality ascertainment,
uncertainties in the accuracy of diagnoses, uncertainties
associated with instrument error in making radiation measurements, uncertainties in the degree to which a radiation
film badge measurement estimates dose to some organ,
uncertainties in estimating various parameters in performing a dose reconstruction, uncertainties in the “transfer” ofrisk estimates from one population to another, uncertainties
in behavioural factors that affect exposure to radioactive
deposits after an accident or residential radon exposure, anduncertainties in the uptake and metabolism of specificradionuclides. In theory, a complete model to correct for
uncertainties would need to take into account all the applicable sources of uncertainty in a given study. However,frequently only limited information is available on themagnitude of these uncertainties, so the researcher has touse whatever information is available to make judgements
about the distributions of the relevant uncertainties. Thisrequires that the researcher make use of information available in the literature. Ideally it would require cooperation
between experts from a variety of disciplines, for example
between statisticians, epidemiologists and dosimetrists, inorder to correctly identify the forms and magnitudes of
the uncertainty distributions. Statistical estimates of thecomposite “credibility interval” that take into account thevarious measured and judged uncertainties can then be made.

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There can be serious systematic error (or bias) in studies
that can produce spurious or misleading results and that may
be difficult or impossible to properly account for in analyses. For example, if persons who developed thyroid cancer
years after exposure to fallout had better recall of past
events and habits, such as of their milk consumption at thetime, than similar persons who are disease-free, or if persons living in high-dose areas were screened for thyroid
cancer but persons living in low-dose areas were not, thiscan lead to serious bias.
24. Methods to deal with the complexities of measurement
error corrections are still evolving. Estimating the combination of various sources of measurement error and their magnitude with respect to individual dose estimates often
requires sophisticated Monte Carlo simulations [H4].
Nevertheless, the new generation of epidemiological studies
has begun to provide estimates of radiation risk corrected
for dose uncertainties (e.g. [G1, L1, L93, P2, S1]), and corrections for other uncertainties are beginning to be made. A
method of wide applicability is first-order regression calibration, in which one substitutes for the “true” dose, D, in
fitted models the expectation of the “true” dose given the“nominal” (measured) one, E[D] | [d] [C12]. As emphasized
by Carroll et al. [C12], this is an approximate method in
non-linear dose–effect relationships. It leads to reasonableadjusted point estimates of the model parameters but does
not fully take account of all the variability induced by themeasurement errors. Within many contexts, for example thatof the LSS data, the extra variability not taken into accountis relatively small [P2, P16]. It is well known that when
dosimetric errors are not too large, the first-order regression
calibration parameter estimates are a good approximation to
the full likelihood-based estimates [C12, K26, R21]. A
Bayesian approach to the measurement error problem hasrecently been developed [R22, R23, R24] that rests on theformulation of conditional independence relationships
between different model components, following the general
structure outlined by Clayton [C15]. In this approach, threebasic submodels are distinguished and linked: the diseasemodel, the measurement model and the exposure model. The
power of this Bayesian approach is that the dosimetric uncertainty is reflected in the variability of the model parameters.
An adapted Bayesian method of correction for measurement
error, the two-stage Bayesian method, has already been
applied to the fitting of generalized relative and absolute risk
models to the LSS data on cancer mortality and incidence;
estimates of population cancer risk and associated uncertainties have been derived from the posterior distribution of
the risk parameters [B18, L17]. The Committee outlines inAppendix E how this method has been used to fit models
to the latest LSS cancer mortality data [P10], and thereby toevaluate uncertainties in population cancer risks.
25. Dosimetric uncertainty analyses do not correct formethodological biases that distort observations and produce
spurious results. Statistical methods to deal with multiple
sources of bias, such as those arising from methodologicalissues, have recently been developed [G11, G14]. However,these are still controversial, as they tend to produce verylarge uncertainties in risks, are not perhaps completelytransparent, and avoid reliance on a full probability modelby using a series of more or less ad hoc “adjustments”(see the remarks of Copas, Spiegelhalter and de Stavola inreference [G11]).
26. Another type of dose measurement error that may
have an impact on studies involving occupational exposure
to radiation, but that has received limited attention, occurs
in the assigning of a value for a dose when the dosimeterreading is below the limit of detection. Designating suchdoses as zero will tend to overestimate the risk per unit
dose and distort the dose–response relationship. Statisticalmethods to assign values for such doses in an unbiased
manner have recently been proposed [M9, X1].
27. Very few studies attempt to take account of natural
background exposure simultaneously with the effect of theother radiation exposures being considered. Low-LET natural background radiation might be expected to contributea dose of about 70–80 mSv over a lifetime. These levelsof dose are small in relation to radiotherapeutic doses,
although not in relation to the average doses received in
occupational settings, or to those received by the survivorsof the atomic bombings. In most cohorts, such doses should
not be correlated with the other doses received, or withother modifying factors, so that they should not materiallyaffect inferences on radiation risk. For those cancers thatare extremely radiogenic, such as thyroid cancer or
leukaemia, natural background exposure may contributematerially to the risk, particularly in cohorts, such as the
LSS, in which the average doses approach backgroundlevels [L96]. A recent analysis of thyroid cancer incidence
among the survivors of the atomic bombings demonstrated
that a substantial proportion (up to 32%) of thyroid cancer
appearing at young age in this cohort might be attributed
to natural background exposure [L96]. Doses from radiological examinations or from radiation therapy are also generally not considered. Surveys of both exposure types havebeen conducted in the LSS [K60, K61], although as yet noaccount has been taken of these doses in any analysis ofhealth end points. Cumulative doses to specific organs (e.g.
colon, stomach) due to radiological examinations in somepersons in the AHS are of the order of 100 mSv or more,which is comparable to the average dose to this cohort due
to the atomic bombings [K60]. However, the doses due toradiological examination are not generally expected to becorrelated with those due to the bombings, thus bias in risk
estimates is unlikely to be appreciable.

E. Use of biodosimetry for epidemiological studiesof radiation risk
28. When individual dose measurements are unavailable
or incomplete, a biodosimetric measure of radiation exposure would be desirable. Ideally the biodosimeter would:

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register uniformly low values in the absence of a radiation
exposure; be sensitive, precise and unbiased in estimatingradiation exposure; and use a biological indicator that has
a long half-life, so that dose estimates could be made someyears after exposure. There are currently no biodosimetricmethods that fulfil all these criteria, although the method
employing electron paramagnetic resonance (EPR) to
measure doses to teeth (see below) arguably comes closest. The measurement of chromosome aberrations inperipheral lymphocytes, whether stable (balanced translocations) or unstable (dicentrics, ring chromosomes), hasbeen much used, for example in studies of the survivorsof the atomic bombings in Japan [K22, S81], in a studyof women irradiated for treatment of benign and malignant
gynaecological disease [K21] and in Chernobyl recovery
operation workers [N23, S27]. G-banding of chromosomes
to detect such aberrations has been performed for a numberof groups, including those of patients receiving radiotherapy [T20]. The technique, developed relatively recently, offluorescence in situ hybridization (FISH) is particularly
useful for assessment of stable chromosome aberrations,and has been used in various studies of nuclear workers[M20, T19], of persons exposed as a result of nuclear
weapons tests [S26] and of Chernobyl recovery operations
worker populations [J4, L18]. The hypoxanthine phosphoribosyltransferase (HPRT) gene mutation frequency inlymphocytes is also sometimes used in an assay of radiation damage [J4]. The glycophorin A (GPA) assay measures somatic radiation inactivation of the GPA gene inerythroid progenitor cells in the bone marrow and has beenused in studies of Chernobyl recovery operation workers[B19, J4]. It has the weakness that it can only be usedamong those (about 50%) of the general population with
the M/N blood type, and it has wide variability in sensitivity between individuals. EPR, also known as electron
spin resonance (ESR), can be used to measure cumulative
radiation doses to tooth enamel. Under experimental conditions and using the latest refinements [H54, H55], thetechnique has a minimum detectable dose of approximately
10 mGy. EPR/ESR has been used in assessing radiation
doses in the LSS cohort [I22, I23], in groups exposed toradiation due to the Chernobyl accident [I24, S82] and inworkers at the Mayak nuclear complex in the RussianFederation [R44, R45]. All these techniques and theirapplications to biodosimetry are discussed in a recent
report of the International Commission on Radiation Units
and Measurements (ICRU) [I21].
29. Biodosimetric data pose at least five particular problems. First, most such measurements show some variability in background levels. The most important source ofvariability for stable chromosome translocations is age. In
particular, a recent collaborative analysis involving a
number of laboratories using the FISH technique demonstrated that age is the main determinant of translocation
yield; other variables, such as smoking and sex, had little
if any influence on aberration yield [E5, L44, W19]. A
comparison of measurement results among some laboratories has been reported [L44] as part of the follow-up to27

the 1994 accident in Estonia involving the exposure of afamily to radiation from a powerful 137Cs source. Aftercorrection to full genome, yields from the participating
laboratories were in reasonable agreement [L44]. A similar comparison of results for blood samples taken from
non-irradiated populations has likewise demonstrated alarge measure of agreement among laboratories [W19]. A
second problem with biodosimeters is that they integratedose from all sources. While in certain circumstances thismight be thought advantageous, the lack of information on
the temporal distribution of exposure can cause difficulties, particularly as for most sites the probability of cancer
occurring varies substantially as a function of age at exposure [U2]. Moreover, the dose under study (for example
that received occupationally) may be similar in magnitude
to the cumulative dose that individuals have received due
to background radiation. Since the dose from external
penetrating background radiation averages about 1 mGy
in a year, by age 50, study subjects have received about
50 mGy on average from background radiation, with perhaps a twofold variation around that value. If the extra
dose under study (e.g. that resulting from occupational
exposure to radiation) is of a similar magnitude, it
becomes difficult to discriminate between the two components. A third problem with biodosimeters is that,compared with physical dosimeters such as film badges,
collection, storage and analysis of the biological material
are relatively expensive. At present it is not practicable tostore and analyse samples for more than a small proportion of most cohorts. Storing samples and then analysing
data from the cases and from a suitably structured set ofcontrols from within the same cohort could alleviate somewhat the problem of expense of analysis. However, it isimportant that samples be taken and stored in comparable
conditions, and if possible at a comparable time. It is also
important that subsequent modifying exposures to radiation or other agents be avoided. This implies that samples
should be taken from all members of an exposed cohort
as soon after the relevant exposure as possible, before disease status is known. A fourth problem with biodosimeters is the difficulty in estimating organ doses following
partial body irradiation. This can be a problem also forphysical dosimeters, unless multiple dosimeters are used.
A fifth problem, but only for certain end points, inparticular unstable chromosome aberrations [L19], is thatthe signal decays over time. Knowledge of when the dose
was received is needed to reliably infer dose. Some earlystudies of HPRT mutations also suggested that thesignal decayed over time [D26, U18], but later studies did
not show this [J4].
30. This last point is very much linked with the lowest
detectable dose, as is also the intrinsic variability in aberration yield. In general, cytogenetic dosimetry based on
the assay of chromosome aberrations in peripheral
lymphocytes cannot reliably detect doses below about
100 mGy [L14]. For example, in spite of more than
258,000 painted metaphases being analysed, there was no
association between aberration yield and recorded dose

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among a population of 118 Estonian workers performing
recovery operations after the Chernobyl accident, who had
an average dose of about 103 mSv, although there was asignificant increase in aberration yield among older recovery operation workers and among smokers [L18]. A recent
acute dose of about 100 mGy can be fairly easily measured by counting dicentric chromosomes, because such adose would treble the background level of ~1 dicentricamong 1,000 cells. Dicentrics have a half-life of about
3 years, but this can be much shorter following high doses.
Therefore, any doses received more than about 5 years
before blood sampling cannot be measured using thisindicator.
31. When translocation yields are measured many years
after an accident, the lymphocytes drawn in the blood
sample will have been derived from stem cells, which at
the time of irradiation are presumed to have been in thebone marrow. This raises two questions. If there is a difference in sensitivity between mature lymphocytes and precursor cells in the bone marrow, it would not be appropriate
to derive in vitro calibration curves using mature lymphocytes. Secondly, the irradiated cells have passed through an
unknown number of divisions to become mature lymphocytes. This means that unstable cells will have been
removed and, if some of these also contained translocations,the yield of translocations might have changed. The workthat has been done on persistence in vivo, particularly whenstable cells only have been scored, shows that neither ofthese problems is of great practical importance [L19, T20].
As indicated above, the major confounding factor forcontrol levels is age, but there is still some extra variation
unaccounted for.
32. The minimum detectable dose is to some extent
related to the number of cells the investigator is preparedto score. If one is prepared to score translocations in alarge number of cells (for example on a group basis), thenone might detect an average dose of 200 mGy, although
500 mGy is a more realistic lower limit. Scoring 1,000
genome equivalents (3,000 cells with 3 pairs of the largest
chromosomes painted, giving 33% efficiency in detecting
translocations [I36]), one would expect to see a control
level of about 10 translocations (in a 60-year-old) and afurther 10 from an added dose of 500 mGy; these are justabout measurable, bearing in mind the Poissonian variability. However, increasing the number of cells scoredwill reduce only the Poissonian variability in counts, andwill do nothing to eliminate the intrinsic variability incontrol levels.
33. In summary, the biodosimetric methods available atpresent seem useful to estimate only moderate to high individual historical doses (doses of perhaps 0.2 Gy or above),although their use to estimate group-averaged doses ofabove 0.1 Gy may be meaningful. Perhaps the most usefulmeasure, which is stable over time and between laboratories, is the assay of chromosome translocations using the
FISH technique.

F. Problem of multiple comparisons in epidemiologicalstudies of radiation risk
34. For a study that makes numerous comparisons (e.g. a
study of radiation exposure and cancer mortality that provides results for many types of cancer), it is popularly supposed that 1 statistical test out of 20 will be statisticallysignificant (at the 5% level) by chance. This is not strictlytrue. Not so widely known are the probabilities of obtaining 1, 2, 3,…n statistically significant results by chancewhen there is no real effect at all. If the comparisons are
independent of each other (as appropriately calculated
estimates of excess mortality or incidence due to various
types of cancer approximately are), then table 3 gives
illustrative results.
35. Table 3 shows, for example, that the probabilities ofobtaining one or more statistically significant results purely
by chance are about 40.1%, 64.2%, 78.5%, 87.1%, 92.3%
and 99.4% with 10, 20, 30, 40, 50 or 100 comparisons,respectively. The corresponding probabilities of obtaining
two or more statistically significant results by chance are
8.6%, 26.4%, 44.6%, 60.1%, 72.1% and 96.3%, respectively, and so forth. There is no simple way to distinguishwith certainty real effects from chance effects. Other criteria must be used to assess whether a particular association
is likely to be causal or due to chance.
36. In assessing the results of analyses, in particular thosethat may have come from multiple testing of a variety ofend points, the Bradford Hill criteria for assessing whether
an association is plausibly causal should always be considered [B20]. Specifically these are as follows: (a) consistency and unbiasedness of findings: confirmation of theassociation by different investigators, in different populations, using different methods; (b) strength of association,and in particular two aspects: the frequency with which thefactor (in this case radiation) is found in association withthe disease, and the frequency with which it is found in theabsence of the disease; the larger the relative risk, the more
the hypothesis is strengthened; (c) temporal sequence: obviously, exposure to the factor (in this case radiation) mustoccur before onset of the disease; in addition, if it is possible to show a temporal relationship, as between exposure
to the factor in the population and the frequency of the disease, the case is strengthened; (d) biological gradient(dose–response relationship): finding a quantitative relationship between exposure to radiation and the frequencyof the disease; the intensity or the duration of exposure may
be measured; (e) specificity: if the factor being studied canbe isolated from others and shown to produce changes in
the incidence of the disease, for example if thyroid cancer
can be shown to have a higher incidence specifically associated with radiation exposure, this is convincing evidenceof causation; (f) coherence with biological background andprevious knowledge: the evidence must fit the facts that arethought to be related, e.g. the rising incidence of dental
fluorosis and the rising consumption of fluoride are coherent; (g) biological plausibility: the statistically significant

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association fits well with previously existing knowledge;(h) reasoning by analogy: sometimes a commonly accepted
phenomenon in one area can be applied to another area;(i) experimental evidence: are similar effects observed in
carefully controlled experiments in a variety of model systems? Criteria (a), (d) and (i) are critical in evaluating
whether a putative radiation effect is likely to represent a
causal association. Sometimes a Bayesian analysis will also
help give a better indication of the meaningfulness of particular results, in that it can present a more realistic pictureof relative risk [G5, W1].
37. Statistical approaches are available to address the
problem of multiple comparisons (e.g. the highly conservative Bonferroni criterion [T2] or the improved approach ofBenjamini and Hochberg [B1]) but have seldom been used,
for a number of reasons. One reason is that they reduce statistical power for any given comparison. Epidemiologicalstudies commonly have limited statistical power for many
cancer end points, and using such approaches to address the
problem of multiple comparisons would reduce it further.
The assumption of independence of the various tests is also
often questionable. Although such methods facilitate theadjustment of tests of statistical significance, they provide
no way to adjust the corresponding confidence intervals.
Notwithstanding these problems, attempts should generally
be made to account for this in assessing the significance ofclaimed findings.

G. Measures of radiation risk, including
lifetime risk
38. Appendix B details the six commonly used measuresof population cancer risk, and their relation to the instantaneous cancer mortality rate, µc(s,t | a,D), expressed as cancer
deaths per year that result for a given cancer type c at age
t for persons of sex s following some instantaneously administered radiation dose D given at age a. This quantity is typically evaluated by fitting a model for radiation risk to data
corresponding to some exposed cohort. As outlined in appendix B, fundamental to the assessment of cancer risk for a
population, one must assume certain underlying mortality
rates that the population would experience in the absence ofradiation exposure, both overall and for each cancer type.
For calculations of population risk for cancer incidence,cancer incidence rates must also be specified. These underlying rates are generally estimated from national morbidity
and mortality rates. It is usual to calculate the consequenceof an instantaneous exposure to a “test” dose, Dt, that isassumed to be administered at some age, a. However, other,more general patterns of exposure are possible. By far themost commonly used population risk measure is the risk of
exposure-induced death (REID) per unit dose; this has been
employed by many scientific committees [I11, U2, U4] andothers [L15, L16, L17]. As discussed in appendix B, thisand the other five measures of risk considered there are nonconstant as a function of the test dose Dt.

29

H. Transfer of radiation risk estimates between
populations, and interactions of carcinogens
39. Despite the relatively large number of data on radiation
risk, the question of how to transfer risk estimates derived
from one population to a different population remains unanswered. The available data suggest that there is no simple
solution to the problem [M23, U4], as indicated below.
40. There does not appear to be an obvious, consistent
relationship between underlying and radiation-related
cancer risk, either across cancer sites within a single population or across populations for a single cancer site. In thefemale Japanese population generally, age-standardized(world) incidence is similar for stomach cancer and breast
cancer, about 31 and 34, respectively, per 100 000 per year
whereas in the United States of America the incidence isabout 3 and 90, respectively [P19]. Among survivors of theatomic bombings, the radiation-related ERR at exposure age
30 at 1 Gy (ERR1 Gy) is 0.34 for stomach cancer incidence
and 0.87 for breast cancer incidence [P48]. Stomach cancer
contributes a substantial proportion of the total radiationrelated risk (about 18%), but that proportion is considerably
less than the proportion of underlying stomach cancer incidence to total underlying cancer incidence (about 27%)
among survivors of the atomic bombings [P48] and among
Japanese people generally [P19]. In the United States, theratio is 2% for males and 1% for females [P19]. For female
breast cancer the opposite is true. The underlying rate inJapan is among the lowest in the world for developed countries, whereas the total cancer rate is not much different
from that in most other countries [P19], while among survivors of the atomic bombings, breast cancer contributes adisproportionately large fraction (about 17%) of the total
radiation-related cancer burden [P48]. In the United Statesand many Western European populations, by contrast,
underlying breast cancer rates are high [P19], but the radiation-related excess risk (in absolute terms) per unit doseamong medically exposed women is similar to that among
the survivors of the atomic bombings [L5, P3] (see also
table 10). That is, the dose-specific, radiation-related component of the total breast cancer risk is likely to be similar
in absolute magnitude for exposed Japanese and Western
populations but, in Western populations, smaller as a proportion of the total breast cancer risk. For stomach cancer,
on the other hand, the United States underlying rate is anorder of magnitude lower than that in Japan [P19], whereas
the limited information on dose-specific, radiation-related
excess risk suggests that, as a multiple of the underlying
risk, it may be comparable to that in the survivors of theatomic bombings [C4, G6].
41. The above information suggests that, for breast cancer,
the radiation-related ERR per unit dose (i.e. the excess risk
per unit dose expressed as a multiple of the underlying risk
for the Japanese population) based on the data from the survivors of the atomic bombings in Japan would overestimate
the risk for an exposed United States population. On theother hand, for stomach cancer, the radiation-related EAR

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Page 30UNSCEAR 2006 REPORT: VOLUME I(i.e. the difference between the risk following exposure and
the Japanese underlying risk) would result in an overestimate for the United States population. For most othercancers there is almost no information of a similar nature.
This is not a trivial matter, because any transfer of a risk
estimate from one population to another requires anassumption, explicit or implicit, about the relation between
the excess and the underlying risk. Moreover, for some sites(e.g. stomach, liver and oesophagus) the underlying rates
can differ markedly between populations [P19].
42. The available information suggests that, depending on
circumstances, relative or absolute transfer of risk between
populations, or indeed the use of some sort of hybrid
approach, such as that employed by Muirhead and Darby[M24] and Little et al. [L21], may be appropriate. Many
regulatory bodies implicitly assume that risk transfer isintermediate between additive and multiplicative [I11,
M23]. In the updated United States National Institutes of
Health (NIH) radioepidemiological tables report [L45], formost cancer sites population cancer risk was calculated byweighting equally all possible linear combinations of themultiplicative (M) and additive (A) transfer model estimates,p × M + (1 – p) × A, by assuming p to be a random variable distributed approximately uniformly between 0 and 1.
This subjective approach was motivated by: (a) the consideration that differences in underlying rates might reflect differential exposure to both cancer initiators (consistent withadditive transfer) and cancer promoters (consistent withmultiplicative transfer), and (b) an almost complete lack of
relevant epidemiological information for most cancer sites.
The general United States Environmental Protection Agency(EPA) approach for site-specific cancer risk was similar, but
on a logarithmic scale, i.e. the logarithm of the excess risk
was assumed to be a linear mixture between the logarithmsof the multiplicative and additive transfer model estimates[E6], where the value of the uncertain mixture parameter p
was assumed to be uniformly distributed between 0 and 1.
The EPA approach tends to yield somewhat lower risk estimates than the approach of the National Cancer Institute(NCI)/Center for Disease Control (CDC) [L45]. For the few
sites where information on population transfer was available, the NCI/CDC approach was to favour one simple
transfer model over the other. For example, for breast
cancer, 0.5 probability was placed on additive transfer and
0.5 on the uniform model; for stomach cancer, 0.33 probability was placed on multiplicative transfer and 0.67 on theuniform model.
43. It should not be surprising that the relationshipbetween radiation-related and underlying risk in different
populations is not consistent for different cancer sites. There
are reasons, as yet poorly understood, why underlying breast
cancer rates are high in the United States, and why underlying stomach cancer rates are high in Japan. These reasonsare almost certainly related to differences in lifestyle.
Haenszel et al. [H35] found that migrants to Hawaii fromJapan continued to have high levels of stomach cancer risk,
but their children, especially those who had adopted

Western-style diets, did not; this suggests that exposures
early in life are critical determinants for this disease. On
the other hand, colon cancer rates among migrants to theUnited States and Australia from countries with low underlying levels have tended, within their lifetimes, to converge
to the higher levels characteristic of the country [H36, T39].
Similar findings have been reported for breast cancer risk
among women migrating to the United States from
European countries with low underlying rates, but forJapanese migrants to Hawaii and California the convergence
was much slower [H34, Z5]. Generally breast cancer rates
from non-white migrants to the United States remain below
United States rates both in the migrants and in their descendants [T39]. In contrast, breast cancer rates in white
migrants to the United States approximate those of UnitedStates whites in the first generation, except for rates inmigrants from the former Yugoslavia [T39]. On the other
hand, the breast cancer incidence rate in the San
Francisco–Oakland metropolitan region among Americanborn women of Japanese descent was, by 1969–1971,
approaching that for the Caucasian population [B24]. The
lifestyle factors affecting the rates for breast and stomach
cancer are probably different, at least in part, and probablyinteract differently with the radiation dose factor.
44. Related to this question is the issue of how one shouldmodel interactions between radiation and other agents inrelation to cancer risk. This was the subject of an extensive
review issued in annex H of the UNSCEAR 2000 Report[U2], which encompassed biological and epidemiologicalevidence for interactions and discussed in detail the implications for statistical modelling. Undoubtedly the most studied of these interactions is that between radiation andcigarette smoking in relation to lung cancer. Analysis by
the BEIR (Biological Effects of Ionizing Radiation) VI
Committee and others of the effects of cigarette smoking
and radon progeny on lung cancer risk in 11 miner cohortssuggested an interaction that was intermediate between
additive and multiplicative [C36, L39]. The preferred modelof the BEIR VI Committee was submultiplicative [C36]. In
fits to the data on Colorado Plateau uranium miners, models
with multiplicative interaction between the effects of exposures to radon progeny and cigarette smoke were preferred
to models with additive interactions, although it was notpossible to rule out either submultiplicative or supramultiplicative models [L39]. Lung cancer mortality among
Mayak workers could be better described with a model ofcarcinogenesis that was submultiplicative in relative risksof smoking and radiation than with a model that was multiplicative [J10]. Studies on domestic radon daughter exposure also suggest that the relationship between the effectsof smoking and exposure to radon progeny in relation tolung cancer risk may be closer to multiplicative than additive [D24, P18]. Analysis of lung cancer in persons treatedfor Hodgkin’s disease demonstrated a multiplicative interaction (on the logistic scale, i.e. where the disease probability, p, is transformed via the expression log[p /(1 – p)])between radiotherapy dose and cigarette smoke in relation
to lung cancer risk; an additive interaction fitted statistically

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significantly worse. Interactions between radiation andchemotherapy were more nearly additive (on the logistic
scale); a model assuming a multiplicative interaction fitted
statistically significantly worse [G23]. In contrast, analysisof the effects of radiation and smoking on lung cancer incidence in the survivors of the atomic bombings suggested
that the interaction was approximately additive, although it
was also consistent with a multiplicative interaction [P17].
However, with only a few tens of radiation-induced excess
lung cancers, the LSS data at present lack the statisticalpower of the miner data to discriminate between multiplicative and additive models of interaction. The BEIR VII
report [C37] also assessed interactions between radiation
and a variety of other factors, including tobacco smoke,
chemotherapy, heritable genetic risk factors, iodine insufficiency and ultraviolet radiation; in general, interactions
ranged between additive and multiplicative effects. The
BEIR VII Committee also adduced from consideration ofstochastic quasi-mechanistic models why this should be so[C37]. However, as this analysis was based on an approximate (deterministic) version of the two-mutation model,
which is known to poorly approximate the cancer risk fromthe exact (stochastic) model [H57], these inferences may
not be correct. The arguments used by BEIR VII [C37] tojustify invariance of relative risk break down when the exact
hazard function is used instead of the approximate (deterministic) hazard function, although the arguments in favourof additive invariance of risk still hold (although not for thereasons given). Caution should be exercised in the application of such inferences to this model, and also to moregeneral multistage cancer models [L25, L26].
45. In general it is not clear in terms of mechanisms or
biology how data on excess risks for one population shouldbe transferred to another population. If one supposes thatradiation acts as the initiating mutation in generalized multistage models of the sort recently developed [L25, L26], theninvariance of EAR would correspond to similar radiationinduced mutation rates between populations [L25, L26,
L27]. Invariance of ERR would correspond to the ratio
between the radiation-induced mutation rates and the underlying mutation rates being invariant [L25, L26, L27].
Mechanistic considerations imply that the interactions
between radiation and the various other factors that modulate the multistage process of carcinogenesis may be complex [C35, L22], so that in general one would expect neither
relative nor absolute risks to be invariant across populations.
In fits of quasi-mechanistic multistage models to the dataon the Colorado Plateau uranium miners, the model fitting
best was one with three rate-limiting stages, with radon
daughter exposure acting to vary the first and second mutation rates, and with cigarette smoking acting on the firstmutation rate [L41]. This mixture of radon progeny andsmoking actions on different stages implies that the interactions between these agents will not conform to a simple
multiplicative or additive pattern. If this model is true, itwould imply that the observed interaction between theeffects of radiation and cigarette smoking will depend on
their relative timing. This might explain why there are31

indications (admittedly not statistically significant) of differences between the forms of interaction in the LSS data,
where an instantaneous radiation exposure was in generalfollowed by cigarette smoke exposure, and in the miner
data, where cigarette smoke exposure was concurrent with,
but also preceded and followed, radiation exposure.
46. Much of environmental, nutritional and occupational
cancer epidemiology is concerned with identifying risk factors that might account for some part of the variation ofsite-specific underlying cancer rates among populations.
While there has been much progress, the problem is vastand there is only limited information on the interaction
between radiation dose and lifestyle, or constitutional factors, in terms of cancer risk. The interactions between radiation exposure and cigarette smoking in relation to lungcancer risk discussed above are among the most well studied of such interactions, although other risk factors, in particular diet, have been studied in relation to radiation
exposure [S42]. Interactions of radiation exposure with constitutional factors are discussed at greater length in section
I.I and also in sections III.L and III.M (on cutaneous
melanoma and non-melanoma skin cancer) below. Thus it
is likely that, for the foreseeable future, the most useful
information relevant to transferring radiation-related risk
coefficients from one population to another will come frommultinational comparisons of site-specific radiation-relatedrisk, rather than from investigations of underlying cancer
risk factors and their interactions with radiation dose.
47. In studies assessing the possible interaction of otherfactors with radiation risk, it can be useful to combine studies with similar designs to attempt to increase statisticalpower, for example by having a wider range of exposures
to some other factor between populations than is available
within any given population. Sometimes a “meta-analysis”,
based on published findings from several studies, may be
performed. Where feasible, as noted below, it is preferable
to combine the original data and analyse them using acommon format, in other words to perform a “pooled analysis”. Pooled analyses have been conducted of various
cohorts of radiation workers [C3, C41], to assess the effectsof radon daughter exposure in relation to lung cancer risk
in underground miners [C36], to assess thyroid cancer risk
in various (mainly medically exposed) cohorts [R6] and toassess breast cancer risk in various populations [H9, L5,
P3]. Less commonly, analyses combining cohort and casecontrol data, for example in relation to leukaemia risk
[L31], have been conducted.
48. The possible influence of confounding and residualbias needs to be considered. The greater power and therefore apparently greater precision of combined studies may
be offset by increased bias resulting from uncontrolled confounding, for example inter-study confounding. Perhaps themost extreme instances of this are correlation studies, which
as discussed in section I.A are prone to “ecological bias”.
For example, this sort of bias is likely to explain theelevated risks, which are large and highly statistically

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significant, in a meta-analysis of leukaemia in relation toradon daughter exposure [H32, M45]. Even where there isno inter-study confounding, if the individual studies arebiased, meta-analysis based on their results can result inseriously biased and misleading results [B56, B57, L87].
One of the main problems in joint analysis can result froma lack of comparability of the component studies owing, forexample, to differences in data collected on exposures andpotential confounders. This is likely to be a particular problem for meta-analysis, or retrospectively assembled cohorts
combined in a pooled analysis. Pooled analyses, in which
the component cohorts are assembled using a common protocol and prospectively followed up, are therefore to be preferred. Another potential problem with retrospective poolingor meta-analysis is publication bias, i.e. selective reportingof results depending on whether the outcome was statistically significant. As noted in section I.B, this arises particularly in small, ad hoc cohorts. This is less likely to be a
problem in a pooled analysis of large cohorts prospectively
followed up.

group were higher than among patients without RB. In thatstudy, the RB group included both those patients treated forbilateral RB, which is presumed to be heritable, and thosetreated for unilateral RB, of which most cases are presumed
to be non-heritable [W11]. About half of the RBs in thisgroup would be expected to be bilateral [W11].

I. Impact of human genetic susceptibility
on radiation risk

50. Only three of the studies considered by Little and colleagues [L9, L20, L23] contained adequate information toassess interactions between radiotherapy and cancer-prone
conditions, all three studies relating to populations treatedin childhood [L24, T10, W11]. The cancer incidence studyof Wong et al. [W11] is an update of an earlier mortality
study of Eng et al. [E7]. Tables 4 and 5 provide details on
RRs of radiation-associated cancer in these studies in relation to whether the patients had a cancer-prone disorder(defined slightly differently in each study).

where Ei is the (population) expected number of second cancers in group i. Here exp[β] is the ratio of the risk in theirradiated group to that in the non-irradiated group among
the non-heritable-RB patients, exp[δ] is the ratio of the riskin the heritable-RB patients to that in the non-heritable-RBpatients, and exp[θ ⋅ β] is the ratio of the risk in the irradiated group to that in the non-irradiated group among theheritable-RB patients. The parameter of interest is the multiplier of radiosensitivity in the heritable-RB group, θ, themaximum-likelihood estimate of which is 1.62 (95% CI:
0.70, >10,000) (table 5); i.e. there is weak evidence that theradiosensitivity of heritable-RB patients is higher than thatof non-heritable-RB patients. The weakness of this evidence
may in part be a consequence of the small number (nine)of cancers in the non-heritable-RB group. It should beemphasized that no account has been taken of radiotherapy
dose in this analysis, reflecting the limitations of the published data. Consequently the conclusions drawn must be
qualified. It is likely that similar conclusions would be
drawn from the updated follow-up [K43]; unfortunately notenough information is given in the published report evento duplicate what has been attempted here for the more
limited follow-up.

51. There were no indications in the studies of Little etal. [L24] or Tucker et al. [T10] that the RR of a second
cancer is higher among those patients with a familial cancer
syndrome. Indeed, in the study of Little et al. [L24], brain
tumour RRs were markedly lower among the patients withcancer-prone disorders compared with those in the nonsusceptible population, at borderline levels of statistical significance (2-sided p = 0.06) (table 4). In the study of Tucker
et al. [T10] there were non-significant indications (2-sided
p = 0.67) of a lower ERR of a bone tumour among patientswith retinoblastoma (RB) than among those patients without, although, as is clear from table 5, EARs in the RB

53. Although not shown in tables 4 and 5, additional information on the interaction between the risk of a radiationrelated second cancer and cancer-prone conditions is given
in a study of survivors of childhood cancer by Kony et al.
[K25], in which there is weak evidence that RRs of radiationassociated second tumours in patients whose close relatives
develop cancer more frequently than average (i.e. who
belong to cancer-prone families) are lower than those inpatients who are not from cancer-prone families [K25]. The
ratio of RRs for second tumours between groups receiving≥0.5 Gy and <0.5 Gy in the cancer-prone families is 1.9,
whereas in the non-cancer-prone families it is 4.1 [K25].

49. The International Commission on RadiologicalProtection (ICRP) [I12] and others [L9, L20, L23] haverecently reviewed the issue of interaction between human
genetic susceptibility and radiation risk. Little and colleagues [L9, L20, L23] paid particular attention to risks
observed in medically exposed populations, where there isoften (particularly in persons treated for cancer) a higher
proportion of persons with heritable cancer syndromes thanin the general population.

52. More limited information is available on the interaction between radiotherapy and heritable RB in the study ofWong et al. [W11]; unfortunately there is insufficient information on radiation dose in the published report. Moreinformation is given in a subsequent report [K43], althoughradiation dosimetry has still not been assessed. To assess
the effects of heritable RB on RRs of a second cancer afterradiotherapy for RB, Little et al. [L9] assumed that the
expected numbers of second cancers in this cohort [W11]
are given by:
Ei in the non-irradiated, non-heritable-RB group;
Ei ⋅ exp[β] in the irradiated, non-heritable-RB group;
Ei ⋅ exp[δ] in the non-irradiated, heritable-RB group;
Ei ⋅ exp[δ + θ ⋅ β] in the irradiated, heritable-RB group;

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54. Although there are indications of rather lower radiogenic ERRs among people with cancer-prone disorders[L9], the radiogenic EAR can be higher. For example, inthe study of Tucker et al. [T10], the ERR can be calculated
as 0.08 Gy–1 among patients with a first cancer other thanRB and as 0.05 Gy–1 among those with RB as their firstcancer [L9]; thus the ratio of ERRs for RB versus non-RB
patients is 0.05/0.08 = 0.6. On the assumption that theunderlying cancer risk in RB patients is 5.6 times that in
non-RB patients (taken from the ratio of risk in heritableRB patients to that in non-heritable-RB patients in the studyof Wong et al. [W11]), this calculation implies that the ratioof EARs for RB versus non-RB patients is roughly (5.6 ×
0.05)/0.08 = 3.5. As discussed in section I.H above, the factthat ERRs are lower in people with cancer-prone disorders
is consistent with a more general pattern observed in epidemiological data, whereby higher underlying cancer risks
are to some extent offset by lower ERRs of radiogenic
cancer [U2, U4]. The ICRP [I12] has recently reviewed
radiogenic cancer risks among genetically susceptible individuals, and suggests that EARs of radiogenic cancers in
people with familial cancer syndromes may be higher by afactor of 5–100 than those in non-susceptible individuals,
with the most appropriate value for this factor being about
10. The ICRP [I12] points out the serious implications of
the higher EAR for such people receiving large doses ofradiation, for example during radiotherapy. This elevatedrisk has to be balanced against the generally high underlying cancer risk in these individuals and the benefits
accruing from radiotherapy.

J. Effects of dose protraction or fractionation
and radiation quality
55. The derivation of cancer risks after exposure to ionizing radiation at low doses and dose rates is critical to thesetting of standards for radiological protection. In annex Gof the UNSCEAR 2000 Report [U2], there was a detailed
discussion of what constitutes “low dose” and “low doserate”, in part derived from previous UNSCEAR reports [U5,U7]. Curvature in the dose response to any end point canbe measured by the ratio of quadratic to linear coefficients,
β /α, which defines the curvature, in fits of the equation:

F(D) = α ⋅ D + β ⋅ D 2(It should be noted that in annex G of the UNSCEAR 2000
Report [U2], curvature was defined as the inverse of thisquantity, i.e. α / β.)56. For chromosome aberrations in peripheral blood lymphocytes exposed to 60Co gamma rays, typically β / α ≈5 Gy–1 [L88], implying that at doses of up to 40 mGy thequadratic term, β ⋅ D2, contributes less than 20% of theexcess. For this reason, the UNSCEAR 2000 Report indicated that 20–40 mGy of low-LET radiation would be considered a low dose [U2]. Pierce and Vaeth [P11] analysed

33

curvature in the LSS cohort adjusting for random dosimetric errors and obtained, for solid cancers, a value for β / αof 0.3 (95% CI: <0, 1.7) Gy–1, and for leukaemia, a curvature of 0.6 (95% CI: 0.1, 3.3) Gy–1. Little and Muirhead[L37] fitted a somewhat different model, arguably moreplausible radiobiologically, to the LSS incidence data, also
taking account of random dosimetric errors, and taking separate account of the effects of neutron dose, Dn, and gammadose, Dγ , using:F(Dγ , Dn ) = α ⋅[Dγ + RBE ⋅ Dn ] + β ⋅ Dγ 2Assuming a neutron relative biological effectiveness (RBE)of 10 and dose errors expressed as 35% geometric standard
deviation (GSD) (similar to assumptions made by Pierce
and Vaeth [P11]) Gy–1, Little and Muirhead [L37] obtained,
for solid cancers, curvatures of 0.10 (95% CI: –0.18, 0.70)
and, for leukaemia, curvatures of 1.95 (95% CI: 0.31,>1000) Gy–1. It has been shown that the curvature forleukaemia in the LSS is consistent with that seen in a
number of data sets of chromosome aberrations in peripheral blood lymphocytes exposed to 60Co gamma rays,
although this is not the case for solid cancers [L100]. Thesefigures suggest that at a dose of 100 mGy the quadratic
terms contribute 3–20% of the total excess, so that a low
dose might consist of any value up to 100 mGy.
57. In the UNSCEAR 2000 Report [U2], microdosimetricanalysis demonstrated that for 60Co gamma rays hitting a
4 µm diameter cell nucleus, doses of 0.8 mGy or less would
ensure that on average no more than about 0.2 radiation
tracks hit the nucleus, resulting in no more than 2% of cell
nuclei having more than one radiation track. On this basis alow dose would correspond to no more than 0.8 mGy. The
BEIR VII report [C37] defined (without justification) a low
dose as 100 mGy or less. The principal definitions to dateas to what constitutes a low dose are summarized in table 6.
58. The UNSCEAR 2000 Report [U2] employed microdosimetric analysis of the number of radiation track coincidences within a cell nucleus to estimate that, in thepresence of DNA repair, dose rates of up to 10–3 mGy/min
would be considered low dose rates, and in order to ensure
only one track per cell in 60 years, dose rates of up to
10–8 mGy/min would be considered low dose rates.
However, it was noted that these considerations only
applied to end points such as chromosome aberrations,mutation or cell killing. For the multistage induction ofcancer, where the probability of an effect might be influenced by a subsequent radiation track, these calculations
break down. Assessment of fractionation effects for induction of leukaemia and solid tumours in animal studies wasused in the UNSCEAR 1986 Report [U7] to suggest that
0.05 Gy/min of low-LET radiation can be considered a low
dose rate. Comprehensive assessment of fractionation
effects in experimental tumour systems and other data wereused in the 1993 UNSCEAR Report [U5] to conclude that
0.1 mGy/min of low-LET radiation averaged over about anhour can be considered a low dose rate. The BEIR VII

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report [C37] defined (without justification) a low dose rate
as 0.01 mGy/min or less. The principal definitions to dateas to what constitutes a low dose rate are summarized intable 7.
59. In extrapolating cancer risks observed in groups (suchas the survivors of the atomic bombings in Japan) exposed
at a high dose rate to low-LET radiation, the ICRP [I11]
recommends application of a “dose and dose-rate effectiveness factor” (DDREF) to obtain cancer risks at low doses
and low dose rates. The ICRP [I11] recommended a DDREF
of 2 on the basis of data from studies of animals, the evidence for curvilinearity in the data from the Japanese survivors of the atomic bombings, and other epidemiologicalstudies. The UNSCEAR 1993 Report reviewed epidemiological and experimental data to conclude that a DDREF
should be applied to estimate tumour risk for low-LET exposures at a dose rate of 0.1 mGy/min or less, whatever the
total dose, or if the total dose was less than 200 mGy, whatever the dose rate [U5]. UNSCEAR did not estimate tissuespecific DDREFs, but suggested that for tumour induction
the available data suggested that the DDREF adopted should,
on cautious grounds, “have a low value, probably no morethan 3” [U5]. The BEIR VII Committee [C37] estimatedwhat they termed an “LSS DDREF” to be 1.5 (95% CI: 1.1,
2.3), on the basis of estimates of curvature from experimental animal data and from the latest LSS data on solidcancer incidence. BEIR VII also conducted a detailed reviewof the experimental literature, and documented a substantial
DDREF for chromosome aberrations and cell mutations (forexample at the HPRT locus) and animal carcinogenesis[C37]. DDREFs in excess of 2 were seen in many cellular
systems; for most of the studies of cancer in animals, theexperimental end point nearest to cancer in humans “yields[DDREF] estimates on the order of 2 to 6, with most values
in the range 4–5” [C37]. Table 8 summarizes the estimatesthat have been made of DDREFs and related quantities.
60. For high-LET radiations, such as neutrons and alphaparticles, no such reduction factor is indicated, because ingeneral the dose response for tumour induction and hereditary effects following exposure to these sorts of radiation
is linear, with no variation in effect with dose fractionation[I11, U5]. The reason for this may be connected with thefact that, at a tissue level, a low dose rate results in mostcells being non-irradiated. For example, a dose of 1 mGyfrom exposure to alpha particles would result in 99.7% of
cells being non-irradiated and in fewer than 1 in 106 cells
being hit more than once [U5]. This would lead one to
expect that, at relatively low tissue doses, cancer risk would
be proportional to the number of cells traversed, and therefore to dose. When a single high-LET particle strikes thecell nucleus, it delivers a large dose (for example 370 mGy
on average for an alpha particle), so that even when thetissue dose is low, at a cellular level those cell nuclei thatare hit receive a high dose.
61. There are no epidemiological studies that permit a
direct internal comparison—to facilitate calculation of

DDREF—between (a) exposures that are high dose andhigh dose rate, and (b) those that are highly fractionated or
protracted. A second-best alternative is to compare risk estimates from the available high-dose and high-dose-rate studies with those from fractionated or protracted dose studies.
In performing comparisons, the Committee has restricted its
attention to studies where there is good quality organ
dosimetry, good follow-up and good case ascertainment.
Tables 9–12 show results for three specific classes oftumour—lung cancer, breast cancer and leukaemia—fromvarious studies involving low-LET exposure. In particular,tables 9, 10 and 12 show results of comparing risks in various medically exposed groups with subsets of the atomic
bombing survivor data for cancer incidence [P4, T1] andmortality [P1] matched for sex, age at exposure and years
of follow-up. These comparisons are taken from the paperof Little [L20], and further details on the methodology are
given there.
62. Table 9 shows that, in general, lung cancer ERRs in
the medically irradiated groups are substantially below
those in similar subsets of the LSS data. This is true for all
four of the medical studies considered. For three of the studies this discrepancy is highly statistically significant (2sided p < 0.001). Of particular interest are the findings thathighly fractionated exposures confer little risk for lungcancer as compared with an acute exposure, both in the
Canadian tuberculosis (TB) fluoroscopy study [H7] and in
the Massachusetts TB fluoroscopy study [D4]. However,caution should be exercised in interpreting the results, as
there may be confounding by smoking habits in both studies. Smoking histories were available in the TB medical
records in the Canadian study [H7] and in the Massachusetts
study [D4], and these showed no confounding with dose.
However, the patients’ subsequent smoking habits may havechanged because of their respiratory illness and could haveaffected the lung cancer outcomes. Nevertheless, theMassachusetts study [D4] obtained smoking informationfrom the patients many years after they had been hospitalized for TB, thus it is unlikely that changes in smoking
habits would have been a factor.
63. Table 10 shows that for breast cancer the picture isvery different. Although the ERR for the survivors of theatomic bombings is higher than that for the medical studies in two instances, it is lower than the corresponding ERR
for another two medical studies, although nowhere is thisdifference statistically significant. Table 11 extends theanalysis of dose-rate effects for breast cancer by reproducing the results of a recent meta-analysis of breast cancer[P3]. (The benign breast disease study considered by
Preston et al. [P3] is excluded from the comparisons given
here because the central value of age at exposure used for
adjustments, 25 years, is considerably different from thevalue, 50 years, used in most of the other studies, making
meaningful comparisons of ERR difficult.) As can be seen,
breast cancer risks in the three high-dose and high-doserate studies are not consistently different from those in the
two low-dose-rate studies, irrespective of whether EARs or

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ERRs are considered. However, when Preston et al. [P3]compared the high-dose-rate thymic irradiation study of
infants with the low-dose-rate haemangioma study of infants,
the differences in the EARs were of the order of sixfold.
64. Little and Boice [L5] previously compared breast
cancer incidence rates in the LSS and the Massachusetts
multiple fluoroscopy study. They found that the ratio of the
ERR per unit dose for the Japan study to that of the
Massachusetts study was 2.1 (95% CI: 1.05, 5.0). However,this occurred primarily because of the lower underlying
rates of breast cancer in Japan. When EARs were compared,
the Japan/Massachusetts ratio of EARs was 0.73 (95% CI:
0.4, 1.4), indicating good comparability. These findings do
not necessarily contradict the findings of Preston et al. [P3],
who used the same Massachusetts fluoroscopy data but aversion of the LSS incidence data with an extra six years
of follow-up, i.e. to the end of 1993. Although Preston etal. used the same Massachusetts TB data, they analysedthem differently. They used breast cancer rates from theConnecticut cancer registry to estimate the underlying (zerodose) rates, in contrast to Little and Boice [L5], who useda parametric model to estimate the term for underlying rates.
In addition, Preston et al. [P3] discarded all person-years
before the age of 20 and all person-years within 10 yearsof exposure. While this last assumption would make little
difference to the Japanese cohort, for whom follow-up only
started in 1958 (over 12 years after the bombings), it mightmake more difference to the Massachusetts data.
65. Table 12 shows that, in general, leukaemia risks
follow the pattern for lung cancer, so that ERRs for the
medically irradiated groups are substantially below those forsimilar subsets of the LSS data. This is true for all six medical studies considered. For three of the studies this discrepancy is statistically significant (2-sided p < 0.05).
66. Thus the risks of cancer induction at certain sites (e.g.
leukaemia, lung) for particular groups undergoing radiotherapy are much less than would be expected from the risks
observed in the LSS. It has been generally assumed that thereason for this is cell sterilization, the effect of which is toremove cells that might otherwise develop into cancer.
However, cancer risks are not lower in all radiotherapy
groups (e.g. [G23, T25, V8]), which implies that in thesecases the effects of cell killing (known to take place at thevery high local cumulative doses in many radiotherapy
regimes [T25, V8]) are being countered by cell repopulation within the irradiated areas. A model recently developedby Sachs and Brenner [S84] proposed a simple and radiobiologically plausible mechanism for repopulation of cells
after radiation exposure that explains why this might
happen, at least for solid tumours. This has been generalized to leukaemia, where it is also necessary to consider therole played by cell migration from blood to bone marrowand vice versa [L91, S85].
67. As noted in section I.H above, it is not clear ingeneral how radiation-induced cancer risks should be

35transferred between populations. Caution should therefore
be exercised when making quantitative inferences about
the effects of dose rate, or any other factor, on the basisof comparisons of the excess cancer risks in different populations. This is especially so when, as is the case forbreast and lung cancer in the Japanese, North Americanand Western European populations considered here, there
are substantial differences in the underlying risks. Anothercomplication in comparing radiation risks across studies isthat the radiation energy spectrum involved varies. For thesurvivors of the atomic bombings, the dose was predominantly from high-energy (>1 MeV) gamma radiation, witha small contribution (1–2%) from high-energy (>1 MeV)
neutrons [L28, R12, R20]. Most of the gamma-ray energy
from the two atomic bombs was in the range 2–5 MeV[R12, R20]. In most of the medical studies considered
here, the photon energy was 300 kVp or less. Higherenergy gamma rays are known to be less biologicallyeffective [N8, S31]. For example, the relatively highenergy gamma rays produced by the atomic devices usedin Hiroshima and Nagasaki would be less biologicallyeffective, by a factor of about 3, than photons with anenergy of 250 kVp [S31].
68. Direct estimation of cancer risks in human populations arising from exposure to radiation at moderate andlow dose rates is possible for only a few exposed populations [U2, U4]. Among the most useful estimations arethose from the various studies of nuclear workers [C3,C41, M12]. Table 13 (adapted from reference [M12])
gives a summary of ERRs from the major published studies on workers to date. Table 13 shows that the ratio of
the leukaemia ERR estimate for the second analysis of the
National Registry for Radiation Workers (NRRW) of theUnited Kingdom of Great Britain and Northern Ireland[M12] to that for the current LSS mortality data on thesurvivors of the atomic bombings [P9, P10] is 1.60 (90%
CI: <0, 5.27) (see figure V). The corresponding ratio forsolid cancers excluding lung cancer is 0.67 (90% CI: <0,2.74) (see figure VI). (Lung cancers are excluded becauseof possible confounding by cigarette smoking.) The threecountry study of the International Agency for Research on
Cancer (IARC) [C3] yields similar values, with slightly
narrower confidence intervals. The IARC 15-country study[C41] yields similar values for leukaemia, although forsolid cancers there are (statistically non-significant) indications of higher RRs than from the LSS: the ratio of RRs
is 3.93 (95% CI: <0, 8.62). These values imply that the
ERRs from the LSS do not markedly underestimate risks
in the nuclear worker studies. There is no strong evidence
for a DDREF greater than 1, although the substantial
uncertainties are certainly consistent with a DDREF of 2
(or indeed ∞). As well as the statistical uncertainties, there
are uncertainties relating to the fact that dose in the worker
studies was measured with film badges, which, because ofanisotropy in the radiation fields to which the workers
were exposed, may not accurately represent whole bodydose, and of course take no account of the contribution
from internal emitters. Another factor that must be

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Figure V. Trends with dose in relative risk (and 90% CI) for leukaemia excluding chronic lymphocytic leukaemia in the
NRRW [M12] and among the survivors of the atomic bombings in Japan [P10]
The results for the atomic bombing survivors are based on the linear component of a linear–quadratic dose response (adaptedfrom Muirhead et al. [M12]). The points represent estimated RRs for certain dose intervals, and the regression line is based on a fit tothese data
6

5

Atomic bombing
survivors

trend

NRRW

trend

CI

CI

RELATIVE RISK

4

3

2

1

0
0.0

0.1

0.2

0.3

0.4

0.5

0.6

DOSE (Sv)considered in comparing the worker studies and the LSS
data is the radiation energy spectrum. As noted above,
most of the gamma-ray energy from the two atomic bombings was in the range 2–5 MeV [R12, R20]. There is considerable variation in the radiation energy spectrum among
the nuclear workforces. Even at the Sellafield site there
was substantial variation in radiation energy, with someworkers exposed to high-energy gamma radiation, with anenergy of up to 7 MeV, although for the majority of workers most of the dose was delivered by photons with anenergy in the range 0.1–1 MeV [K28]. As noted above,
higher-energy gamma rays are known to be less biologically effective [N8, S31]. For example, the relatively highenergy gamma rays produced by the atomic devices usedin Hiroshima and Nagasaki would be less biologicallyeffective by a factor of about 2 than photons with anenergy of 0.5 MeV [S31].
69. As an alternative to deriving values for DDREF bycomparing cancer risks in groups exposed at high dose rates(such as the survivors of the atomic bombings) with thosein groups exposed at lower dose rates, attempts have been
made at assessing the curvature in the dose–response relationship for cancer derived from the LSS data in order toassess cancer risks at low doses [C35, L37, P1, P11, V5].
Most of these attempts use various versions of the LSS dataon cancer mortality.

70. Pierce and colleagues [P11, V5] and Little andMuirhead [L37] fitted linear–quadratic and linear models
to the LSS data and derived estimates of a quantity called
the low-dose extrapolation factor (LDEF), which is theamount by which the low-dose (linear) slope of thelinear–quadratic model is overestimated by the slope ofthe linear model, and so is somewhat analogous to
DDREF. Pierce and Vaeth [P11] analysed the LSS Report
11 mortality data and derived values for LDEF of about
1.8 (95% CI: 1.0, 6.0) for leukaemia and about 1.2 (95%
CI: <1, 3.4) for solid cancers. Vaeth et al. [V5] analyseda preliminary version of the older cancer incidence data
[P4, T1] and derived values for LDEF of about 2.5 (95%
CI: 1.3, 8.4) for leukaemia and about 1 (95% CI: <1, 1.4)
for solid cancers. Little and Muirhead [L37] analysed theolder version of the cancer incidence data [P4, T1] andderived values for LDEF of 2.47 (95% CI: 1.24, >1000)
for leukaemia and 1.06 (95% CI: <1, 1.62) for solid cancers. When attention was restricted to the 0–2 Gy dose
range, Little and Muirhead derived values for LDEF of
1.73 (95% CI: <1, 147.67) for leukaemia and 1.21 (95%
CI: <1, 2.45) for solid cancers. The value of 2 for DDREF
recommended by the ICRP is consistent with these values[I11]. For solid cancers, values of DDREF much greaterthan 2 would not be consistent with the LSS data.
Moreover, a value for DDREF of 1 would also be consistent with these data.

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37

Figure VI. Trends with dose in relative risk (and 90% CI) for all malignant neoplasms other than leukaemia and lung cancer
in the NRRW [M12] and among the survivors of the atomic bombings in Japan [P9]The results for the atomic bombing survivors are based on a linear dose response, without adjustment for dose rate (adapted from Muirhead
et al. [M12]). The points represent estimated RRs for certain dose intervals, and the regression line is based on a fit to these data

1.5

Atomic bombing
survivors

trend

NRRW

trend
CI

1.25

RELATIVE RISK

CI

1.00

0.75

0.50
0.0

0.1

0.2

0.3

0.4

0.5

0.6

DOSE (Sv)K. Thresholds and other departures fromlinear–quadratic curvature
71. It has been customary to model the dose–response
function, F(D), in fits to biological data [U5] and epidemiological data [U2, U4] by the linear–quadratic expression:

F(D) = α ⋅ D + β ⋅ D 2(1)

It should be noted that this is a model for cancer induction
whose parameters bear no relation to the α and β values
commonly used in radiotherapy to describe cell killing by
fractionated radiotherapy. While the linear–quadratic dose
response (with upward curvature) that is found forleukaemia is perhaps the most often employed departurefrom linearity in analyses of the shape of the dose–response
curve for cancer in radiation-exposed groups [C35, P1, P11,
S3], there are various other possible shapes for thedose–response curve. Some use has been made of exponential adjustments to the linear–quadratic term in thedose–response function, described by:F(D) = [α ⋅ D + β ⋅ D 2 ] ⋅ exp(γ ⋅ D)

(2)

72. This form has been employed in fits to biological data
[U5] and epidemiological data [B5, L29, L30, L31, S32, T21,
W2]. In particular, there is evidence of cell sterilization

effects in the dose response for non-melanoma skin cancer
among the survivors of the atomic bombings [L30] and forleukaemia in a pooled analysis of the survivors and two
medically exposed cohorts [L31]. The α ⋅ D + β ⋅ D 2 component represents the effect of (carcinogenic) mutationinduction, while the exp(γ ⋅ D) term represents the effect ofcell sterilization. In general, the cell sterilization coefficientis <0. Variant forms of the cell sterilization term, exp(γ ⋅ D),
incorporating higher powers of dose, D, i.e. exp(γ ⋅ D k) for
k>1, are sometimes employed [L30, U5].
73. Evidence has been presented for possible hormetic or
beneficial effects of low doses of ionizing radiation, whether
in respect to cancer [D23, H29, M2] or other end points[M25], although these interpretations of the data have beenchallenged [U5]. For the class of deterministic effects
defined by the ICRP [I11], it is assumed that there is a
threshold dose below which there is no effect, so that, generalizing the above, the dose–response function could takethe form:

F(D) = [α ⋅[D − Dt ] + β ⋅[D − Dt ]2 ] ⋅exp(γ ⋅[D − Dt ]) ⋅1D > Dt

(3)

74. This form of dose response assumes that the radiation-induced excess risk will be zero up until dose Dt , after

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UNSCEAR 2006 REPORT: VOLUME I

which it smoothly varies. Such a form of dose response has
also been employed in analyses of brain damage and smallhead size among those exposed in utero to the atomic bombings at Hiroshima and Nagasaki [O3, O4]. There are anumber of cancers, such as rectal cancer and non-Hodgkin's
lymphoma, which have generally only been observed inexcess following relatively high therapeutic doses of radiation [U2, U4]. It is possible that this reflects variations insusceptibility to radiation-induced cancer and indeed significant differences in the shape of the dose–response curvefor different cancers.
75. Little and Muirhead [L29, L33, L34] fitted
linear–threshold and linear–quadratic–threshold models to
the LSS incidence data (for solid cancers and leukaemia),adjusting also for measurement error. There was no evidence of threshold departures from linearity in the solidcancer data, with fairly tight upper bounds (≈0.2 Sv) on
the magnitude of a possible threshold. Pierce and Preston[P12] also fitted linear–threshold models to the LSS solidcancer incidence data, with an extra seven years of followup (to the end of 1994). Perhaps because of the extra years
of follow-up data, Pierce and Preston [P12] observed asomewhat tighter upper bound of about 0.06 Sv on thepossible threshold when fitting a linear–threshold model.
However, Little and Muirhead [L29, L33] found evidence
at borderline levels of statistical significance ( p =0.04–0.05) for departures from linear–quadratic curvature
for leukaemia incidence. In fits to the LSS Report 12
mortality data, Little and Muirhead [L35] found no evidence for threshold departures from linear–quadratic curvature ( p = 0.16) for leukaemia, and as with the incidence
data there was no evidence for threshold departures fromlinearity for solid cancers, with fairly tight upper bounds(≈0.15 Sv) on the magnitude of a possible threshold. As
Little and Muirhead [L35] document, the LSS leukaemia
mortality and incidence data are fairly similar (see figureVII) (most leukaemia cases were fatal in the 1950s and1960s). Little and Muirhead [L34, L35] concluded that themost likely explanation of the difference in findings
between the leukaemia incidence and mortality data is thefiner disaggregation of dose groups in the publicly available version of the mortality data compared with theincidence data (14 versus 10).
76. Similar models have also been fitted to the LSS incidence data by Hoel and Li [H30] and by Baker and Hoel
[B21]. Hoel and Li [H30] did not adjust for measurement

Figure VII. Relative risk for leukaemia mortality and incidence, derived from data on survivors of the atomic bombings inJapan, as a function of the average true bone marrow dose, with 95% CI (shielded kerma dose < 4 Gy and colon dose
< 4 Sv)
Upper panel: all data; lower panel: low-dose region of upper panel. (Reproduced from Little and Muirhead [L34, L35])
50
Mortality

40

Incidence
Relative risk = 1

30
20

RELATIVE RISK

10
0
0

1

2

3

4

0.0

0.1

0.2

0.3

0.4

3

2

1

0

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error, which may invalidate the results of their analysis, as
discussed by Little [L36] and as elaborated below. Baker
and Hoel [B21] fitted a variety of dose–response models,
one of which allowed for a dose-dependent RBE for neutrons. The findings of Baker and Hoel [B21] were generally
similar to those of Little and Muirhead [L29, L33, L34] andof Pierce and Preston [P12], the main difference being thatwhen using the variable RBE model there was evidence fora threshold for solid cancers. As pointed out by Little [L36],
there are certain methodological difficulties associated withthe use of threshold models, since the asymptotic (χ2) distribution of the deviance difference statistic employed forsignificance tests is not guaranteed, owing to the lack of sufficient smoothness in the likelihood function [S33]. This
problem is circumvented by the likelihood-averaging (regression calibration) techniques used by Little and Muirhead[L29, L33, L34, L35] and by Baker and Hoel [B21] to takeaccount of measurement error, at least when the GSD for
dose is assumed to be non-zero. C2 smoothness of the likelihood is a sufficient condition that guarantees asymptotic
properties of maximum-likelihood estimates [S33].
However, it is not a necessary condition, and in practicemaximum-likelihood parameter estimates and uncertainties
obtained without likelihood smoothing in this data set, forexample those obtained using 0% GSD errors, are reasonably similar to those obtained with non-zero errors [L29].
77. One way in which epidemiological evidence for a
threshold can be assessed is by examination of the lowest
dose at which a statistically significant positive dose
response can be detected. Pierce et al. [P1] used thisapproach on the LSS mortality data. It suffers from thedefect alluded to above, i.e. that one is to some extent estimating the dose threshold Dt from the data, and the lack ofsufficient smoothness in the likelihood as a function of thisparameter means that the asymptotic (χ2) distribution of
associated deviance–difference statistics is not guaranteed.
More refined versions of the tests performed by Pierce etal. [P1] have also been proposed [L89, P45].
78. These problems notwithstanding, this report now briefly
reviews the evidence for the lowest dose at which excess
cancer risk has been observed, for the most part restricting
attention to the LSS data. Simple linear RR models werefitted to the LSS mortality and solid cancer incidence data
[P10, P48], in which the number of cancer cases or deaths
in stratum s and dose group d (with average organ dose D)
is given by PYsd ⋅ λs ⋅ (1 + α ⋅ D), where PYsd is the numberof person-years of follow-up (adjusting the cancer incidence
data for migration out of the two cities). The λs are stratum-specific underlying cancer rates; in all the analyses thestratification is defined by city, sex, attained age and age at
exposure. These data are summarized in table 14 for various cancer sites using the latest LSS DS02 cancer mortality and solid cancer incidence data [P10, P48]. The tableshows that for all solid cancers a statistically significant(2-sided p = 0.05) positive trend occurs over the 0–0.2 Svdose range in the cancer mortality data, and in the 0–0.25
Sv dose range in the incidence data. For subsites of solid

39

cancer, the lowest dose ranges for which there exist statistically significant positive dose trends are generally higher,
although for colon cancer and female breast cancer the dose
response also attains statistical significance over 0–0.25 Sv.
There might appear to be contradiction with the previous
findings of Pierce and Preston [P12], who derived an apparently statistically significant solid cancer dose response
down to about 0.1 Sv in a previous follow-up of the incidence data, using the previous (DS86) dosimetry. The technique used by Pierce and Preston relied on fitting an RR
model with semi-parametric dose response (RR constantwithin each dose interval), and with parametric adjustments
for sex and age at exposure, over the full dose range. Thatdone, Pierce and Preston smoothed the resulting RRs using
a weighted moving average, taking account of the (Wald,
likelihood-based) standard errors to compute uncertainty
bounds. This should be contrasted with the somewhat simpler approach adopted here, in which the data set is progressively truncated, by omitting survivors who received
more than a certain dose, and then simple linear RR models
are fitted to the truncated data sets. In the method used here,
not taking into account the variability by sex and age at
exposure somewhat inflates the uncertainty in ERR coefficients, and this probably accounts for the discrepancy
between these two assessments.
79. Direct epidemiological evidence exists of excess
cancer risk in a number of groups exposed at low doses or
low dose rates, as reviewed in a recent ICRP task groupreport [I25]. In particular, excess cancer risk is associatedwith radiation doses of the order of a few tens of milligraysfrom X-ray pelvimetry in the Oxford Survey of Childhood
Cancers (OSCC) and in various other groups exposed inutero [H56, M16, S11]. However, these in utero studies arecontroversial [I33, M48], in particular because: (a) there isno specificity in risk; risks for all childhood cancers are
increased by about 40%, implying a possible bias; (b) thereis apparent inconsistency with the largely negative findings
for the atomic bombing survivors exposed in utero [D14];(c) risks are not appreciably higher in studies of data on
twins [I26, M57, R46], despite the presumably much higher
prevalence of pelvimetry in this group; (d) risks associatedwith pelvimetry are elevated in case-control studies, but
not generally in otherwise similar cohort studies [C42,D45]; and (e) risk is equally elevated for tumours such as
Wilm’s tumour and neuroblastoma of early embryonal
origin; this is implausible given that most of the radiationdose is delivered in the third trimester [B42]. The ICRP
[I33] has carefully reviewed all these studies, in particular
the OSCC, where it has noted a number of methodological problems, in particular possible selection and recall
biases that may operate. Doll and Wakeford [D37] andWakeford and Little [W23] also carefully reviewed the literature and concluded that most of the criticisms of thesestudies could be addressed, in particular the five statedabove. Doll and Wakeford [D37] concluded that “there isstrong evidence that low dose irradiation of the foetus inutero … causes an increased risk of cancer in childhood.”
However, the ICRP was more cautious and concluded that

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because of the combination of biological and statisticaluncertainties involved, they raise a serious question ofwhether the great consistency in elevated RRs, including
embryonal tumours and lymphomas, may be due to biases
in the OSCC study rather than a causal association” [I33].
Wakeford and Little estimated the ERR coefficient forchildhood (<15 years of age) cancer obtained from the
OSCC to be around 50 Gy–1, leading to a risk coefficientfor total incidence of about 8% Gy–1; however, the statistical, dosimetric and modelling uncertainties in these risk
estimates are considerable [W23].
80. Increased breast cancer risk has been observed among
young women exposed to high cumulative doses from multiple thoracic fluoroscopic X-ray exposures, delivered in
fractions that were, on average, of the order of 10 mGy [B3,
H9, L5]. Increased breast cancer risk has also been observedin a study of patients given multiple X-rays as part of thediagnosis of scoliosis; doses in this study were due to conventional X-rays rather than fluoroscopic X-ray exposures[D17]. A typical chest fluoroscopic exposure given in the
period between 1930 and 1950 would last about 15 s, andpatients would receive 0.01–0.10 Gy [L5]. These fluoroscopic exposures were not low-dose-rate exposures (seesection I.J above), although as the fluoroscopic exposureswould be every two weeks for three to five years, the wide
temporal separation of such fractionated low-dose exposure
should theoretically result in a linear dose–response relationship directly applicable to the estimation of low-dose
effects [N16, U5], as discussed in section I.J above. Excess(absolute) breast cancer risks per unit of total dose in thesegroups are comparable to those among survivors of theatomic bombings [L5, P3]. However, there is no comparable excess risk of lung cancer among fluoroscopy patients,
even though lung doses were comparable to breast doses[D4, D6, H7]. This difference between the findings forbreast and lung cancer among fluoroscopy patients suggeststhat there may be variation in results among cancer sites interms of fractionation effects. However, it should be kept
in mind that exposure to tobacco smoke is by far the dominant risk factor for lung cancer. It is possible that among
TB patients who underwent lengthy courses of lung collapse therapy associated with high cumulative radiation dosefrom fluoroscopic examinations, below-average exposure totobacco smoke might mask a radiation-related increase inlung cancer risk. As discussed in section I.J, attempts weremade to control for smoking in some of the analyses, butthese were based on fairly crude measures such as“ever/never” smoking [D4, H7], so that residual confounding cannot be ruled out. Nonetheless, the mean doses for
smokers and non-smokers, for both men and women, wereremarkably similar, and there was no difference in the percentage of smokers by lung dose over six categories of dose
up to and greater than 3 Sv [H7].
81. As discussed above, there are a number of studies ofoccupationally exposed persons, who generally receive low
doses of ionizing radiation at low dose rates [C3, C36, C41,

M12]. For example, in the IARC 15-country study [C41],
average cumulative doses were 19.4 mSv, and fewer than
5% of workers received cumulative doses exceeding 100
mSv. As noted above, risks observed in these studies are
generally consistent with those seen in the LSS, as well as
being consistent with much lower risks.
82. Recently the ICRP has carefully reviewed the issue ofpossible thresholds and their effect on risk estimates ([I25],
but see also [L99]). A survey of the epidemiological data
indicates that, as discussed above, there are a number ofgroups exposed to low doses and dose rates that exhibit
excess risk compatible with extrapolations from risks
observed at high doses and dose rates (such as in the LSS[I25]). They present an illustrative exercise in quantitative
uncertainty analysis, in which the various uncertain components of estimated cancer risk associated with low-dose,
low-LET radiation exposure are combined. Attention is paid
to the resulting uncertainty distribution for ERR per unit
dose, with and without allowing for the uncertain possibility of a universal low-dose threshold below which there
would be no radiation-related risk. Illustrative calculations
demonstrate that assuming various subjective probabilities
of a low-dose threshold of between 20% and 80% makes
very little difference to the upper 95% confidence limit ofcancer risk. Even when a low-dose threshold is assumed
with 80% subjective probability, the upper 95% confidence
limit of cancer risk is about 5% Sv–1, compared with the
95% upper confidence limit of about 9% Sv–1 if no lowdose threshold is assumed [L99] (see figure VIII). In theexample used, which considers risk from all cancers combined, including leukaemia but not non-melanoma skincancer, the major contributors to uncertainty in the overallrisk factor are: statistical variation in the estimated ERR at
1 Gy for the population of survivors of the atomic bombings; subjective uncertainty with respect to the DDREF to
be applied at low doses and dose rates; and the postulated
uncertainty concerning the existence of a universal threshold at some dose above that for which the calculation was
being made. The ICRP concluded that, unless the existenceof a threshold was assumed to be virtually certain, the effectof introducing the uncertain possibility of a threshold wasequivalent to that of an uncertain increase in the value of
DDREF, i.e. a variation on the result obtained by ignoring
the possibility of a threshold [I25].

L. Effect of age at exposure, latency and timesince exposure
83. When estimating population cancer risks from epidemiological data, one of the principal uncertainties is dueto the fact that few radiation-exposed cohorts have been followed up to the end of life of all study subjects. For example, 55 years after the atomic bombings of Hiroshima andNagasaki, 45% of the survivors were still alive [P10]. In
attempting to estimate lifetime population cancer risks, it istherefore important to predict how risks might vary as a

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41

Figure VIII. Effect on the probability distribution of excess lifetime risk per unit dose of assuming the possible existenceof a low-dose threshold, with probability p = 0.2, 0.5 or 0.8 (reproduced from Land [L99])

1.0

95% upper limit

CUMULATIVE PROBABILITY

0.8

0.6

0.4

0.2

0
2

4

6

8

10

12

14

ESTIMATED LOW-DOSE MORTALITY RISK (% Sv–1)

function of time after radiation exposure, in particular for thatgroup of people for whom the uncertainties in projecting risk
to the end of life are most uncertain, namely those who were
exposed in childhood.
84. Analyses of solid cancers in the LSS and otherexposed groups have found that the radiation-induced
excess risk can be approximately described by a constant
RR model [I11, U2]. The time-constant ERR model assumes
that if a population is irradiated, then, after some latent
period, there is an increase in the cancer rate, the excess
rate being proportional to the underlying cancer rate in a
non-irradiated population. For leukaemia, this model provides an unsatisfactory fit to observations, and consequently, for a group of similar malignancies, a number ofother models have been used, including one in which theexcess cancer rate resulting from exposure is assumed to beconstant rather than proportional to the underlying rate, i.e.
the time-constant EAR model [U6].
85. For solid cancers there is a large body of evidence
that ERRs diminish with increasing age at exposure [L51,
L52, U2]. In particular, this pattern of risk is observed in
the LSS data for both solid cancer incidence and mortality,

for many solid cancer sites and for all solid cancers as awhole [P1, P10, P48, T1] (see also figure X in section II
below), and in a variety of other groups (e.g. radiotherapy
patients) [L51, L52]. The pattern of variation of EARs withage at exposure is generally the reverse of this. For constant attained age the EAR for solid cancers or solid cancer
mortality increases with increasing age at exposure, as seenin the LSS [P10, P48] (see also figure X).
86. For leukaemia, ERRs also generally diminish withincreasing age at exposure [L51, U2]. In particular, this pattern of risk is observed in the LSS data for both solid cancer
incidence and mortality [P1, P4, P10], as well as in a variety of other groups (e.g. radiotherapy patients) [L51, L52,
U2]. The pattern of variation of EARs with age at exposure is generally the reverse of this. EAR increases withincreasing age at exposure, whether for constant attained
age or constant time since exposure, in both the incidence
and the mortality data sets of the LSS [P4, P10]. Patternsof variation of risk by leukaemia subtypes are not so wellunderstood, in part because of a lack of statistical power.
In a combined analysis of three cohorts—the LSS cohort(using incidence data) [P4], the United Kingdom ankylosing spondylitis patients [W2] and a group of women treated

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for cervical cancer [B5]—different patterns of variation ofrisk were seen for the three main radiogenic subtypes [L31].
For acute myeloid leukaemia (AML) and chronic myeloid
leukaemia (CML), the ERR was described by negative
powers of years since exposure (–0.9 and –2.7, respectively), implying no extra variation with age at exposure.
However, for acute lymphoblastic leukaemia (ALL), theERR was described by a negative power (–6.3) of attained
age, implying a reduction of risk with increasing age at
exposure [L31].
87. To some extent related to these issues is that of the“latency period”. This may be defined as the minimum
period following exposure after which an excess risk is
detectable, but is often taken to be the minimum period following exposure after which a statistically significant excess
risk is detected. As such, it will obviously depend on themagnitude of the dose administered and on other factors,
e.g. the magnitude of the ERR and the underlying cancer
rate. For this reason, the latency period may not be a very
useful quantity. Bearing this out, certain groups exposed toradiation due to the Chernobyl accident [K52] and other(medically exposed) cohorts [L98] provide evidence ofshorter latency periods when exposures are higher. Excess
solid cancer mortality is statistically significant for the LSS
cohort already in the period 5–10 years after exposure[P10]. For example, for the period 1950–1952, the ERR per
unit colon dose (with 35% GSD errors), calculated using astratified linear RR model, is 0.41 (90% CI: –0.01, 0.99)
Sv–1; for 1950–1955, the ERR is 0.38 (90% CI: 0.07, 0.75)
Sv–1; and for 1950–1960, the ERR is 0.24 (90% CI: 0.05,0.45) Sv–1. In other words, there is evidence of excess risk
within 10 years of exposure, and a suggestion of an excess(i.e. not quite statistically significant) within 7 years. An
excess of thyroid cancer about 5 years after the Chernobyl
accident has been observed among residents of heavily contaminated areas of the Ukraine [S90]. Given that thyroid
doses due to the Chernobyl accident averaged 1 Gy or more
to some groups (e.g. the 1986 evacuees in Belarus andUkraine [C50, U2]) compared with the much lower doses(e.g. about 0.2 Sv) in the LSS [P48], the apparent discrepancy in latency period is easily explained. Latency periodsof much longer than 10 years are statistically inconsistent
with the LSS breast cancer data [L78]. For solid cancers,
excess risk is manifest between 5 and 10 years after exposure in a number of therapeutically irradiated groups [L51,
W8]. However, BEIR VII [C37] presents evidence fromvarious studies that indicate shorter latency periods for solidcancers, and it assumes a latency period of 5 years for solidcancers when estimating cancer risks for the United States
population.
88. Excess leukaemia risks within 5 years of exposure havebeen observed in the ankylosing spondylitis cohort in the
United Kingdom [D53], and there are suggestions of excess
leukaemia risks in Hiroshima and Nagasaki within 5 yearsof the bombings, albeit based on an open city sample that
includes some people not resident in the cities at the timeof the bombings and with no estimates of dose [F18].

89. For those exposed in childhood, there is evidence thatsolid cancer ERRs may eventually decrease with increasingtime after exposure [L16, L53, L90], although this has notbeen seen in all such groups [S7]. For those exposed in
adulthood, risks are more approximately constant over time
[L51], although again exceptions have been seen [W8]. As
will be seen later (in table 45), the optimal generalized RR
models for solid cancers, fitted to the latest LSS mortality
data [P10], are ones assuming that—as a function of dose,D, age at exposure, e, and years since exposure, t—the
ERR = α ⋅ D ⋅ t1.0 ⋅ [t + e]–2.6, or that the ERR = (α ⋅ D + β ⋅D2) ⋅ t1.0 ⋅ [t + e]–2.6. This implies in either case that the ERR
increases up until approximately 0.6 ⋅ e years after exposure,
after which it decreases. In particular, this means that the
RR decreases sooner for those exposed in childhood thanfor those exposed in adulthood, which is consistent withobservations from the LSS and studies of other irradiatedgroups.
90. Solid cancer EARs generally show marked increases
over time for all ages at exposure. For example, this patternis observed in the latest LSS mortality data [P10] (see table
45 and figure X), and also for many solid cancer sites in
the incidence data [P48] (see tables 47–58). As can be seenfrom table 45, the optimal generalized EAR models for solidcancers, fitted to the latest LSS mortality data [P10], areones assuming that—as a function of dose, D, age at exposure, e, and years since exposure, t—the EAR = α ⋅ D ⋅ t 0.7 ⋅[t + e]2.4, or that the EAR = (α ⋅ D + β ⋅ D2) ⋅ t 0.7 ⋅ [t + e]2.3.
91. The ERRs for leukaemia generally peak very shortly
after exposure, consistent with the short latency period forthis cancer, and then decrease with increasing time after
exposure. This pattern is observed in the LSS incidence andmortality data [L29, P4, P10], in the United Kingdom
ankyosing spondylitis mortality data [W2], in the international cervical cancer case-control study [B5] and in a variety of other groups (generally radiotherapy patients) [L51,
L52, U2]. Patterns of variation of risk over time byleukaemia subtype are not so well understood, in part
because of a lack of statistical power. The combined analysis of the three (LSS, United Kingdom ankylosing spondylitis and international cervical cancer) cohorts discussed
above documented different patterns of variation of riskover time for the three main radiogenic subtypes (AML,
CML and ALL) [L31]. For AML and CML, the ERR was
described by a negative power of years since exposure, witha more strongly negative exponent (–2.7) for CML than for
AML (–0.9). For ALL, the ERR was described by a negative power (–6.3) of attained age, implying a very marked
reduction of risk with increasing time after exposure [L31].
As can be seen from table 46, the optimal generalized RR
models for leukaemia, fitted to the latest LSS mortality data
[P10], are ones assuming that—as a function of dose, D,
age at exposure, e, and years since exposure, t—the ERR =α ⋅ D2 ⋅ [t + e]–1.6, or that the ERR = (α ⋅ D + β ⋅ D2) ⋅[t + e]–1.6. This implies in either case that the ERR decreases
with increasing time after exposure. In interpreting this it
should be noted that the first 5.1 years of follow-up are

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

missing in the LSS data set [P4, P10], so that the early rapid
increase in leukaemia ERR is probably missing.
92. The pattern of variation of leukaemia EAR is generally similar, with a pronounced decrease in EAR withincreasing time after exposure. This pattern is observed in
the LSS incidence and mortality data [P4, P10], at least for
all leukaemia subtypes together. Patterns of variation of
EAR over time by leukaemia subtype are more complex. In
the LSS incidence data there are indications that the EAR
for AML increases over time in the group with the oldest

43(>40) age at exposure, although the EAR decreases withtime in groups with younger ages at exposure [P4]. As canbe seen from table 46, the optimal generalized EAR models
for leukaemia, fitted to the latest LSS mortality data [P10],
are ones assuming that—as a function of dose, D, and years
since exposure, t—the EAR = α ⋅ D2 ⋅ t –0.7, or that theEAR = (α ⋅ D + β ⋅ D2) ⋅ t –0.6. This implies in either case that
the EAR decreases with increasing time after exposure, but
as mentioned above, the problems that result from the missing first 5.1 years of follow-up in the LSS data set [P4,P10] should be noted.

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II. NEW OR UPDATED STUDIES
ble change in the shape of the dose–response curve or in
the age and time patterns of excess risk [P10]. A few highlights of the report can be summarized in selected figuresfrom it. Of the total of 10,127 deaths due to solid cancers
in the cohort (considering all survivors, including those witha shielded kerma dose of greater than 4 Gy), about 5% (479)
would be attributable to radiation exposure [P10].

A. Survivors of the atomic bombings in Japan (LSS)
93. Since the UNSCEAR 2000 Report was issued, thesolid cancer mortality experience of the LSS has been
updated by another 10 years, to the end of the year 2000.
There have been two substantial reports on LSS mortality,
the first updating follow-up to the end of 1997 [P9] and thesecond taking follow-up to 2000 [P10]. The first report
described an increase in the number of deaths due to solidcancers (in the group with a shielded kerma dose of under
4 Gy) from 8,040 in the year 1990 to 9,335 in the year
1997, an increase of 16% [P9]. The second report described
an increase in the number of deaths due to solid cancers to
10,127 in the year 2000, a further increase of 8% over theprevious follow-up [P10].

95. The excess risk of solid cancer appears to be linear
in dose, even in the dose range 0–150 mSv. Figure IX plots
the dose–response data for the ERR, giving the best-fitting
linear dose–response slope and showing a smoothed nonparametric dose–response fit to the data points along witherror bounds on the non-parametric curve. In view of thefact that the upper and lower confidence bounds around thesmoothed curve are drawn at one standard error, most of
the points and the fitted regression line would be within
95% bounds (which would be about twice the width).
Hence there is no indication of upward curvature below
0.5 Gy. The dose response appears to be slightly steeper
up until 0.2 Gy, as described previously by Pierce et al.
[P1]. They commented that there might possibly be adifferential bias in ascertainment of death among low-dose
survivors compared with higher-dose survivors, which
would account for this downward curvature in the dose
response in this region.

94. The major change made in the latest LSS mortality
report [P10] is the use of the new set of dose estimates forthe survivors of the atomic bombings, the DS02 dosimetry[R12]. This differs slightly from the DS86 system, for bothneutron and gamma doses, generally by no more than 20%
in the range up to 1500 m from the two hypocentres, where
survivors received the highest doses [C13, R12]. Analysesof the LSS data for solid cancer and leukaemia mortality
using the new dosimetry indicate that estimates of cancer
risk might fall by about 8% as a result, with no apprecia-

Figure IX. Solid cancer dose–response function (taken from Preston et al. [P10])
The left panel presents dose-category-specific ERR estimates based on DS02 (circles) and DS86 (triangles) with locally weighted
regressions. The right panel displays the DS02 dose response for a low dose range together with linear fits based on dose ranges of
0–1 Sv and 0–2 Sv and the linear–quadratic fit based on the 0–2 Sv range. These curves fit the data about equally well

DS02 smoothDS86 smooth

1.0

DS02 parametric and non-parametric dose response
Low dose range

0.5

EXCESS RELATIVE RISKEXCESS RELATIVE RISK

DS02 and DS86 non-parametric dose response
0-2 Sv range

0.5

DS02 smoothLinear 0-2 Sv
Linear 0-1 Sv
Linear-quadratic

0.4

0.3

0.2

0.1

0.0

0.0
0.0

0.5

1.0

1.5

0.0

2.0

0.2

0.4

0.6

0.8

WEIGHTED COLON DOSE (Sv)

WEIGHTED COLON DOSE (Sv)

45

1.0

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UNSCEAR 2006 REPORT: VOLUME I

Figure X. Primary descriptions of the excess risk of solid cancer (reproduced from Preston et al. [P10])
The left panel presents fitted sex-averaged ERR estimates using both DS86 (dashed lines) and DS02 (solid lines) doses, for ages 10, 30
and 50 at exposure. The right panel presents fitted EAR estimates for the same dose groups

Sex-averaged solid cancer Excess Relative Risk

Sex-averaged solid cancer Excess Absolute Risk
60

DS86
DS02

1.5

1.0

Age at exposure 10 a

Age at exposure 30 a

0.5
Age at exposure 50 a

0.0

EXCESS ABSOLUTE RISK (104 PY Sv)–1

EXCESS RELATIVE RISK AT 1 Sv

2.0

DS86
DS02

Age at exposure 30 a

40

Age at exposure 10 a

20
Age at exposure 50 a

0
40
M:F ERR ratio 1.9:1

60

80

30

97. At present the analysis of cancer mortality using DS02
dose estimates has been conducted only for solid cancers
and leukaemia [P10]. An evaluation for more detailed
cancer end points was conducted in the previous follow-up,

70

90

ATTAINED AGE (a)ATTAINED AGE (a)96. The extended follow-up continues to confirm that the
ERR per unit dose is modified both by age at exposure and(more weakly) by attained age (i.e. age at observation).
Figure X shows the marked trend of decreasing ERR for
solid cancer with increasing age at exposure; this is highly
statistically significant (p < 0.001) (see appendix D, table
D1). After adjustment for age at exposure, there is evidence
at borderline levels of statistical significance for a decline
in the solid cancer ERR with increasing attained age(p = 0.04) (table D1). However, if EAR models are fitted
instead, the EAR per unit dose increases with attained age
and with age at exposure, both of these effects being highly
statistically significant (p < 0.001 and p = 0.002, respectively) (table D1). After adjustment of EAR for attained age,
however, EAR decreases with increasing age at exposure,as can be seen in figure X. For those exposed before age
20, the estimated number of radiation-related deaths has
approximately doubled in each of the last three decades.
The ERR and EAR estimates are greater for women thanfor men, and for ERR this difference is statistically significant (p < 0.001) (table D1). For EAR, without adjustment
for time since exposure and age, there is also evidence ofa difference in EAR between the sexes (p = 0.003) (table
D1). However, after adjustment for time since exposure andattained age, the difference in EAR estimates between thesexes is no longer statistically significant (p > 0.5). Thissuggests that the greater estimate of ERR for women may
occur because the underlying cancer rates in Japan are lower
for women than men.

50

M:F EAR ratio 1.1:1

using DS86 dose estimates [P9]. Figure XI shows the best
estimates of ERR for a number of solid tumour sites takenfrom this earlier report [P9]. The numerical values corresponding to these estimates and their confidence intervals
are given in the respective tables of this annex for thesetumour sites. It is notable that analyses showed that the risk
estimates for nearly all the tumour types were generallycompatible with the estimate for solid cancers as a whole,
namely an ERR of 0.47 (90% CI: 0.37, 0.57) Sv–1. The
ERRs for breast cancer and lung cancer have somewhat
higher values, while the ERRs for cancers of the uterus andpancreas have lower values, as shown in figure XI [P9].
Nevertheless, the variation in the ERRs among the 14 solid
2 = 28.8,
cancer sites depicted is statistically significant (χ 13
p = 0.01). The largest contribution to the χ 2 heterogeneity
statistic is from cancer of the uterus (6.0) followed bycancer of the pancreas (4.6).
98. The solid cancer incidence data have recently beenreanalysed using the DS02 dosimetry [P48]. This extends
the follow-up to 1998 from the previous 1994 follow-up
of these data [P12], resulting in a total of 18,645 cases,
13,454 of which were among people within 10 km of therespective hypocentres at the time of bombing, for whom
doses were estimated using the DS02 dose assessment
methodology. (It should be noted that these numbers differ
from those given in table 19 because survivors with dosesof less than 0.005 Sv are omitted from all of tables 19–44.)
By comparison, the previous follow-up had 11,455 cases
among people within the 10 km range [P12]. Section IVof this annex presents evaluations of population cancer
risks for a variety of populations using risk models derived
from these latest mortality and incidence data sets
[P10, P48].

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B. Mayak worker study
99. Major new reports are available concerning lung andliver cancer risks for workers at the Mayak nuclear complex in relation to both external radiation and plutonium
exposure, and these reports are discussed in the sections
below for the respective organ sites [G2, G12]. The researchis especially important in that it is the only study that has
a large enough number of persons with moderate to highplutonium exposures to be informative regarding the healtheffects of plutonium exposure. The dosimetry is being
improved [K23, K24, R2], and the first overall assessmentof cancer end points has appeared [S28], albeit only in relation to external dose. Internal doses have been calculated
for only a few organs. Shilnikova et al. [S28] studied cancer
mortality among all the approximately 21,500 people who
worked at the Mayak nuclear complex between 1948 and1972. This included workers in the nuclear reactor complex(4,396 workers), the radiochemical plant (7,892 workers),
the plutonium production plant (6,545 workers) and two
auxiliary plants (2,724 workers), the water treatment facility and the mechanical repair plant. The latter two groups
had relatively low radiation exposures. The average cumulative external dose among those monitored for externalradiation exposures was 0.8 Gy. About 24% of the cohort
were women, and their mean cumulative dose was similar

47

to that of the men. Workers in the radiochemical and plutonium production facilities had a potential for significant
internal exposures from inhaled plutonium (239Pu) aerosols
as well as from external gamma radiation. Approximately
one third of those potentially exposed to plutonium weremonitored for plutonium exposure. Among those monitored,
the mean body burden was 2.1 kBq, considerably higher
than body burdens in other worker series in the United
Kingdom or the United States.
100. The follow-up until 1997 of the workers has been ofgood quality: only 10% of the entire group have been lostfrom the follow-up, and the cause of death is documented
for 97% of the deceased. The workers have been followed
for an average of roughly 40 years. There were 7,067 deathsin all, including 1,730 due to solid cancers and 77 due toleukaemia (66 excluding chronic lymphocytic leukaemia(CLL)). The largest numbers of cancer deaths were due tocancer of the lung (569) and of the stomach (308). The
deaths due to solid cancers included 668 deaths from cancers in the organs of primary plutonium deposition (569
lung, 67 liver and 32 skeletal cancers).
101. The dose–response analyses for external gamma
radiation took into account exposures to plutonium, using
measured values when available or an ordered score

Figure XI. Estimates of the site-specific solid cancer ERR with 90% CIs and 1-sided p-values for testing the hypothesis of
no dose response
Except for sex-specific cancers (breast, ovary, uterus and prostate), the estimates are averaged over both sexes. All estimates andp-values are based on a model in which the effects of age at exposure and of attained age were fixed at the estimates for all solidcancers as a group. The dotted vertical line at 0 corresponds to no excess risk, while the solid vertical line indicates the sex-averaged risk
for all solid cancers (reproduced from Preston et al. [P9])
Deathsp value

All solid cancers

9 335

p = <0.001

Oesophagus291

p = <0.001

Stomach2 867

p = <0.001

Colon

478

p = <0.001

Rectum370

p = 0.14

Liver

1 236

p = <0.001

Gall bladder328

p = 0.007

Pancreas407

p = 0.29

Lung

1 264

p = <0.001

Breast275

p = <0.001

Uterus518

p = 0.20

Ovary

136

p = 0.004

Prostate104

p = 0.42

Bladder150

p = 0.02

Other solid911

p = <0.001
–0.2

0.0

0.5

1.0
ERR (Sv–1)

1.5

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judging the potential for plutonium exposure when measurements were not available. For total solid cancers, theERR estimate for external exposure (adjusted for plutonium
exposure) was 0.15 (90% CI: 0.09, 0.20) Gy–1. However, it
showed a downturn at higher doses (concave upward curve).
The addition of a quadratic component to the fit produced
an estimate for the linear component of the ERR of 0.30
(90% CI: 0.18, 0.43) Gy–1, twice the simple linear estimateof 0.15 (90% CI: 0.09, 0.20) Gy–1 [S28].
102. Even after adjusting for plutonium exposure, theexternal gamma risk estimate for lung, liver and skeletalcancers combined was greater than that for other cancers.
This may be because plutonium deposition could only partially be adjusted for by using the surrogate exposure measure. The linear ERR estimates were 0.30 (90% CI: 0.18,
0.46) Gy–1 for lung, liver and skeletal cancers, and 0.08
(90% CI: 0.03, 0.14) Gy–1 for other solid cancers. For bothgroups of cancers, there were suggestions of concave
upward curvature, such that the linear terms in linear–quadratic models of dose response were approximately twice
those from the simple linear models [S28]. An evaluationof effect modifiers on radiation risk showed no differenceby age or by time since exposure, but did show a significant decline in risk with older age at hire. The limited data
available suggested that smoking was not a major confounding factor for the radiation effect in this study.
103. There was an approximately 40% excess mortality
for leukaemia excluding CLL. The estimated ERR was 0.99
(90% CI: 0.45, 2.12) Gy–1. There was a suggestion of concave upward curvature, but it was not statistically significant (p = 0.1). There was a strong temporal effect, such thatthe risk from doses received in the most recent 3–5 years
was observed to be more than 10 times that from doses
received more than 5 years earlier.
104. The risk estimates are somewhat lower than thosefrom the LSS cohort, but the authors cautioned that any
comparison should be regarded as tentative in view of thedosimetric uncertainties for the Mayak cohort. Lung doses
due to plutonium are extremely high for this cohort, so highthat in some cases fibroses developed. It appears that subjects receiving higher doses were more often autopsied thanthose receiving lower doses; therefore this group may have
had better ascertainment of causes of death. Given thesefactors, it is difficult at present to compare the results ofthis study with others.

C. Techa River study
105. The dosimetry and epidemiological procedures arebeing improved for the study of persons exposed to effluents of the Techa River in the Russian Federation [D8, D22,
K5, K6]. Internal doses have been estimated from autopsy
samples collected from 1951 onwards (i.e. from very close
to the time of maximum exposure in the early 1950s), fromin vivo beta measurements in teeth from 1959 onwards, andfrom a large number of whole-body-counter (WBC) measurements of 90Sr based on bremsstrahlung from the decayof 90Y [D22]. About half the original Techa River cohort
has such individual measurements [D22]. Internal doses forthis cohort were estimated by scaling 90Sr intakes for a reference village (Muslyumova) by the average WBCestimated 90Sr skeletal body burdens in other settlements,and similarly for other shorter-lived radionuclides, giving
what are fundamentally age-specific village-level internal
dose estimates. External doses were computed on the basisof measurements made near the shoreline and in individual
villages, and on the basis of estimates of radionuclide transport from the site of release [D22]. Estimates of annual village-level mean doses were computed on the basis of details
on the distribution of distances of houses from the shoreline within each village. Dose estimates for cohort members were individualized by taking into account factors suchas their residence history, length of follow-up and age. An
updated dosimetry system, TRDS-2000 [D22], was developed several years ago. Internal doses from 90Sr, which
accounts for most of the red bone marrow dose received bythis cohort, do not change markedly using the TRDS-2000
dosimetry system [D22, K6]. There is much more change
in the external dose estimates, which are generally lower
using TRDS-2000 [D22]. While some questions have beenraised about the external dose component of TRDS-2000[J5, M22], dose estimates have been validated on a village
level using physical measurements on bricks [J5]. A recent
review of the system [B66] suggested that the basic methodology was sound, although the reviewers indicated that thevalues of risk estimates using the system should be considered preliminary.
106. The first reports on health effects using TRDS-2000
have appeared [K49, K50, O2], and the preliminary risk
estimates provide evidence of increased solid cancer andleukaemia risks following protracted low-dose exposures.
There are, however, likely to be changes to the risk estimates from this cohort associated with the fact that, as indicated above, the dose estimates are based on individualized
village-level mean radionuclide intake and external exposure estimates. While genuine individual doses are clearly
preferred, using the individualized dose estimates probably
results in Berkson errors. In general, Berkson errors resultin little, if any, bias in the dose–response estimates, butrather lead to a reduction in the statistical power to detect
an effect if it exists.
107. The patterns of variation of risk for solid cancer inthis cohort are unusual, with indications that ERR increases
both with age at first exposure (2-sided p = 0.08) and withattained age (2-sided p = 0.03) [K50]. These patterns are
not observed for leukaemia, although there is a suggestionof an increase in ERR with increasing age at first exposure(2-sided p = 0.10). Such patterns are the reverse of what isobserved in the cancer mortality data for the survivorsof the atomic bombings [P9, P10] and in many otherradiation-exposed groups [U2]. The efforts currently under

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way to provide increasingly individualized dose estimates
and to improve mortality and morbidity ascertainment
should make this cohort more informative regarding cancer
risks at low dose rates.
108. A nested case-control study of leukaemia risk has
been performed using incidence data for this cohort, basedon the older TRDS96 dosimetry system [O13]. There are asomewhat larger number of cases (83) than from the recent
mortality data [K50] (49 non-CLL, 12 CLL), although only
50 of these cases are of known cell type, and 20 of these
50 cases are CLL. The results confirmed an increase in risk
with red bone marrow dose, for both internal and external
exposure. No increase in risk was observed with age at thetime of maximum releases.

49

the exposed group. Bauer et al. observed elevated risks thatwere statistically significant for all solid cancers(ERR = 0.81 (95% CI: 0.46, 1.33) Sv–1), stomach cancer(ERR = 0.95 (95% CI: 0.17, 3.49) Sv–1) and lung cancer(ERR = 1.76 (95% CI: 0.48, 8.83) Sv–1) [B58]. The ERR
was statistically significantly increased with increasing age
at exposure (p < 0.0001). Such patterns are the reverse ofwhat is observed in the cancer mortality data of the survivors of the atomic bombings [P9, P10] and of many otherradiation-exposed groups [U2]. Taken together with thegenerally higher ERR for solid cancers in this cohort compared with that observed in the atomic bombing survivor
data, again the reverse of what might be expected following protracted exposure, this suggests that “ecological bias”
may be operating preferentially in groups with older ages
at exposure.

D. Semipalatinsk weapons test site fallout
E. International worker study
109. To date there have been a number of publications
about dosimetry [G4, S10] and health follow-up [G7, S9]
in populations in Altai (Russian Federation) and Kazakhstan
exposed to radioactive fallout from the nuclear weapons
tests at Semipalatinsk, although only the recent report of
Bauer et al. [B58] assesses health effects in relation toreceived dose. The cohort consists of inhabitants of 10
exposed villages near the Semipalatinsk test site (STS) andof six comparison villages some hundreds of kilometres distant from the STS. For both exposed and comparisongroups, persons had to have been born before 1961 and tohave been permanently resident in one of the villages. Dosereconstruction for the exposed subcohort is based on historical data for levels of radionuclides in food and the environment and on semi-empirical models for radionuclide
accumulation and metabolism. Doses due to radionuclide
ingestion and inhalation were estimated for the thyroid (dueto 131I), the whole body (due to 137Cs) and bone marrow(due to 90Sr). Most internal dose was due to 131I. For thecomparison group, settlement-specific dose estimates could
not be obtained, so a per caput cumulative dose of 20 mSv
due to fallout was assigned to all persons. Even within the
exposed group the doses were estimated for subgroups
according to their age at main exposure and settlement, so
that, as constituted at present, the study is fundamentally an“ecological” one. The possibilities of bias in such studies
are well known [G13, P15]. The extent of variation of dose
within each settlement is not clear, although there iscertainly substantial variation (by at least three orders ofmagnitude) of, for example, thyroid dose over time [G4].
Hence substantial “ecological bias” cannot be discounted.
110. Bauer et al. present two sets of analyses: those internal to the 10 exposed villages, and those for both exposedand comparison villages [B58]. Results are similar for bothsets, although excess risks tend to be higher if the full cohort
is used rather than only the exposed group. Because of
deficiencies in the dosimetry for the comparison group,
the Committee has concentrated on results internal to111. Following an earlier pooled analysis of data on radiation workers at selected sites in three countries [C3, I2],
a larger international collaborative study has been conducted based on workforces from 15 countries working in
any of 154 nuclear facilities, numbering 407,391 workers
monitored for external photon (X and gamma) radiation
with personal dosimeters [C41]. This study, which included
most of the cohorts included in the earlier three-country
study [C3, I2], has attracted considerable attention, including a substantial editorial by Wakeford [W37]. The study
excluded 190,677 workers because they had not been
employed in one or more of the facilities for at least one
year, or because they had not been monitored for external
exposure, or because they had potential for substantial exposure from internal emitters or neutrons (amounting to morethan 10% of the effective dose). The study followedmortality in the cohort, and accumulated 5.2 million personyears of follow-up. The average individual effective dose
was 19.4 mSv, with 90% of the workers receiving cumulative doses of less than 50 mSv and with fewer than 0.1%of the workers receiving doses of more than 500 mSv. There
were 6,519 deaths from cancer excluding leukaemia, and
196 from leukaemia excluding CLL.
112. Cardis et al. estimate the ERR for cancers excluding
leukaemia to be 0.97 (95% CI: 0.14, 1.97) Sv–1, for all solidcancers to be 0.87 (95% CI: 0.03, 1.88) Sv–1 and forleukaemia excluding CLL to be 1.93 (95% CI: <0, 8.47)
Sv–1 [C41]. As noted in table 13, while the difference fromthe LSS risks in a comparable group (male, age at exposure 20–60 years) is not statistically significant, there areindications that the solid cancer risks observed are 4 times
higher than those in the LSS. As pointed out by Wakeford[W37], since the worker risks relate to exposure at low dose
rates, a DDREF of 2 might be indicated [I11] (see section
I.J above), so that the true discrepancy with LSS solidcancer risks may be about a factor of 8, but with very wide
confidence limits. The ERR for solid cancer is strongly

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influenced by that for lung cancer, 1.86 (95% CI: 0.26, 4.01)
Sv–1. The ERR for cancers excluding leukaemia, lung andpleural cancers is 0.59 (95% CI: –0.29, 1.70) Sv–1 [C41].
However, smoking-related cancers other than lung cancer
exhibit an ERR of 0.21 (95% CI: <0, 2.01) Sv–1. Set against
this, the ERR for non-malignant respiratory disease is 1.16
(95% CI: –0.53, 3.84) Sv–1 and that associated with chronic
obstructive bronchitis and emphysema is 2.12 (95% CI:–0.57, 7.46) Sv–1, both of these groupings of diseases thatare related to smoking. As Cardis et al. indicate, “Taken
together, these findings indicate that a confounding effectby smoking may be partly, but not entirely, responsible forthe estimated increased risk for mortality from all cancers
other than leukaemia” [C41]. Therefore caution is suggested
in interpreting the study results.
113. As noted by Wakeford [W37], the Canadian data
have “a surprisingly large influence on the ERR for all cancers other than leukaemia”. Indeed, although the Canadiandata contribute 400 deaths from cancers other than
leukaemia (6% of the total deaths from this cause), andnotwithstanding the fact that the Canadian workers have an
average individual effective dose (19.5 mSv) that is virtually the same as the full cohort (19.4 mSv), removing theCanadian cohort results from the estimation of solid cancer
ERR produces a value of 0.58 (95% CI: –0.22, 1.55) Sv–1,
i.e. a reduction of 40% from the overall central estimate
value. This estimate is still larger than the corresponding
estimate from the LSS data, although it is no longer statistically significant [C41]. The fact that this study has sucha large influence on the results, given the small size (interms of relative numbers of deaths, person-years of followup, person-dose (that is to say, the sum of the cumulative
dose per person over the cohort)) of the Canadian cohort,appears to reflect the low precision in the findings from theother cohorts. Figure 2 in the paper [C41] shows that the
Canadian cohort has a solid cancer ERR of >6 Sv–1 with alower 97.5% centile confidence limit of >2 Sv–1. A previously published study of Canadian nuclear workers [Z6]gave a lower risk estimate for solid cancers (ERR = 2.80
(95% CI: –0.038, 7.13) Sv–1), of only borderline statisticalsignificance (p = 0.054). Detailed analyses have been conducted aimed at understanding the apparent differences inrisk estimates for the Canadian nuclear worker cohort
between Zablotska et al. [Z6] and the 15-country study[C41]. These analyses show that the difference is related tothe exclusion of Ontario Hydro workers from analyses ofsolid cancers in the latter study, owing to the lack of information on socio-economic status (SES) for this group of
workers. Several studies of radiation workers (e.g. [C3,M12]) have shown that both solid cancer risk and occupational radiation dose are related to SES, and hence SES isa confounding factor. All other differences between references [Z6] and [C41] in analytical approaches, dosimetricquantities and definition of study populations had very little
impact on the results [E12]. In the Canadian National Dose
Registry, which includes a large number of other personnel(e.g. medical and dental radiographers) not included in the
15-country study, the ERR for cancers other than leukaemia

among males was also large, 2.5 (90% CI: 1.1, 4.2) Sv–1
[S8], as was that for mortality from all cancers among
males, 3.0 (90% CI: 1.1, 4.9) Sv–1 [A8]. However, whereas
many non-cancer causes of death (including infectious andparasitic diseases, and accidents) were correlated with dosein analyses of the Canadian National Dose Registry, suggesting the possibility of bias in vital status ascertainment,
this was not the case for the Canadian component of the
15-country nuclear worker study [E12]. The ERR for
Canadian workers in the latter study appears to be unusually high and the lower confidence bound does not include
the combined estimate. Reviews of historical dose records
have raised possible concerns about the completeness ofrecords in one Canadian facility (Atomic Energy of Canada
Limited) that may have biased the Canadian ERR. This iscurrently being evaluated. It should be stressed that there
are substantial uncertainties in the risk estimates derived
from the 15-country study. Consequently, not too muchshould be made of the apparent discrepancies with risks
observed in other studies, such as the LSS.

F. United States medical radiologic technologists
114. The cohort of 146,022 United States “radiologic
technologists”, of whom 106,884 (73.2%) are female, wasdrawn from those certified by the American Registry ofRadiologic Technologists (ARRT) during 1926–1982 [M10,
M31, S29]. The vital status at the end of 1997 of 99.3% of
the technologists was established and includes 12,624
deaths [M31]. A study of cancer incidence based on individuals who responded to two questionnaire surveys in the
periods 1983–1989 and 1995–1998 (or who died between
the first and second surveys) identified 2,651 cancer cases[S29] among the respondent subcohort of 90,305 persons.
Individual dose reconstructions are being conducted but are
not yet available, so year of entry to the ARRT is used asa crude surrogate for dose, since exposure levels were considerably higher in earlier years. About 1.6% of the cohort
was first certified before 1940, 3.9% in 1940–1949, 13.1%
in 1950–1959, 28.1% in 1960–1969, 48.3% in 1970–1979
and 5.1% in 1980 or later [M31]. About half had worked
as radiologic technologists for 10 or more years [M31, S29].
As with most working populations, the rates of death fromall cancers were lower than expected in the general population, for both sexes [D3, M31]. No specific cancer typeshowed an overall excess risk.
115. Mortality from all cancers combined, and separatelyfrom breast cancer, lung cancer and leukaemia excluding
CLL, was examined in more detail among those who hadcompleted the initial questionnaire survey, which permitted
control for other disease risk factors [M31]. The results
showed that the cumulative number of years of work as aradiologic technologist was not associated with the risk of
any of these cancer categories, nor was there any association between year of first certification as a radiologic
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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER[M31]. Mortality risks of all cancers combined showed amodest but statistically significant increase (2-sided p =0.04) with earliest calendar year first employed, as also didbreast cancer mortality (2-sided p = 0.002). In addition, thenumber of years worked before 1950, when exposures werelikely to have been higher, was positively associated withboth breast cancer risk (2-sided p = 0.018) and risk ofleukaemia excluding CLL (2-sided p = 0.05) [M31].
116. There are substantial methodological concerns withthese related data sets. The year of first entry into the profession (entry to the ARRT) is largely confounded by yearof birth. Substantial birth cohort effects would be expected,
for example effects associated with changes in reproductive
patterns over this period, although some of these lifestylefactors (age at first childbirth, age at menopause, family history of breast cancer) were adjusted for in the breast cancer
mortality study [M10]. There being as yet no radiation dose
estimates for this cohort, the putative radiation effect is
implicitly derived from comparisons of persons entering the
ARRT prior to 1940 with those entering later, and so may
be difficult to separate out from the effect of year of birth.
In addition, because there are relatively few persons in theolder age groups among those entering the profession later(for example after 1960), there will be little overlap in theseolder age groups with those entering before 1940, so thatage-specific adjustment (for example by comparison ofcancer rates at similar ages in the post-1960 versus pre-1940
birth cohorts) is not possible. Cancer incidence rates were
estimated from a combination of death certificates, questionnaire responses and medical records from physicians andhospitals [S29]. These were compared with Surveillance,
Epidemiology and End Results (SEER) population-based
incidence rates. As these incidence rates were calculated forvarious metropolitan regions that may not reflect the geographical distribution of the ARRT cohort, but in any casehave much more uniform (and higher quality) ascertainmentof cases, it is possible that biases would be introduced in
the calculation of standardized incidence rates (SIRs).

G. Chinese radiologists and technologists
117. Wang et al. [W3] have updated their study of cancer
incidence among medical X-ray workers in China to include
the years 1950–1995. An important aspect of the new updateis that group doses are now available, which should permit
risk estimates to be calculated, although these have yet tobe taken into account in the analyses of cancer risk [W3].
The study group consisted of 27,011 medical diagnostic
workers, including both radiologists and technicians,
employed between 1950 and 1980 in 24 provinces of China.
A control group consisted of 25,782 workers from othermedical specialties who did not use X-ray equipment intheir work. Eighty per cent of X-ray workers and 69% ofcontrols were males. Seventy per cent of the diagnosed
cancers had histological confirmation; most of the otherdiagnoses were made by X-ray examination.

51

118. Since there was no systematic individual dose monitoring before 1985, a retrospective dose reconstruction was
performed [Z1] by measuring exposures to a dose phantom
at 608 X-ray machines and 1,632 workplaces with simulated historical working conditions. In addition, 3,805
X-ray workers were randomly chosen to be interviewed
concerning details of their occupational exposure histories.
To assess the validity of their dose reconstruction, stable
chromosome analysis was performed for 96 workers using
G banding and fluorescence in situ hybridization (FISH)
techniques [W4]. A correlation between the biodosimetry
and physical dose estimates was found, although thephysical dose estimates were consistently about 50% higher.
The estimated mean cumulative doses for those who began
practising radiology prior to 1960, in 1960–1969 and in
1970–1980 were 758, 279 and 83 mGy, respectively.
119. An excess of total cancers (RR = 1.19; 95% CI: 1.1,
1.3, n = 836) was found. There was also a significant excessof leukaemia among X-ray workers, with 44 cases versus
25 in the control group (RR = 2.17; approximate 95% CI:
1.6, 2.9) [W3]. The RR for leukaemia was greatest among
those employed as X-ray workers before age 20 and
declined progressively for those first employed at older
ages. The RR for leukaemia was greater (RR = 2.4) for
those employed before 1970 than for those first employed
in 1970–1980 (RR = 1.7). The excess leukaemia incidence
rate in the irradiated group was not attributable to a deficit
in the control group, as the leukaemia rate in the control
group was at least as high as in the general population.
120. Significant excess risks were also reported for female
breast cancer (RR = 1.34, n = 46), non-melanoma skincancer (RR = 4.05, n = 18), oesophageal cancer (RR = 2.65,
n = 39), liver cancer (RR = 1.20, n = 155), lung cancer(RR = 1.20, n = 151) and bladder cancer (RR = 1.84, n =21). Age at exposure appeared to be an effect modifier forthyroid and lung cancer, as those first employed at the
youngest ages had nominally higher RRs. The RRs for total
solid cancers and for cancers of the liver, skin, bladder andthyroid were somewhat higher in the earlier cohort (first
employed before 1970) of X-ray workers. However, cancers of the stomach were very much higher in the younger
cohort (first employed in 1970–1980). The reported statistical significance of the results in this study, however,should be treated cautiously, as it appears that calculations
were performed without taking into account the variance
contributed by the control group. The inconsistent trends inrisk in the later compared with the earlier groups imply
some problems with this study, perhaps in relation to thecomparison group.

H. Studies of aircrew
121. Because aircrew receive elevated doses, which canrange up to 6 mSv per year, with a substantial neutron component (representing 25–50% of the absorbed dose) [B22,UNSCEAR REPORT-PART 1.qxp

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G15], there has been much interest in studies of this group.
To date there have been various, generally small, studies ofaircrew, whether pilots or flight attendants. The largest studies to date are three large pan-European studies, the first offlight attendants [Z4], the second and third of male cockpit
crew [B23, L48]. The two studies of male cockpit crew differ
principally in that the first [B23] used length of employment
as a relatively crude analogue for exposure, whereas thesecond [L48] used total flying time and radiation dose,
although these measures were only available for a subset ofthe full cohort (excluding cohorts from Greece and the United
Kingdom for which insufficient information was available).
Radiation dose was estimated on the basis of “block hours”,
a measure of time spent on the aircraft (including time on
the runway), the type of aircraft a pilot was licensed to fly
in a particular year, and a job–exposure matrix based on typical routes of each national airline for each type of aircraft
in a specific year and at typical flight altitudes [L48]. The
first study, of flight attendants, found a statistically nonsignificant increase in mortality from melanoma (standardizedmortality ratio (SMR) = 1.93; 95% CI: 0.70, 4.44) among
male crew, but no suggestion of increased risk among female
staff (SMR = 0.36; 95% CI: 0.04, 1.37) [Z4]. The second
study, of male cockpit crew, found a statistically significant
increase in mortality from melanoma (SMR = 1.78; 95% CI:
1.15, 2.67) [B23]. No consistent association between employment period or duration and cancer mortality was observed,
whether for melanoma or any other end point, in either study[B23, Z4]. In the third study, none of the SMRs were significantly elevated, nor were there any trends of mortality
with dose for any cancer site [L48]. If anything, there wereindications of a negative trend in the risk of all cancers combined with increasing radiation dose (p = 0.101), so that, forexample, the RR associated with doses of greater than 25
mSv was 0.74 (95% CI: 0.51, 1.06) [L48]. In some of thestudies of groups nested within this cohort, the dosimetry
based on hours spent in certain types of flight (i.e. lowaltitude, intermediate-distance, long-distance) has been
evaluated [P21]. There is in general no assessment of solar
exposure or constitutional factors, a serious problem in evaluating skin cancer risk. The aircrew studies have recently
been reviewed, and evidence has been found of consistentexcess risk of melanoma, non-melanoma skin cancer andbreast cancer [S35]. However, as with the three large
studies discussed above, there is generally no relation withduration of employment. In the absence of individual information on radiation dose and solar exposure in most of thestudies, as well as reproductive histories, it would be difficult to ascribe the excess risks observed in these studies toionizing radiation exposure [S35].

I. Patients treated with radiation
122. Patients treated with radiation are providing opportunities to learn about the mechanisms of carcinogenesis as
well as providing opportunities to estimate risks of asecond cancer following both high and low doses [A37,

B67, C51, I34, T49, T50, V6]. Radiation doses to specificorgans can be estimated with precision, scatter doses toorgans outside the treatment beams are low, the numbersof exposed patients are large, and the relatively high survival rates of children and young adults provide opportunities to study the patterns of risk expression over long
periods of time. Large-scale international studies ofpatients treated with radiation exhibit risk estimates thatare generally lower than those from the studies of the survivors of the atomic bombings in Japan; these lower risks
have been attributed in large part to cell killing andfractionation effects [L20, L23].
123. A new study of cervical cancer patients showed anincreased rate of leukaemia, other than CLL, that occurs
within a few years after treatment [K57]. This study of over
16,000 women treated with radiation in the United States andfollowed within the SEER cancer registration system also
found no evidence that CLL was increased at any time after
exposure, similar to the absence of excess risk reported inprevious studies of leukaemia after cervical cancer [B5, K1].
Also consistent with previous studies [B8, B11], radiotherapy for cervical cancer has contributed to the increased risk
among long-term survivors for subsequent primary cancersof the stomach, rectum, urinary bladder, and bone and joints.
124. Recent studies of patients receiving radiation treatment for Hodgkin’s disease (HD) continue to provide new
information on risks for second cancers. The risks of breast
cancer are elevated in a dose-dependent manner following
radiotherapy, and ovarian ablation associated with radiotherapy and chemotherapy substantially reduces the risk
[T25, V8]. A family history of breast cancer does not appear
to influence radiation risk [H59]. Estimates of cumulative
absolute breast cancer risk have been developed to assist
physicians in counselling patients [T51]. Lung cancer incidence rates have also been found to be elevated in longterm survivors of HD, even after very high therapeutic doses,
although estimates of excess risk per unit dose are much
lower than reported in lower-dose studies [T3]. Cigarettesmoking and high lung dose enhanced the risk of lung cancer
in a near-multiplicative fashion [G23]. Leukaemia is also a
potential consequence of treatments for HD, although therisk from chemotherapy was generally much greater thanthat associated with radiotherapy [S46, T7].
125. Children and young adults treated for cancer are surviving much longer than in years past; this allows time forincreasing numbers of late effects to be detected [G32,
M58]. High-dose radiotherapy for childhood cancer
increases the risk of thyroid cancer, but a downturn in risk
is observed above about 30 Gy, attributable in all likelihood to cell killing [S88], consistent with previous studies[T5]. Significant increases in the incidence rate of second
tumours that occur in the brain are associated with treatment for cancer, with children showing higher risks thanadults [I20, N14, N20, W35]. Treatment for childhood HD
can result in higher risks of breast cancer occurring in later
life [G29, V8]. Treatment for retinoblastoma results in

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increased rates of sarcoma and other malignancies, whichsuggests a possible interaction with an underlying genetic
susceptibility [K43, W11]. Long-term survivors of childhood cancer are showing increased risks of second cancers
that are now persisting late into life; continued follow-up
and study has been recommended to quantify risks andlearn about patterns of risk expression over long periodsof time [G30, M59, N21, S47].

J. Worker and public exposure to uranium
126. Many workers employed during the early years ofuranium processing, manufacturing and milling potentiallyinhaled or ingested relatively large amounts of uranium butwith minimal exposure to radon gas. Because of recent concern about the possible health effects of exposure to depleted
uranium, studies of uranium workers (excluding underground miners) have been carefully evaluated in various
meta-analyses [H60, I35, T32]. Fourteen epidemiologicalstudies were conducted of more than 120,000 workers at
uranium processing, enrichment, metal fabrication andmilling facilities [T32]. These studies, overall, did not findthe rate of any cancer to be significantly increased. The total
risk for all cancers taken together was close to that expected;that is, 7,442 cancers were observed compared with 8,178
expected (SMR = 0.91) [T32]. There was reasonable consistency among the findings from the 14 epidemiologicalstudies of workers employed throughout the world [T32].
Although there were weaknesses in these studies because oflimited dosimetry, the absence of time response analyses andthe inherent difficulties associated with accounting for thehealthy worker effect, the results were consistent with alarge-scale case-control study of 787 lung cancer cases
among workers at four uranium processing operations, whichfound no association with estimated lung dose [D43]. A
recent study of workers in the early days of nuclear energy
development incorporating comprehensive dosimetry forinternal emitters also revealed no statistical evidence forincreased cancer risks, although the numbers were not especially large [B68, B69]. In contrast to the negative findings
from studies of uranium workers other than miners, studies
of underground uranium and other hard rock miners haverevealed consistent and substantial increases in lung cancer
attributable to radon and its decay products [C36, L8].
127. The primary occupational exposures in uranium mills
were to uranium, silica and vanadium. A recent study ofColorado Plateau millers was conducted by the National

53

Institute of Occupational Safety and Health (NIOSH) of
1,484 men who worked at one of seven uranium mills on
or after 1 January 1940 [P25]. Increased numbers of deaths
were found for non-malignant respiratory diseases, lungcancer, lymphoma and kidney disease. The authors wereunable to show conclusively whether these deaths resulted
from working in the mills, because increased risk was not
associated with length of employment.
128. The extraction of uranium from ore produces solidand liquid wastes, called tailings. The wastes contain theradionuclides present in the ore, including thorium, radiumand other decay products. Tailings ponds, runoff collection
ponds, ore transport and mills (extraction facilities) present
the potential environmental exposure pathways to humans[N22]. Concerns surrounding mill activities include possible increased exposure to ionizing radiation from uranium
and its decay products, possible contamination of groundwater and vegetation, and possible increased levels ofindoor radon. Descriptive correlation studies, however, findno excess cancers among populations residing near uranium
milling, mining or processing facilities [B29, B30, B31,
M60]. Studies of populations with increased levels of uranium and other radionuclides in drinking water also havenot found associations with any cancers or overt kidney disease [A25, A26, K56, K58, K59].
129. There has been much controversy surrounding theuse of depleted uranium, especially on the battlefield. This
topic has been comprehensively reviewed by the Royal
Society [T32, T52] (see also [H60, I35]). The Royal Society
concluded that doses from depleted uranium are unlikely tobe high, even in the most unfavourable (battlefield) conditions, so that lung cancer risks are unlikely to be more thandoubled [T32]. There is potential non-radiological risk
associated with exposure to depleted uranium, in particularassociated with its nephrotoxicity, although there is little orno evidence of this in practice [T52].
130. There appear to be several possible reasons why uranium is not conclusively found to cause cancer in humans
and why it is not considered a human carcinogen [I35]:
uranium is not very radioactive (having such a long halflife of billions of years, 238U decays very slowly), and its
chemical properties are often such that any inhaled oringested uranium is excreted rather quickly from the body
[H60]. Some compounds of uranium are relatively insoluble and can be retained in the body. Nonetheless, there islittle or no epidemiological evidence for an association
between uranium and any cancer.

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III. SITE-SPECIFIC CANCERS
131. Table 15 summarizes the principal features of cohortand case-control epidemiological studies of the carcinogenic
effects of exposure to low-LET radiation. Table 16 provides
a similar summary of the studies for high-LET exposure.
The sections below on specific cancer sites consider thesestudies in greater detail. table 17 summarizes the strengths
and weaknesses of cohort and case-control studies for lowLET exposure, and table 18 provides a similar summary of
the strengths and weaknesses of studies for high-LET exposure. Most of these studies were considered in the
UNSCEAR 2000 Report [U2].

summary ERR and EAR measures, the following simple
linear models were fitted to each data set in which the
expected disease rate (i.e. numbers of cases or deaths perperson-year of follow-up) in the stratum with age a, city cand sex s is given by:

h0 (a,c, s) ⋅[1 + α ⋅ D]

(4)when assessing ERR, and by:h0 (a,c, s) + α ⋅ D(5)when assessing EAR. In both cases, in general h0(a,c,s) has
the form:

132. As much as possible in tables 19–44, estimates ofcancer risk per unit dose (in Gy–1 or Sv–1) are those givenin the original publications, but for publications that did not
calculate risk estimates, the methods described in Section
I.C of Annex A of the UNSCEAR 1994 Report [U4] havebeen employed. In particular, if O denotes the observed
number of deaths or cancer cases in the exposed population, E denotes the corresponding expected number basedon age- and sex-specific rates in the reference population(typically the general population), D denotes the average
dose and PY denotes the number of person-years of followup, then the ERR at 1 Sv is estimated by (O – E)/(E · D),
and the EAR per unit dose and per unit time at risk is estimated by (O – E)/(PY · D). Instances where this approachhas been implemented are indicated by a footnote in tables
19–44. It should be noted that the results based on thismethodology might differ from those based on adose–response analysis if those data were available. A particular problem with this approach occurs when exposed
populations are explicitly or implicitly selected for good
health (e.g. working populations or higher-social-statusgroups, respectively) and the expected values are derived
from the general population. In such cases, the risk estimates will tend to be biased in a downward direction andtherefore the true risks may be masked. Risk estimation thatused the general population statistics to derive the expectedvalues is therefore indicated by a footnote.

When fitting to the mortality and incidence data for boneand salivary cancers and for melanoma, because of the small
number of cases and deaths for these end points, slightly
simplified versions of the model for underlying rates,h0(a,c,s), were assumed, in which κ1 = κ2 = κ3 = κ6 = κ7
= 0, i.e. in which h0(a,c,s) = exp[κ0 + κ4 ·1n[a] + κ5·1n[a]2].
The same was done for thyroid cancer mortality data. For
bone cancer the models fitted were of purely quadratic form,
so that the bone cancer rate for the ERR model (4) is, given
by h0(a,c,s) ·[1 + α ·D2] and for the EAR model (5) is given
by h0(a,c,s) + α ·D2. The tables also provide 90% profilelikelihood confidence intervals [M21] on the fitted ERR andEAR (the parameter α). It should be noted that in deriving
these simple summary measures (ERR and EAR), there is
no implication that the corresponding models (4) and (5) fit
the various data sets well. As discussed in Section I.L, ingeneral the sex, age at exposure and time since exposure
substantially modify both ERR and EAR for most of thedata presented.

133. Risk estimates have been made from the LSS mortality and incidence data in tables 19–44, wherever possible using the latest DS02 dosimetry and follow-up (i.e.
1950–2000 for the mortality data [P10] and 1958–1998 forthe solid cancer incidence data [P48]). For site-specific solidcancers, mortality risks were estimated using the previous(DS86) dosimetry and the 1950–1997 follow-up [P9]. Fora few cancers (e.g. non-Hodgkin’s lymphoma, Hodgkin’s
disease, multiple myeloma and leukaemia), older incidence[P4] and mortality [P1] data are employed. In calculating

134. In fitting to the latest mortality and incidence data
[P10, P48], as also to the previous (pre-DS02) mortality data
[P9], the doses used were adjusted truncated doses, calculated using the methodology described by Pierce et al. [P2].
In particular, the adjustment factors used in this process
were derived from the previous (DS86) dosimetry [P2].
Prior to models being fitted to the latest incidence data
[P48], as also to the previous (pre-DS02) mortality data
[P9], the respective data sets were collapsed over strata
defined by age, sex, attained age, age at exposure and years

h0 (a,c, s) = exp[κ 0 + κ1 ⋅ s + κ 2 ⋅ c + κ 3 ⋅ s ⋅ c + κ 4 ⋅ln[a] + κ 5 ⋅ ln[a]2 + κ 6 ⋅ s ⋅ ln[a] + κ 7 ⋅ s ⋅ ln[a]2 ](6)55

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of follow-up. In other words, person-years and cases ordeaths were summed over these strata, and doses in eachstratum were replaced by person-year weighted averages
over the stratum. To estimate the “expected” LSS cases ordeaths for these two data sets [P9, P48], in these tables the
RR model (4) was fitted, and the sum∑ h (a, c, s)
0evaluated, corresponding to the cases predicted at zero dose.
In all tables this sum, and also the sums of cases or deaths
and person-years of follow-up, are over those survivors withrespective organ doses of greater than 0.005 Sv. Throughout
tables 19–44, the LSS cohort is assigned to the category of“external low-LET exposures”. Of the dose received by survivors, 1–2% is due to neutrons, most of the rest being due
to high-energy (mostly 2–5 MeV) gamma radiation [R12].
Even after application of a neutron RBE of 10 (as is donein most of the analysis presented here), the dose in thiscohort results predominantly from external low–LET exposures. Similar simplifications are made for various otherstudies. It should be noted that, while the above procedures
were used to estimate risks for the LSS given in tables
19–44, results given in the main text are in general basedon the published reports [P2, P9, P10, P48] wherever possible. These may be slightly different. Many of the slightdifferences relate to the 0.005 Sv cut-off used in the tables,
which because of the grouped nature of the publicly available data file will result in groups that do not always correspond precisely to the set of survivors with this dose. It
should also be noted that, for solid cancers (table 19), theTecha River cohort [K50] is assigned to the category of“external low-LET exposures”, since 75% of the stomachdose is thought to be from this source (with most of therest from 137Cs). For leukaemia (table 44), the Techa cohort
it is assigned to the category of “internal low-LET exposures”, since 92% of the bone marrow dose is thought tobe from internal beta emitters [K50].

A. Total solid cancers
135. The solid cancer mortality experience of the LSS ofsurvivors of the atomic bombings up until the end of 2000
has been reported [P10]. This represents an additional three
years (1998–2000) of follow-up since the previous report[P9]. There are 10,127 deaths from solid cancer and 296
deaths from leukaemia. If attention is restricted to survivors
who received a shielded kerma dose of less than 4 Gy, there
are 10,071 solid cancer deaths and 284 leukaemia deaths.
Preston et al. [P10] estimate that about 479 (~5%) of the
10,127 solid cancer deaths would be attributable to radiation exposure. Among survivors with (DS02 or DS86) colon
doses of greater than 5 mSv, about 8% of solid cancer
deaths would be attributable to exposure, a figure very similar to that of the previous follow-up [P9]. In general,
although risk estimates are somewhat lower than before,
for both solid cancers and leukaemia the patterns of the

distribution of excess risk by age and time are very similar to those of the previous follow-up [P9]. A striking feature of the solid cancer data is that, in a lower-dose (lessthan 2 Sv) group, there is statistically significant upwardcurvature [P10]. This is not an artefact of the new dosimetry: the same finding had been observed in the previous
follow-up of the LSS mortality data, using the DS86
dosimetry [W20]. As noted in the previous report [P9], thesolid cancer radiation risks are highest among those exposed
as children, and as before there is a steep decline in ERR
with increasing time after exposure in this group (figure X).
136. As noted in table 19, both the ERR and the EAR for
total solid cancers are somewhat higher (by about a factorof 2) for women than for men.

B. Salivary gland cancer

1. General background137. Cancers of the salivary gland are rare. Annual agestandardized world rates are fewer than 1.5 and 1.3 cases
per 105 persons for men and women, respectively, in thevast majority of tumour registries represented in Parkin etal. [P19]. Rates tend to be slightly higher in developed
countries. Among the highest rates are in parts of Australia,
where annual age-standardized rates of 1.9 per 105 persons
are recorded for men, and in parts of Canada, where rates
of 3.8 per 105 persons are recorded for women [P19]. Rates
are somewhat lower for developing countries. For example,in Martinique, age-standardized rates of 0.4 and 0.2 per 105
persons are recorded for men and women, respectively[P19]. Benign tumour rates are 2–3 times higher, withtumours appearing at somewhat younger ages [B48]. Apartfrom ionizing radiation, causes of salivary gland cancer are
not clear. There have been suggestions of associations withthe use of hair dyes or mouthwash and with certain occupational factors, but few suggestions of associations withdietary factors, tobacco or alcohol use [B27].

2. Summary of UNSCEAR 2000
138. Salivary gland cancer was not considered in the
UNSCEAR 2000 Report [U2].

3. New or updated studies(a) External low-LET exposures139. A number of early studies, mostly based on small
numbers of cases, have suggested an association between
salivary gland tumours and radiation exposure at young
ages [H41, J6, M54, M55, S53, S71, S72]. These publishedresults were the basis for: (a) a meta-analysis that resulted

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in estimates of 0.26 ± 0.06 excess malignant tumours and0.44 ± 0.11 excess benign tumours following childhood
exposures of 104 PY Gy [L82]; and (b) estimation of theprobability of causation for radiation-related salivary glandcancer following childhood exposure [N11]. Concurrently
and more recently, Hildreth et al. [H26] estimated an RRof 5.5 for benign salivary gland tumours associated withtherapeutic X-ray treatment in infancy for enlarged thymus.
Preston-Martin et al. [P7] compared reported histories ofdental X-ray examinations in patients with benign andmalignant parotid gland tumours and matched controls, estimating RRs of 5.6 and 1.5 for malignant and benign
tumours, respectively, associated with exposures of greaterthan 0.5 Gy. No dose–response analyses were presented ineither study. In a study of occupational exposures and mortality due to salivary gland cancer among African-Americanand white workers in the United States, Wilson et al. [W34]found a positive trend (p = 0.08) among white workers withprobability of exposure to ionizing radiation, as measured
by a job–exposure matrix.
140. Results from an incidence and pathology study ofbenign and malignant salivary gland neoplasms in the LSS
population are presented in table 20 [L83, S73]. Informationfrom the LSS Tumor Registry was supplemented by additional case findings, with pathology review, from autopsy,
from biopsy and from surgical specimens maintained at theRadiation Effects Research Foundation and elsewhere. The
incidence of malignant tumours (ERR = 3.5 (90% CI: 1.5,
7.5) Gy–1; based on 31 cases with estimates of radiation
exposure) and of benign tumours (ERR = 0.7 (90% CI: 0.1,1.7) Gy–1; based on 64 cases) both increased significantlywith radiation dose, and no modifying effects of exposure
age, attained age, sex or time since exposure were observed.
Remarkably, most of the evidence for a malignant tumour
dose response pertained to mucoepidermoid carcinoma(ERR = 8.3 (90% CI: 2.6, 29.6) Gy–1; based on 11 cases),and most of the evidence for a benign tumour response pertained to Warthin’s tumour (ERR = 3.1 (90% CI: 0.6, 10.3)
Gy–1; based on 12 cases). Both of these tumours occur onlyin the parotid glands. Dose response for residual malignant
tumours was of only suggestive significance (ERR = 1.4(90% CI: 0, 4.7) Gy–1; based on 20 cases; p = 0.11), whilethat for residual benign tumours (ERR = 0.3 (90% CI: –0.1,
1.2) Gy–1; based on 52 cases; p = 0.29) was positive but
not statistically significant.
141. Schneider et al. [S74] studied radiation dose
response for incidence of salivary gland tumours in a cohortof 2,945 persons medically irradiated as children between
1939 and 1962, mainly for treatment of enlarged tonsils
and adenoids. Twenty-two patients developed malignant
salivary gland tumours that were verified by pathology
after surgery, including 9 cases of mucoepidermoid carcinoma, and 66 developed benign salivary tumours (including only 2 cases of Warthin’s tumour). The incidence ofmalignant tumours was not significantly associated withradiation dose (ERR = –0.06 (95% CI: undetermined, 4.0)
Gy–1), even though 22 cases were observed versus 0.39

57

expected according to age- and sex-specific population
rates. Conversely, the incidence of benign tumours was significantly associated with dose (ERR = 19.6 (95% CI: 0.16,
undetermined) Gy–1). The very large numbers of malignant
and benign tumours observed relative to the expectation
based on population rates were partly ascribed by theauthors to notification and screening programmes for the
study population. These began in 1974 and resulted in athreefold increase in the numbers of cases diagnosed after
1974. In a group irradiated in childhood for treatment of
tinea capitis, there was no statistically significant elevationin the RR for salivary gland tumours (6 tumours among
those irradiated versus 2 tumours in the control group (RR= 1.8; 95% CI: 0.4, 8.9)) [S68].
142. In comparing the results from the LSS and frommedically irradiated populations discussed in the previous
two paragraphs, it is illuminating to consider the distribution of dose within the two populations. As generally for
the LSS cohort [P9, P10, R20], the distribution of salivary
gland doses among the exposed population is highly
skewed, and the mean doses and inter-quartile ranges fordifferent types of tumour differ markedly from those of
the population as a whole. By contrast, in the medically
irradiated population, doses are much higher than in the
LSS population, and they are more closely and more symmetrically concentrated around the mean value. Thus, as
stated by Schneider et al. [S74], the LSS results are notdirectly comparable to theirs, and extrapolation of theirresults to lower doses may not be justified. One possible
explanation offered for the difference, that the LSS doses
are partially due to neutrons, seems less tenable in viewof the reduced role assigned to neutrons in the most recent
refinement of that dose reconstruction system [P10]. Thereremains a possibility, however, that the small neutroncomponent might be of some importance, since the
gamma-ray estimates are influenced by the choice of the
RBE for neutrons [W20].
143. Salivary gland tumours have been studied for anumber of nuclear worker cohorts. In particular, a statistically significant excess risk (2 cases versus 0.19 expected)
has been observed in a group of workers at the Lawrence
Livermore National Laboratory in the United States [W39].
There is no analysis of dose response in relation to salivary
gland tumour risk. However, in three other United Kingdom
cohorts, there were no statistically significant elevations inrisk (2 cases versus 2.23 expected [M4], 1 case versus 2.97
expected [M5] and 2 cases versus 0.38 expected [M6]).

4. Summary
144. The available evidence indicates that the salivary
gland is susceptible to the induction of cancer by ionizing
radiation; the evidence for this comes almost entirely fromstudies of external low-LET exposure. There is little evidence for the modifying effects of sex, age at exposure ortime since exposure.

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C. Oesophageal cancer

3. New or updated studies(a) External low-LET exposures

1. General background145. Rates of oesophageal cancer vary widely by country and ethnic group [M32], with low rates in many countries but extremely high rates among Chinese and certain
Central Asian groups, and intermediate rates in black populations [M32]. For example, age-standardized world ratesof 183.8 and 123.1 cases per 105 persons for men andwomen, respectively, have been observed in parts of China[P19], whereas the rates are fewer than 10 cases per 105
persons [P19] in many European countries. Since
oesophageal cancer is generally fatal, mortality rate is agood surrogate for incidence rate. The major known risk
factors for oesophageal cancer are heavy alcohol consumption, tobacco use and chewing of betel nut [M32].
Other possible risk factors, but where the weight of evidence is less strong, are consumption of pickled foods andnutritional deficiency [M32].

2. Summary of UNSCEAR 2000
146. The UNSCEAR 2000 Report stated that the LSS data
did not provide convincing evidence of a link between
oesophageal cancer and radiation, although a significant
excess in oesophageal cancer mortality occurred in the earlyyears of follow-up, i.e. from 5 to 12 years after exposure.
Cancer incidence data from the LSS, which began 12 years
after exposure, do not show a significant excess risk ofoesophageal cancer [T1]. The LSS mortality data also
showed a higher ERR for this cancer in females than inmales, although not significantly so.
147. The United Kingdom ankylosing spondylitis study
was the only study of medically exposed populations toreport a significant risk of radiation-associatedoesophageal cancer [W8]. Regarding internal low-LET
exposures, little epidemiological information was available. The data from patients treated with 131I for adulthyperthyroidism [R3] showed no increased risk ofthis cancer, but the doses received by the oesophagus
were small.
148. Oesophageal cancer data were available from several
worker studies following high-LET exposures. In a study ofthree groups of workers exposed to plutonium in three
United Kingdom nuclear industry workforces, no clear
excess of oesophageal cancer was seen (23 observed versus
21.3 expected deaths), nor was any excess seen among
workers monitored for exposures to uranium, polonium,
actinium and other radionuclides (apart from tritium)(9 observed versus 16.1 expected deaths), although doses
to the oesophagus were probably small [C40].

149. The updated LSS [P9] identified 171 oesophageal
cancer deaths among those with at least 5 mSv of exposure. Since the underlying mortality rates of oesophageal
cancer are considerably higher for males than females, theestimates of ERR were lower among males (0.55 Sv–1) thanfemales (1.40 Sv–1) (table 21).
150. Several studies of workers exposed to external radiation have reported data on the risks of oesophageal cancer(table 21). Although these were reported before 2000, in
some cases, they were not discussed in the UNSCEAR 2000
Report, so the Committee considers them here. Of these,
three studies reported data based on “internaldose”–response comparisons. The NRRW [M12] reported adose–response association non-significant in the negative
direction that was based on 120 cases of oesophageal cancer
in informative strata (strata defined by age group, sex, interval of follow-up, etc., with at least one cancer death and at
least two dose groups with persons contributing to thefollow-up) and a mean dose of 0.03 Sv. A smaller UnitedStates study of workers at Los Alamos National Laboratory[W6] reported a marginally positive dose response (p < 0.1)
but a deficit compared with the United States population (22observed and 27.4 expected cases). A study of oesophageal
cancer incidence among workers in the Canadian National
Dose Registry [S8] reported a null dose–response association based on 22 observed cancers, and an update of the segment of the Registry concerning nuclear power industry
workers also produced a null result [Z6]. Other studies ofworkers (workers at Oak Ridge National Laboratory in the
United States [F2], radiation workers at Électricité de France[R54], Japanese nuclear workers [I14], and radiologic technologists in Japan and the United States [M31, Y5]) reported
deficits in oesophageal cancer mortality rates based on comparisons with reference general populations. Only the studyof Chinese medical X-ray workers reported an excess ofoesophageal cancer among both early workers (mean dose
0.55 Sv) and more recent workers (0.08 Sv) [W3]. It isnotable that the workers in this study had higher radiation
exposures than those in the other studies, and hence there
was a greater potential to observe excess cases. In a United
Kingdom study of Springfields uranium workers [M5], no
excess of oesophageal cancer was seen (25 observed versus
34.54 expected cases) (table 21).
151. A United States study of women treated with radiation for primary breast cancer documented RRs of 2.83 (95%
CI: 1.35, 5.92) and 2.17 (95% CI: 1.67, 4.02) for squamouscell oesophageal cancer occurring between 5 and 9 years andat 10 or more years, respectively, following radiation therapy [Z11]. This increase was mainly due to tumours locatedin the upper and middle thirds of the oesophagus. No assessment of radiation doses has been carried out for this cohort.

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER(b) Internal low-LET exposures152. The only new data on internal exposures andoesophageal cancer are those from the study of persons
exposed to weapons test fallout in the Semipalatinsk areaof Kazakhstan [B58], which reported a highly statisticallysignificant trend of increasing risk with dose in women(p = 0.003), although not for men (p = 0.46). The aggregate ERR based on an internal analysis was 2.37 (95% CI:
1.47, 3.63) Sv–1; however, when analysis was restricted tothe exposed group, based on individual dose estimates, thetrend estimate was much reduced and no longer statisticallysignificant: 0.18 (95% CI: –0.09, 0.66) Sv–1. As noted in
section II.D, “ecological bias” may operate in this study, sothese findings should be treated with caution.

4. Summary
153. The new or updated data are broadly supportive of
the conclusions in the UNSCEAR 2000 Report. There is anassociation of radiation exposure and oesophageal cancer inthe LSS, although since oesophageal cancer is relatively
rare, there was insufficient statistical power to detect anexcess in the several low-dose occupational exposurestudies. There are insufficient data to characterize the shapeof the dose–response curve or to establish a dose-rate
effectiveness factor. Virtually no human data are available
on the magnitude of effects following exposure tohigh-LET radiation.

D. Stomach cancer

1. General background154. Stomach cancer is the fourth most common malignancy worldwide and appears to be the second leading
cause of cancer mortality [N10, S59]. Rates are higher
among men than women and show a sharp increase withage. The incidence rate of stomach cancer varies considerably with geographical location and among different
ethnic groups within the same locality [S59].
Approximately 60% of all stomach cancers occur in developing countries. The highest rates are found in Eastern
Asia, the Andean regions of South America, and Eastern
Europe, while low rates are found in North America,
Northern Europe and most countries in Africa and SouthEast Asia [P19, S59]. For example, annual age-standardized
world rates of 145.0 and 34.5 cases per 105 persons formen and women, respectively, have been observed in partsof China [P19], whereas in many European countries therates are fewer than 30 cases per 105 persons [P19]. Studiesof migrants suggest that environmental factors may belargely responsible for the variation in rates [N10]. Of particular interest is the fact that the Japanese people have had

59much higher rates of stomach cancer than people inWestern countries. In most countries, including Japan,
stomach cancer incidence and mortality rates have declined
markedly over the past 50 years [N10, S59]. These changes
are likely to reflect changes in diet, including increased
consumption of fresh vegetables and fruits and decreased
salt intake (which case-control studies have shown to belinked to reduced stomach cancer risks [K36]). Dietary factors are important, and infection with Helicobacter pylori[S69], especially with certain genetic or physiologicalcofactors, has been associated with elevated risks ofstomach cancer [C18, K36].

2. Summary of UNSCEAR 2000
155. The Committee reported in 2000 that the dose
response seen in the LSS incidence data up to 1987 wasconsistent with linearity, and that the ERR per unit dose
was higher for females than for males, decreased withincreasing age at exposure, and did not vary significantlywith the time since exposure [T1]. The findings for
mortality rates up to 1990 were very similar [P1].
156. The major studies of patients whose stomachs were
irradiated with moderately high doses—particularly thestudies of patients treated for cervical cancer [B8], ankylosing spondylitis [W8] and peptic ulcer [G6]—produced
estimates for EAR per unit dose that were appreciably lower
than those from the LSS, but the ERR estimates of thesestudies and of the LSS were statistically compatible.
157. The Committee also reported in 2000 that there wasa suggestive excess of stomach cancer among Mayak workers with external doses exceeding 3Gy [Z3]. However,studies of workers exposed to lower doses have not provided evidence of a dose–response relationship for stomach
cancer [C3].
158. The Swedish study of patients treated with 131I for
hyperthyroidism reported increased incidence [H6] andmortality rates [H24] from stomach cancer, with some indication of a dose–response trend. In general, however, theepidemiological data were too sparse to quantify a dose or
dose-rate effectiveness factor or to characterize risks frominternal low-LET or high-LET exposures.
159. Studies of persons exposed to 224Ra [N2, W15] andthe diagnostic contrast medium Thorotrast [A5, V3, V4]
provide little evidence of elevated risks of stomach cancer.
A study of 11 cohorts of underground miners found excess
mortality rates from stomach cancer in comparison withnational and local rates, but no evidence of an increase inmortality rates with increasing cumulative radon exposure[D10]. Because doses to the stomach from radon are
estimated to be very low, it seems likely that the excess isdue to other factors, such as other exposures in mining
environments or smoking.

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3. New or updated studies(a) External low-LET exposures160. A summary of results from both old and new studies is shown in table 22. In the updated mortality assessment of the LSS up to 1997, 1,685 stomach cancer deaths
occurred among those people who received doses of atleast 5 mSv. Of these deaths, it was estimated that about
100 were attributable to the radiation exposure [P9]. The
ERR was greater for females (0.65 Sv–1) than for males(0.20 Sv–1), as was the EAR (3.3 (104 PY Sv)–1 and 2.1(104 PY Sv)–1, respectively). For the ERR, the patterns ofvariation of radiation effects with age at exposure andattained age were not significantly different from those forsolid tumours as a whole. Specifically, the ERR per unit
dose declined substantially with increasing age at exposure but declined very little with increasing attained age,as shown in figure XII. For the EAR, there was no significant increase or decrease with age at exposure, a pattern that differed from that for all solid cancers combined(figure XII). The EAR showed a steep increase withattained age, similar to that for all solid cancers as a group.
The difference in the patterns for the ERR and EAR withage at exposure is related to the decline in underlying rates
with birth cohort, a variable that is confounded with age
at exposure.
161. An update of the United States peptic ulcer study [C4]reported as its main result an ERR of 0.06 (95% CI: 0.02,
0.10) Gy–1 based on persons with 10 or more years offollow-up. However, among patients treated with 1–10 Gy,
the ERR per unit dose was somewhat higher: 0.20 (95%
CI: 0.0, 0.73) Gy–1. This estimate should be treated withcaution, however, as the numbers of deaths were relatively
small (47 stomach cancer deaths among 1941 patients, orfor 1–10 Gy, 11 deaths among 309 patients), the mean dosein that group was high (14.8 Gy overall, 8.9 Gy among the
1–10 Gy group), and the patients were being treated for a
stomach condition that may cause hyperplasia or other cellular responses that potentially could alter carcinogenic susceptibility. The irradiated patients were predominantly male(78%), and a quarter had a history of stomach surgery. The
H. pylori status of the patients was not known. The ERR
per unit dose estimates in the lower-dose group arecompatible with those based on male survivors of theatomic bombings; the EAR was not evaluated.
162. Several studies of occupational radiation exposure
have reported data on stomach cancer incidence or mortality. Most studies, including the IARC [C3], NRRW
[M12] and Canadian National Dose Registry [A8] studies,
provide little evidence of a dose–response relationship forstomach cancer, but this may be due to the low doses andlimited statistical power. A recent study of United States
nuclear power industry workers [H44] indicated a large
but non-significant ERR per unit dose based on 16 deaths.
In a study of Japanese nuclear industry workers [I14], therisk of stomach cancer was not elevated in comparison
with the general population, but the dose response basedon 428 deaths was statistically significant; however, thefinding was no longer significant when a Bonferroni

Figure XII. Patterns of stomach cancer mortality with age and time among the survivors of the atomic bombings in Japan(reproduced from Preston et al. [P9])The dark curves are fitted age–time patterns in the ERR (left panel) and EAR (right panel). The light dashed curves are the patterns obtained
when the age and age-at-exposure effects are constrained to equal those for all other solid cancer. The curves are sex-averaged
estimates of the risk at 1 Sv for people exposed at ages 10, 30 and 50, with attained ages corresponding to the follow-up period

Excess Relative Risk

Excess Absolute Risk
Stomach2.5Site-specific effects
Solid cancer effects

1.5

Age at exposure 10 a

1.0

Age at exposure 30 a

8

EAR (104 PY Sv)–1

2.0

ERR (Sv–1)

10

Age at exposure 10 a

64
Age at exposure 50 a

Age at exposure 30 a

0.5

2

Age at exposure 50 a

0.0

0
30

40

50

60

ATTAINED AGE (a)

70

80

30

40

50

60

ATTAINED AGE (a)7080

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procedure was applied to take account of the multiple statistical tests that were performed. The authors note the possibility of confounding by dietary and socio-economic
factors. Although not reported in the UNSCEAR 2000
Report [U2], the 1997 study of Artalejo et al. [A32]reported a slight deficit of stomach cancer mortality among
workers for the Spanish Nuclear Energy Board. The SMR
was 0.81 (95% CI: 0.49, 1.26) but was based on only 19
cancer deaths, of which 7 were among the 27% of thecohort who had been miners and may have been exposed
to alpha radiation [A32].
163. Two relevant studies of occupational exposure in
medicine have recently been reported, in the United States[M10, S29] and in China [W3]; in neither study have individual dose estimates been derived, so their utility for quantitatively understanding radiation risks is questionable. The
Chinese study of medical X-ray workers showed no excess
among those employed before 1970, when exposures werehigh (estimated mean cumulative dose of 0.55 Gy), but anexcess was reported among those first employed during
1970–1980 (estimated mean cumulative dose of 0.08 Gy)[W3]. Among United States radiologic technologists, bothmales and females had lower stomach cancer incidence[S29] and mortality [M10] rates than the general population. Rogel et al. [R54] reported a deficit (at borderline
levels of statistical significance) of stomach cancer mortality compared with French national rates among radiation
workers of Électricité de France (3 observed versus 7.2
expected deaths; SMR = 0.41; 90% CI: 0.11, 1.07).
(b) Internal low-LET exposures
164. The only new data on internal exposures and stomach cancer are those from the study of persons exposed toweapons test fallout in the Semipalatinsk area of Kazakhstan[B58], which reported a highly statistically significant trendof increasing risk with dose in women (p = 0.0016),
although not for men (p = 0.36). The aggregate ERR basedon an internal analysis was 1.68 (95% CI: 0.83, 2.99) Sv–1;
however, when analysis was restricted to the exposed group,
based on individual dose estimates, the trend estimate wassomewhat lower at 0.95 (95% CI: 0.17, 3.49) Sv–1. As noted
in section II.D, “ecological bias” may operate in this study,
so these findings should be treated with caution.
(c) Internal high-LET exposures165. Travis et al. [T30] studied patients injected withThorotrast during radiographic procedures in Denmark,Sweden and the United States. The stomach cancer incidence rate in a group of Thorotrast-exposed patients in
Denmark and Sweden was significantly elevated comparedwith a control group, but there was no evidence of a trendof increasing stomach cancer incidence with a surrogate
measure of cumulative radiation dose. Stomach cancer was
not evaluated with respect to the mortality rate data thatwere available for the United States.

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166. Auvinen et al. [A36] studied cancer epidemiology inrelation to radon, uranium and other radionuclides in drinking water in a cohort of persons who used water from wells
drilled into bedrock in Finland. Activity concentrations of226Ra, radon and uranium were assessed by radiometricanalysis of samples from each well. There was no relationship seen between stomach cancer incidence and levelsof any of the three radionuclides. If anything, there was aninverse relationship: the hazard ratio in the group exposed
to 130–299 Bq/L radon relative to the group exposed to lessthan 130 Bq/L was 0.54 (95% CI: 0.25, 1.18), and thehazard ratio in the group exposed to 300–15,000 Bq/L radon
relative to that exposed to less than 130 Bq/L was 0.48
(95% CI: 0.25, 0.94). Similar inverse relationships between
exposure and stomach cancer risk were observed for 226Ra
and uranium.

4. Transfer of risk estimates across populations
167. Although the appropriate way to generalize or“transfer” risk estimates from one population to another isa general issue, it is especially important when there is amajor discrepancy between the underlying cancer rates inthe two populations. Stomach cancer is a prime example
of such a situation. For example, lifetime risk estimatesfor stomach cancer based on transfer of absolute risks aspresented in the UNSCEAR 2000 Report for the UnitedStates and the United Kingdom were several times higher
than those based on transfer of RRs. The UNSCEAR 2000
Report observed that the attributable risk estimates in thestudies conducted in Western populations were appreciably lower on average than those in the LSS, suggestingthat using the ERR model may be a better way to “transfer” stomach cancer risk than using the EAR model.
Updated data from the peptic ulcer study [C4] confirm that
ERR estimates are very similar to those based on survivorsof the atomic bombings. However, although RRs appear
to be more comparable than absolute risks, other
differences in the study populations may confound thiscomparison, particularly the much higher doses in somemedical studies.

5. Summary
168. Updated data from the LSS [P9] and the peptic ulcer
study [C4] continue to provide evidence of a positive dose
response. Within the LSS, the ERR decreases with increasing age at exposure, but the EAR does not. Past studies ofcervical cancer patients [B8] also provide evidence ofexcess stomach cancer risk from radiation exposure. Most
studies of nuclear workers do not show an association ofexcess stomach cancer with low-dose protracted exposure,but this may be due to the limited statistical power of thesestudies. Weak associations are suggested by new studies ofUnited States [H44] and Japanese [I14] nuclear power
workers.

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E. Cancer of the small intestine,
including the duodenum

1. General background169. Cancer of the small intestine is only slightly less rarethan cancer of the salivary gland, with annual agestandardized world rates of less than 4.0 and 2.0 cases per
105 persons for men and women, respectively, in the tumour
registries represented in Parkin et al. [P19]. The cancer canbe induced in experimental animals by high-dose irradiation of exteriorized intestinal loops [O11], and the small
intestine therefore is an organ susceptible to radiogenic
cancer. However, the small intestine appears to have characteristics involving selective retention of template DNA
strands and providing protection of the stem cell genome inintestinal crypts, which render it highly resistant to carcinogenesis at low to moderate levels of exposure to radiation and other environmental carcinogens [C31, P41]. A
second line of defence, in which mutated stem cells are
eliminated by radiation-induced apoptosis in the stem cellzone of intestinal crypts, may also come into effect [P42].
There are well known hereditary risk factors, in particularfamilial adenomatous polyposis (FAP), and certain otherchronic diseases, in particular Crohn’s disease [S57].

2. Summary of UNSCEAR 2000

anything, there are indications of a negative trend with dose.
An earlier study by Smith and Doll [S75] of 2,068 women
treated with radiation for benign gynaecological disorders
found 3 deaths from cancer of the small intestine versus 0.4
expected. Cancer of the small intestine was not discussed
in the most recently published follow-up study of this irradiated group [D7].
172. Despite the experimental evidence for induction byradiation of cancer of the small intestine, the weak epidemiological evidence, in particular the lack of any trendwith dose in the international cervical cancer case-control
study [B8], and the lack of the expected increase in risk
with time in the cohort study, indicates that the small
intestine is not susceptible to radiogenic cancer induction,even at high doses. It is possible that the very high dosesin the cervical cancer study resulted in cell sterilization,
which might partially explain the negative trend in thecase-control study, although the trend was not statisticallysignificant.

3. Summary
173. The available evidence indicates that cancer of thesmall intestine is not strongly inducible by ionizing radiation. However, the available evidence comes almost entirely
from studies of external low-LET exposure at relatively
high doses, and it is possible that cell sterilization may
partially account for the largely negative findings.

170. Cancer of the small intestine was not considered in
the UNSCEAR 2000 Report [U2].
(a) External low-LET exposures171. In an international cancer registry study of second
primary cancer incidence in a group of very-long-term survivors of cervical cancer [K1], the incidence of second cancers was evaluated in a group of 86,000 cervical cancer
patients reported to 13 population-based cancer registries infive countries. Of these patients, 49,800 had received radiotherapy, with typical average organ doses of between 10
and 20 Gy to the small intestine; 16,700 had not been given
radiotherapy; and 19,700 had missing treatment data. For
the small intestine, 22 cases of second cancer were observed
among radiotherapy patients, versus 12.3 expected from
population rates (ratio of observed to expected (O/E) = 1.8;
90% CI: 1.3, 2.6), and 2 cases versus 2.7 expected wereseen among women who had not been given radiotherapy[K1]. Among the radiotherapy patients, virtually the same
O/E ratios were obtained for the period within 9 years after
cervical cancer diagnosis (O = 9, E = 4.9, O/E = 1.8; 90%
CI: 0.96, 3.2) as after 9 years (O = 13, E = 7.5, O/E = 1.7;
90% CI: 1.03, 2.8). In the parallel case-control study [B8],
there is no evidence of increased risk. The RR is 1.0 (90%
CI: 0.3, 2.9) among the 22 cases, despite the very high doses
received (estimated to be several hundred grays on average). There is no evidence of a dose response (p = 0.47
for trend among all survivors, or 10-year survivors); if

F. Colon cancer

1. General background174. The colon resembles the small intestine in that stem
cells deep in the intestinal crypts produce a continuous flowof new and relatively short-lived crypt cells that migrate
towards the top of the crypt. However, survival and repair
of DNA damage in the crucial stem cells is the rule rather
than apoptosis and replacement (regeneration) as in thesmall intestine. This is perhaps because the latter strategy
would be more error-prone in the colonic environment,
which includes a much higher concentration of genotoxic
molecules [P41]. In any case, cancer of the colon is much
more frequent than cancer of the small intestine, and ionizing radiation exposure is an established risk factor.
Underlying rates tend to be higher in developed countries(whether in North America, Europe, Oceania or Japan),with age-standardized world rates lying generally between
20 and 40 cases per 105 PY, whereas rates are generally
lower—fewer than 5 cases per 105 PY—in Africa andSouthern Asia [P19]. As with cancers of the small intestine, there are well established hereditary risk factors, inparticular familial adenomatous polyposis (FAP) [S58].
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high-fat and low-fibre diets and diets deficient in fruit andvegetables, and also risk factors associated with certain
other chronic diseases, in particular ulcerative colitis andCrohn’s disease [S58].

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observed compared with 29 expected among 1,893 women
treated with radium implants or X-rays for benign gynaecological disorders [W30], and an incomplete follow-up of
women treated with radium for benign uterine haemorrhage
found no excess rates of mortality due to colon cancer[D41].

2. Summary of UNSCEAR 2000
175. The UNSCEAR 2000 Report stated that the LSS
found a dose response consistent with linearity for bothcolon cancer incidence [T1] and mortality [P1]. However,the cervical cancer [B8] and peptic ulcer [G6] studies withcolon doses of several grays showed little evidence of elevated risk, possibly owing to a cell-killing effect. There wasno clear pattern in the variation of ERR per unit dose by
sex, age at exposure or time since exposure among the survivors of the atomic bombings. However, the EAR per unit
dose for mortality increased with time since exposure.
Changes over time in underlying rates in Japan make it difficult to determine how to transfer risks across populations.
176. The LSS Tumor Registry incidence analysis for
1958–1987 [T1] gave an estimated ERR of 0.72 (95% CI:
0.29, 1.28) Sv–1 (p < 0.001), with no significant variation
between the sexes. The ERR per unit dose declined withincreasing age at exposure and with attained age, but thedecrease with attained age was statistically significant whilethat with age at exposure was not. The estimated ERR atage 50, after exposure at age 30, was 1.88 (90% CI: 0.69,
3.86) Sv–1. The EAR per unit dose increases with time in
the LSS mortality study [P1]. Nakatsuka et al. [N12]analysed colon cancer incidence for 1950–1980, obtaining
results similar to those of Thompson et al. [T1] when thedata were restricted to the period 1959–1980, but with asignificant decrease in ERR per unit dose with age at exposure. An interesting finding was that very similar linear
dose–response coefficients were obtained for cancers
located in the caecum and ascending colon (ERR = 0.80
Sv–1; 90% CI: 0.07, 1.96; p = 0.06 for trend), transverseand descending colon (ERR = 1.09 (90% CI: 0.17, 2.59)
Sv–1; p = 0.04) and sigmoid colon (ERR = 0.96 (90% CI:
0.33, 1.87) Sv–1; p = 0.003).
177. Indications of radiation-related colon cancer risk
were obtained from studies of patients irradiated for treatment of benign pelvic disease, including 267 patients followed for an average of 16 years, in which 4 intestinal
cancer deaths were observed versus 1 expected [B49]. Moreconvincing evidence came from a follow-up of patientstreated for metropathia haemorrhagica [S75], which foundno excess rates of mortality due to colon cancer within 5
years after treatment, but observed 21 colon cancer deaths
versus 13.5 expected 5 or more years after treatment. The
most recent follow-up of this series [D7] found 2 coloncancer deaths versus 1.7 expected within 5 years after treatment, and 45 versus 31.2 expected 5 or more years after
treatment (O/E = 1.44; 90% CI: 1.1, 1.8). The estimatedaverage colon dose was 3.2 Gy, with an 80% mid-range of
2.4–3.7. In other studies, only 32 colon cancers were178. The UNSCEAR 2000 Report [U2] concluded that
there was strong evidence of an effect on colon cancer risk
due to ionizing radiation exposure that was consistent witha linear dose response. The effects of sex, age at exposureand time since exposure on the ERR per unit dose are notclear. The UNSCEAR 2000 Report [U2] considered the evidence for colon cancer risk by radiation type and concluded
that there was little precision in the low-dose studies ofexternal exposure to low-LET radiation and of internal
exposure to low-LET and high-LET radiation, which limits
the conclusions that can be drawn with respect to thesemodes of exposure.

3. New or updated studies
179. This section considers several studies (table 23) published well before the UNSCEAR 2000 Report [U2],although these studies were not considered there.
(a) External low-LET exposures180. In the latest follow-up of the LSS cohort, coloncancer mortality rates during the period 1950–1997
increased with neutron-weighted (weight = 10) radiationdose (p < 0.001), with negligible difference between thesexes [P9]. The estimate for ERR per unit dose based on alinear model, for exposure at age 30 with no assumed variation with attained age, was 0.54 (90% CI: 0.13, 1.2) Sv–1
for males and 0.49 (90% CI: 0.11, 1.1) Sv–1 for females,with a 25% decrease per decade of age at exposure.
181. In their tumour registry study of benign gastrointestinal tumour incidence among survivors of the atomic
bombings, Ron et al. [R35] observed 215 histologically confirmed cases of benign colon tumour diagnosed between
1958 and 1989. There was little evidence of a radiation dose
response (ERR = 0.14 (95% CI: –0.20, 0.76) Gy–1).
However, 74% of the tumours were diagnosed between
1985 and 1989, presumably reflecting a more frequent useof colonoscopy. The dose response was positive for theperiod 1958–1984 (ERR = 0.64 (95% CI: –0.11, 2.46)
Gy–1), whereas that for the period 1985–1989 was negative(ERR = –0.20 (95% CI: undetermined, 0.47) Gy–1.
182. In their reports of cancer mortality among ankylosing spondylitis patients, for whom the estimated averagecolon dose [W8] was 4.1 Gy, Court Brown and Doll [C32]and Smith and Doll [S32] tended to discount their consistent observation of excess colon cancer mortality, becauseof known associations between spondylitis and ulcerative
colitis and between ulcerative colitis and colon cancer.

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Smith and Doll [S32] observed 16 colon cancer deaths 9 ormore years after treatment, as opposed to 10.4 expected, anon-significant excess. However, 12 deaths in contrast to
6.9 expected were seen within the first 8 years after treatment, of which 6 occurred within the first 2 years after treatment, when 2.5 would have been expected. The most recent
follow-up [W8] estimated RRs of 1.30 (95% CI: 1.07, 1.55),
or ERR = 0.08 (95% CI: 0.02, 0.14) Gy–1, 5 or more years
after treatment, with a significant decrease in RR over time
following treatment.
183. With an estimated typical average organ dose of
24 Gy, the colon was one of the heavily irradiated sites in
an international cancer registry study of the risk of second
primary cancer occurring among very-long-term survivorsof cervical cancer [K1], although as generally in this study,no organ dose estimates were used in the analysis. Among
patients treated with radiation, 178 colon cancers were diagnosed within 9 years following cervical cancer diagnosis,
as opposed to 162.7 expected (O/E = 1.09; 90% CI: 0.96,
1.2), and 296 were observed versus 267.7 expected 10 ormore years after cervical cancer diagnosis (O/E = 1.12; 90%
CI: 1.01, 1.2). Among the smaller number of cervical cancer
patients not given radiotherapy, the findings were very similar: 39 observed versus 37.3 expected within 9 years after
cervical cancer diagnosis (O/E = 1.05; 90% CI: 0.77, 1.32),
and 56 observed versus 53.1 expected 10 or more years
after diagnosis (O/E = 1.05; 90% CI: 0.82, 1.29). Thus thisstudy suggests that, at very high colon doses, there is little
or no excess risk of colon cancer. The parallel case-control
study assessed 409 cases and 759 controls but reported noincrease in risk (RR = 1.02; 90% CI: 0.7, 1.6), despite thefact that sizeable numbers of cases were exposed at very
high doses (e.g. 44 cases received doses of greater than
40 Gy). There was no observed trend of risk with dose
(p = 0.22); if anything, the risk of colon cancer appeared
to decrease with increasing dose [B8].
184. Although not considered in the UNSCEAR 2000
Report [U2], the study of Artalejo et al. [A32] reported aslight deficit of colon cancer mortality among workers for
the Spanish Nuclear Energy Board; the SMR was 0.83 (95%
CI: 0.33, 1.72) but was based on only 7 cancer deaths, ofwhich 1 was among the 27% of the cohort who had beenminers and may have been exposed to alpha radiation[A32]. Rogel et al. [R54] reported no significant differencesin colon cancer mortality rates compared with Frenchnational rates among radiation workers of Électricité deFrance (8 observed versus 8.3 expected deaths; SMR = 0.97;
90% CI: 0.48, 1.75).
(b) Internal low-LET exposures
185. A study of 6,841 Swedish, French and Italian patientstreated with a combination of conventional (external beam)
radiotherapy and 131I for thyroid cancer documented amodest, but not statistically significant, increase in colorectal cancer incidence (SIR = 1.3; 95% CI: 0.9, 1.6; 69
cases) [R38]. However, there was a statistically significant

trend of increasing colorectal cancer risk with administeredquantity of 131I. Adjusted for external radiotherapy, the ERR
per activity of 131I administered was 0.10 (95% CI: 0.08,0.27) per gigabecquerel. There was a statistically significant trend also among those who received no external radiotherapy, for whom the ERR per activity of 131I administered
was 0.15 (95% CI: 0.02, 0.38) per gigabecquerel [R38].
Unfortunately there was no breakdown of the values forcolorectal cancers into those for colon and rectal cancers
separately for this cohort, but it is likely that the vast
majority of these cancers were colon cancers.
(c) Internal high-LET exposures186. The International Thorotrast Study [T30] did not findany elevation in colon cancer mortality risk. There were 16
cases in both the Thorotrast-exposed and the comparisongroup in the Denmark–Sweden part of this study, resulting
in an RR of 1.5 (95% CI: 0.7, 3.0) [T30]. There were
5 deaths in the Thorotrast-exposed group and none in thecomparison group for the United States part of this study,
resulting in an undefined RR with a lower 95% CI of 0.5[T30]. No colon (or other organ) dose estimates have been
made for this study, and no trend with administered
Thorotrast volume was reported.

4. Summary
187. The available evidence continues to indicate thatcolon cancer is inducible by ionizing radiation, compatible with a linear dose response. The evidence for thiscomes almost entirely from studies of external low-LET
exposure, in particular from the LSS mortality data on thesurvivors of the atomic bombings. The LSS data suggestthat the ERR per unit dose decreases with increasing age
at exposure.

G. Rectal cancer

1. General background188. Cancer of the rectum occurs about half as frequently as cancer of the colon. Risks tend to be higher indeveloped countries (whether in North America, Europe,
Oceania or Japan), with age-standardized world rates generally lying between 5 and 25 cases per 105 PY, whereas
rates are generally lower—less than 5 cases per 105 PY—
in Africa and Southern Asia [P19]. Many of the risk factors for colon cancer apply also for rectal cancer. In
particular, there are well-known hereditary risk factors(e.g. familial adenomatous polyposis (FAP)), dietary risk
factors (high-fat and low-fibre diets, diets deficient in fruitand vegetables) and risk factors associated with certain
other chronic diseases (e.g. ulcerative colitis and Crohn’sdisease) [S58].

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2. Summary of UNSCEAR 2000
189. Rectal cancer was not considered in the UNSCEAR
2000 Report [U2]. The Committee has therefore considered
a number of earlier studies, which are reported in table 24
and discussed in the text.
(a) External low-LET exposures190. Although statistical data on cancers of the colon andrectum are often presented together as “colorectal cancer”,
the radiation dose–response behaviours of the two cancers
differ considerably. In the most recent analysis of cancer
mortality among the survivors of the atomic bombings [P9],rectal cancer mortality was not associated with radiationdose among men. Based on 172 deaths during the period
1950–1997, a linear model estimate for ERR was –0.25(90% CI: <–0.3, 0.15) Gy–1 for exposure at age 30 in amodel with no dependence upon attained age. However, it
was positively and significantly associated with dose among
women. Based on 198 deaths, the ERR was 0.75 (90% CI:
0.16, 1.6) Gy–1, again for exposure at age 30. The tumour
registry analysis covering the period 1958–1987 [T1] foundno significant dose response based on 351 cases of coloncancer arising evenly between the two sexes, with no difference between the sexes, and no significant trends withage at exposure or attained age.
191. In sharp contrast to the data on the survivors of theatomic bombings, there was a highly significant excess ofrectal cancer among cervical cancer patients treated withradiation [K1]: the typical average organ dose was 30–60
Gy, and 340 cases were observed versus 205.5 expected(O/E = 1.7; 90% CI: 1.5, 1.8); whereas, among patients not
given radiotherapy, there were 58 cases versus 43.1
expected (O/E = 1.3; 90% CI: 1.1, 1.6). No excess was seenamong the radiotherapy patients 1–9 years after cervicalcancer diagnosis (66 observed versus 81.5 expected), butthere were 274 observed versus 124 expected 10 or more
years after diagnosis (O/E = 2.2; 90% CI: 2.0, 2.4) (90%intervals calculated from table 5 of reference [K1]). In theparallel case-control study, individual organ doses could not
be estimated, because small changes in the position of theradium inserts would lead to large changes in rectal dose
[B8]. Doses are likely to be very high, of the order of hundreds of grays. Nevertheless, rectal doses could be grouped
into broad ranges, and using these there is a trend with doseof high statistical significance (p = 0.002 for 10-year
survivors) [B8].
192. Increased incidence of rectal cancer has been
observed in two studies of prostate cancer patients given
radiotherapy, based on data from the SEER cancer registry[B50, B51]. Brenner et al. [B50], using SEER data for theperiod 1973–1993, found an increase that was not statistically significant in rectal cancer risk more than 5 years after
prostate cancer diagnosis for patients given radiotherapy(O/E = 73/77 = 0.95) compared with those receiving surgery only (O/E = 86/121 = 0.75). The RR at 5 years after

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diagnosis of prostate cancer patients given radiotherapy compared with those receiving only surgery was 1.35 (95% CI:–0.01, 1.86), but at 10 years after prostate cancer diagnosis,
the RR became 2.05 (95% CI: 1.09, 3.92) [B50]. A more
recent analysis by Baxter et al. [B51] used SEER registrydata from the nine SEER registries that contributed data inor before 1991 to identify prostate cancer cases diagnosed
during the period 1973–1994 who were alive 5 or more years
after their diagnosis, who had not been diagnosed with acolorectal cancer during the first 5 years after prostate cancer
diagnosis, and who had undergone radiotherapy or surgicaltreatment not limited to orchidectomy. Kaplan–Meier curves
representing the time from prostate cancer diagnosis until
development of colorectal cancer were compared between
the patients undergoing surgery and those receiving radiotherapy. The observed hazard ratio for rectal cancer following radiation therapy compared with surgery only was 1.7(95% CI: 1.4, 2.2). The corresponding hazard ratios for“potentially irradiated” colorectal sites (rectosigmoid junction, sigmoid colon and caecum) and non-irradiated sites (therest of the colon) were 1.08 (95% CI: 0.92, 1.26) and 0.95
(95% CI: 0.78, 1.15), respectively. The radiation dose to therectum would be highly non-uniform, but at the point ofhighest exposure would approximate that to the prostate
gland [B52]. In a “conventional” 70 Gy prostate treatment
plan of around 1990, perhaps 40% of the rectum received
more than 60 Gy, and 80% more than 40 Gy [P43].
Currently, with a 75 Gy intensity-modulated radiation treatment (IMRT) plan, perhaps 20% of the rectum receives morethan 60 Gy, and 50% more than 40 Gy [L84].
193. In a group of Scottish women treated with X-rays
for metropathia haemorrhagica (uterine bleeding), 14 deaths
from rectal cancer were observed compared with 12.36
expected (SMR = 1.13; 95% CI: 0.62, 1.90). The average
doses to the rectum were high: 4.9 Gy. There was a suggestive, though not statistically significant, trend of increasing mortality with dose: the ERR is 0.04 (95% CI: –0.09,
0.16) Gy–1. A group of United States women treated withintrauterine radium to control uterine bleeding also had generally high rectal doses: mean = 3.0 Gy. This group exhibited little or no excess rectal cancer risk (15 observed
deaths, SMR = 1.0), and there was no trend of excess risk
with time since exposure or with radiation dose: the ERR
is 0.03 (95% CI: –0.14, 0.19) Gy–1 (1-sided p = 0.45). A
group treated for ankylosing spondylitis probably also had
fairly high rectal doses (mean colon dose = 2.58 Gy) [W8].
There was no evidence of excess risk of rectal cancer mortality: there were 62 deaths compared with 56.9 expected(SMR = 1.09; 95% CI: 0.83, 1.39). There was no trend ofexcess risk with time in this group, but no dose–response
analysis has been reported [W8].
194. In a cohort of United Kingdom radiation workers,
there was no suggestion of increased rectal cancer risk incomparison with the national population: there were 123
deaths compared with 155.58 expected (SMR = 0.79; 95%
CI: 0.66, 0.94) [M12]. However, there was a trend (at borderline levels of statistical significance) of increasing rectal

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cancer mortality with dose in this cohort: the ERR is 1.69
(95% CI: –0.12, 5.01) Gy–1 (1-sided p = 0.067) [M12].
There was no suggestion of excess cancer incidence rate incomparison with national rates in a group of Canadian radiation workers (145 cases observed compared with 199.0
expected, SIR = 0.73; 90% CI: 0.63, 0.84), but as for otherend points in this study, there was a very strong trend ofincreasing rectal cancer incidence with external dose: the
ERR is 13.8 (95% CI: 3.7, 33.6) Sv–1 [S8]. As with theparallel analysis of the mortality data associated with thiscohort [A8], concerns have been expressed about the reliability of record linkage, a possible source of bias [G16].

198. The great majority of primary liver cancers in adults
are hepatocellular carcinomas (HCCs); about 75–80% of
HCCs are aetiologically associated with chronic infection
with the hepatitis B virus (HBV) [L72]. Infection with thehepatitis C virus (HCV) is responsible for about 10–20%
of viral-associated HCCs, and plays an important role insome countries, notably in Japan. Other aetiological factors
include heavy alcohol consumption, liver cirrhosis, the presence of liver flukes and exposure to aflatoxins. HCC is 4–5
times more frequent in men than in women.

2. Summary of UNSCEAR 2000(b) Internal high-LET exposures
195. The International Thorotrast Study [T30] did not findany elevation in rectal cancer mortality risk. There were 8
cases in the Thorotrast-exposed group and 7 in the comparison groups in the Denmark–Sweden part of this study,
resulting in an RR estimate of 1.8 (95% CI: 0.6, 5.3) [T30].
No rectal (or other organ) dose estimates have been madefor this study, and no trend with administered Thorotrast
volume was reported.

3. Summary
196. There is little or no information on radiation-related
risk of rectal cancer at doses of less than about 1 Gy, but
it is reasonably clear that there is a radiation-related excess
risk for rectal doses of tens of grays. It is also clear thatthe small intestine, colon and rectum vary greatly in theircarcinogenic responses to ionizing radiation. There are few
data on risks in relation to anything other than external lowLET exposure.

H. Liver cancer

199. The UNSCEAR 2000 Report [U2] had limited dataon liver cancer from external exposures to low-LET radiation, but far more information was available on internal
high-LET exposures from Thorotrast. None of the studies
on medically or occupationally exposed populations suggested an association between radiation exposure and livercancer once dose–response relationships were examined,
although the difficulty in distinguishing primary frommetastatic liver cancers may have obscured any association.
200. The UNSCEAR 2000 Report [U2] stated that the
LSS provided the most convincing evidence for excess livercancers following exposure to low-LET radiation. The LSS
showed that liver cancer was the third largest cancer risk
due to radiation, after stomach and lung cancer. A significant dose response was found for liver cancer, with an ERR
of 0.52 Sv–1 for males and 0.11 Sv–1 for females. The relationship was strengthened by the analysis of incidence data
based on histologically and clinically verified primary livercancer cases, mostly HCCs [C25]. In the latter study, thedose response was linear and the ERR was estimated to be
0.81 Sv–1 (liver dose). Males and females had a similar RR
so that, given a threefold higher underlying incidence rate
for males, the radiation-induced excess incidence rate wassubstantially higher for males. The excess risk peaked for
those exposed in their early 20s, with essentially no excess
risk for those exposed before age 10 or after age 45.

1. General background197. There is wide geographical variation in liver cancer
incidence rates. The disease is very common in many partsof Asia and Africa, but is infrequent in Western Europe andthe United States [P31]. For example, in parts of Thailand,
annual age-standardized world rates are as high as 88.0 and
35.4 cases per 105 persons for men and women, respectively, but in most of the United States, age-standardized
world rates are fewer than 5 cases per 105 persons [P19].
Overall, primary liver cancer is the fifth most common
cancer worldwide [L72]. Accurate data on primary livercancer are difficult to obtain. Mortality data are unreliablebecause the liver is one of the most frequent sites formetastatic cancer. Up to 50% of liver cancers reported ondeath certificates are metastatic rather than primary livercancers, and tumour registries vary in their success in distinguishing primary and metastatic liver cancers.

201. Studies of Thorotrast-exposed patients consistently
showed increased risks of liver cancer due to exposure toalpha radiation, but in contrast to the LSS, the livercancers associated with Thorotrast exposure were mostcommonly cholangiocarcinoma, followed by angiosarcoma
and HCC. There was also an indication that Mayakworkers exposed to plutonium had an excess of liver cancer,
although the numbers were small and the doses were notwell characterized [G2].

3. New or updated studies(a) External low-LET exposures202. Epidemiological data on liver cancer associated withexternal exposure to low-LET radiation continue to be

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limited. The data available up to the 1990s were presented
in table 9 of the UNSCEAR 2000 Report [U2].
203. Liver cancers following external radiation exposure
were primarily HCCs in the LSS [S70], but in the Thorotrast
studies they have consisted mainly of angiosarcomas andcholangiocarcinomas [D36, T30]. The high prevalence of
HBV or HCV infections found in Japan may act as confounding factors for the radiation effects in the LSS [F13].
HCC arises from liver parenchymal cells, while intrahepaticcholangiocarcinomas arise from epithelial cells of the bileduct. It is also likely that the differing histological distributions, with a predominance of cholangiocarcinomas,
reflect the fact that for Thorotrast patients, areas of the liver
containing bile ducts, from which cholangiocarcinomas
arise, receive a daily dose of alpha particle radiation about
15 times higher than that received by hepatic cord tissue[D35].
204. In the latest LSS report on cancer mortality [P9],there were 1236 deaths from liver cancer, the leading causeof cancer death after cancers of the stomach and lung. A
significant dose response is found for liver cancer, with an
ERR of 0.39 Sv–1 for males and 0.35 Sv–1 for females, both
exposed at age 30 years. Data on risk stratified by sex andspecific age categories, or by specific latency periods, were
not presented.
205. A detailed study of HBV and HCV infections in the
LSS showed that both types of viral infection conferred alarge risk for HCC: odds ratio (OR) = 5.5 (95% CI: 2.6,12) and OR = 6.2 (95% CI: 2.8, 14), respectively. Even
with the strong main effect of HCV infection, among those
without cirrhosis there was a statistically significant interaction with radiation dose such that HCV-infected subjects
were at a 58-fold (95% CI: 2.0, ∞; p = 0.017) higher riskof HCC for a sievert of radiation dose [S70]; such an interaction was not found for patients with cirrhosis. Regardlessof the presence of cirrhosis, there was little evidence of aninteraction between HBV infection and radiation exposurefor HCC.
206. Also compatible with an interaction between radiation and hepatitis infection are data relating clearance of
HBV and radiation exposure [F13]. The presence of both
hepatitis B surface antigen (HBsAg) (indicating current
infection) and of anti-hepatitis-B core antibody (a marker
for both cured and current infections) increased with radiation dose, whereas that of anti-hepatitis-B surface antibody(indicating cured infection) did not. Although these datasuggest that radiation exposure may reduce the likelihoodof clearing a subsequent HBV infection, the authors urged
further study.
207. Although not considered in the UNSCEAR 2000
Report [U2], the study of Artalejo et al. [A32] reported aslight excess of mortality from liver cancer among workers
for the Spanish Nuclear Energy Board; the SMR was 1.51
(95% CI: 0.86, 2.46), but this was based on only 16 cancer

67

deaths, of which 4 were among the 27% of the cohort who
had been miners and may have been exposed to alpha radiation [A32]. Rogel et al. [R54] reported a statistically nonsignificant deficit of mortality due to liver cancer comparedwith French national mortality rates among radiation workers of Électricité de France (3 observed versus 5.0 expected
deaths; SMR = 0.60; 90% CI: 0.16, 1.54).
(b) Internal low-LET exposures
208. Epidemiological data with regard to liver cancer andinternal low-LET exposures continue to be rare. As summarized in the UNSCEAR 2000 Report, in the United States
thyrotoxicosis study, 21,000 hyperthyroid patients treatedwith 131I were followed up for 45 years; 39 liver cancer
deaths were observed, with an SMR of 0.87 [R3, U2]. The
doses received by the liver were not estimated but were presumably very low. An increasing, albeit not statistically significant (p = 0.78), trend in liver cancer mortality with dose
was observed in the study of persons exposed to weapons
test fallout in the Semipalatinsk area of Kazakhstan [B58].
The aggregate ERR based on an internal analysis was 0.45
(95% CI: –0.18, 1.71) Sv–1; however, when analysis was
restricted to the exposed group, based on individual dose
estimates, the trend estimate was negative, –0.08 (95% CI:–0.41, 1.00) Sv–1. As noted in section II.D, “ecological bias”
may operate in this study, so these findings should betreated with caution.
(c) Internal high-LET exposures
209. As noted previously [U2], 232Th is a primordial,alpha-emitting radionuclide with a physical half-life of
more than 10 billion years. Thorotrast—colloidal (232Th)
thorium dioxide—was used widely as an intravascular
contrast agent for angiography in Europe, the UnitedStates and Japan from the late 1920s to 1955. Thorotrast
aggregates injected intravascularly tend to be incorporated
into the tissues of the reticuloendothelial system, mainly
the liver, bone marrow and lymph nodes. Deposition
results in continuous alpha particle irradiation throughout
life at a low dose rate. The radiation dosimetry is complex because of the non-uniform distribution of thorium
dioxide in the liver, bone marrow and lymph nodes [C34].
It has been estimated that the typical annual dose fromalpha radiation following an injection of 25 mL of
Thorotrast is 0.25 Gy to the liver [K41, M46], but a reevaluation of liver organ mass has indicated that theannual dose is 0.40 Gy [K42]. A revised whole-body organ
partition of 232Th has shown a small reduction in the relative partition to the liver, but the estimated liver dose
remains essentially the same [I19]. Patients from the late
1920s to 1955 who were administered Thorotrast havebeen followed in Germany, Portugal, Denmark, Sweden,
Japan and the United States. The results of studies conducted in Germany [V3, V4, V7], Portugal [D15],
Denmark [A5, A28, A29] and Japan [M14, M19, M47]
were reviewed in detail in the UNSCEAR 2000 Report[U2], and are summarized in table 25.

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210. Results describing cancer incidence in the Swedish
Thorotrast study were recently published [N1], and wereincorporated in a later combined analysis of the Danish,
Swedish and United States patients [T30]. The combined
cohort consisted of 3,042 patients who had been injectedduring cerebral angiography with either Thorotrast (n =1,650) or a non-radioactive agent (n = 1,392) and who survived two or more years. SIRs for Thorotrast-exposed (n =1,204) and 1,180 comparison patients (Denmark andSweden) were estimated, and RRs, adjusted for population,
age and sex, were calculated with multivariate statisticalmodels. For United States patients (n = 446 exposed, 212
not exposed), comparable procedures were used to estimate
SMR and RR. In Denmark and Sweden, 136 primary livercancers were diagnosed in the Thorotrast-exposed group andnone in the comparison group (SIR = 108.9; p < 0.05;
RR = ∞; 95% CI: 44.2, ∞). RRs were similar for all cancer
sites for males (RR = 3.6; 95% CI: 2.8, 4.8) and females(RR = 3.3; 95% CI: 2.6, 4.2), but for liver cancer they were
not presented separately for each sex. In the United States,
22 deaths due to primary liver cancer were reported among
the Thorotrast-exposed patients and none in the comparisongroup (SMR = 22.5; 95% CI: 1.8, 464.3). The RR of primaryliver cancers (Sweden and Denmark) increased with timeafter angiography (p < 0.001 for trend), and significant
excesses (SIR = 4.0) persisted for 50 years. The actuarialrisk for all liver cancers after 50 years of follow-up
increased with the amount of Thorotrast injected (68.8%
after >20 mL, 68.5% after 11–20 mL and 33.8% after
3–10 mL) (p for non-homogeneity in dose category < 0.0001).
Increasing cumulative radiation dose (expressed as volume
of injected Thorotrast in millilitres × max[0, time since
injection in years—5 years] × 102) was associated with anincreasing risk of primary liver cancer (p trend = 0.001).
211. As summarized earlier [U2], liver cancer mortality wasstudied among about 11,000 workers exposed to both internally deposited plutonium and to external gamma radiation at
the Mayak nuclear plant in the Russian Federation [G2]. Livercancer risks were elevated among workers with plutonium
body burdens estimated to exceed 7.4 kBq, compared withworkers with burdens of below 1.48 kBq (RR = 17; 95% CI:
8.0, 36), based on 16 deaths in the former group. In addition,
trend analyses using plutonium body burden as a continuous
variable indicated an increasing risk with increasing burden(p < 0.001). However, because of limitations in the current
methodology for plutonium dosimetry, it was possible neither
to quantify liver cancer risks from plutonium exposure interms of organ dose, nor to make a reliable evaluation of therisk from external radiation in this cohort [G2].

4. Summary
212. An association of liver cancer with radiation exposure has not been demonstrated in studies of groups ofpeople medically or occupationally exposed to external orinternal doses of low-LET radiation. However, the updated
mortality data from the LSS of survivors of the atomic

bombings continue to indicate a strong dose response(p < 0.001). Studies of Thorotrast-exposed patients consistently show increased risks of liver cancer that persist for
50 years due to alpha particle radiation exposure.
213. While the most frequent type of liver cancer associated with Thorotrast exposure is typically cholangiocarcinoma, followed by angiosarcoma and hepatocellular
carcinoma, the excess risk associated with low-LET radiation exposure among the survivors of the atomic bombings
is primarily expressed as HCC. Underlying rates of livercancer are high in Japan, especially among males, and thehigh rates have been attributed to hepatitis viral infection,
particularly infection with HCV. In transferring liver cancer
risk estimates from one population to another, differencesin the underlying liver cancer rates, as affected by the prevalence of hepatitis viral infection, should be considered. The
significant interaction between radiation dose and HCV
infection in the development of liver cancer among patients
without cirrhosis merits further study.

I. Pancreatic cancer

1. General background214. The pancreas consists of two separate functional entities—an endocrine portion that produces (most importantly)
insulin and glucagon, and an exocrine organ that is an integral part of the digestive system, producing enzymes suchas trypsin, chymotrypsin, amylase and lipase [A33]. Cancerof the pancreas can be considered virtually synonymous withexocrine adenocarcinoma of the pancreas, since endocrine
neoplasms are relatively rare [A33]. Pancreatic cancer is oneof the most rapidly fatal cancers, and its presentation andcourse are marked by severe pain. There is less than a 20%
chance of surviving one year from diagnosis [A21, A33].
However, pancreatic cancer is relatively rare, with annual
age-standardized world rates generally fewer than 10 cases
per 105 persons for both men and women [P19]. There isrelatively small variation in incidence rates between countries, or between men and women, with age-standardized
world rates ranging from about 1 case per 105 persons inparts of Africa and Asia to about 15 cases per 105 persons
among some male United States black populations [P19].
Pancreatic cancer incidence and mortality rates increased in
the United States between 1920 and 1965 [K51], but rates
have been largely stable since then [A21]. The most consistent risk factor for pancreatic cancer is smoking, but diet,and in particular dietary fat, coffee and alcohol consumption, has also been indicated as a risk factor [A33].

2. Summary of UNSCEAR 2000
215. Pancreatic cancer was not considered in the
UNSCEAR 2000 Report [U2].

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3. New or updated studies

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pancreatic cancer mortality, and in particular no statisticallysignificant trend in risk with dose [G8].

(a) External low-LET exposures216. As shown in table 26, there is no statistically significant excess pancreatic cancer mortality or incidence inthe LSS [P9, P48]. For example, 163 deaths from pancreatic cancer were recorded in the LSS up to 1997 [P9].
Preston et al. [P9] report an ERR for pancreatic cancer inmales of –0.11 (90% CI: <–0.3, 0.44) Sv–1, and an ERR for
females of –0.01 (90% CI: –0.28, 0.45) Sv–1. The same istrue for many other groups. In a case-control study ofwomen receiving radiation treatment for cervical cancer,
there was an OR for radiation exposure of 1.39 (90% CI:
0.7, 2.7), equivalent to an ERR of 0.21 (90% CI: –0.16,0.89) Gy–1 (table 26) [B8]. There was no statistically significant (p = 0.37) trend of OR with dose in this study [B8].
In a cohort of British radiologists, there was a statisticallysignificant SMR among the “earliest entrance group” (those
first registered in the period 1897–1920), when presumably
doses would have been highest (5 deaths versus 1.29
expected, SMR = 3.88) (2-sided p < 0.05); there was no
statistically significant excess among the radiologists registering after 1920 [B2]. The United States peptic ulcer study
demonstrated a strong exposure-related increase in pancreatic cancer mortality; the ERR was 0.04 (95% CI: 0.0, 0.08)
Gy–1 [C4]. However, when attention was restricted to
exposed patients only, there was no evidence of a positive
trend: the ERR was –0.03 (95% CI: –0.10, 0.05) Gy–1 [C4].
This lack of dose response is possibly a consequence of thehigh doses and generally narrow spread of doses received
by the exposed group of patients [C4]. Inskip et al. analysedcancer mortality in a group of women treated with intrauterine 226Ra capsules for uterine bleeding, and did not observe
any statistically significant excess risk of pancreatic cancer
with dose: the ERR was 0.14 (90% CI: –2.76, 28.84) Gy–1
[I4]. A large and highly statistically significant trend ofincreasing pancreatic cancer incidence with radiation dose
was observed in a Swedish group treated for haemangioma
in infancy: the ERR was 25.1 (95% CI: 5.5, 57.7) Gy–1
[L10]. However, this finding was based on only 9 tumours,
and as the authors note, might well be due to chance. A
study of benign breast disease among Swedish women
observed a negative trend of pancreatic cancer mortality
with dose, based on 30 deaths (14 in the exposed group,
16 in the unexposed group): the ERR was –0.37 (95% CI:<–0.37, 0.8) Gy–1 [M3].
217. There was a trend of increasing pancreatic cancer
mortality risk with cumulative film badge dose thatapproached conventional levels of statistical significance(1-sided p = 0.07) among workers at the Hanford site in the
United States [G10]. The authors were inclined to treat theassociation as spurious, in view of the large number of end
points studied and the lack of any prior basis for assuming
a risk of pancreatic cancer [G10]. Combined analysis of thedata on the Hanford, Oak Ridge National Laboratory andRocky Flats weapons plant workforces in the United States
did not indicate any statistically significant excess risk of

218. A large and (for males) statistically significant
excess pancreatic cancer incidence has been seen in the
Canadian National Dose Registry [S8]. The ERR for males
was 9.2 (90% CI: 0.10, 36.8) Sv–1, based on 58 cases; formales and females combined, the ERR was 6.9 (90% CI:<0, 27.1) Sv–1, based on 76 cases. An increase of similar
magnitude, although not statistically significant, wasobserved for males in the parallel mortality data, fromwhich the ERR was 7.3 (90% CI: –4.4, 19.0) Sv–1, basedon 72 deaths [A8]. There was no suggestion of an increased
risk for females from these data: the ERR was –0.2 (90%
CI: –18.7, 18.3) Sv–1, based on 15 deaths [A8]. As noted
in section II.E above, similarly elevated ERRs per unit dosewere found for many other cancer end points and for causesof death that included infectious diseases and accidental
deaths, thus raising the question of bias in this study.
219. There was no statistically significant trend in pancreatic cancer mortality with cumulative film badge dose ina stratified cohort of United Kingdom radiation workers:
the ERR was –0.003 (90% CI: –1.12, 2.31) Sv–1, based on
129 deaths [M12].
220. There was a positive, but not statistically significant(1-sided p = 0.115), trend in pancreatic cancer mortality
with radiation dose for the IARC three-country nuclear
worker study [C3], based on 191 deaths from pancreaticcancer.
221. There was excess mortality due to pancreatic cancer
at borderline levels of statistical significance compared with
French national mortality rates among radiation workers ofÉlectricité de France (11 observed versus 6.6 expected
deaths; SMR =1.66; 90% CI: 0.93, 2.74) [R54].
(b) Internal low-LET exposures
222. There was no statistically significant excess of pancreatic cancer mortality in the United States thyrotoxicosis
study, with 161 deaths versus 153.13 expected [R3]. There
were no statistically significant trends in pancreatic cancer
with administered 131I in this study [R3].
(c) Internal high-LET exposures
223. There was a statistically significant increasing riskof pancreatic cancer mortality with increasing cumulative
radon daughter exposure in a combined cohort of 11 groupsof underground miners (p < 0.05); the ERR was 0.07% per
working level month (WLM) (95% CI: 0.01, 0.12) [D10].
However, as there is little previous epidemiological basis
for an association of pancreatic cancer with radon daughter exposure, and as this was the only one of 28 examinedcancer sites to yield a statistically significant increased
risk, the authors were inclined to view this as a chancefinding [D10].

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224. A statistically significant increase in pancreatic cancer
mortality was observed among workers at a thorium processing plant (5 cancers observed, 1.21 expected; SMR =4.13; 95% CI: 1.34, 9.63) [P37]. However, as with all studies involving occupational exposure to radiation, comparisons with cancer rates for the general population (which
includes both workers and non-workers) may be misleading.
Given the absence of information about risk in relation to
cumulative exposure, it is difficult to interpret this finding.

228. Ionizing radiation has been linked with cancers of
the trachea, bronchus and lung in numerous epidemiological studies. Dose–response relationships have been demonstrated for exposure to low-LET radiation, and also for
exposure to inhaled high-LET alpha emitters, includingradon (and its progeny) and plutonium.

225. Cancer incidences in a combined cohort of Danish,
Swedish and United States patients given the diagnostic
contrast medium Thorotrast have recently been published[T30]. The combined cohort consisted of 3,042 patients who
had been injected during cerebral angiography with either
Thorotrast (n = 1,650) or a non-radioactive agent (n =1,392)
and who survived two or more years. A total of 14 pancreatic cancer cases were observed in the Thorotrastexposed group, and 8 in the comparable control group.
There were marginally statistically significant (p = 0.07)
trends of increasing pancreatic cancer incidence with timeafter injection of Thorotrast, and (p = 0.05) with [injectedThorotrast volume] × [time since injection] [T30].

(a) Summary of UNSCEAR 2000

4. Summary
226. There is little, if any, evidence for associations
between pancreatic cancer and radiation dose, whether in
relation to external or internal low-LET radiation, or tointernal high-LET radiation.

J. Cancers of the trachea, bronchus and lung

1. General background227. Lung cancer is both the most common malignant disease and the leading cause of cancer mortality worldwide.
Rates tend to be higher in developed countries and lower
in developing countries. For example, in parts of the UnitedStates, annual age-standardized world rates are as high as
107.0 and 40.8 cases per 105 persons for men and women,
respectively, but in most of Africa and South Asia, rates
are fewer than 15 cases per 105 persons [P19]. The wide
range of geographical, temporal and sex differences in lungcancer mortality largely reflect variations in patterns of cigarette smoking, the main cause of the disease. Lung cancer
incidence has increased rapidly since the beginning of the20th century, but lung cancer mortality in males has begun
to decline in several countries, including the United States,
the United Kingdom and Finland. In most countries, lungcancer incidence rates are higher among people of lower
socio-economic classes, probably because of differences insmoking prevalence. Lung cancer has also been linked with
exposure to asbestos, with air pollution and with low
consumption of vegetables and fruits [B34, S59].

2. External low-LET exposures229. Lung cancer has been strongly linked with radiation
exposure in several studies, including those of the LSS
cohort of survivors of the atomic bombings in Japan. Cancer
incidence data from the LSS cohort for the period
1958–1987 indicated that the dose response was consistent
with linearity, that the ERR (Sv–1) for females was nearlyfour times that for males, and that there was little evidence
that the ERR depended on either age at exposure or attained
age [T1]. Results based on mortality data [P1] were similar, although the ratio of risk for females compared withthat for males was not as striking. The analyses noted above
did not take account of smoking habits. Efforts to do so[K35, P26, U2] suggested that the effect of the interactionof smoking and radiation was better described by an additive model than a multiplicative one, but could not definitively distinguish between the two models.
230. Lung cancer risk has been linked with radiation instudies of patients treated with radiation for ankylosing
spondylitis and in patients receiving radiotherapy for
Hodgkin’s lymphoma. A noteworthy finding from the ankylosing spondylitis study was the decline in the RR 25 years
after the first treatment [W8]. A limitation of this study isthat data on smoking habits were not available. In a casecontrol study of lung cancer among Hodgkin’s lymphoma
patients, van Leeuwen et al. [V2] found a statistically significant supramultiplicative effect of radiation and smoking
based on small numbers (30 cases, of whom 8 were either
non-smokers or light smokers).
231. The UNSCEAR 2000 Report [U2] provided a
detailed discussion of studies of lung cancer mortality
among patients who received multiple fluoroscopies in thecourse of treatment for tuberculosis in Canada [H7] and theUnited States (Massachusetts) [D4]. The lung doses, mean
age at exposure and follow-up were similar to those in the
LSS cohort. Neither study found evidence of an association
between lung cancer mortality and radiation dose. The
Canadian study was large enough (25,000 subjects withlung doses in excess of 10 mSv) to demonstrate that estimates of the ERRs per unit dose were incompatible withthose based on LSS data. These studies are importantbecause, in contrast to the LSS cohort, in these cases the
exposure was protracted. Howe [H7] explored several
sources of potential bias, including dose measurement error,
misclassification of lung cancer deaths as deaths from tuberculosis, smoking habits, differences in underlying rates, andUNSCEAR REPORT-PART 1.qxp

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differences between patients with tuberculosis and healthy
persons. No clear evidence of bias from any of these sources
was found, but the possibility that the dose response might
be different for patients with a lung disease (tuberculosis)
cannot be excluded.
232. Studies of several cohorts with protracted exposures
were reported and included a large international study ofradiation workers [C3], studies of a selected group of early
workers exposed at considerably higher doses at the Mayaknuclear plant in the former Soviet Union [K8, K17], and astudy of natural radiation exposure in the Yangjiang areaof China [T12, T14]. None of these studies indicated anelevated risk of lung cancer from low-dose, protracted
exposure.
(b) New or updated studies
233. Risks of both lung cancer occurrence and mortality
due to lung cancer have been strongly linked with radiationdose in the LSS cohort of survivors of the atomic bombings.
On the basis of the most recent evaluation of mortality datafrom the LSS cohort [P9], the ERR per unit dose (Sv–1) for
females was about twice that for males, whereas the EARs
per unit dose (Sv–1) were similar for the two sexes. In contrast to many other solid cancers, for lung cancer there wasonly a very small decline in the ERR per unit dose with age
at exposure, but the decline with attained age was comparable to that for all solid cancers as a group. By contrast,
the EAR showed a pronounced increase with attained age(stronger than for most solid cancers) and a clear declinewith age at exposure. As shown in figure XIII (from refer-71

ence [P9]), the sex-averaged EAR for a person exposed atage 30 is about 2 (104 PY Sv)–1 at attained age 60, but rises
to about 7 (104 PY Sv)–1 at attained age 70. Preston et al.
[P9] note that underlying lung cancer mortality rates in the
LSS cohort have increased with birth cohort, and that thismay confound evaluation of the effects of age at exposure.
234. Pierce et al. [P17] evaluated the joint effects of smoking and radiation exposure on lung cancer incidence up to1994 in a subset of about 45,000 members of the LSS cohort
for whom data on both radiation doses and smoking habitswere available. In analyses that took account of age at exposure, attained age, birth cohort and sex, they found that theeffects of smoking and radiation exposure were significantly
submultiplicative and consistent with an additive model.
These investigators also found that adjustment for smoking
reduced the ratio of the ERR per unit dose (Sv–1) for females
and males from 5.8 to 1.6; about 85% of the men and 16%of the women were smokers. In addition, after adjustment
for smoking, there was evidence of a strong decline in the
ERR per unit dose with increasing attained age, but no evidence of modification by age at exposure. Without adjustment, the decline with attained age was weaker and the ERR
increased with age at exposure. Pierce et al. note that theageing of the cohort and the higher smoking levels among
more recent birth cohorts provide a stronger basis for evaluating the joint effects of smoking and radiation exposure
than was possible in earlier analyses [K35, P26, U2].
235. Carr et al. [C4] evaluated risks of cancers of several
exposed organs in patients (78% male) treated with radiation for peptic ulcer. This study updated a previous

Figure XIII. Patterns of lung cancer mortality with age and time among the survivors of the atomic bombings in Japan(reproduced from Preston et al. [P9])The dark curves are fitted age-time patterns in the ERR (left panel) and EAR (right panel). The light dashed curves are the patterns obtained
when the age and age-at-exposure effects are constrained to equal those for all other solid cancer. The curves are sex-averaged
estimates of the risk at 1 Sv for people exposed at ages 10, 30 and 50, with attained ages corresponding to the follow-up period

Excess Relative Risk

Excess Absolute Risk
Lung

3.0
Site-specific effects
Solid cancer effects

2.0
ERR (Sv–1)

50 a

30 a

8

1.5
Age at exposure 10 a

EAR (104 PY Sv)–1

2.5

30 a

10

Age at exposure 10 a

6
10 a

50 a

4

1.0
Age at exposure 30 a2

0.5

Age at exposure 50 a

0.0

0
30

40

50

60

ATTAINED AGE (a)

70

80

30

40

50

60

ATTAINED AGE (a)7080

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analysis by Griem et al. [G6], and the number of lung cancer
deaths increased from 99 to 125. Lung cancer mortality risk
was significantly elevated compared with the risk among
patients who were not treated with radiation, but there was
no evidence of a dose response from analyses that wererestricted to exposed subjects. Evaluation of the interactionof smoking and radiation exposure indicated that the data
were compatible with a multiplicative interaction model.
236. Lung cancer risks were addressed in two recent casecontrol studies of Hodgkin’s lymphoma patients and one
such study of breast cancer patients. Swerdlow et al. [S60]conducted a case-control study that included 88 lung cancer
cases and 176 matched control subjects with Hodgkin’s
lymphoma treated in the United Kingdom. No estimates ofradiation doses were made, and data on smoking habits wereavailable for only 39% of the subjects. There was no significant relation between risk and “radiation volume”, used
as a surrogate for radiation dose.
237. Travis et al. [T3] conducted an international
population-based lung cancer case-control study that
included 222 cases and 444 matched controls. Strengths ofthis study were the existence of dose estimates for the specific site of the lung tumour (or comparable location inmatched controls), and of detailed data on both chemotherapy and tobacco use. The study showed a clear increase inrisk with increasing dose after adjustment for chemotherapy and smoking habits, and suggested a multiplicativeinteraction of radiation exposure and smoking.
238. Gilbert et al. [G23] conducted additional analyses
addressing the radiation effect on the basis of the 199 casesand 393 controls from the study by Travis et al. [T3] withadequate radiation dosimetric data and an additional 28
cases and 62 controls from a previous case-control study by
van Leeuwen et al. [V2] (summarized in reference [U2]).
There was little evidence of a departure from linearity orof modification in the ERR per unit dose (Gy–1) with sex,time since exposure (after an initial 5-year latency period),age at Hodgkin’s lymphoma diagnosis, or age at lung cancer
diagnosis. There was evidence of a significant radiationdose response for all histopathological types of lung cancer
evaluated (squamous cell, small cell, adenocarcinoma andlarge cell), and little evidence that the ERR per unit dose
varied with type. The interaction of radiation exposure andsmoking was consistent with a multiplicative relationship,
but not with an additive one (p < 0.001). In contrast, theinteraction of radiation exposure and chemotherapy was
found to be well described by an additive relationship. The
authors caution that the relevance of these findings for otherpopulations may be limited owing to the very high doses(mean dose of 25 Gy) and the immunodeficiency inherent
to Hodgkin’s lymphoma and associated with chemotherapy.
239. Ford et al. [F15] conducted a case-control study (280cases and 300 controls) of patients treated for breast cancer
at the M.D. Anderson Cancer Center at the University ofTexas in the United States. Their analyses suggest a

supramultiplicative interaction between radiotherapy treatment and smoking. The study did not include quantitative
information on either radiation exposure or smoking habits,
and also did not consider possible modification of the risks
by the length of period between breast and lung cancer
diagnosis (latency), which was 0.5 to 10 years for 55% of
the cases.
240. Zablotska and Neugut [Z8] conducted a cohort study
using data from the SEER registry in the United States toinvestigate lung cancer incidence in groups of women
treated for breast cancer with radiotherapy. After 10 years
of follow-up, risk to the ipsilateral lung was significantly
elevated for women treated after mastectomy (table 27), but
not for women treated after lumpectomy, where doses tothe lung are likely to have been much lower.
241. Several investigators have evaluated lung cancer
mortality in a cohort of Russian workers at the Mayaknuclear facility. A difficulty in estimating the effects of the
protracted external doses for this cohort is that many workers also received large doses from internal plutonium exposure, and only 40% of these workers were monitored forthis exposure. Early analyses reviewed in reference [U2]showed little evidence of a relationship between lung cancer
risks and external dose [K8, K17]. More recently,
Kreisheimer et al. [K34] analysed data on 4,212 male workers in the main plants at the Mayak facility who were hiredin the early period of operations (1948–1958) and for whom
doses to the lung from exposure to plutonium could be estimated either because they had been monitored or because
they had no potential for plutonium exposure. Using analyses that were adjusted for the lung dose due to plutonium,
these authors found no significant association between lungcancer mortality and external dose.
242. Gilbert et al. [G12] evaluated lung cancer risk for agroup of 21,790 Mayak workers, expanding the group evaluated in reference [K34] by adding females, persons hiredin the period 1959–1972, auxiliary plant workers (with little
potential for exposure) and workers potentially exposed toplutonium who were not monitored for this exposure. To
adjust for plutonium exposure in the last group, a surrogate
measure based on occupational histories was developed.
These investigators found a highly significant dose response
for external dose (p < 0.001). There was no evidence that
the ERR per unit dose (Gy–1) depended on sex, age at hireor attained age, although the power to address this was limited. An estimate for ERR per unit dose based only on
workers whose doses due to plutonium could be estimated
was 0.10 (<0, 0.29), similar to that obtained by Kreisheimer
et al. [K34]. The authors note the possibility of bias due toinadequate adjustment for plutonium exposure, which might
result from uncertainties in estimating doses due to plutonium as well as from using the surrogate measure. Parallel
analyses of Mayak workers (external dose) and the LSS
cohort indicated that both the level of risk and the patternsof risk for the ERR and EAR with sex and attained age
were remarkably similar in the two cohorts.

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243. Studies of nuclear workers exposed to low radiation
doses generally provide little evidence of a dose response
for lung cancer; this may be due to limited statistical power.
In addition to the large international [C3, C41] and NRRW
(United Kingdom) [M12] studies, recent studies of nuclear
power industry workers in the United States [H44] andJapan [I14] showed no evidence either of excess risk incomparison with the general population or of dose response
for lung cancer. Although the estimates of ERRs per unit
dose (Sv–1) for lung cancer [S8] and lung cancer mortality[A8] from the Canadian National Dose Registry were large,
as noted in section II.E above, similarly elevated ERRs perunit dose (Sv–1) were found for many other causes of death,
which included infectious diseases and accidental deaths,
thus raising the question of serious bias in this study.
244. Although not reported in the UNSCEAR 2000
Report [U2], the study of Artalejo et al. [A32] reported aslight deficit of lung cancer mortality among workers for
the Spanish Nuclear Energy Board; the SMR was 0.98 (95%
CI: 0.71, 1.31), based on 45 cancer deaths, of which 24
were among the 27% of the cohort who had been miners
and may have been exposed to alpha radiation [A32]. Rogelet al. [R54] reported a statistically significant deficit of mortality due to lung cancer compared with French nationalmortality rates among radiation workers of Électricité deFrance (23 observed deaths versus 47.5 expected; SMR =0.48; 90% CI: 0.33, 0.69); there was no statistically significant trend in respiratory cancer mortality with dose (ERR
= 0.1 (90% CI: –7.5, 17.4) Sv–1).
245. In a study of United States medical radiologic technologists, lung cancer mortality risk was not elevated compared with that of the general population, and there was noevidence of trends with either the length of radiation workor the year of first employment [M31]. The analyses were
controlled for smoking habits as well as attained age, calendar year, race and sex. Lung cancer incidence was not elevated [S29]. Doses were not available for this study. A recent
update of a study of cancer incidence among medical X-ray
workers in China found elevated lung cancer risks in comparison with a control population of surgeons, physicians andotolaryngologists [W3]. However, the excess was largest for
those workers who began their employment after 1970, whendoses would have been smaller than in the earlier period. The
authors note that their findings may be due to factors otherthan radiation exposure, such as smoking. The latest updateof the mortality study of radiologists in the United Kingdom
found excess lung cancer mortality among radiologists who
had first registered before 1920 (based on 7 deaths), but noexcess among those first registered in later years [B2].

3. Internal low-LET exposures(a) Summary of UNSCEAR 2000
246. Studies of persons treated with 131I were reviewed.
Little evidence of excess risk was found, possibly because

73

doses to the lung were low. Studies of cancer incidence near
the Three Mile Island nuclear plant in the United States
were also reviewed, with the conclusion that such studies
were uninformative regarding radiation and lung cancer, andfailed to provide convincing evidence that radionuclides
released as a result of the accident contributed to lungcancer risk.
(b) New or updated studies
247. An increasing and highly statistically significant(p = 0.0001) trend of lung cancer mortality with dose wasobserved in the study of persons exposed to weapons test
fallout in the Semipalatinsk area of Kazakhstan [B58]. The
aggregate ERR based on an internal analysis was 2.60 (95%
CI: 1.38, 4.63) Sv–1; when analysis was restricted to the
exposed group, based on individual dose estimates, the trend
estimate was somewhat reduced, 1.76 (95% CI: 0.48, 8.83)
Sv–1. As noted in section II.D, “ecological bias” mayoperate in this study, so these findings should be treatedwith caution.

4. Internal high-LET exposures (plutonium)(a) Summary of UNSCEAR 2000
248. Studies of workers at the Mayak nuclear plantdemonstrated clear evidence of a dose response for exposure to plutonium [K8, K17]. Studies of workers exposed
to plutonium at the Sellafield plant in the United Kingdom[O1] and at Los Alamos National Laboratory in the UnitedStates [W6] failed to provide evidence of plutonium-related
lung cancer risk, a finding that may be due to the relatively
low doses and limited statistical power in these studies. Theinternal doses due to plutonium for workers in the United
Kingdom and the United States were far lower than forworkers at Mayak.
(b) New or updated studies
249. Three new analyses of data on workers at the Mayaknuclear plant quantify lung cancer mortality risk as a function of dose to the lung, and make use of improved internal dose estimates that became available in the year 2000.
As noted above, Kreisheimer et al. [K34] evaluated lungcancer risks for a subcohort of Mayak workers whose plutonium doses could be estimated and who were hired in the
period 1948–1958. In analyses that were adjusted for both
external dose and smoking habits (i.e. yes or no), a linear
dose–response relationship was found to describe the datawell. Gilbert et al. [G12] evaluated a larger group of workers (see above), although the evaluation of the plutonium
dose response was necessarily based on those workers whose
plutonium doses could be estimated. These investigators
confirmed the good fit of the linear model, and the estimated
ERR per unit dose was similar to that obtained byKreisheimer et al. They also fitted EAR models, and evaluated the modifying effects of sex, age at hire, attained age

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and time since exposure on both the ERR and the EAR. The
ERR per unit dose for females was about 4 times higher
than that for males, whereas the EAR (expressed as excess
deaths for 104 PY Gy) for females was less than half thatfor males. The ERR per unit dose showed a strong declinewith attained age, whereas the EAR increased with attained
age until about age 65 and then decreased. Neither the ERR
nor the EAR depended on age at hire. The ratio of coefficients for the effects of the internal dose due to plutonium
and the external dose (i.e. the RBE) was estimated to be 33(95% CI: 14, 98). Parallel analyses of Mayak workers, forwhom plutonium dose estimates were adjusted by the quality factor of 20 recommended by the ICRP, and the LSS
cohort indicated that the ERRs were reasonably similar in
the two cohorts, although the decline with attained age was
not observed in the LSS cohort. However, the pattern of theEAR with attained age was markedly different in the twocohorts. At younger ages (under 65 years), the EAR was
higher for the Mayak workers, whereas at older ages, theEAR was higher for the LSS cohort. Comparisons were also
made with risks observed for 11 cohorts of undergroundminers exposed to radon [C36]. The overall level of risk wascompatible for the two types of exposure, and the decline in
the ERR with attained age was very similar. After accounting for the effect of attained age, there was no evidence from
the Mayak workers of the decline with time since exposurethat was observed in the study of underground miners.
However, this may have been because it was not possible
to measure the pattern of lung dose accumulation due to plutonium in individual workers. Jacob et al. [J10] analysed thedata using a two-stage “clonal expansion” model. In contrast
to the other two analyses, the preferred model in this analysis was submultiplicative in the RRs due to smoking and toplutonium radiation dose, and resulted in a markedly lower
estimate of the ERR per unit dose.
250. Wing et al. [W22] examined cancer risks in relation
to work involving potential exposure to plutonium at the
Hanford site in the United States. They used information on
work location and job title to assess the likelihood of plutonium exposure. For most end points evaluated, including
lung cancer, risks were significantly lower for workers
judged to have potential plutonium exposure than for workers with no such potential. However, at ages 50 and above,the duration of employment in jobs with potential for plutonium exposure was found to be associated with mortality
due to several other disease categories, with that due to lungcancer showing the largest increase. Because Wing et al.
considered several alternative age cut-offs, this finding may
be due to chance. Since workers with potential for exposure
to plutonium were supposed to be monitored for this exposure, it is not clear whether the surrogate measure of plutonium exposure used was meaningful. No analyses of lungcancer risks in relation to plutonium monitoring data werereported, and no data on smoking habits were available.
251. Brown et al. [B35] conducted a case-control study
for lung cancer among plutonium workers at the Rocky
Flats plant in the United States. Annual doses to the lungdue to plutonium, americium and uranium were estimatedfor the 180 cases and 720 matched controls included in thisstudy, with most plutonium doses in the range 0–1 Sv.
There was no evidence of increased risks from exposure toamericium or uranium. Analyses of the cumulative dose due
to plutonium using lag periods of 5, 10 and 15 years
resulted in elevated (usually non-significantly) odds ratios
for several dose categories, but did not show a consistentincrease in risk with increasing dose. Because of concern
regarding a differential healthy worker effect depending on
duration of employment, analyses for three separate categories of employment duration were performed. Analyses
restricted to those employed for 15–25 years produced asignificant dose response, but analyses based on those
employed for shorter or longer periods indicated no evidence of a dose response (the direction of the trend was
negative). The results of trend tests were occasionally noted,
but risk estimates per unit dose were not presented.
Although Brown et al. allude to supplementary analyses thatwere adjusted for smoking, analyses presented in the paper
were not so adjusted.

5. Internal high-LET exposures (Thorotrast and radium)(a) Summary of UNSCEAR 2000
252. Studies of persons exposed to Thorotrast and 224Ra
were summarized and found to provide little evidence ofelevated risks of lung cancer. The statistical precision inthese studies was limited by the small numbers of lungcancers.
(b) New or updated studies
253. Travis et al. [T30] studied patients injected withThorotrast during radiographic procedures in Denmark,Sweden and the United States. The lung cancer incidence
rate among Thorotrast-exposed patients in Denmark andSweden was significantly elevated compared with incidence
rates among the general population, but not in comparison
with that in a control group. Lung cancer mortality rates inUnited States patients were non-significantly elevated inrelation to both the general population and the control
group. There was also no evidence of a trend of increasinglung cancer risk with a surrogate measure of cumulative
radiation dose.

6. Internal high-LET exposures (radon)(a) Summary of UNSCEAR 2000
254. The UNSCEAR 2000 Report [U2] summarized theresults of various epidemiological studies of undergroundminers and of people exposed in residences, as well as many
relevant biological data, and concluded that there was strong
evidence for an association between lung cancer risk andexposure to radon daughters.

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255. In particular, the results of a comprehensive analysis of miners conducted by the BEIR VI Committee [C36]were reviewed in the UNSCEAR 2000 Report [U2].
Summary data are given in table 28. The BEIR VI
Committee re-examined the pooled data from 11 cohort
studies of radon-exposed miners by Lubin et al. [L8],
including updated data from China, the Czech Republic,
France and the United States (Colorado Plateau) (see table
10 in reference [U2]). The BEIR VI models were based ona linear ERR model, but incorporated adjustments for
effects of the time since exposure by differentially weighting exposures to radon received 5–14 years, 15–24 years
and 25 or more years earlier. The models also allowed forvariation in the exposure–response effects with attained age,with duration of exposure or with average radon concentration. The BEIR VI Committee derived two separate
models, designated the “exposure–age–duration” model andthe “exposure–age–concentration” model, but proffered no
preference [C36]. The pooled data included nearly 1.2 million person-years of follow-up, from which there were 2,674
lung cancer deaths among workers with prior radon exposure, and 113 lung cancer deaths among workers without
prior radon exposure. The large number of cases permitted
detailed examination of many factors that may modify therisk of radon-induced lung cancer. The ERR per unit radon
exposure (WLM–1) decreased with increasing time since
exposure and attained age, and with increasing averageradon concentration (the exposure–age–concentration
model) or with decreasing duration of exposure (the exposure–age–duration model). There was no variation in the
ERR per unit radon exposure (WLM–1) with age at first
exposure; however, except for the cohort of Chinese tin
miners, the range of ages at first exposure was limited, withmean age at first exposure more than 25 years in all cohorts.
The joint effect of radon exposure and smoking on lungcancer risk was evaluated for six cohorts where information
on smoking habits was available. The joint association for
the RR was greater than additive and less than multiplicative, although the precise modelling of the joint effects wasdifficult to quantify definitively owing to the small numberof miners who had never smoked and to the limited quantitative information on tobacco use. On the basis of differences in ERR per unit radon exposure (WLM–1) in“ever-smoker” and “never-smoker”, the BEIR VI Committee assigned a twofold greater ERR for never-smokers.
Any modifying effects of exposure to other agents encountered in mines were not clear, although the ERR per unit
radon exposure was lower after adjusting for arsenic exposure [L8]. Because of an absence of data, effects of radon
exposure for females could not be evaluated.
(b) New or updated studies
256. Since the BEIR VI Report appeared, follow-ups ofseveral of the miner cohorts have been extended andreanalysed, and new analyses have been conducted onrelated populations. A nested case-control study of lungcancer was selected from a cohort of non-smoking miners
employed in the uranium mining industry of the Colorado

75

Plateau region [G3]. Results for non-smokers were consistent with results from the Colorado Plateau and New
Mexico cohort studies in the United States, and showed
increased lung cancer risk with radon exposure (WLM), as
well as evidence of a decreasing radon exposure–response
relationship with increased exposure rate. Tomasek
analysed the S (older, higher-exposed) and N (new, lowerexposed) cohorts of the Czech miner study [T33]. These
data extend the follow-up of a subset of the Czech cohort
included in the pooled analysis to the end of 1999. Results
showed decreasing risk with time since exposure and withage at exposure, and a (non-significant) twofold greater
ERR per unit radon exposure (WLM–1) for non-smokers.
Investigators added six years of follow-up to the Frenchuranium miner cohort, identifying a total of 125 lung cancer
deaths, nearly tripling the number of lung cancer deaths thecohort contributed to the pooled analysis [R39]. Results
showed a decreasing ERR per unit exposure with time since
exposure and with exposure rate, although the exposure-rate
effect disappeared after 1956, when exposure assessment
improved owing to more frequent and more comprehensive
measurements. These results are difficult to interpret since
the mean annual exposure among French miners was 23.9
WLM per year prior to 1956 and 1.5 WLM per year afterwards. A new, very large cohort study of miners of the
Wismut uranium mining company in the former German
Democratic Republic has recently been initiated [K37]. On
the basis of year of initial employment, 60,000 subjects
were selected from an estimated 400,000 total worker population covering three periods (1946–1954, 1955–1970 and
1971–1989), which represented the “wild” years (whenradon exposures were high and reached 300 WLM peryear), the “transition” years (when radiation protection procedures were introduced, radon measurements were started
and exposures were reduced) and the “consolidation” years(when employment was stable and exposure levels were
estimated as generally below 2 WLM per year), respectively. Cohort analyses have not yet been published,
but given its size, this study should yield important new
information.
257. Data used in the miner analyses were drawn fromstudies of a broad range of populations, including workers
at uranium, tin, iron and fluorspar mines. For each study inthis diverse group, the relationship between radon exposureand lung cancer mortality risk was consistent with linearity, and estimates of the ERR per unit radon exposure(WLM–1) were statistically consistent with homogeneity of
the radon effect [C36]. Nonetheless, concerns have beenraised about the consequences of radiation exposures fromsources other than radon, e.g. thoron (220Rn and its decay
products) and gamma radiation, for lung cancer risks to uranium miners [D29]. However, the consistency of resultsfrom the pooled analyses and from a comparison of Czech
tin and uranium miners [T34] suggests a limited impact
from these other radiations on estimates for lung cancer risk
due to radon exposure. If exposures to gamma radiation
were a significant contributor to the total radiation
exposure of uranium miners, then one might anticipate an

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excess incidence of leukaemia, which to date has not been
observed [D10, L54].
258. The presence of an inverse exposure-rate effect in
the BEIR VI models has important implications for the
extrapolation of risk from studies of miners to populations
exposed in residences. This effect implies that, for equal
total exposure, the risk is higher when the exposure isreceived over a longer rather than a shorter period of time.
The inverse exposure-rate effect was seen, to varying
degrees, in all of the miner studies, except for the Frenchcohort, where miners often worked for many years at low
exposure rates. However, a reanalysis of the data from the
Beaverlodge uranium mine in Canada based on revised
exposure estimates [H18] provided no evidence of an
inverse exposure-rate effect. It should be noted that thehighest exposure rates, which generally gave rise to thehighest cumulative exposures, occurred in the earliest yearsof mining, when the fewest measurements were made anduncertainties in dose estimation were probably greatest.
These greater exposure errors would bias the observed risks
towards the null for these high exposure rates and potentially induce an inverse exposure-rate effect. However,adjustments by Lubin et al. [L8, L59] by calendar year offirst exposure, calendar years of exposure, attained age andyears since the last exposure did not markedly influence theeffects. In a reanalysis of the Colorado cohort, Stram et al.
directly adjusted for exposure uncertainties and found thatthe inverse exposure-rate effect remained, although it was
smaller [S61]. It therefore seems unlikely that measurement
error entirely explains the inverse exposure-rate effect.
259. Results of experimental studies using animals support
the inverse exposure-rate effect, having shown that a longer
duration of radon exposure at a lower rate induced morelung cancers than a shorter duration of exposure at a higher
rate [C19, C20, M38, M42]. Regarding possible mechanisms, Moolgavkar et al. [M39, M40] suggested, on the basisof the two-stage initiation–progression model for carcinogenesis, that extended duration allows time for the proliferation of initiated cells and thus for higher excess incidenceof disease. Brenner and Sachs postulated that the inverse
exposure-rate effect is a consequence of the “bystander”
effect, whereby irradiated cells send signals that can resultin damage to nearby cells [B36, B40]. The model postulates
that: (a) the bystander signalling emanates from cells whose
nucleus is directly hit by an alpha particle, and additional
hits do not increase bystander response; (b) at any given
time, a subpopulation of target cells is hypersensitive in their
response to the bystander signal; and (c) cells in the hypersensitive subpopulation are also hypersensitive to direct radiation damage, such that alpha particle traversal of a nucleus
results in cell death [B40]. On the basis of the miner data,
the model estimates that about 50 cells are signalled by thecell with the traversed nucleus [B40]. At low exposures, thebystander effect would be expected to dominate risk estimation; however, this effect has already been empiricallyincorporated into the BEIR VI models, and thus the BEIR
VI extrapolations would not be expected to underestimate

the risks of exposure to radon in residences [B40]. A contrasting view is given by Little, who believes that the inverse
exposure-rate effect can be explained using a linear RR
model with adjustment for attained age and age at first exposure, without the need to resort to a complex bystander effect[L47]. The bystander effect and other “non-targeted” effects
are discussed at greater length in annex C of the UNSCEAR
2006 Report, “Non-targeted and delayed effects of exposure
to ionizing radiation”.
260. The biologically based, two-stage clonal expansion
model has previously been applied in analysing data fromthe cohort study of Colorado Plateau uranium miners in the
United States [L71] and experimental studies of radon exposure in rats [H45, K40]. Application of this model has now
been extended to cohort studies of French [B60, H48],
Czech [B60, H48] and Chinese [H47] miners. Precise interpretation of the results, however, remains problematic,owing to heterogeneity of parameter estimates across animalstrains [K40] and among miner cohorts [H48], although thispoint is controversial [B60]. However, results generally suggest that radon exposure affects the initiation rate, but its
dominating influence is on promotion (clonal expansion)[H49], while it does not affect the rate of transformation of
initiated cells [H48]. Little et al. have raised concerns about
these results, in particular with respect to the Colorado
Plateau uranium miner data, as they found an improved fit
to the data using a three-stage model compared with thetwo-stage model and an effect of radon exposure on thesecond-mutation rate [L41].
261. Since publication of the UNSCEAR 2000 Report[U2], several new epidemiological case-control studies ofradon in residences and lung cancer have been reported,
supplementing the already existing case-control studies (seetable 29). While it remains important to assess lung cancer
risk and radon concentration for a variety of populations
that involve different lifestyles, smoking habits, occupations
and other potential confounding factors, several consortia
of investigators have reported results from the pooling oforiginal data from China [L61], Europe [D24, D30] andNorth America [K38, K39]. These reports jointly represent
the best available characterization to date of lung cancer
risk and residential exposure to radon. These pooling projects were the result of extensive and ongoing planningworkshops held between 1989 and 1995 and sponsored by
the Office of Health and Environmental Research of theUnited States Department of Energy, and the Radiation
Protection Programme, Commission of European
Communities [D31, D32, D33, D34]. The goals of thesemeetings were: to minimize study heterogeneity by making
the protocols for radon measurement and for collection ofother data as consistent as possible across studies; todevelop a common data format for the pooling of data; andto create a collaborative environment to facilitate analyses.
Combined data for the studies of residential exposure
included 12,282 lung cancer cases (China, 1,053; Europe,
7,148; North America, 3,662) and 21,486 controls (China,
1,997; Europe 14,208; North America, 5,281).

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262. The importance of these pooling projects cannot be
overemphasized. By the late 1980s, investigators had clearly
identified elevated exposure to radon and radon progeny as
a risk factor for lung cancer among underground miners[C34, I18, N13]. Surveys of radon concentrations in indoor
air of residences, early epidemiological studies using surrogate markers of radon exposure and extrapolations usinglung cancer risk models based on miner data suggested thatthe general population may carry a substantial burden ofincreased risk of lung cancer from radon exposures indwellings [C34]. Owing to differences in environmental conditions between mines and dwellings, and in patterns of
exposure between miners and the general population, there
was substantial uncertainty about the application of models
for estimating lung cancer risk based on miner data to general populations. The mean radon exposure of miners fromthe pooled data analysed by the BEIR VI Committee was
162 WLM [N2], which is 20–30 times the exposure from
25 years of residence in a typical dwelling. It should be recognized, however, that although mean exposures were higher
for miners, 13.2% (353 out of 2,674) of the lung cancer
deaths among exposed miners occurred among those
exposed to less than 50 WLM. In comparison, long-term residence in dwellings with concentrations in the range 400–500
Bq/m3 results in a radon exposure of about 50 WLM; therange reflects varying assumptions on residential conditions[D29, K39]. Thus cumulative exposures for some miners
were comparable to cumulative exposures for long-term residents of dwellings with high radon concentrations. Thisoverlap of the ranges of exposure for mines and dwellings
helps to reduce the uncertainty associated with extrapolating
beyond the ranges of observable data for miners.
Nonetheless, owing to the potentially large number of individuals exposed to this known human carcinogen in thehome, it was important to provide independent confirmatory
information of the risk projections based on miner data bydirectly evaluating risks from epidemiological studies ofradon exposure in dwellings. Lubin et al. [L62, L63] suggested, however, that epidemiological studies would have toovercome two substantial problems: (a) very low expectedexcess lung cancer risks from radon exposure, since theradon concentrations in the indoor air of most homes were
low compared with those in mines; and (b) substantial uncertainties in estimating current and historical radon exposuresfor 20–30 years and more in the past, because some previous homes no longer exist or cannot be measured, andbecause of the natural temporal and spatial variability ofradon concentrations in indoor air. As a result of these twolimitations, Lubin et al. emphasized the need for sufficient
statistical power to test for significant risk from radon exposure and to evaluate modifications in these effects by conducting studies with large sample sizes and by poolingoriginal data from multiple studies [L62, L63]. The threecurrent pooling studies effectively address these limitations.
263. Criteria for inclusion, as well as exposure assessment
procedures, differed slightly for the three pooling projects.
The pooling of Chinese studies included the two casecontrol studies conducted in China, which used air alpha77

track detectors accumulating exposure over 1 year, and collected comprehensive information on smoking habits andother personal characteristics [L61]. Exposure assessment
focused on an “exposure time window” (ETW), defined as
the period 5–30 years prior to disease occurrence for cases
or prior to the year of interview for controls. For the
Shenyang study [B37], investigators measured the radon
concentration in air of one home only, either the current
home if it was occupied for 5 or more years, or the previous home if it was occupied for 5 or more years. Because
cases were ascertained in the period 1985–1987, before theimportance of an ETW was fully appreciated, investigators
recalculated exposures for the pooled analysis based on the
5–30 year ETW. The European study pooling included all
13 European studies that enrolled 150 or more cases andcontrols, ascertained detailed smoking histories and demographic and other information, and sought radon measurements in all homes occupied in the previous 15 years ormore [D24, D30]. Exposure assessment relied mostly onradon concentrations measured using 1-year alpha track
detectors, although two Swedish studies (nationwide [P18]and “never-smokers” [L65]) used 3-month detectors in
winter, the Spanish study used 5-month detectors [B39], andthe French [B41] and United Kingdom [D13] studies used
6-month detectors. The ETW was defined by the 30-year
period 5–34 years prior to study enrolment. The North
American pooling included all seven studies that enrolled
200 or more cases, and ascertained detailed smoking histories and demographic and other information [K38, K39]. It
relied primarily on 1-year air alpha track detectors [K38,K39]. In the Winnipeg study, investigators based radon
exposure assessment on two alpha track detectors placed
consecutively for 6 months each [L64], while in the New
Jersey study in the United States, the investigators surveyed
the homes of 8% of subjects using a 4-day charcoal canister detector [S62]. However, data included in the North
American analyses were limited to subjects whose exposure
assessment was based, at least in part, on measurements
using long-term alpha track detectors. The ETW was
defined by the period 5–30 years prior to study enrolment.
It should be noted that, except for the two Swedish studies, investigators who used detectors in place for less than
1 year either staggered measurements throughout the years
or conducted seasonal adjustment. Thus the main influenceon exposure assessment of using detectors in place for lessthan 1 year would be a slight increase in variability ofassessed exposures, but no introduction of bias.
264. Table 29 summarizes mean radon concentrations in
air of residences of both cases and controls, and values ofexcess odds ratios (EORs) for 100 Bq/m3 based on a linear
OR model. Although the range of estimates was wide,
19 studies estimated a positive trend with radon concentration, while three studies estimated a (non-significant) negative trend with concentration. The model for the summary
ORs for each pooling was consistent with linearity andstatistically significant (as shown in figure XIV). The estimated EORs were 0.13 (95% CI: 0.01, 0.36) for 100 Bq/m3
for the Chinese pooling, 0.08 (0.03, 0.16) for the European

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pooling and 0.11 (0.00, 0.28) for the North American pooling. Within each of the pooled analyses, the estimates of
EOR were consistent with homogeneity of the radon effectacross studies. The p-values for the test of the null hypothesis of homogeneity of the EORs for 100 Bq/m3 were 0.29
(China), 0.94 (Europe) and 0.56 (North America). Eachpooled analysis also evaluated variations in the EORs for
100 Bq/m3, and found no significant variations on a multiplicative scale for the radon effect by sex, age or smoking
status. For example, the EORs for 100 Bq/m3 for males andfemales, respectively, were 0.16 and 0.08 for the Chinesepooling, 0.11 and 0.03 for the European pooling, and 0.03
and 0.19 for the North American pooling. For “eversmokers” and “never-smokers”, the EORs for 100 Bq/m3
for males and females, respectively, were 0.13 and 0.13 for
the Chinese pooling, 0.08 and 0.11 for the European pooling, and 0.10 and 0.10 for the North American pooling. It
is worth noting that these patterns differed from those foundin studies of miners, where analyses exhibited declining
radon effects with age and greater radon effects for nonsmokers [C36]. The reason for this difference is unknown.
In both the European and the North American residential
studies, the radon exposure–response relationship was
greater for small cell carcinoma cases, although variations
by histology were not statistically significant in either data
set. Histology was not accurately assessed in all subjects in
the Chinese studies and was not analysed.

265. Lubin et al. showed that a linear ERR model, with
an ERR estimate of 0.0117 (WLM–1), provides a good
approximation to the BEIR VI models for exposures under
50 WLM [L60]. Using standard assumptions for occupancy,
equilibrium factors for radon and its progeny, and differences between mine and dwelling conditions [C36], residing for 30 years in a dwelling with a radon concentrationof 100 Bq/m3 results in about 12 WLM of exposure, andan EOR of 0.14 for 100 Bq/m3 based on miner models[K39]. Since lung cancer is a rare disease and often rapidly fatal, the estimate of the ERR for lung cancer mortality is comparable to the EOR for lung cancer incidence, andthus the miner-based estimate of 0.14 for 100 Bq/m3 is inexcellent agreement with estimates from the residential
pooling analyses of 0.13 for China, 0.08 for Europe and0.11 for North America.
266. Assessment of residential radon exposure for many
years in the past is subject to substantial uncertainties.
Radon measurements vary spatially within rooms of adwelling, between rooms and between dwellings, and overtime. In addition, there is variability associated with themeasurement device and the measurement processing. Thisvariation introduces random variability when assessing
long-term mean radon concentrations [D24]. In addition,
uncertainties in exposure assessment may also arise fromlifestyle changes of residents, structural changes in homes
or long-term systematic changes in radon concentrations.

Figure XIV. Odds ratios for categories of residential exposure to radon and fitted linear odds ratio models based onsummary results of pooled analyses of original data from China [L61], Europe [D24] and North America [K38]4Pooled results() China() Europe() North America

ODDS RATIO

3

2

1

0
0

100

200

300

400

500

RADON CONCENTRATION (Bq m–3)

600

1 200

1 300

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Uncertainties in the estimation of radon exposure are influenced by gaps in residential histories for which no measurement exists because measurement protocols may exclude
short-term residences, houses that no longer exist or are nolonger used as residences, or houses for which the current
owners refused measurement. Finally, uncertainties may
also arise from ignoring exposures that may contribute torisk, for example exposures beyond 30 or 35 years in thepast. The approaches to addressing the consequences ofthese uncertainties differed among the pooled analyses. The
European project used replicate measurement data from theCzech Republic, Italy, Sweden and the United Kingdom toestimate measurement variability for all the study populations [D30], and integrated those estimates into their exposure–response modelling using either regression calibration[C12] or integrated likelihood [R19] methods. With theiradjustment for random uncertainties, the estimated radon
effects increased the EOR from 0.08 to 0.16 for 100 Bq/m3.
Assessments of exposure uncertainties in the North
American pooling and the Chinese pooling were conductedby restricting subjects on the basis of length of occupancy
in the current house, under the assumption that contemporary measurements of radon more accurately reflect true
concentrations throughout the ETW period for long-term
residents, and also by restricting subjects to those withincreased coverage of the ETW with measurement data,
under the assumption that greater coverage of the ETW
resulted in less supposition for values of missing data [K38,K39]. In the North American pooling, risk estimates
increased consistently with increasing stringency of coverage of the ETW, and when subjects were limited to thoseresiding in one or two homes in the ETW. For example,
EORs were 0.11 per 100 Bq/m3 with no residency restriction, and 0.14 for subjects with 20 years or more of coverage of the ETW. For subjects residing in one or two
homes, EORs were 0.15 per 100 Bq/m3 with no residency
restriction, and 0.18 for subjects with 20 years or more ofcoverage. In the Chinese pooling, the overall EOR was
0.13 per 100 Bq/m3; it increased to 0.32 for subjects with
25 years of coverage of the ETW and to 0.33 for subjects
who lived in exactly one residence. In a separate evaluation, investigators for the Gansu study in China conducteda 3-year radon measurement study to evaluate temporal andspatial variation [L66]. The adjustment for uncertainties
increased risk estimates by 50–100%, similar to the impact
found in the European pooling and the North American
pooling.
267. Alternative methods for reducing uncertainties
include the use of an improved dosimeter and improved
study design. A surface dosimeter measures residual radiation from 210Po, which is embedded in glass artefacts, suchas glass mirrors and picture frames, following recoil fromdecay of 210Pb [L67, M43]. It is believed that measurementsof residual radiation in glass objects that are retained anddisplayed over many years and in multiple homes provide
a more accurate estimate of cumulative radon exposures,
although concerns have been raised about the effects ofincreased particulate levels from the presence of smokers

79

on plate-out rates [W26]. In the United States, a study ofMissouri women reported an EOR of 0.63 (95% CI: 0.1,
1.9) per 100 Bq/m3 using a glass surface monitor, but foundno excess risk when dosimetry was based on standard air
radon detectors accumulating exposure over a year [A9].
A Swedish study estimated an EOR of 0.33 (95% CI: –0.12,2.0) per 100 Bq/m3 with dosimetry based on radon measurements in air, and 0.75 (95% CI: –0.04, 4.30) withdosimetry based on surface monitors [L67]. Surface monitors may offer an improved measurement technology, but
do not eliminate temporal uncertainties from misspecification of the age of the artefact, or address spatial uncertainties from the exact location of the artefact and within-homevariation. Uncertainties can also be reduced through study
design. In the United States, the Iowa radon study enrolled
only long-term (20 years or more) residents of a single
dwelling, thereby minimizing uncertainties from residential
mobility [F12], and carried out radon measurements
throughout the house, adjusting for residential occupancy
and time spent in other buildings and outdoors [F12, S63].
The EOR ranged from 0.16 (95% CI: 0.0, 0.6) per 100
Bq/m3 for all subjects to 0.33 (95% CI: 0.02, 1.23) per 100
Bq/m3 for living subjects [F12, S63]. A study in Finland
also restricted participation to persons with 20 years or moreof residency in their current dwelling, and estimated an
EOR of 0.11 (95% CI: 0.09, 1.3) per 100 Bq/m3 [A26].
268. Recent works have largely resolved the decade-longdebate over results of “ecological studies” [M44]. Startingin the early 1990s, Cohen published a series of reports
showing decreasing lung cancer mortality rates in UnitedStates counties with increasing average radon concentrations in dwellings grouped by counties [C14, C21, C22,
C23]. Indeed, the “ecological” model predicted a protective
effect of radon concentrations above 50 Bq/m3 relative tolower radon concentrations. The most recent of these correlation analyses used combined mortality data for the years
1979–1994 [C23]. Radon measurements were based on datafrom three sources: a University of Pittsburgh project conducted in the period 1986–1991; survey measurements
made by the United States Environmental Protection
Agency; and measures made by state agencies [H46].
Smoking data were not available either for individuals orfor counties, but were extrapolated for each county using
data from a 1985 survey and using models that included
county-specific socio-economic factors and state-level cigarette smoking data. The smoking estimates were further
adjusted to reflect prevalence in the 1960–1970 period.
Results from the correlation analyses contrast markedly
with results from all cohort studies of radon-exposed miners
and nearly all case-control studies of lung cancer and residential radon concentration, where data on radon exposure
and on smoking and other factors are specifically collected
on individuals.
269. Arguments against the validity of the “ecologicalstudies” were based on both theoretical and practicalgrounds. “Ecological analysis” involves grouped data, andcan be related directly to individual-level effects only when

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the relationship between exposure and outcome is linear[L68]. In the case of lung cancer and radon, where a linear
relationship does not hold, results are subject to a varietyof biases, many of which do not exist for studies of individuals. Radon studies are particularly vulnerable to biases
associated with the use of radon levels averaged over geographical areas, because of extreme variation in radon levels
within areas. Greenland and Robins illustrated that theabsence of, or adjustment for, confounding at the grouplevel does not imply the elimination of confounding at theindividual level [G13]. This is particularly important in thecase of indoor radon, because of the dominant role of smoking habits on lung cancer risk. Whereas smoking habits are
the main potential confounder in an individual-level study,
the corresponding potential confounder in an “ecologicalstudy” consists of the smoking-risk-weighted distribution ofhistorical radon concentrations for smokers and “neversmokers” within each area [L69]. Thus adjustment for theeffects of tobacco use in “ecological analyses” of radon andlung cancer is not likely to be adequate without detailed
information on smoking habits and radon exposure histories within counties, for example from independent population surveys [P27, S64]. Lubin demonstrated the potential
for “ecological bias” theoretically by showing that aggregate disease rates may be strongly influenced by small correlations of factors within groups [L68]. There has been afurther exchange of correspondence between Cohen andLubin in relation to this study [C49, L97]. Muirhead et al.
[M45] and Piantadosi et al. [P28] demonstrated that correlations between factors could be greatly affected, evenresulting in a reversal of sign, when the unit of analysis was
subject to further aggregation.
270. More recent criticisms of Cohen’s results havefocused more directly on the “ecological” regression
between radon concentration and lung cancer. Smith et al.
[S65] reported that a negative correlation seen in the stateof Iowa disappeared when mortality data were replaced byincidence data, although the value of these data has beendisputed [C24, F17]. In a particularly revealing analysis,
Puskin explored the adequacy of Cohen’s adjustment forsmoking by evaluating the regression of mortality rates fora variety of cancer sites grouped by the strength of theirassociation with cigarette smoking [P29]. Puskin foundstrongly negative correlations with average county indoor
radon concentrations for cancers (lung, oral cavity and pharynx, larynx and oesophagus) strongly linked to smoking,moderately negative correlations for cancers (bladder andpancreas) moderately linked to smoking, and essentiallyzero correlations for cancers (prostate, colon and breast) not
linked to smoking. Since the lung is the only cancer sitethat has been associated with radon exposure [C36],
Puskin’s study indicates that Cohen’s results are very likely
to be the consequence of incomplete control for the smoking factor. There has been a further exchange of correspondence between Cohen and Puskin and others in relation
to this study [C47, P49]. In a report coordinated by theUnited States National Council on Radiation Protection andMeasurements, Heath et al. reanalysed Cohen’s data andshowed that, after adjustment for smoking, the negative
trend was largely confined to counties with mean concentrations of below about 50 Bq/m3, and the regression was
generally flat from this level to about 175 Bq/m3. Data were
too sparse to evaluate above 175 Bq/m3 [H46]. The analysis suggested that the trend may be influenced by confounding from smoking, which was greater for the counties
with lower average radon concentrations. It suggests that“systematic errors and uncertainties in Cohen’s data andanalysis … preclude estimating to what degree or in what
direction lung cancer mortality is altered by exposure to …
radon” [H46]. Cohen has responded to these criticisms,
questioning a number of the statements made by Heath etal. in relation to his analysis, and also disputing the flatness of the dose response in the range 50–175 Bq/m3 [C48].

7. Transfer of risk estimates271. Estimates of ERR per unit dose for lung cancer fromseveral studies involving medical exposures in predominantly Caucasian patients are lower than those based on survivors of the atomic bombings (table 27). Although thismight indicate that absolute risks are more comparable thanRRs, the lower ERR estimates may also have resulted fromother differences in the study populations, particularly themuch higher doses in several of the medical studies. Lungcancer rates in Japan have increased in the past few decades.
Because of this increase, lung cancer rates for the LSS
cohort are generally lower than current Japanese rates, an
important consideration in transferring risk estimates for the
LSS cohort to another population.
272. Because much of the variation in underlying lungcancer rates among countries is likely to be due to differences in smoking habits, the finding that the joint effect ofsmoking and radiation exposure on lung cancer risks in survivors of the atomic bombings is well described by an additive model [P17] lends support to the use of absolute risk
transfer. Nevertheless, studies of lung cancer risks in underground miners exposed to radon [C36] or in Hodgkin’s disease patients treated with high doses of radiation [G23]rejected additive interactions and found that multiplicativeinteractions were compatible with the data. However, thehigh doses involved in these studies may make them lessrelevant for estimating risks of low-dose exposures.

8. Summary
273. Lung cancer risk has been associated with external
low-LET radiation in survivors of the atomic bombings, inpersons exposed at high doses for medical reasons and in
Mayak workers exposed at high doses. Based on data forthe survivors of the atomic bombings, the ERR per unit
dose (Sv–1) was larger for females than for males, but theEARs were similar for both sexes. Unlike the case of manyother solid cancers, there is little evidence that the ERR forlung cancer declines with increasing age at exposure. The

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evidence regarding the interaction of radiation and smoking
is conflicting, with data on survivors of the atomic bombings supporting an additive interaction, while studies of persons exposed therapeutically support a multiplicative, andpossibly even a supramultiplicative, interaction. Most studies of low-dose protracted exposure have failed to demonstrate dose–response relationships for lung cancer, but thismay be because of limited statistical power. Particularly
noteworthy is the lack of dose response for lung cancer
among tuberculosis patients who received multiple chest
fluoroscopies, where it was possible to demonstrate that the
ERR per unit dose was incompatible with that based on
survivors of the atomic bombings. However, findings forpatients with a lung disease may not be typical for thegeneral population.
274. With regard to high-LET radiation, there is little evidence that lung cancer risk is related to internal exposure
from Thorotrast or radium, although this may be due to limitations in the available data. However, lung cancer risk has
been strongly linked with internal exposure, predominantly
via inhalation, to plutonium in studies of Mayak workers in
the Russian Federation, and there is a wealth of data linking lung cancer risk with exposure to radon and its progeny. More is said about radon dosimetry and risks inannex E of the UNSCEAR 2006 Report, “Sources-to-effects
assessment for radon in homes and workplaces”.

K. Malignant tumours of the bone
and connective tissue

1. General background275. Malignant tumours of the bone account for about
0.5% of malignant neoplasms in humans [M56], while softtissue sarcomas, which include connective tissue malignancies, account for about 1% of all malignancies [Z9]. Thereis not much variation in incidence rates worldwide: annual
age-standardized world incidence rates vary from less than
0.3 per 100,000 among both men and women in some partsof Japan to more than 3 per 100,000 among men in partsof Italy [P19]. Among bone sarcomas, dissimilarities in cell
type between osteosarcoma and Ewing’s sarcoma indicatethat these tumours have different origins. The role of genetic
susceptibility has been identified through molecular andcytogenetic studies of the gene loci for these types of sarcoma, as well as by the linkages of osteosarcoma withhereditary retinoblastoma and the Li–Fraumeni syndrome[M56]. Li–Fraumeni syndrome has also been investigated
together with connective tissue malignancies [Z9]. As will
be described below, a variety of studies on external lowLET and internal high-LET exposures have established thatbone sarcomas can be induced by radiation. Human andanimal studies have suggested a possible association
between exposure to chromium and nickel and the risk ofbone and soft-tissue malignancies [M56].

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2. Summary of UNSCEAR 2000
276. Among the survivors of the atomic bombings overall,although not reported in the incidence data [T1], the estimated trend in risk per unit dose is statistically significantly
positive, but is based on very small numbers (34 cases). There
are indications that the risk is higher for exposure in childhood than in adulthood [T1]. Statistically more powerful
information comes from studies of patients treated for cancer
in childhood. Three studies with reasonably large numbersof cases [H27, T10, W11] have reported a statistically significant trend of increasing risk with (external low-LET)
dose, based on mean doses of between 10 and 30 Gy; anothersuch study reported similar results, although with fewer
details [D16]. However, few studies of adult external lowLET exposure are informative, owing in part to the rarity ofmalignant tumours of the bone or connective tissue. For
example, the study of cervical cancer patients involved meandoses comparable to those in the above childhood cancer
studies [B8]; in that instance, no significant trend of increasing risk with dose was found. Among ankylosing spondylitis patients in the United Kingdom, the total number of deaths
was significantly greater than expected from national rates,
but the data were not analysed in relation to estimates of dose
[W8]. In a group of over 120,000 women in Sweden treatedfor breast cancer, the incidence rate of soft-tissue sarcomas
was about double that expected from national rates [K18].
277. In relation to the effects of internal high-LET exposure, there is strong evidence that large intakes of radium
have induced increased numbers of bone sarcomas in a groupof patients in Germany [N2, S79] and in radium dial workers in the United States [C11, F4, R18, R27]. Because of thelong half-lives of 226Ra and 228Ra (the source of the highLET exposures in the United States study) relative to the halflife of 224Ra (the source of exposure in the German study),
it is easier to model risks using the latter study. Analysis of
the 224Ra data indicates that the EAR decreases with increasing time since exposure (beyond about 12 years) and age at
exposure, and that the effect on risks of exposure rate is small
at doses below around 10 Sv. The 224Ra data are consistent
with a linear dose response over a range up to more than
100 Sv, although there is uncertainty in extrapolating the findings down to doses of a few sieverts. The United States study
on 226Ra and 228Ra offers little evidence of an elevated risk
at these lower doses, although it is difficult to evaluate thedose associated with any “practical threshold” in risk.

3. New or updated studies
278. Table 30 summarizes the risk estimates for cancer
and cancer mortality based on epidemiological studies ofradiation exposure.
(a) External low-LET exposures279. An excess risk of bone and soft-tissue cancers, inparticular angiosarcoma, has also been found in other recent

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studies of women treated with radiotherapy for primarybreast cancer [E2, H3, Y8], although detailed dosimetry islacking in these studies.

1.1, 6.4) was based on only 6 cases of cancer, of which
3 were among the 27% of the cohort who had been miners
and may have been exposed to alpha radiation [A32].

280. Virtanen et al. [V11] studied bone and soft-tissue sarcomas among 295,712 Finnish patients who had been treatedfor certain cancers during the period 1953–2000, and identified 147 cases against 88.5 expected from Finnish national
rates, the excess becoming apparent 10–14 years after treatment. Patients who received radiotherapy alone constituted
43% of the total person-years of follow-up, those who
received chemotherapy alone 5%, and those who received
both radiotherapy and chemotherapy 3%. The SIR for those
who were treated with radiation alone was 2.1 (95% CI: 1.6,
2.6), with those diagnosed below 55 years of age having an
SIR of 3.4 (95% CI: 2.5, 4.6). When the cancer rate forthose patients treated with radiation alone was comparedwith that for patients who had received neither radiotherapy
nor chemotherapy, the crude RR was 1.6 (95% CI: 1.0, 2.6),
and the RR adjusted for age, sex and type of primary cancer
was 1.5 (95% CI: 0.9, 2.6). There was no statistically significant difference between the effect of radiation upon therisk of bone versus soft-tissue sarcoma [V11].

(b) Internal high-LET exposures

281. In an international study of second cancers after treatment for Hodgkin’s lymphoma [D46], elevated SIRs for bone
cancers (3.8; 95% CI: 1.7, 7.2) and soft-tissue cancers (5.1;
95% CI: 3.5, 7.2) were found for a group of 32,591 patients.
The SIR of 7.0 (95% CI: 3.3, 10.5) for bone and soft-tissue
sarcomas in patients who had been treated with radiationcompares with an SIR of 3.4 (95% CI: 2.0, 5.3) among those
who were not, an SIR of 15 being apparent among thosereceiving radiotherapy 10–19 years after treatment. The RRof bone and soft-tissue sarcomas decreased significantly withincreasing age at treatment [D46]. In a similar study of a
British cohort of 5,519 survivors of Hodgkin’s lymphoma,
Swerdlow et al. [S77] found a raised SIR for bone cancers(10.7; 95% CI: 3.3, 24.8) and for soft-tissue sarcomas (3.9;
95% CI: 1.0, 10.1), and all the cases occurred in patients who
had been treated with radiation. The SIR for bone and softtissue sarcomas combined was greatest for those treated
before the age of 25 years and was significantly elevated in
the period 5–14 years after first treatment [S77].
282. In a cohort of 6,597 persons treated for breast cancer
in France, 12 bone or soft-tissue sarcomas developed after
high-dose radiotherapy (doses of more than 10 Gy) [R52].
There is a trend of increasing risk of bone/soft-tissue sarcoma with radiation dose, although the ERR is not large(ERR = 0.05 (95% CI: indeterminate, 1.18) Gy–1; the lower
confidence bound did not converge). The best fit was
obtained with a quadratic dose–response model. Excluding
three cases of women with Stewart–Treves syndrome, thetrend was highly statistically significant (p < 0.01).
283. Although not considered in the UNSCEAR 2000
Report [U2], the 1997 study of Artalejo et al. [A32] reported
an excess of bone tumours among workers for the Spanish
Nuclear Energy Board. This excess (SMR = 2.95; 95% CI:

284. Workers at the Mayak nuclear complex in the
Chelyabinsk region of the former Soviet Union were exposed
to high levels of external radiation and plutonium (239Pu)
during the production of weapons materials, especially during
the early years of operations in the late 1940s and the 1950s.
Substantial doses to the lung, liver and skeleton werereceived from 239Pu. Koshurnikova et al. [K46] examinedmortality risks from bone cancer before 1997 among 10,797
Mayak workers employed during the period 1948–1958.
Nineteen bone cancers together with four deaths fromtumours sited in soft tissues close to bone surfaces were
included in the analysis; 21 of these deaths occurred among
9,381 workers monitored for exposure to external sources ofradiation (mean recorded cumulative dose with a two-year
lag = 1.23 Sv), and five deaths were in the group of
954 workers with cumulative external doses in excess of 3 Sv.
285. Of 5,521 workers with plutonium body burdens thatwere considered to be known (i.e. either the workers weremonitored for exposure to plutonium or worked in areas
with a low potential for exposure), 2,207 had detectablelevels of plutonium in urine samples (mean body burden =4.5 kBq, mean bone surface dose from plutonium =3.8 Gy), and six bone cancers occurred in this exposedgroup [K46]. Three bone cancers were in the group of
251 workers with plutonium body burdens in excess of
7.4 kBq. A further 5,276 workers were considered to have
had the potential to have been exposed to plutonium, butwere unmonitored, and 13 bone cancer deaths occurredin this group. Seven of these deaths were among 2,142
workers in the plutonium plant, where the highest
exposures tended to be experienced [K46].
286. Uniformly raised levels of bone cancer mortality rates
were found for the various groups of Mayak workers whencompared with either Russian or United States reference
rates, but given the potential for bias when comparing with
rates based upon external populations, most reliance shouldbe placed upon the findings using comparisons within the
Mayak workforce [K46]. Indications of an increase in bone
cancer risk with increasing cumulative external dose, treatedas a categorical variable, were found, but because fullaccount could not be taken of the influence of the dose fromplutonium, reliable conclusions could not be drawn.
287. Further analyses treating the estimated plutonium
body burden as a continuous variable indicate an increasing
risk of bone cancer with increasing body burden(p < 0.001) [K46]. Overall, the evidence from this study
strongly suggests that exposure to high levels of plutonium
at Mayak has increased the risk of bone cancer, but risk coefficients cannot at present be determined, because of the lackof comprehensive estimates of doses to bone surfaces from

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plutonium. Shilnikova et al. [S28] also examined cancer mortality among the Mayak workforce, but they considered bone,
liver and lung cancers (i.e. those cancers most likely to berelated to plutonium deposition) as a group, so that the study
does not provide information on bone cancers alone.
288. An update of mortality data for Portuguese patients
injected with Thorotrast [D27] found a statistically significant (p < 0.001) SMR for bone cancer (12.8) when using
Portuguese mortality rates as a comparison, but the ratio of
this SMR to that for unexposed patients was not significant:rate ratio = 7.60 (95% CI: 0.85, 359). Travis et al. [T30]
studied cancer incidence and mortality rates for Thorotrast
patients from Denmark, Sweden and the United States, andfound a statistically significant (p < 0.05) SMR for bone
cancer among United States patients (13.9, based upon
2 deaths), but also found no case of bone cancer among the
Scandinavian patients (although the expected number ofcases, while not presented, would have been small). They
pointed out that 224Ra, a bone-seeking radionuclide, is present in the decay chain of 232Th, and that the total skeletaldose from all radionuclides in the decay chain could be in
the range from 3 to 9 Gy, so that an excess risk of bone
cancer among Thorotrast patients is plausible.

4. Summary
289. As in the UNSCEAR 2000 Report [U2], studies ofpatients treated for childhood cancer demonstrate anincreasing risk of bone and soft tissue sarcomas with dose,over a range of several tens of grays (low-LET). These studies are not informative about risks at doses below a few
grays, but a study of retinoblastoma patients in particular
indicates that genetic predisposition may affect risks associated with high-dose therapeutic radiation exposure. Other
studies of external low-LET exposure are less informative,
although there is some suggestion that the RR is lower for
exposure in adulthood than in childhood. Studies of persons
receiving high-LET radiation, in particular 226Ra, 228Ra and224Ra, strongly suggest an exposure-related increased riskof bone tumours. The major new study to appear in relation to internal high-LET exposure is that of the Mayakworkers exposed to 239Pu, which also suggests a radiogenic
excess bone tumour risk. However, until the bone dosimetry for this cohort is established, in particular identifying
the components of dose due to 239Pu and to external lowLET radiation exposure, quantitative risk estimates cannot
be derived from this study.

L. Cutaneous malignant melanoma

1. General background290. Cutaneous malignant melanoma is a comparatively
rare tumour in many populations, although incidence rates

83

are increasing around the world [A14]. The incidence ofmalignant melanoma is strongly related to ultraviolet radiation (UVR) exposure, with exposure at all ages likely to
be important for various stages of development of thetumour (initiation, development of naevi, and invasive
melanoma) [T22]. For this reason, possible depletion of
atmospheric ozone may exacerbate these trends [A15]. The
incidence of malignant melanoma is strongly correlatedwith skin pigmentation, but it is about 10 times lesscommon than non-melanoma skin cancer. Age-standardized
world annual incidence rates for melanoma vary from about
0.5 per 100,000 persons in Algeria to over 40 per 100,000
in parts of Australia [P19, T22]. Unlike many tumours ofadults, melanoma arises relatively frequently among theyoung and the middle aged. Malignant melanoma incidence
rises steeply with age until about age 50, after which therate of increase slows [A18]. Much of the increase in incidence in the last few decades appears to be due to solar
exposure [A16]. A number of recent case-control studies
have provided corroborating evidence for this proposition,
but have indicated that the exposure–response relationshipis complex [A17, A18, A20, T22]. In contrast to nonmelanoma skin cancer, both cumulative exposure and intermittent exposure of untanned skin are risk factors for the
disease [A17, A18, A20, T22]. Melanoma can usually beclassified into one of three histopathological types: superficial spreading melanoma, lentigo malignant melanoma(also known as Hutchison’s melanotic freckle melanoma),and nodular melanoma, although this classification is controversial [A18]. As noted above, skin pigmentation is avery important risk factor [A18], and there is considerable
evidence also for familial susceptibility, hormonal factors(e.g. use of oral contraceptives and reproductive status) andimmune suppression as risk factors [A18, T22]. Some studies have suggested associations with diet, and in particularthat intake of vitamin E is a protective factor for the disease [A18]. Further details on the epidemiology are to befound in reference [A18].

2. Summary of UNSCEAR 2000
291. The UNSCEAR 2000 Report indicated that no relationship of melanoma with radiation exposure has been
demonstrated in the major exposed groups [U2], including
the survivors of the atomic bombings [R25]. As shown intable 31, there is a moderate ERR of melanoma within the
LSS cohort of 0.21 (90% CI: <0, 3.15) Sv–1, with wide confidence intervals, based on 13 cases (6 with unweighted
colon doses of more than 0.01 Gy) [R25, T1].
292. In the past there were concerns that an excess incidence of cutaneous malignant melanoma at the Lawrence
Livermore National Laboratory in the United States might
be due to radiation exposure [A19]. However, a later study
concluded that the supposed excess was most likely due tofactors relating to host constitutional factors, such as skinreactivity and number of moles, and to exposure to sunlight[M28].

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3. New or updated studies(a) External low-LET exposures
293. An association between external ionizing radiationand melanoma risk was suggested by a study of UnitedStates radiologic technologists who had first worked before
1950 (RR = 1.8; 95% CI: 0.6, 5.5), particularly among those
who worked 5 or more years before 1950 (RR = 2.4; 95%
CI: 0.7, 8.7; 2-sided p = 0.03) [F11]. Beginning work before
1940 was associated with a greatly increased risk (RR =8.6; 95% CI: 1.0, 72.7), but this observation was based on
only 4 cases. Risk was also moderately elevated among
technologists who did not customarily use a lead apron
when they first started employment (RR = 1.4; 95% CI: 0.8,25) [F11]. As with the various other analyses of this cohort,
no individual doses had been estimated. The study relies on
self-reported diagnoses, although pathological records wereobtained for a sample of 160 (66%) of the 243 reported
melanomas; 140 of these 160 cases had the diagnosis confirmed. Information on hair and eye colour, skin tone andfamily history of melanoma was only requested in thesecond (of two) questionnaires; no information on historyof sunburn was collected. In view of this limited information with which to adjust for solar exposure and constitutional factors, the association with ionizing radiation is notconvincing.
294. The analysis of cancer incidence in relation to occupational dose in the National Dose Registry of Canada has
documented a statistically significant increased SIR for
melanoma of 1.16 (90% CI: 1.04, 1.30) [S8]. The trend withdose of melanoma incidence in this cohort is not statistically significant: there is a high ERR of 4.3 (90% CI: <0,19.6) Sv–1, with wide confidence intervals [S8]. However,as with the parallel analysis of the mortality data associatedwith this cohort [A8], concerns have been expressed about
the reliability of record linkage, a possible source of bias
[G16]. Moreover, there is no information on solar exposureand constitutional factors in this study.
295. Analysis of cancer incidence in a small group of children who underwent cardiac catheterization yielded an SIR
among males of 4.87 (95% CI: 1.0, 14.2). However, there
were no cases (of any cancer) among the female children,and the authors did not calculate an overall SIR for the combined group, so that it is difficult to interpret this finding.
No radiation dose estimates exist for this cohort [M27].
There is also no information on constitutional factors or
exposure to sunlight in this study, so that it is difficult toinfer any link between melanoma and ionizing radiation
exposure from the results of this study.
296. Analyses of melanoma incidence in a group of 4,401
survivors of childhood cancer treated at French and British
centres and 25,120 survivors of cancer treated before theage of 20 at various centres in the Nordic countries found
16 melanoma cases. An excess risk at borderline levels of
statistical significance was observed at high local doses,>15 Gy, for which the OR was 13 (95% CI: 0.94, 174)
[G31]. Likewise, a continuous model fitted to these datasuggested a trend of risk that increased with dose at borderline levels of statistical significance (2-sided p = 0.05)[G31].
(b) External high-LET exposures
297. Because aircrew receive elevated radiation doses,
which can range up to 6 mSv per year, with a substantial
neutron component (25–50% of the absorbed dose) [B22,G15], there has been much interest in studies of this group.
To date there have been various, generally small, studies ofaircrew, whether of pilots or flight attendants. The largest
studies to date are three large pan-European studies, the firstof flight attendants [Z4], the second and third of male cockpit crew [B23, L48]. The first study, of flight attendants,
found a statistically non-significant increase in mortality
from melanoma (SMR = 1.93; 95% CI: 0.70, 4.44) among
male crew, but no suggestion of increased risk amongfemale staff (SMR = 0.36; 95% CI: 0.04, 1.37) [Z4]. The
second study, of male cockpit crew, found a statisticallysignificant increase in mortality from melanoma (SMR =1.78; 95% CI: 1.15, 2.67) [B23]. No consistent association
between employment period or duration and cancer mortality was observed, whether for melanoma or any other end
point, in either study [B23, Z4]. In the third study, there
was no indication of a trend of melanoma risk with radiation dose (p = 0.481), so that, for example, the RR associated with doses of greater than 25 mSv was 0.33 (95% CI:
0.06, 1.85) [L48]. Radiation doses were measured only inthe third study [L48]. There is no assessment of solar exposure or constitutional factors in any of these three studies.
The aircrew studies have recently been reviewed, and evidence has been found of a consistent excess risk ofmelanoma, non-melanoma skin cancer and breast cancer[S35]. However, as with the three large studies discussed
above, there is generally no relation with duration of
employment. Since the only study implying a risk of cutaneous melanoma did not estimate radiation doses [B23], andin the absence of individual information on solar exposurein all three studies [B23, L48, Z4], it would be difficult toascribe the excess risks observed in these studies toionizing radiation exposure [S35].

4. Summary
298. Solar UVR has the potential to seriously confoundthe ionizing radiation dose response for melanoma, because
it is a known risk factor for this end point and may well
be correlated with cumulative ionizing radiation dose. In
general, there will be appreciable positive bias in any estimated radiation dose response if solar UVR is not taken
into account.
299. As for the UNSCEAR 2000 Report [U2], there
remains only weak evidence that cutaneous melanoma is
inducible by ionizing radiation. Most of the studies that sug-

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gest that there might be such risks do not have adequate
radiation dosimetry, and do not properly control for constitutional factors and sunlight exposure.

M. Non-melanoma skin cancer

1. General background300. Non-melanoma skin cancer (NMSC) is extremelycommon in Caucasian populations but relatively rare in populations with highly pigmented skin [S36]. The two maintypes of NMSC are squamous cell carcinoma (SCC) andbasal cell carcinoma (BCC) or epithelioma (otherwise
known as a rodent ulcer as it appears to erode the surrounding skin) [L42]. Both SCC and BCC of the skin are
derived from keratinocytes [P20, S37]. SCC occurs as aresult of the neoplastic transformation of cells in the epidermis—the suprabasal cells; this tumour may occasionallymetastasize to other organs. BCCs are particularly slow
growing and originate from the basal cells of the epidermis
or hair follicles; this tumour does not usually metastasize.
In Caucasian populations, the incidence of BCC is almost
always greater than that of SCC. Scotto and colleagues[S38] reported a sex- and age-adjusted rate for SCC in eightregions of the United States as 41 per 100,000 persons per
year, compared with a rate of 192 per 100,000 per year for
BCC. The BCC:SCC incidence ratio was about 4:1 formales and about 6:1 for females [S38]. Very similar ratios
have been reported in a number of other surveys ([A21],
but see also the reviews in references [L42, S36]). However,
because of the higher fatality rate for SCC than for BCC(principally because of the greater metastatic potential of
SCC), the numbers of deaths due to SCC are generally
rather higher than for BCC [W18]. Annual age-standardized
world incidence rates for NMSC vary from about 0.8 per
100,000 persons in parts of China to over 100 per 100,000
in parts of Switzerland [P19]. Since most NMSC cases areroutinely treated in doctors’ surgeries, whereas cancer registries routinely rely on inpatient records from hospitals,
reporting of NMSC is often very incomplete, and somecancer registries do not report it at all. Therefore populationbased estimates of NMSC incidence require special
surveys involving the collection of data from office records
and outpatient files [S36].
301. NMSC is believed to be induced predominantly by
exposure to UVR [A20]. NMSC incidence rates rise rapidly with age, with such cancers being common among theelderly [S36]. Over the past decades, there has been a substantial increase in the incidence rate of NMSC, by about
15–20% over a decade [A21, M29]. Much of the increase
in incidence appears to be due to sun exposure. Total accumulated exposure appears to be the main risk factor for
SCC, although for BCC a combination of cumulative exposure and intermittent exposure is more relevant [A20].
NMSC is a generally treatable malignancy with a very high85

cure rate: fewer than 1 in 500 patients with SCC dies fromthis cancer [P20]. In the United Kingdom there were 46,741
diagnosed cases of NMSC in 1999, and in the same year
368 deaths [O5, O6]. Several chemical carcinogens havebeen linked to an increased risk of NMSC, in particulararsenic, polycyclic aromatic hydrocarbons (PAHs) and psoralens [S36]. Cigarette smoking and diet have also been suggested as risk factors in some studies [S36]. As noted above,
constitutional factors, in particular skin pigmentation, arevery important risk factors [S36], and risks are also
increased in persons with certain rare genetic disorders, inparticular naevoid basal cell carcinoma syndrome and xeroderma pigmentosum [E4, S36]. Immune status is also
clearly important, with increased risks seen in various
groups with immune suppression [S36]. Further details on
the epidemiology are to be found in references [L42, S36].

2. Summary of UNSCEAR 2000
302. An association between external ionizing radiation
and NMSC risk has been demonstrated in the LSS of thesurvivors of the atomic bombings [L30, L42, R25], the New
York (United States) and Israeli tinea capitis studies [R16,S15], the Rochester thymus study in the United States [H26,
S22] and in various other groups (reviewed in references[L42, U2]).
303. In the latest data from the LSS, a strong
dose–response relationship was demonstrated for BCC(ERR = 1.9 (90% CI: 0.83, 3.3) Sv–1) (table 32), but not
for SCC (ERR ≤–0.1 (90% CI: <–0.1, 0.1) Sv–1) [R25].
There was non-linearity in the BCC dose response [R25].
A dose–response curve having two slopes (with the change
in slopes at 1 Sv) marginally improved the fit (p = 0.09);
a linear model with a threshold at 1 Sv did not fit the data
as well [R25]. In earlier evaluations of all NMSC in the
LSS, non-linearity was highly statistically significant; theindicated models had non-zero thresholds in dose, or werefunctions involving powers of dose that were greater than1, combined with exponential terms representing cellsterilization [L30]. The ERR decreased strongly and highly
statistically significantly (p < 0.001) with increasing age at
exposure [L30, R25].
304. There is evidence that the risk of BCC in the LSS
cohort is lower for parts of the body exposed to the sun[R25], in contrast to the evidence presented by an ICRPTask Group [I13]. As discussed by Little et al. [L42], thereis evidence that the ICRP analysis may have been confounded by the effects of age at exposure. Most (all butone) of the sites exposed to UVR considered by the ICRP
[I13] were for exposures in childhood, whereas most (allbut one) of the sites shielded from UVR were for exposuresin adulthood. As noted above, there is an appreciable reduction of ERR with increasing age at exposure. A complication in comparing UVR exposure status for the LSS withthat for other groups is that the patterns of solar radiation
exposure in the Japanese population may be different from

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those in most Caucasian populations [L42]. Present-day
Japanese women are rarely exposed to UVR, because theyuse parasols when outside even for short walks; Japanese
men often use wide-brimmed hats when working in the sun.
However, it seems that the patterns of solar radiation exposure in the Japanese population four or five decades ago
may have been appreciably different from the present pattern. For example, 50 or so years ago it was common forJapanese manual labourers to be clad only in a fundoshi, asimple loincloth, particularly in summer when much ofJapan can be quite humid [L42].
305. To date, there has been little indication of an association between ionizing radiation and SCC, but the data
are sparse [L42]. As with many other cancers [U2], the ERRof BCC decreases with increasing age at exposure [R25].
Data on the dose–response relationship for BCC suggest
non-linearity, but more data are needed to better characterize the shape of the dose response, to further evaluate therole of ionizing radiation in the development of SCC, andto clarify the role of UVR relative to ionizing radiation.

3. New or updated studies(a) External low-LET exposures306. The New York tinea capitis study has recently been
updated [S7]. There were 128 cases of NMSC in the groupof 2,224 irradiated persons, and 21 in the control group of
1,380 persons. Of the 128 irradiated people with NMSC,
125 were Caucasian and 3 African-American; of the people
with NMSC in the control group, all 21 were Caucasian,i.e. none were African-American [S7]. Almost all the cases
among the Caucasians were of BCC: 124 out of 125 cases
among Caucasians in the exposed group were of BCC. The
ratio of EAR associated with ionizing radiation exposurefor the Caucasians relative to the African-Americans was
10.0 (95% CI: 3.2, 31), which the authors take as implying
a large enhancement of radiation risk for persons ‘effectively’ exposed to UVR (i.e. for those whose skin was notshielded by melanin). This does not necessarily contradict
the findings from the LSS data. Shore et al. [S7] calculate
EARs, whereas in the LSS [R25] the measure used is ERR.
The number of BCCs occurring on skin unexposed to solar
UVR will be very much less than the number occurring on
skin exposed. Thus the ERRs could well be much greaterthan on UVR-exposed skin, yet the EARs on UVR-shieldedskin be rather less on UVR-exposed skin. The ERR for BCC
on the scalp of 1.7 Gy–1 is slightly but not statistically significantly (p = 0.24) greater than the ERR of 0.6 Gy–1 forthe margins of the scalp, which are presumed to receive
more solar UVR, in support of the findings from the LSS
data [R25]. Shore et al. [S7] argue for considering normalized risk, i.e. excess BCCs per unit area of skin per unit
dose, similar to the measure proposed by the ICRP [I13].
If this is done, then EARs for UVR-exposed skin are greaterthan for skin unexposed to UVR. Case ascertainment
was via four surveys. About 88.1% of the people in theoriginal exposed group and 84.4% of those in the control
group were contacted and answers to questionnaires
obtained. In the exposed group, 94.4% of reported cases
were medically verified. This is undoubtedly a highquality study. However, the very small number of cases of
NMSC (3) among African-Americans and possible lifestyledifferences between this group and the Caucasian groupmean that caution should be exercised in ascribing the differences in radiation risk between these groups to their UVR
exposure status.
307. An association between exposure to external ionizing radiation and risk of BCC was suggested by a study ofwhite United States radiologic technologists. The risk of
BCC adjusted for the total numbers of years worked
decreased in a statistically significant manner with earlier
calendar years of first employment [Y4]. There were nosuggestions of increased risks for SCC [Y4]. Among thoseworking before 1950, there was no suggestion of a dose
response for BCC based on the number of years worked.
The RR for those working for up to 5 years was 1.45 (95%
CI: 1.06, 1.97), compared with an RR of 1.14 (95% CI:
0.74, 1.75) for those working for more than 5 years [Y4].
Among those working in the period 1950–1959, there weremore indications of a dose response for BCC. The RR for
those working for up to 5 years was 1.29 (95% CI: 1.03,
1.62), compared with an RR of 1.59 (95% CI: 1.23, 2.06)
for those working for more than 5 years [Y4]. The risk of
BCC associated with exposure to ionizing radiation (basedon years first worked) was not modified by UVR exposure
as an adult or in childhood, although there were significant
modifying effects due to skin pigmentation. As with otheranalyses of this cohort [F11], the study is reliant on selfreported diagnoses, although confirmatory pathologicalrecords were obtained for a sample of 668 (49%) of the
1,355 reported BCC cases and 79 (29%) of the 270 reported
SCC cases [Y4]. Information on hair and eye colour was
only requested in the second (of two) questionnaires. Solar
ultraviolet B (UVB) exposure in adulthood was estimatedon the basis of information about the state within the UnitedStates in which residence was held and the length of that
residence. Solar UVB exposure in childhood was estimatedfrom the state of birth. No information on sunburn or family
history was collected [Y4]. In view of the limited information with which to adjust for solar exposure and constitutional factors, and the lack of ionizing radiation
dosimetry, the association with ionizing radiation exposure
is not convincing.
308. There is a small, and statistically non-significant,excess risk of NMSC mortality for United Kingdom radiologists in the early years of practice, 1897–1920, specifically 2 deaths compared with 0.46 expected (SMR = 4.35)[B2]. These deaths are very likely to be cases of SCC.
Yoshinaga et al. [Y5] reviewed all the radiologist and radiologic technologist studies and concluded that several studies provide evidence for a radiation effect on the risk of
NMSC, in particular the studies of United States radiologists [M30] and of Chinese medical X-ray workers [W3].

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However, the only one of the cohorts considered by
Yoshinaga et al. [Y5] that had individual dose measurements was the small cancer incidence study by Andersson
et al. [A6] of 4,151 persons employed at two radiotherapy
departments in Denmark, in which the trend of NMSC risk
with dose is not statistically significant.
309. In the United States, a case-control study in New
Hampshire has evaluated risks of BCC and SCC in relation
to previous therapeutic exposure [L43]. Persons with BCC
or SCC diagnosed from a population-based ascertainment
programme [K29] and age- and sex-matched controls wererecruited. Information was collected by interview on medical history (including previous radiotherapy treatment), sun
exposure history and sun sensitivity. Medical records ofthose reporting treatment with radiotherapy were obtained.
Although limited radiation dosimetry appears to exist (probably only treatment planning or skin entrance doses), nodose–response analysis has been attempted. Excess risks ofboth BCC and SCC in relation to previous radiotherapeutic
exposure are suggested. For BCC, excess risk was noted
both among those who tend to burn in sunlight and among
those who tend to tan [L43]. In contrast, for SCC, excess
risk was noted only among those who tend to burn in sunlight, and not among those who tend to tan [L43]. The main
problem with this study is the lack of proper radiation
dosimetry, which makes it difficult to evaluate NMSC risks
quantitatively.
(b) External high-LET exposures
310. Because aircrew receive elevated doses, which canrange up to 6 mSv per year, with a substantial neutron component (25–50% of the absorbed dose) [B22, G15], there
has been much interest in studies of this group. To datethere have been various, generally small, studies of aircrew,whether pilots or flight attendants. The largest studies todate are three large pan-European studies, the first of flightattendants [Z4], the second and third of male cockpit crew
[B23, L48], but these consider mortality risks only, and so
are not very useful for study of the risk of NMSC. The only
large study to assess cancer incidence is that of Nordic aircrew by Pukkala et al. [P21]. (This meta-analytical study
includes a number of previously studied national cohorts.)
There was a statistically significant increase in SIR of 2.08
(95% CI: 1.74, 2.79) based on 27 cases. However, inPoisson regression analyses, there was no significant trendof NMSC risk with dose (2-sided p = 0.14), nor was there
for BCC (2-sided p = 0.17) [P21]. In the absence of dataon solar exposure or constitutional factors for individuals,
the findings of this study are difficult to interpret.

4. Summary
311. As for melanoma, solar UVR has the potential toseriously confound the ionizing radiation dose response,
because it is a known risk factor for this end point and may
well be correlated with cumulative ionizing radiation dose.

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In general, one would expect appreciable positive bias inany estimated radiation dose response if solar UVR were
not taken into account.
312. As for the UNSCEAR 2000 Report [U2], there isstrong evidence that NMSC, and specifically BCC, is
inducible by ionizing radiation, with the RR strongly
decreasing with increasing age at exposure. There are suggestions of upward curvature in the BCC dose response. An
unresolved issue is that of interaction between exposure tosolar UVR and to ionizing radiation. The available data
[R25, S7] suggest that ERRs may be lower for sites exposed
to sunlight, whereas EARs may be higher for such sites.

N. Breast cancer

1. General background313. Breast cancer is the most commonly diagnosed
cancer among women in most countries. Rates vary considerably between regions, with standardized rates for North
America and Western Europe being at least two to threetimes higher than those in East Asian countries and higher
still in comparison with those seen in African countries[A30, P19]. For example, annual age-standardized world
incidence rates for breast cancer vary from fewer than 25
per 100,000 women in many parts of Africa to over 100
per 100,000 in parts of the United States [P19]. Despite thewide variation across populations, breast cancer incidence
rates exhibit a fairly consistent pattern of increase with age
that differs from that seen for most other cancers. In particular, rates increase markedly up to about age 50, after
which the rates increase much less rapidly. For most othersolid cancers, incidence rises steeply until age 70 or 80,
after which there is some slackening of the rate of increase.
The well-documented dependence of breast cancer rates on
age and on reproductive factors (including the associationof increased risk with decreasing parity and increased age
at first full-term pregnancy [P39, S76], and the transient
increase in risk seen during the five years following childbirth [L76]) highlights the importance of hormonal factors
for breast cancer risks. This has been demonstrated more
directly in a number of recent studies [C29, K45, N15].
Other non-hereditary factors for which there is evidence ofan association with breast cancer risk include factors related
to energy balance (e.g. height, weight and obesity, diet andactivity levels) [D39, S76] and history of benign breastdisease [P40].
314. There are well-established effects on breast cancer
incidence from and clear associations with a family historyof breast cancer [C30]. In a recent study of breast cancer
risks in twins, it was suggested that about one quarter ofall breast cancer cases are associated with genetic effects
or gene–environment interactions [L77]. A number of cell
cycle and DNA repair genes have been found to be

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associated with breast cancer susceptibility, including
BRCA1, BRCA2 and ATM. However, it is currently believedthat only about 20% of breast cancer cases are attributableto mutations in known susceptibility genes [T37].
315. Breast cancer rates among women have been increasing for many decades and were recently estimated to haveincreased by 30–40% between the early 1970s and the late
1990s [A30]. The increasing trends have been especiallysharp in Asian countries [A30]. This increase, particularly indeveloped countries, has been generally attributed toincreased detection using mammographic screening, while
the increase in countries where the incidence was previously
low, e.g. Japan, may have been due to changes in lifestylefactors. These factors, together with genetic differences, arethe most plausible explanation for the large variation in rates
across populations. Ionizing radiation is well documented asa cause of radiation-induced breast cancer in women, which
is one of the most closely studied cancers, as described inreference [U4] and in reference [R32]. These references provide an extensive review of the current understanding of risks
due to radiation exposure and factors that modify these risks.

2. Summary of UNSCEAR 2000
316. The UNSCEAR 2000 Report [U2] concluded that
there was strong evidence of an effect of ionizing radiationon breast cancer risks that was consistent with a linear dose
response. It was also concluded that the ERR per unit dose
exhibited a strong dependence on age at exposure, with thelargest risks for those exposed as children or young adults,
and smaller RRs for women who were over 40 at the timeof exposure. On the basis of the comparison of results fromstudies of populations from Japan and from studies of otherpopulations it was noted that, while RRs varied considerably, excess rates appeared to be less variable, and that dose
fractionation had little apparent effect on the risk per unit
dose. The UNSCEAR 2000 Report contained no explicit
discussion of interactions between radiation and other risk
factors, although it was noted that interpretation of radiation effects in some reports “is complicated by the potential for confounding as a consequence of reproductive
factors or other exposures”.
317. Results from the LSS for breast cancer were basedon case follow-up for the period 1958–1987 [T1] and mortality follow-up for the period 1950–1990 [P1]. The LSS
incidence and mortality results were broadly similar. The
summary risk estimates clearly indicate that the RR depends
on age at exposure and may increase with time since exposure. However, interpretation of the results in relation to thetime since exposure is complicated by the correlation
between age at exposure and time since exposure.
318. With the exception of a study that involved thymic
irradiation of infants [H10], the estimates of ERR per unit
dose from the other studies considered in the UNSCEAR
2000 Report were generally statistically significant but

much smaller than those from the LSS. These other studies involved North American and European populations
whose members received therapeutic [B10, B11, B16, D17,
L7, M8, M17, S5, S20, W8], diagnostic [B3, H9] or occupational [C3] exposures to ionizing radiation. It was notedthat studies of internal low- and high-LET exposures [H6,
H10, H24, N2, R3] have failed to provide any indicationof increased breast cancer risks.

3. New or updated studies
319. Table 33 summarizes the risk estimates for breast
cancer and breast cancer mortality from epidemiologicalstudies of radiation exposure.
(a) External low-LET exposures320. An update of the LSS data on breast cancer incidence was published in 2003 [L78]. That paper was basedon follow-up between 1950 and 1990. However, the primary analyses focused on risks after 1958, since the tumour
registries did not begin operating until 1958, and the authors
considered that there were indications that the minimal
latent period might be of the order of the widely acceptedvalue of 10–12 years [R32]. The paper gives an estimate
for ERR of 1.7 (90% CI: 1.3, 2.1) Gy–1, without allowing
for variation in the ERR with either age at exposure orattained age. A major emphasis in this paper concerns therelative importance of attained age and age at exposure as
modifiers of the ERR. It noted that there are statisticallysignificant decreases in the ERR per unit dose with increasing attained age or age at exposure, and that, even after
allowing for such effects, there is still evidence for verylarge RRs for cases diagnosed under the age of 35. It concluded that, after allowing for this early onset effect, there
is no statistically significant variation in the ERR per unit
dose with attained age, but that the ERR still exhibits a statistically significant decrease with increasing age at exposure. After allowing for an 8.5-fold (90% CI: 2.3, 48)
increased risk for the early onset, the authors suggested that
the ERR per unit dose decreases by about 30% (90% CI:–50%, –10%) per decade increase in age at exposure. While
there was a marked decrease in risk with age at exposure,the data suggested that risks for women exposed at ages of
50 or more remain elevated, with increases of 40–50%. A
non-parametric estimate of the joint dependence of the ERR
per unit dose on age at exposure and on attained age givenin this paper suggested that the ERR per unit dose forwomen exposed after age 40 is 0.5 (90% CI: 0, 1.4) Gy–1.
An ERR of this magnitude is comparable to that seen formany other solid cancers in the cohort of survivors of theatomic bombings and in other populations.
321. The other publication presenting LSS results is apooled analysis of incidence data from eight major breast
cancer cohorts [P3]. This analysis used the LSS breast
cancer incidence data for the period 1958–1993 together
with data on tuberculosis patients who received multiple

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chest fluoroscopies as part of their treatment [B3], womenwith benign breast disease [M8, S5], and infants who
received radiation therapy for an enlarged thymus [H10] orskin haemangioma [L4, L7]. The analysis considered variation in both the ERR and the EAR with both attained age
and age at exposure, and developed pooled ERR and EAR
models for the risk based on the underlying studies. Their
final ERR model allowed for: a decrease in the ERR
inversely proportional to the square of the attained age, andno variation with age at exposure for the LSS, tuberculosis
and thymic irradiation cohorts; a large effect of age at exposure for the Swedish benign breast disease cohort; and novariation with either age or age at exposure for the mastitis
or haemangioma cohorts. As for other pooled analyses ofsome of these data sets [L5, L79], the RRs for the survivorsof the atomic bombings were significantly higher than thosefor United States and European populations. The authors
recommended the use of a pooled EAR model in which the
EAR increases with attained age, with a reduction in therate of increase after age 50 for all cohorts and a 40%
decrease in the EAR per unit dose for every 10-year increasein age at exposure for the LSS, fluoroscopy and thymic irradiation cohorts. There was a much more rapid decrease withage at exposure in the Swedish benign breast disease cohort
and a non-significant increase in the mastitis cohort. Risks
per unit dose were low in the haemangioma cohort evenafter allowing for infancy at the time of exposure. In general, the results suggested that no relatively simple pooledmodel can adequately describe the risks of all the cohorts,
and that factors such as a history of breast disease may havea marked effect on risk. The ERR results suggested that
more attention needs to be given to descriptions of breast
cancer risks that allow for the effects of both attained age
and age at exposure.
322. Studies of second primary cancers diagnosed among
Hodgkin’s disease (HD) survivors have been an importantsource of information on the risks of breast cancer following high-dose exposures. Updated results have been published for several of the major HD survivor cohorts. These
include analyses of cancer incidence in a United Statescohort of 1,380 childhood HD survivors (including 480
women with an average follow-up of 17 years per person)
treated before age 16 [B46], and a United Kingdom HD
cohort that includes 5,519 survivors (including 2,085 women)of all ages with an average follow-up of about 8.5 years perperson [S77]. Both incidence [V8, V9] and mortality [A31]risks have recently been examined in a Dutch cohort thattakes in 1,261 people (including 539 women) treated prior
to age 41 with an average follow-up of about 20 years perperson. The nested case-control study with 48 breast cancer
cases and 175 controls based on the Dutch cohort of van
Leeuwen et al. [V8] is one of the most important since,
unlike other studies of HD survivors, it makes use of individual dose estimates, and the authors make a concerted
effort to investigate effect modification by chemotherapy
and other factors. The study of those women treated under
the age of 30 forms part of a meta-analysis of HD survivors[T25]. In 2000, Metayer et al. [M52] presented results of a89

pooled analysis of cancer incidence among 5,925 European
and North American paediatric HD survivors who were
under 21 years of age at the time of treatment. The pooled
analysis cohort includes 2,737 women with an averagefollow-up of about 9.5 years per person.
323. Despite the problems in separating the effects ofchemotherapy and radiotherapy, all of these studies provide
clear indications of large, statistically significant increases
in breast cancer risk from high-dose radiotherapy. There arealso indications that the risks decrease with increasing age
at exposure. The pooled analysis of paediatric HD survivors[M52] reports an O/E of 14 (p < 0.05). In the United
Kingdom study [S77], the SIR estimate for breast cancer
associated with radiotherapy among women treated prior toage 25 is 14 (95% CI: 6, 29), while for women aged
between 25 and 55 the estimated SIR is about 2 and not
significantly greater than 1. The Dutch study of breast
cancer incidence [V9] reports an SIR of 17 (95% CI: 8, 32)
for paediatric HD cases and of about 4 for women treatedafter age 20. The United States study of paediatric HD survivors [B46] finds an SIR of 52 (95% CI: 40, 76).
324. In a cohort of 1,814 female 3-year survivors of childhood cancer in France and the United Kingdom, 16 persons
developed breast cancer [G29]. Radiation doses to the breast
averaged 5.06 Gy. There was a trend of increasing breast
cancer risk with dose at borderline levels of statisticalsignificance; ERR = 0.13 (95% CI: <0, 0.75) Gy–1 (2-sided
p = 0.06).
325. As noted above, the Dutch nested case-control study[V8] is the only HD follow-up study to make use of individual dose estimates. The authors provided an estimate for
the ERR of 0.06 (95% CI: 0.01, 0.13) Gy–1 among women
treated using only radiotherapy. They also noted that risk
estimates were about 50% lower for women who received
both chemotherapy and radiotherapy. They carried out
analyses which suggested that this difference is largely
attributable to early onset of menopause induced by thechemotherapy. These estimates of ERR per unit dose andof the SIR and O/E discussed above are considerably lower
than the risks that would be predicted on the basis of linear
risk estimates from the LSS or from other populations withlower doses (i.e. less than about 5 Gy), supporting theconcept of effects due to cell-killing at high doses.
326. Initial results from a cohort study of more than
90,000 United States radiologic technologists employed
between 1926 and 1982 have been published in recent years[M10, S29]. Analyses indicated that the breast cancer incidence rate for this population was higher than that for
women recorded in the SEER cancer registries in the UnitedStates, with an overall SIR of 1.16 (95% CI: 1.09, 1.23)
based on 177 cases. The breast cancer risks were particularly high for women employed in earlier years and declinedwith later years of initial employment. This pattern lends
support to the idea that the increased risks are associatedwith occupational exposures to radiation. Since there are

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currently no individual dose estimates for cohort members,
this study does not yet estimate dose response.
327. As noted in Section II.H, because aircrew receive elevated doses, which can range up to 6 mSv per year, with asubstantial neutron component (25–50% of the absorbed
dose) [B22, G15], there has been much interest in studies ofthis group. To date there have been various, generally small,studies of aircrew, whether pilots or flight attendants. Breast
cancer mortality in a large pan-European study of flightattendants was slightly elevated, but this was not statisticallysignificant: the SMR was 1.11 (95% CI: 0.82, 1.48), basedon 59 deaths [Z4]. There was no trend of breast cancer mortality with years of service [Z4]. Likewise, breast cancer incidence in a cohort of Norwegian airline cabin attendants
demonstrated a slight, but statistically non-significant,increase in breast cancer incidence: there were 38 cases compared with 34.0 expected (SIR = 1.1; 95% CI: 0.8, 1.5).
Again there was no trend of incidence with duration of
employment; for example, the RR for 15 or more years ofservice compared with less than 5 years of service was 1.0(95% CI: 0.3, 3.0) [H58]. In a study of Icelandic cabin attendants, there is a more pronounced (but still statistically nonsignificant) elevation in risk associated with increased yearsof service, so that the relative breast cancer risk among thosewith 5 or more years of service compared with those withless than 5 years of service was 2.10 (95% CI: 0.93, 4.73)[R53]. For those with 5 or more years of service before 1971
compared with those with less than 5 years of service before
1971, the RR was 5.24 (95% CI: 1.58, 17.38). This study isunusual among studies of these cohorts in that reproductive
history (nulliparity and age at first birth) was adjusted forin the analysis. A study of Finnish airline cabin attendants
adjusted for reproductive history, and for familial andlifestyle risk factors, and, unusually, also had individual radiation dose estimates. However, there was no suggestion ofincreased risk associated with radiation dose. The adjusted
OR was 0.93 (95% CI: 0.68, 1.27) for 10 mSv [K55]. In theabsence of individual information on radiation dose andlifestyle factors for most of these groups, it would be difficult to ascribe the generally modest excess risks observed inthese studies to ionizing radiation exposure [S35].
328. As discussed above, age at exposure is widely
acknowledged as an important modifier of the radiationdose response for breast cancer. The LSS provides someindication of especially high RRs for early onset (diagnosis
prior to age 35) of breast cancer among women exposed
early in life [L78]. An early onset effect is also suggestedby the Dutch cohort study [V8, V9]. Such an effect may besuggestive of a genetically susceptible subgroup, but may
also reflect a modification of the ERR by attained age. Moreanalyses are needed to address this issue.
329. The most comprehensive analysis of interactions
between known radiation risk factors and radiation effects
remains the study of 196 breast cancer cases and 566
matched controls conducted using the LSS data [L80, L81].
The results of this study suggested that the presence ofknown protective factors, such as early first childbirth andmultiple births, reduces the excess risk of breast cancers
due to radiation exposure at least as much as it reduces theunderlying risks of breast cancer. The recent Dutch HD
analyses [V8] mentioned above suggested that this reduction might be even greater than that suggested from the LSS
data. The results of the recent pooled analysis [P3] suggestthat a history of benign breast disease may increase the riskof radiation-associated breast cancer. This observation is
given some support by the findings of a recent casecontrol study of the effects of medical exposures to radiation on breast cancer risks [H51]. This study reported thata significant association between medical radiation exposures and breast cancer risks was seen only among womenwith a history of benign breast disease. However, the study
was based on self-reported radiation exposure histories, so
there is some possibility of recall bias.
330. Breast cancer is quite rare among men, accounting
for less than 0.5% of all cancers in men and less than 1%of all breast cancers [P19]. Because it is so rare, it has
seldom been considered in analyses of radiation-associatedcancer risks. However, a recent report on male breast cancer
in the LSS of survivors of the atomic bombings [R33] noteda statistically significant increase in the risk with increasing radiation dose. Because of the small number of cases,
the risk estimate is extremely imprecise.
(b) Internal low-LET exposures331. A study of 6,841 Swedish, French and Italian patientstreated with a mixture of conventional (external beam) radiotherapy and 131I for thyroid cancer recorded a statisticallysignificant increase in breast cancer incidence (SIR = 1.3;
95% CI: 1.0, 1.5; 128 cases) [R38]. However, there was no
trend of increasing breast cancer risk with administeredquantity of 131I: adjusted for external radiotherapy. The ERR
was –0.01 (95% CI: indeterminate, 0.04) GBq–1 of 131I (the2.5 percentile estimate did not converge). There was a (statistically non-significant) positive trend with administered
131I among those people who did not receive external radiotherapy. The ERR was 0.002 (95% CI: indeterminate, 0.07)
GBq–1 of 131I (the 2.5 percentile estimate did not converge)[R38]. A highly statistically significant (p = 0.004) trend ofincreasing breast cancer mortality with dose was observedin the study of persons exposed to weapons test fallout in
the Semipalatinsk area of Kazakhstan [B58]. Based on aninternal analysis, the aggregate ERR was 1.28 (95% CI:
0.27, 3.28) Sv–1. However, when the analysis was restricted
to the exposed group and based on individual dose estimates,
the trend estimate was slightly reduced and no longer
statistically significant: 1.09 (95% CI: –0.05, 15.8) Sv–1.
As noted in section II.D, “ecological bias” may operate inthis study, so these findings should be treated with caution.
(c) Internal high-LET exposures
332. As noted in the UNSCEAR 2000 Report, there arefew published data on the effects of internal high-LET

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exposures to ionizing radiation on breast cancer risks. The
primary published data concern the effects of doses arisingfrom 224Ra administered for therapeutic purposes [N3]. This
study found no indication of elevated risks associated withthe radiation exposure.
333. The potential for studies of the Mayak worker [K2]and Techa River [D40, K6] cohorts to provide informationon breast cancer risks from internal radiation exposures wasnoted in the UNSCEAR 2000 Report [U2]. However, whilesome information on risks for these cohorts is now available [G2, G12, K46, S28], the reports do not provide information on breast cancer risks.

4. Summary
334. Radiation effects on female breast cancer risks havebeen widely studied because breast tissue appears to be relatively radiosensitive and because breast cancer is the mostcommon cancer among women. As outlined above andrecently reviewed in reference [R32], there is compelling evidence for effects of radiation exposure on breast cancer rates.
The dose response appears to be linear for doses of up to
several grays, while epidemiological studies of populations
who received radiotherapy suggest that cell-killing may
reduce the relative effectiveness at very high doses. There isaccumulating information to delineate the complex modifying effects of age at exposure and attained age. There seems
to be fairly strong evidence supporting the notion that age at
exposure is an important risk factor, with younger women
having higher risks than women exposed later in life.
However, more attention should be paid to characterizationof the ERR as a function of attained age, and of the relative
effect of attained age and age at exposure on the risk ofradiation-associated breast cancer. Comparison of the LSS
results with those from studies on European and United States
populations suggests that radiation may act additively withrespect to many of the factors responsible for differences
between the underlying breast cancer rates of Japanese andof Western populations. On the other hand, the limited dataon the joint effects of radiation and known risk factors forbreast cancer suggest that radiation may act multiplicatively
with respect to reproductive factors. Furthermore, somefactors, such as a history of benign breast disease, may markedly increase the risk of radiation-associated breast cancer.

O. Uterine cancer

1. General background335. Uterine cancer includes cancer of the body (corpus)of the uterus and cancer of the uterine cervix. Most cancersof the uterine corpus are adenocarcinomas of the lining of
the uterus (endometrium); sarcomas arise in the muscular
tissue of the corpus (myometrium) but are rare [G25]. Most

91

cancers of the uterine cervix are SCCs [S51]. Annual agestandardized world incidence rates for corpus uterine cancer
vary from less than 5 per 100,000 women in most of Asia
to more than 20 per 100,000 in parts of the United States[P19]. Annual age-standardized world incidence rates for
cervical cancer vary from less than 15 per 100,000 womenin most of Western Europe to over 30 per 100,000 in partsof South Asia [P19].
336. Cancers of the uterine cervix and corpus have very
different aetiologies. Human papillomavirus (HPV) appears
to be involved in nearly all cervical cancers, although otherfactors must also be involved, since HPV infection is much
more common than cervical cancer [S51]. Different strainsof HPV have different degrees of oncogenicity [S51]. The
usual mode of transmission is sexual intercourse. Cigarettesmoking is also associated with risk [D5, L2]. With theintroduction of cervical cytological screening (“Pap smear”)
programmes, the incidence and mortality rates for cervicalcancer have declined precipitously in developed countries;
nonetheless, cervical cancer is the second most common
cancer in women worldwide [P38, S51]. Unlike cervicalcancer, corpus cancer appears to be more common in
women of higher socio-economic status [G25]. Risk factors
for endometrial cancer include menstrual and reproductive
characteristics, obesity, use of hormones and certain medical conditions [A2, G25]. Risk factors for uterine sarcomas have not been studied extensively and are poorly
understood. Data on uterine cancer logically should be subdivided into those for the uterine cervix and for the uterine
corpus; however, a number of the available radiation studies have combined data on cervical and corpus cancers.
Table 34 notes when the data for the two types werecombined in the various studies.

2. Summary of UNSCEAR 2000
337. Uterine cancer was not considered in the UNSCEAR
2000 Report [U2].

3. New or updated studies(a) External low-LET exposures
338. Neither cancer of the uterine corpus nor cancer ofthe uterine cervix appeared to be related to radiation exposure in studies of the survivors of the atomic bombings [T1].
Corpus cancer showed a non-significant inverse association
with radiation dose (ERR at 1 Sv = –0.25; EAR = –0.26(104 PY Sv)–1. A non-significant negative association also
was seen for cervical cancer: ERR at 1 Sv = –0.07 (95%
CI: –0.29, 0.27) and EAR = –0.37 (95% CI: –1.57, 1.38)(104 PY Sv)–1. For all cancers of the uterus combined, there
was no significant modifying effect of age at exposure, timesince exposure or attained age. In the most recent mortality analysis for the LSS cohort [P9], the ERR for all uterine cancers combined was 0.17 (95% CI: –0.10, 0.52) Sv–1,

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and the EAR was 0.44 (95% CI: –0.27, 1.3) (104 PY Sv)–1.
Cancer of the uterine corpus is uncommon in Japan [P19].
339. Within the AHS subset of the LSS cohort, the incidence of benign uterine myoma was associated with radiation dose, and the association did not appear to be readily
explicable in terms of better detection among the more
highly exposed women [Y3]. If a high proportion of womenwith myomas went on to have hysterectomies, this could
introduce a downward bias in the dose response for uterine
cancer, particularly for corpus cancer.
340. Cancer of the uterine corpus was increased significantly 15 or more years after radiotherapy for cervical cancer(RR = 6.0), and the RR increased with dose (p = 0.14) [B5].
Most women in the study received radiotherapy for their cervical cancer, and doses were extremely high; indeed, womenwith doses to the uterus of up to 100 Gy constituted the reference group for dose–response analyses. Controls for theuterine corpus cases had to have an intact uterus at the timeof diagnosis of the matched case. There was some indication that the risk was greater for adenocarcinoma of theuterus than for sarcoma of the uterus, but this comparison
was limited by the small number of sarcoma cases.
341. Several studies have reported increased incidence[W30] and mortality rates [D7, I4] of uterine cancer among
women irradiated for benign gynaecological disorders associated with excessive or irregular uterine bleeding.
However, interpretation is complicated by the possible relation between uterine cancer and the underlying gynaecological conditions for which the radiotherapy was given.
These include hyperplasia of the endometrium, uterine
fibroids and endometrial polyps, all of which are thought
to be related to hormonal factors [K44]. Furthermore, thefrequency of hysterectomy for women with such disorders
might differ from that for women in the general population.
Wagoner [W30] reported a significantly elevated incidenceof uterine cancer among Connecticut (United States) women
irradiated for benign gynaecological disorders (observed =83, expected = 29.3, SIR = 2.8 (p < 0.01)). The risk of uterine sarcoma or carcinosarcoma was especially high relative
to that for women in the general population (observed = 12,
expected = 1.5, SIR = 8.0 (p < 0.01)). Approximately halfof the women were irradiated by external beam X-rays andhalf by intracavitary 226Ra. Among women from
Massachusetts or Rhode Island (United States) irradiated byintrauterine radium, Inskip et al. [I4] reported a significantly
elevated overall SMR of 1.8, with some indication of anincreasing risk with increasing follow-up time. However,there was little evidence of a dose response (ERR = 0.006(90% CI: –0.01, 0.05) Gy–1). The median dose to the uterus
was 32 Gy. Death due to cervical cancer occurred less often
than expected (SMR = 0.5). In extended follow-up of acohort of Scottish patients irradiated with X-rays for
metropathia (with a mean dose to the uterus of 5.2 Gy),
Darby et al. [D7] observed a non-significantly elevated
SMR for cervical cancer (SMR =1 .31; 95% CI: 0.67, 2.28)
and for all uterine cancer combined (SMR = 1.41; 95% CI:

0.91, 2.08). The estimated ERR for uterine cancer was 0.09
(95% CI: –0.02, 0.19) Gy–1, and there was no clear trendof increasing RR with increasing follow-up time.
342. A statistically non-significant, negative trend ofuterine cancer incidence with radiation dose was observedin a Swedish group treated for haemangioma in infancy:
22 such tumours were observed [L10].
343. In general, no significant trends of uterine cancer risk
with external radiation dose have been observed in various
groups of radiation workers. For example, in the United
Kingdom there were 15 deaths due to uterine cancer in the
NRRW, compared with 14.9 expected. There was a large
but statistically non-significant trend with external film
badge dose: the ERR was 16.8 (90% CI: <–1.95, 130.3)
Sv–1 [M12]. Likewise, in the IARC three-country nuclear
worker study, there were positive trends with dose for bothuterine cervix and other uterine cancer deaths, which forthe latter end point approached statistical significance(1-sided p = 0.092) [C3].
344. Rates of cancer of the uterine corpus were slightly,
but not significantly, increased among women treated withradiation to the ovaries and pituitary gland for infertility(SIR = 1.44; 95% CI: 0.52, 3.13) [R30]. The mean dose to
the uterus was 0.97 Gy, and there was no indication ofincreasing risk with increasing dose. Excess cancers of theuterine corpus also have been observed following ovarian
ablation therapy for breast cancer [E9], but not among ankylosing spondylitis patients [W8].
345. Cancer of the uterus (including corpus and cervix
cancer) did not occur more often than expected in a cohortof 69,524 radiologic technologists compared with the incidence rate in the general female population (SIR = 0.80;
95% CI: 0.69, 0.90) [S29]. The risk of cancer of the uterus(including cervix) was not associated with low-dose radiation exposure (mean dose = 1.75 mSv) in a cohort of occupationally exposed women from Canada (SIR = 0.71; 95%
CI: 0.63, 0.80) [S8]. Only 77 women in this cohort had
doses of 100 mSv or greater.
(b) Internal low-LET exposures
346. A study of cancer incidence following radioiodine
treatment for hyperthyroidism [F1] reported that there was
no overall excess of uterine cancer in the treated group compared with the general population but did find adose–response association: for ≤220 MBq, SIR = 0.52 (95%
CI: 0.28, 0.96); for 221–480 MBq, SIR = 0.73 (95% CI:
0.41, 1.32); for >480 MBq, SIR = 2.11 (95% CI: 1.2, 3.7);
p = 0.002 for trend. The uterus does not concentrate radioiodine, and it is questionable whether it would receive meaningful exposure from this treatment. Death due to cancer ofthe uterus occurred significantly less often than expected inanother large cohort of hyperthyroid patients treated withradioiodine compared with mortality rates in the general
female population [R3].

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347. The International Thorotrast Study [T30] did not
find any elevation in uterine cancer incidence rates associated with Thorotrast administration. There were 6 casesof uterine cervical cancer in the Thorotrast-exposed group
and 9 in the comparison group in the Denmark–Sweden
part of this study, resulting in an RR of 0.6 (95% CI: 0.2,
1.8). There were 5 cases of uterine corpus cancer in theThorotrast-exposed group and 10 in the comparison groupin the Denmark–Sweden part of the study, resulting in an
RR of 0.6 (95% CI: 0.2, 1.8) [T30]. No uterine (or otherorgan) dose estimates have been made for the study, andno trend with administered Thorotrast volume wasreported.

4. Summary
348. Available evidence indicates that there is no strong
ionizing radiation dose response for uterine cancer [B44,
T1]. An absence of association between cervical cancer
risks and radiation exposures is a consistent finding,including exposures at very high doses. The evidence is
not quite so universally negative for cancer of the uterine
corpus but suggests that, if there is an effect, it is largely
confined to the region of very high doses, i.e. in the tensof grays or more. These inferences must be tempered by
the possibility that radiation dose is also related to treatment of conditions that lead to hysterectomy, which would
preclude the possible future occurrence of uterine corpus
cancer. Dose-dependent removal of the organ at risk could
exert a downward bias in the dose response for uterine
cancer.

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2. Summary of UNSCEAR 2000
350. Ovarian cancer was not considered in the UNSCEAR
2000 Report [U2].

3. New or updated studies(a) External low-LET exposures
351. The number of epidemiological studies providing
results for ovarian cancer risk is quite limited. The largest
number of cases comes from the case-control and cohort
studies of women treated with radiation for cervical cancer[B8, K1]. Excess risk of ovarian cancer due to radiation
exposure was not observed; however, the doses were exceptionally large (e.g. 32 Sv), which probably resulted in cellkilling. A non-significant excess risk was observed in agroup of women treated with 226Ra for uterine bleeding. The
ERR was 0.4 (95% CI: –0.7, 1.5) based on 37 cases [I4].
352. The best evidence for an effect due to radiation exposure comes from the studies of the incidence and mortality
data for the survivors of the atomic bombings (LSS). Up tothe end of 1987, 66 cases of ovarian cancer were observedin women with exposures of greater than 0.01 Sv. The estimated RR was 0.99 (95% CI: 0.12, 2.34) at 1 Sv [T1]. For
mortality, there is longer follow-up of the data (up to theend of 1997), and 85 deaths due to ovarian cancer wereobserved among women receiving more than 0.005 Sv [P9].
A significant ERR of 0.94 (95% CI: 0.07, 2.0) at 1 Sv wasestimated (see table 35). In a previous mortality analysis[P1], this dose–response relationship was clearly linear,although the numbers of cases were limited. There was alsoa suggestion, although statistically non-significant, that
exposures at either young (less than 10 years old) or older(more than 40 years old) ages were a greater risk.

P. Ovarian cancer(b) Internal low-LET exposures
1. General background349. Ovarian cancer will affect 1–2% of women indeveloped countries during their lifetime [W25]. Annual
age-standardized world incidence rates for ovarian cancer
vary from fewer than 6 per 100,000 women in most of
China to more than 10 per 100,000 in most of the UnitedStates [P19]. The ovarian cancer mortality rate is high,and it is the fourth most common cause of cancer mortality in women, accounting for 1% of their total mortality. There has been a steady increase in mortality fromovarian cancer in industrialized countries [Y2]. There areseveral histological types, with epithelial tumours clearly
dominating. Risk factors, other than reproductive patterns
and hormone levels, are not well understood. It has beenshown that occupational exposures to asbestos and talc
may be associated with this cancer. Further details on theepidemiology of ovarian cancer can be found in Weisset al. [W25].

353. Very few data are available to relate ovarian cancer
and internal low-LET radiation exposures. Patients treatedfor hyperthyroidism [R3] showed no increase in ovariancancer incidence rates (based on 86 cases), but the dose waslow (less than 0.1 Sv). A (statistically non-significant)positive trend of ovarian cancer incidence rates with radiation dose was observed in a Swedish group treated forhaemangioma in infancy: 15 such tumours were observed,
yielding an ERR of 0.62 Gy–1 [L10].
354. In general, no significant trends of ovarian cancer
with external radiation dose have been observed in various
groups of radiation workers. For example, in the United
Kingdom there were 13 deaths due to ovarian cancer in the
NRRW, compared with 16.2 expected; there was a large
but statistically non-significant trend with external film
badge dose: the ERR was 82.8 (90% CI: <–1.95, 2583) Sv–1
[M12]. Likewise, in the IARC three-country nuclear worker
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trends of ovarian cancer mortality with dose (1-sided
p = 0.312) [C3].
(c) Internal high-LET exposures
355. The International Thorotrast Study [T30] found elevated risks of ovarian cancer associated with Thorotrast
administration. There were 9 cases of ovarian cancer in theThorotrast-exposed group and 3 in the comparison group in
the Denmark–Sweden part of this study, resulting in an RRof 4.3 (95% CI: 1.1, 24.3) [T30]. No ovarian (or otherorgan) dose estimates have been made for this study, andno trend with administered Thorotrast volume was reported.

4. Summary
356. Although the body of evidence is not strong, the LSS
provides evidence that ovarian cancer is inducible by ionizing radiation.

2. Summary of UNSCEAR 2000
359. Studies of the risk of prostate cancer following radiation exposure are limited by the fact that prostate cancer
largely occurs at older ages, and that few studies have sufficient follow-up or sample size to have appreciable power
to detect excess risk at levels comparable to that seen for
most other cancers linked with radiation exposure. It wasnoted that several studies have provided some evidence
for a radiation effect on prostate cancer incidence, mostnotably the long-term follow-up of the United Kingdom
cohort of ankylosing spondylitis patients who received Xray treatments [W8], and United Kingdom Atomic Energy
Authority workers with internal low-LET radiation exposures [R14]. However, it was concluded that there is nocompelling evidence for a radiation effect on prostatecancer risks. This conclusion was based on the lack of asignificant dose response in the studies on survivors of theatomic bombings [T1], in a United States study of men
who had received radiotherapy for peptic ulcers [G6]and in the study of the large pooled cohort of nuclear
workers [C3].

Q. Prostate cancer
3. New or updated studies
1. General background357. Prostate cancer is one of the most commonly occurring cancers among men in Europe, Africa and theAmericas, with particularly high incidence rates in the
United States, especially among black people. Rates are
considerably lower throughout Asia, particularly in Japanand China [P19], where, despite increases in recent years,
they remain an order of magnitude or more lower than thosein the United States and other high-risk countries [H28,
S78]. For example, annual age-standardized world incidence
rates for prostate cancer are less than 10 per 100,000 men
in most of China, whereas in some parts of the UnitedStates, rates exceed 180 per 100,000 [P19]. Because of relatively effective treatments, mortality rates are lower thanthose of lung, stomach and other relatively common cancers. There are some indications that in recent years prostatecancer mortality rates are declining in many developed
countries [B47]. Both the increase in incidence rates andthe possible decline in mortality rates reflect, at least in part,the effects of increased screening.
358. Prostate cancer is extremely rare before age 40, after
which rates increase more rapidly than for other cancers.
The rate of increase with age is largely independent ofregional variations in rates [P19]. Little is known about risk
factors for prostate cancer; however, migration and family
studies [M53, R34] have suggested that, while genetic predisposition has some role in explaining the large regional
differences in the risk of prostate cancer, other factors arealso important. Dietary factors, particularly levels of bodyfat [R34, V10, W33], and levels of sex hormones [H38] aresuspected risk factors.

(a) External low-LET exposures360. Table 36 summarizes the risk estimates for prostatecancer and prostate cancer mortality from epidemiologicalstudies of radiation exposure.
361. While mortality among survivors of the atomic
bombings has been presented in recent publications [P9,P10], these reports do not explicitly consider prostate cancer
risks. However, the Radiation Effects Research Foundation
has released the data set used in the analyses for reference[P9], and this data set does contain information that permits analyses of possible radiation effects on prostate cancer
mortality risks for the period 1950–1997. An update of thepeptic ulcer study on cancer mortality has been publishedrecently [C4]. Unfortunately, since the publication of the
UNSCEAR 2000 Report [U2], there have been no newreports on prostate cancer incidence for the survivors of theatomic bombings, nor have any of the other studies considered in the UNSCEAR 2000 Report been updated.
Prostate cancer is routinely considered in many follow-up
studies of cancer survivors (e.g. [A31, V9]), but because of
the relatively short follow-up period and the ages of mostmembers of these cohorts, they do not provide useful information on the relation between radiation exposure andprostate cancer risks.
362. The latest follow-up data on mortality among thesurvivors of the atomic bombings [P9] include 53 deaths
due to prostate cancer among 19,992 male members of thecohort with dose estimates of greater than 5 mGy. The estimate for ERR per unit dose of prostate cancer reported by
Preston et al. [P9] is 0.21 (90% CI: <–0.3, 0.96) Gy–1.

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As with the incidence data [T1], there is no indication of astatistically significant increase in prostate cancer risk withradiation exposure. The point estimate for the ERR per unit
dose is about half of that seen for all solid cancer deaths
among men in the LSS, but the uncertainty on both
estimates is such that there is no evidence that the ERR
for prostate cancer is significantly lower than that for all
solid cancers.
363. The study of cancer mortality in the peptic ulcer
study has been updated to include follow-up to the end of
1998 [C4]. The cohort includes 2,914 men, half of whom
received radiotherapy, with an average of 25 years offollow-up per person. There have been 72 prostate cancer
deaths. As in the earlier analyses of this cohort, theobserved number of deaths due to prostate cancer was
higher than expected number based on the general malepopulation for both the radiotherapy and the nonradiotherapy group, with the ratio of observed to expected
smaller (1.24) in the radiotherapy group than in the nonradiotherapy group (1.47). These results do not suggestthat radiation exposure is increasing prostate cancer rates
in this cohort.
364. Prostate cancer and prostate cancer mortality risks
have been considered for the 21,000 men in the cohort of
United States radiologic technologists. Individual dose estimates are not yet available for this cohort. However, comparisons with expected numbers of cases based on national
incidence [S29] and mortality rates [M10] do not suggestthat prostate cancer rates are associated with occupational
exposures for this cohort. Based on 222 cases of prostatecancer, the estimated SIR was 1.02 (95% CI: 0.89, 1.16),
while the SMR for the 87 deaths due to prostate cancer was
0.89 (95% CI: 0.7, 1.1). Recent studies of mortality among
47,000 male workers in the United States nuclear powerindustry led to a large negative estimated ERR of prostatecancer of –2.50 (95% CI: <–2.51, 26.4) Sv–1, which was
not significant, however [H44]. No significant effects due
to radiation exposure were found for prostate cancer in thelatest analysis of mortality data among Japanese nuclear
workers [I14].
365. Atkinson et al. [A22] have studied mortality followup in a cohort of 51,367 United Kingdom Atomic Energy
Authority workers, extending a previous study of Beral etal. [B59]. The trend of prostate cancer risk with dose failed
to be statistically significant (2-sided p = 0.13), although
the trend remained statistically significant for those workers followed up until 1979 (2-sided p < 0.01) [A22], somewhat corroborating the previous findings in this cohort[B59]. A study of 12,540 workers at the Capenhurst uranium enrichment facility in the United Kingdom did notsuggest any elevated risk of prostate cancer: the trend of
prostate cancer risk with cumulative external dose wasnegative [M4]. Likewise, a study of 13,960 radiation workers at the Springfields uranium production facility in the
United Kingdom did not suggest any increased incidenceor mortality rates for prostate cancer [M5]. A study of95

2,628 workers at the United Kingdom Chapelcross site
found a statistically significant positive trend of mortality
due to prostate cancer with cumulative external dose
(1-sided p = 0.036 for a 10-year lag, adjusted for age, sex,
calendar year, industrial status, worker status and timesince first exposure), based on 8 deaths [M6]. However,with increasing lag time the significance of the trend progressively decreased, so that with a 20-year lag time, thetrend was no longer conventionally statistically significant(1-sided p > 0.05) [M6]. None of the 8 deaths had beenmonitored for tritium, 51Cr, 59Fe, 60Co or 65Zn, theradionuclides suggested by the study of Rooney et al.
[R14] as being associated with elevated risk. Some ofthese cohorts include substantial groups who wereheavily exposed to tritium [A22, B59, M4, M6], althoughin general, doses from tritium do not appear to havebeen estimated.
366. Although not considered in the UNSCEAR 2000
Report [U2], the study of Artalejo et al. [A32] reported aslight deficit of prostate cancer mortality among workers
for the Spanish Nuclear Energy Board; the SMR was 0.73
(95% CI: 0.29, 1.51), based on only 7 cancer deaths, ofwhich 1 was among the 27% of the cohort who had beenminers and who may have been exposed to alpha radiation[A32].
(b) Internal high-LET exposures
367. The International Thorotrast Study [T30] found elevated risks of prostate cancer associated with Thorotrast
administration. There were 14 cases of prostate cancer in
the Thorotrast-exposed group and 4 in the comparison groupin the Denmark–Sweden part of this study, resulting in an
RR of 4.5 (95% CI: 1.6, 16.3) [T30]. There was a single
death from prostate cancer in the Thorotrast-exposed groupand there were 2 in the comparison group in the UnitedStates part of this study, resulting in an RR of 0.2 (95%
CI: 0.0, 5.1) [T30]. Prostate (or other organ) doses were notestimated for this study, and no trend with administered
Thorotrast volume was reported.

4. Summary
368. There is little indication of effects due to radiation
exposure on prostate cancer risks. Despite the relatively
long follow-up and large cohort size, the power of the studies of the survivors of the atomic bombings is somewhat
limited by the low underlying rates of prostate cancer inJapan and the relatively low mean dose for the survivors.
It is of some interest that the United Kingdom ankylosing
spondylitis study led to a statistically significant result, withan estimate for the ERR per unit dose that is similar to thatseen from the LSS. Occupational cohorts and studies ofpeople who received radiotherapy provide little indicationthat external or internal radiation exposure increases
prostate cancer risks.

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R. Cancer of the urinary bladder

1. General background369. Bladder cancer accounts for less than 5% of totalcancer incidence and less than 2% of total cancer mortality in industrialized countries. There is wide internationalvariation in bladder cancer incidence, with high rates in
Europe and North America and low rates in Latin Americaand Asia. Incidence rates increase steeply with age, and thiscancer is substantially more common among men than
women: in some countries the ratio can reach 5:1 [H37,
P19]. The incidence rate increased from the 1960s to the
1980s, but recently has begun to stabilize. Mortality rates
have been decreasing for both men and women and for all
ages. The temporal trends are influenced by changes indetection and improvements in survival.
370. Cigarette smoking is a leading cause of bladdercancer. In Western countries, approximately 50% of thecancers in men and 30% in women would be attributableto smoking. Occupational exposures to carcinogens, particularly to aromatic amines, and urinary tract infections,especially among women, also are associated with anincreased risk of bladder cancer. Use of phenacetincontaining analgesics and cyclophosphamide, as well as
exposure to arsenic in drinking water and Schistosoma
haematobium infection, are also suspected risk factors for
bladder cancer [H37, M33, S44].

2. Summary of UNSCEAR 2000
371. The UNSCEAR 2000 Report concluded that there
was convincing evidence of a relation between low-LET
radiation exposure and bladder cancer risk based on the
LSS incidence and mortality data [P1, R10, T1], as wellas on studies of several populations medically exposed toradiation for benign diseases [D7, I4, W8] and populations
receiving radiotherapy for malignant diseases [B8, B50,
N9, T27, T28]. The risk estimates from the studies of thesurvivors of the atomic bombings were generally greaterthan those from most other studies. However, this difference may be related to the phenomenon of cell killing
arising from the very high doses involved in many of the
medical studies.
372. In the LSS, the effects of age and sex on bladdercancer risk were unclear. A statistically significant difference between the risks for the two sexes, with the ERR for
females exceeding that for males by a factor of about 5,
was seen in the incidence data; however, no significant difference was observed when an EAR model was used [T1].
Based on the mortality data, the point estimates of the
ERRs and EARs for males were higher than those forfemales, although the differences were not statistically significant [P1]. Neither the mortality data [P1, S3] nor theincidence data [T1] exhibited statistically significant

variation with age at exposure for either the ERR or the
EAR. The UNSCEAR 2000 Report indicated that potential interactions between smoking and radiation exposure
needed to be studied.
373. Information on bladder cancer risks from internal
low-LET radiation exposure was limited, and there waslittle evidence of a link between bladder cancer and exposure to 131I [D18, H2, H6, H24, R3], with the exception of
two relatively small studies of thyroid cancer [E8] and of
hyperthyroid patients [F1] treated with 131I.
374. The risk of bladder cancer associated with exposure
to high-LET radiation was unclear. No risk was seenamong patients exposed to Thorotrast as a contrast medium[A5, D15, M14, V4]. In one study of patients treated with
224Ra [N2], there was a suggestion of an elevated risk, but
this was not found in another study of similarly treatedpatients [W15].

3. New or updated studies(a) External low-LET exposures
375. The results from studies reported in the UNSCEAR
2000 Report and the new and updated studies are presented
in table 37. The most recent LSS report stated that 150
bladder cancers occurred between 1950 and 1997. Of these,99 occurred among survivors exposed to 5 mSv or more,of which about 16% would be attributable to radiation exposure [P9]. While there was little difference in the ERR
between the sexes for exposure at age 30, the estimatedEAR for males was about twice that for females (0.7 and
0.33, respectively). No information on time patterns wasprovided in this report.
376. The Chicago study of mortality due to peptic ulcer
was updated in 2002 [C4]. Based on a small number ofdeaths due to bladder cancer among irradiated and nonirradiated patients (13 and 8, respectively), the RR for radiotherapy was estimated as 1.49 (95% CI: 0.50, 4.4) in the
period 11–62 years after treatment. With a mean bladderdose of 0.2 Gy, an ERR of 2.5 (90% CI: <0, 17.2) could
be estimated.
377. Although individual organ doses frequently are notavailable, several, but not all, studies of second cancers
have reported an association between bladder cancer risk
and high therapeutic radiation doses. As described in the
UNSCEAR 2000 Report, elevated risks of bladder cancer
were associated with radiotherapy in studies of patients with
non-Hodgkin’s lymphoma [T29], or with cancers of theovary [K31, T27], cervix [B8], testis [T28] or prostate [B50,
N9, P23]. Results from two new studies of bladder cancer
following radiotherapy for prostate cancer are inconsistent.
Pickles and Phillips [P24] observed an elevated risk of bladder cancer 10 or more years after radiotherapy for prostatecancer. However, no excess risk was reported in a much

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smaller series of patients from the Mayo Clinic in the UnitedStates [C16].
378. No clear excess of bladder cancer incidence or mortality has been shown in a number of studies of nuclear
radiation workers, including those of the Canadian National
Dose Registry [S8], the United Kingdom NRRW [M12], thecombined analysis of workers in Canada, the United
Kingdom and the United States [C3], and several smaller
studies [A22, F2, I14, M4, M5, M6, M34, W6]. An elevated risk of bladder cancer has been reported among
Chinese radiology workers, particularly those who worked
before 1970 [W3]. In contrast, neither bladder cancer incidence nor mortality was increased in a cohort of UnitedStates radiologic technologists [M31, S29].

97

bladder cancer risk, but because the radiation exposure ofthese workers was low, the statistical power of the studies
is quite limited. One relevant study of occupational exposure in medicine with presumably high exposures hasreported an excess incidence of bladder cancer. In the recent
BEIR report [C37], the estimate of lifetime mortality due
to bladder cancer, 0.90% (95% CI: 0.3, 2.90) Sv–1, is similar to that proposed by the ICRP [I11], and is between thetwo estimates proposed in the UNSCEAR 2000 Report,
although it is much closer to the estimate based on an
absolute risk transfer model.

S. Kidney cancer(b) Internal low-LET exposures
1. General background379. High doses of 131I are often used to treat thyroid
cancer. Because the bladder is one of the few organs thatconcentrate iodine [U2], the 131I dose to the bladder fromthis treatment is about 2 Gy. An excess risk of bladdercancer was reported in one small study of thyroid cancer
patients [E8], but not in two others [D18, H2]. In the only
new study of low-LET radiation exposure, bladder cancer
incidence rates were elevated, but the lower confidence
interval did not include unity (SIR for 131I therapy
compared with no 131I therapy = 1.6; 95% CI: 0.6, 4.5)
following 131I exposure during treatment for thyroid
cancer [R38]. This study was the largest conducted to dateand included cohorts of patients from France, Italy andSweden.
(c) Internal high-LET exposures380. In an analysis of Danish, Swedish and United States
patients injected with Thorotrast as a contrast medium, bladder cancer incidence and mortality rates did not differ significantly from those observed in a comparison group [T30].
These results are consistent with earlier studies of internal
high-LET exposure from Thorotrast [A5, D15, M14, V4].
381. In a Finnish study of persons exposed to dissolved
radioactive material (predominantly 222Rn, but also 234U,
238U, 226Ra, 210Po and 210Pb), an elevated incidence rate ofurinary bladder cancer was not statistically significantly associated with ingested aggregate quantities of radon, radium or
uranium, or with the aggregate bladder dose [K56].

4. Summary
382. Updated mortality information from studies of thesurvivors of the atomic bombings continues to demonstrate
a positive radiation dose response for bladder cancer. In theaggregate, studies of cancer patients treated with high-doseradiotherapy also demonstrate an association between radiation exposure and risk of bladder cancer. Studies of nuclear
workers do not provide evidence of a radiation-related

383. The estimated annual number of cases of kidneycancer worldwide is approximately 189,000, and the associated annual number of deaths is about 91,000 [F14]. The
incidence rate of renal cell carcinoma is about eightfold
higher in developed countries than in developing ones, witha worldwide range of annual age-standardized world incidence rate from 0.5 per 100,000 persons in parts of India
to 20.0 and 10.2 per 100,000 men and women, respectively,in parts of the Czech Republic [P19]. Part of this difference is due to the relative availability of ultrasound, computerized tomography (CT) and magnetic resonanceimaging (MRI) scans [G26]. Well-documented risk factors
for the disease include cigarette smoking, obesity, hypertension and acquired polycystic kidney disease. Risk factors for which there is some evidence, but for which links
are as yet unproven, are renal transplantation, infection withhuman immunodeficiency virus (HIV), exposure to heavy
metals (especially to cadmium and lead), chlorinated solvents, asbestos and phenacitin analgesics, and urinary tract
infections. Other factors, such as higher levels of physicalactivity, of vegetable consumption, and of calcium and vitamin E supplements, may be protective [G26, M50]. Thereis a clear familial component to the disease: the RR for asibling, but not for the parents, of an affected person isabout 2.5, thereby suggesting recessive genetic risk [H50].
A study in Iceland reported that nearly 60% of kidneycancer patients also had a first or second degree family
member with kidney cancer [G27]. At the molecular level,common findings in familial and sporadic renal cell carcinoma are a loss of the terminal portion of the small arm ofchromosome 3, sometimes with a translocation near the
breakpoint 3p13 in familial cases, and/or a somatic mutation or hypermethylation in the 3p segment on or near the
von Hippel–Lindau (VHL) gene locus [G26].

2. Summary of UNSCEAR 2000
384. Kidney cancer was not assessed in the UNSCEAR
2000 Report [U2].

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3. New or updated studies(a) External low-LET exposures
385. The data are quite sparse for radiation exposure andkidney cancer risk. In the LSS cohort, the association
between radiation dose and kidney cancer incidence was notstatistically significant, although the point estimate of theeffect was similar to that seen for many other sites (ERR
= 0.71 Sv–1) [T1] (see table 38). Similarly, in the LSS mortality data, the dose–response association was not statistically significant for either males or females, although therisk was nominally larger for females (ERR = 0.97 (90%
CI: <–0.3, 3.8) Sv–1) than for males (ERR = –0.02 (90%
CI: <–0.3, 1.1) Sv–1) [P9]. Studies of several cohorts of cervical cancer patients receiving radiotherapy did not indicate
significant elevations in risk (compared with general population rates or non-irradiated comparison groups) [B11, K1,
S20]. However, a case-control study nested within thelargest cervical cancer cohort study showed a positive but
not statistically significant (p = 0.17) dose–response relationship (ERR = 0.71 (95% CI: 0.03, 2.2) Sv–1) [B8]. The
United Kingdom ankylosing spondylitis study also showed
an elevation in kidney cancer risk in association with generally high (radiotherapeutic) doses (ERR = 0.10 (95% CI:
0.02, 0.20) Sv–1) [W8]. Two smaller studies of radiotherapy for uterine bleeding or peptic ulcer did not exhibit
raised risks [I4, C4], but they had low statistical power.
386. A number of studies of radiation workers haveshown no positive dose–response association or clear excess
of kidney cancers. For example, the United Kingdom
nuclear worker study [M12] observed 83 deaths due tokidney cancer compared with 89.7 expected, and a statistically significant negative trend with external film badge
dose: the ERR was <–1.95 (90% CI: <–1.95, –0.96) Sv–1.
Likewise, there is a negative trend, albeit not a statisticallysignificant one (p = 0.848), with increasing film badge dosein the IARC three-country study, based on 88 deaths fromkidney cancer [C3]. The Canadian National Dose Registry[S8] reported 69 kidney cancer deaths versus 91.1 expected(SMR = 0.76; 90% CI: 0.61, 0.93) among male workers,
and 21 kidney cancer deaths versus 26.5 expected (SMR =0.79; 90% CI: 0.53, 1.14) among female workers. Generally(non-significant) negative trends of kidney cancer mortality
with external dose were observed in a United Kingdom
cohort of workers at a uranium production facility. Only
with a 20-year lag is the trend with dose (non-significantly)
positive [M5]. Likewise, generally negative trends of kidneycancer mortality rates with external film badge dose areobserved among workers at the Chapelcross plant in the
United Kingdom [M6]. The study of Artalejo et al. [A32]reported a slight excess of kidney cancer mortality among
workers for the Spanish Nuclear Energy Board; the SMR
was 1.26 (95% CI: 0.34, 3.21), based on only 4 cancer
deaths, of which 2 were among the 27% of the cohort who
had been miners and may have been exposed to alpha radiation [A32]. The statistical power of all of the occupational
studies is limited by the low levels of dose.

(b) Internal low-LET exposures
387. Three studies have examined kidney cancer risk inrelation to internal exposure to low-LET radiation. A
Swedish study of cancer incidence following 131I treatment
for hyperthyroidism reported significantly more kidney cancers in the 131I-treated group than expected on the basis ofgeneral population rates. However, a dose–response analysis was not reported, so it is unknown whether the excess
was associated primarily with hyperthyroidism or with radiation exposure [H6]. A United States study of mortality following hyperthyroidism treatment showed no excess risk
for kidney cancer [R3]. A study of 6,841 Swedish, French
and Italian patients treated with a mixture of conventional(external beam) radiotherapy and 131I for thyroid cancer
recorded a modest, and statistically significant, increase in
kidney cancer incidence rate (SIR = 2.6; 95% CI: 1.7, 3.8;
31 cases) [R38]. However, there was no relation with 131I
exposure; risks were comparable in the group treated withand that treated without 131I (SIR = 2.6 in both cases) [R38].
(c) Internal high-LET exposures
388. The only recent study of kidney cancer risk in relation to internal high-LET radiation exposure was of a groupof Danish, Swedish and United States patients who received
the diagnostic contrast medium Thorotrast, and a companion group who received a non-radioactive contrast medium[T30]. There were 12 cases of kidney cancer in the exposedgroup and 4 in the control group, representing an RR of
5.7 (95% CI: 1.9, 21.0, p < 0.05) [T30]. The RR also
increased with increasing interval of follow-up (p < 0.001),
suggesting a causal association between Thorotrast exposure and the risk of kidney cancer; however, there was no
statistically significant trend with increasing volume of
injected Thorotrast (p = 0.23). No statistically significant
excess of kidney cancer has been observed in German orJapanese Thorotrast-exposed groups [M19, V4]. Ishikawa
et al. [I15] have estimated that the kidney in Thorotrast
patients would typically receive a relatively modest radiation dose, of about 1.5 mGy per year. Given that the kidney
appears to be relatively radio-resistant, it is unlikely thatthe excess risk observed in the three-country study iscausally associated with the Thorotrast exposure.
389. In a Finnish study of persons exposed to dissolved
radioactive material (predominantly 222Rn, but also 234U,
238U, 226Ra, 210Po and 210Pb), the incidence of kidney cancer
was not statistically significantly associated with ingested
aggregate quantities of radon, radium or uranium, or withthe aggregate kidney dose [K56].

4. Summary
390. There is weak evidence linking the risk of kidneycancer with radiation exposure. The strongest evidence isfrom those studies (on patients with cervical cancer andankylosing spondylitis) where the kidney doses were likely

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to have been high (in the radiotherapy range), suggestingthat there is no strong dose response for kidney cancer.

T. Brain and central nervous system tumours

1. General background391. The most common types of tumour of the brain andcentral nervous system (CNS) are gliomas, meningiomas
and schwannomas. Schwannomas (also known as neurolemmomas) and most meningiomas are benign, whereas
gliomas are malignant. Depending on tumour location,
benign and malignant tumours of the CNS can have similar symptoms and outcomes. As a result, the two types oftumour are not always easily distinguished, and many
tumour registries routinely include benign and malignant
histological types in their evaluations of CNS tumour incidence [I20, P32].
392. Annual age-standardized world incidence rates for
CNS cancers range from about 1.0 to about 10 per 100,000
persons, but, since the quality of medical care varies fromcountry to country and the reporting of benign tumours isinconsistent among registries, international comparisons ofreported CNS tumour incidence rates can be misleading[P19]. The fact that lower incidence rate values are reported
primarily by cancer registries with uncertain completenessof ascertainment suggests that country-to-country variation
is probably considerably smaller than current reporting indicates. Over the last few decades, brain tumour incidence
and mortality rates have increased, especially for the elderly, but whether this is a real increase or a result of better
diagnosis and reporting is controversial [I20, P32].
Meningiomas are more common in females than in males,but malignant CNS tumours occur more frequently amongmen [P19]. This section will consider both benign andmalignant CNS tumours occurring within the cranium(brain, cranial nerves and cranial meninges), spinal cord,
spinal meninges and peripheral nervous system, because of
the potential problem of misclassification or inconsistentclassification according to tumour behaviour. In addition,
since the rates used for comparison in some studies arederived from tumour registries that combine all CNS
tumours into one category, results are reported for all CNS
tumours together and not only for malignant tumours.
393. Primary malignancies of the CNS are among themost lethal of cancers. In the United States, 5-year survival
for malignant CNS tumours is approximately 30% [R28].
Survival for benign meningiomas has improved considerably over the past few decades but, depending on tumour
size and location, the quality of life can be severely
impaired [L74].
394. While the aetiology of CNS tumours remains
elusive, therapeutic irradiation of the head and neck99

during childhood is an established risk factor [I20, P32].
Findings of inverse associations between risk of glioma andself-reported history of asthma or allergies has promptedinterest in the possible importance of immune system
factors [B45, S52, W31, W32]. A small proportion ofbrain and nervous system tumours occur in association
with family cancer syndromes, such as Li–Fraumeni syndrome, neurofibromatosis types 1 and 2, and hereditary
retinoblastoma [L73].

2. Summary of UNSCEAR 2000(a) External low-LET exposures
395. A significant relationship between radiation dose and
CNS tumour risk was demonstrated in the Israeli tinea capitis study [R17] and in various other studies of radiotherapy
used to treat non-malignant conditions [A7, H10, H26, K14,
K15, L4, S45, S53, S54, S68]; however, CNS tumour
incidence rates were not elevated in a Swedish study of
persons treated for haemangioma in childhood [L10].
396. A higher than expected number of second primary
CNS tumours among survivors of childhood cancers has
been noted in several studies [B43, D16, D19, E7, L16,
L24, N14, W11]. There is evidence of risk being higher for
benign CNS tumours than for malignant CNS tumours [L24,
R17]. Data on adult exposures are considerably more limited. Following high dose (~40 Gy) fractionated radiotherapy, an excess risk of CNS tumours was observed among
pituitary adenoma patients [B13], but lower doses, of 0.6
Gy, are not associated with an increase in CNS tumour incidence or mortality rates in two small cohorts of infertile
women whose pituitary glands and ovaries were irradiated[R29, R30]. Ankylosing spondylitis patients did not havean excess of mortality from spinal cord tumours after theirspinal cords were exposed to high radiation doses [W8].
397. Radiation workers in general receive low, fractionated doses with relatively little exposure of the brain. To
date, most occupational studies have been negative withrespect to brain cancer [C3, M12, W29].
398. Dental diagnostic X-ray exposures have beenassessed in several studies conducted by Preston-Martin etal. in relation to various types of CNS tumour [P33, P34,
P35, P36]. They found associations between meningioma
and both frequent full-mouth X-ray examinations and X-ray
examinations performed many years ago, when radiation
doses were relatively high. In other studies, however, brain
tumour cases did not have a history of dental X-ray exposure significantly more often than controls [R31].
399. The issue of whether CNS tumours are related tofoetal exposure to radiation remains controversial. Doll andWakeford [D37] carefully reviewed the literature and concluded that in utero exposure to a mean dose of approximately 10 mGy increases the risk of childhood cancer. ThisUNSCEAR REPORT-PART 1.qxp

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conclusion was largely based on the Oxford Survey ofChildhood Cancers (OSCC). In the OSCC, mortality fromchildhood CNS tumours was associated with foetal irradiation (RR = 1.4; 95% CI: 1.2, 1.7) [B2]. Miller and Boice[B42, M48] expressed concern about the OSCC results,
noting that all childhood cancers were increased by about
40%, whereas such commonality is not seen in either animalor human studies. Among survivors of the atomic bombings exposed in utero, an association between dose andcancer mortality risk has not been found, but the in utero
survivor cohort is small, with consequently limited statistical power, and the negative result is therefore compatible
with a wide range of possible risk values [D14].
(b) Internal low-LET exposures400. Little is known about brain and CNS tumours following internal exposure to low-LET radiation. A small
increased risk of CNS tumours was observed among
35,000 Swedish patients receiving diagnostic 131I examinations, although since the dose to the brain was less than
10 mGy, the observed excess is not likely to be due to theradiation exposure [H8]. Significant excess risks were notdemonstrated for patients receiving 131I therapy for hyperthyroidism [H6, H24, R3] or thyroid cancer [D38, E8, G24,
H2]; however, among 10-year survivors, the brain tumour
incidence rate was significantly elevated in the Swedish
hyperthyroid patients [H6].
(c) Internal high-LET exposures
401. Danish patients exposed to Thorotrast, a radiographic
contrast agent associated with internal exposure to alphaparticle-emitting radionuclides, had a significantly elevatedincidence of brain tumours, but the fact that these tumours
developed very soon after the Thorotrast examination suggests that they are related to the underlying disease or tobetter ascertainment rather than to the Thorotrast itself [A5].
Thorotrast was given in conjunction with cerebral angiography because of a suspected brain disorder. Often thisdisorder was later found to be a brain tumour, especiallyamong epileptic patients. Brain malignancies and other CNS
tumours have not been linked to exposure to radium [S50]or radon among miners [D10].

3. New or updated studies(a) External low-LET exposures
402. As summarized in table 39, the epidemiologicalliterature provides evidence for an association between
ionizing radiation and tumours of the CNS.
403. A detailed investigation of CNS tumours in the LSS
updated data on tumour incidence up to 1995, and included
thorough tumour ascertainment and a pathology review[P33]. The intracranial tumours included 43 gliomas,
88 meningiomas and 33 schwannomas. There were nearlystatistically significant elevations in risk for glioma (ERR
= 0.56 (95% CI: –0.2, 2.0) Sv–1) and meningioma (ERR =0.64 (95% CI: –0.01, 1.8) Sv–1, and a stronger association
for schwannoma (including both intracranial and others,
ERR = 4.5 (95% CI: 1.9, 9.2) Sv–1 (table 39). For nervous
system tumours other than schwannomas, the linear
dose–response model fits very well, while, for schwannomas, the dose–response relationship tended to curve downwards at high doses (>2 Sv), albeit not significantly(p = 0.09) [P33]. For nervous system tumours other thanschwannomas, there was a suggestion of greater risk following radiation exposure at earlier ages (p = 0.06 fortrend), such that those exposed before age 20 had ERR =1.2 (95% CI: 0.3, 2.9) Sv–1, and those after age 20 had ERR= 0.2 (95% CI: <–0.2, 1.0) Sv–1. There was no indicationof modification of risk due to time since exposure, suggesting that elevated risks may persist throughout the lifetime. For nervous system tumours other than schwannomas,
there was a greater radiation risk for males than females(ratio of ERRs per unit dose = 14, p = 0.05). The dose
response for tumours of the nervous system remained
significant when analysis was limited to persons with brain
doses of less than 1 Sv.
404. It was estimated that 14% of the first primary
tumours of the CNS and pituitary gland occurring in the
LSS cohort would have been attributable to radiation [P33],
and clinical characteristics of the tumours occurring in thisstudy population were similar to those of spontaneous
tumours in population-based studies [Y6]. While in North
America and Europe, tumours of neuroepithelial origin predominate, meningioma is the most common neural tumour
in Japan.
405. As in earlier reports, the most recent analysis of mortality data from the survivors of the atomic bombings does
not show a statistically significant association between mortality due to tumours of the brain or CNS and radiation dose
[P9]. An earlier analysis showed virtually no association
with brain tumour risk but a non-significant positive association with the risk of CNS tumours other than those of
the brain [P1].
406. A significant relationship between radiation dose and
CNS tumour risk was demonstrated in the latest follow-up
of the Israeli tinea capitis study [S48]. The mean age at thetime of irradiation was 7.1 years. Risks of both benign
meningioma and malignant brain tumours were associatedwith dose in this tinea capitis cohort [S48]. The
dose–response relationship was stronger for meningioma
than for malignant brain tumours. The ERR was 4.63 (95%
CI: 2.43, 9.12) Gy–1 for meningioma and 1.98 (95% CI:
0.73, 4.69) Gy–1 for malignant tumours. The EAR was 0.48
(95% CI: 0.28, 0.73) (104 PY Gy)–1 for meningioma and0.31 (95% CI: 0.12, 0.53) (104 PY Gy)–1 for malignant brain
tumours. The ERR for malignant tumours was inversely
associated with age at irradiation, varying from 3.56 Gy–1
for those under age 5 at the time of exposure to 0.47 Gy–1
for those over age 10. The ERR per unit dose for meningioma

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showed little relation to age at exposure. The risk of bothtypes of tumour remained elevated after 30 years. The EAR
increased with increasing follow-up time, reaching 0.31 (104PY Gy)–1 and 2.03 (104 PY Gy)–1 after 30 years for malignant brain tumours and meningioma, respectively. The ERR
per unit dose did not appear to differ between the sexes. The
malignant brain tumours were predominantly (75%) ofneuroepithelial origin. The results of this study are therefore
consistent with earlier reports of larger risks for benign brain
tumours than for malignant brain tumours [L24].
407. Recent follow-up of a cohort of 4339 Dutch patients
given nasopharyngeal radium irradiation did not reveal
evidence of increased brain cancer incidence or mortality
rate [R4, R41]. The average dose to the brain was 1.8 cGy.
A smaller study from Maryland (United States) noted anelevated number of brain tumours, three of which were
malignant and four benign, but the RR estimate was highly
unstable (RR = 14.6; 95% CI: 0.76, 286.3) [Y7].
408. For patients irradiated for hereditary retinoblastoma,
the risk of developing a brain cancer was 16 times that for
the general population [K43]. Young children who received
cranial irradiation as a conditioning regimen before bonemarrow transplantation were found to have a significantly
elevated RR of developing brain or other CNS cancers.
However, it was likely that earlier cranial radiotherapy totreat acute lymphoblastic leukaemia prior to bone marrowtransplantation (and associated whole-body irradiation)
played an important role in the development of these neuralmalignancies [C26].
409. Data on adult exposures are considerably more limited. Longstreth et al. [L75] reported an association between
meningioma risk and having had six or more full-mouth
X-rays 15–40 years before diagnosis, but little evidence ofa dose–response relationship. These data somewhat support
the earlier studies of Preston-Martin et al. discussed above[P33, P34, P35, P36].
410. As was true of the earlier studies of radiation workers, most occupational studies to date have been negativewith respect to this site of cancer, in particular two studiesof radiologists and radiologic technologists [S29, Y5]. A
recent study of 191,333 workers in Canada exposed occupationally to very low doses of radiation (mean dose of 6.64
mSv) did not show an increased incidence rate of brain
cancer relative to the general population (SIR = 0.79; 95%
CI: 0.67, 0.93) [S8].
411. Although not considered in the UNSCEAR 2000
Report [U2], the study of Artalejo et al. [A32] reported aslight excess of brain and CNS cancer mortality among
workers for the Spanish Nuclear Energy Board; the SMR
was 1.33 (95% CI: 0.61, 2.52), based on 9 cancer deaths,
of which 2 were among the 27% of the cohort who had
been miners and may have been exposed to alpha radiation[A32]. Rogel et al. [R54] reported excess mortality due tobrain and CNS cancer at borderline levels of statistical101

significance compared with French national mortality
rates among radiation workers of Électricité de France(16 observed versus 10.3 expected deaths; SMR = 1.56;
90% CI: 0.98, 2.37); there was no statistically significant
trend of mortality with dose (ERR = –4.1 (90% CI: –9.9,
28.9) Sv–1).
(b) Internal low-LET exposures
412. There have been no new studies since the
UNSCEAR 2000 Report [U2].
(c) Internal high-LET exposures
413. There has been a recent analysis of cancer mortality
in a group of Danish and Swedish patients who underwent
cerebral angiography with Thorotrast and in a comparisongroup of patients who underwent cerebral angiography witha non-radioactive contrast agent [T30]. The RR was not significantly different from 1.0, suggesting that apparent
increases seen in previous analyses [A5] may have been due
to pre-existing brain tumours rather than to radiation exposure. Radiation doses to the brain were very low relative tothose to the liver, spleen or bone marrow.

4. Summary
414. Ionizing radiation can induce tumours of the CNS,
although the relationship is not as strong as for several othertumours, for example breast and thyroid cancer or
leukaemia, and most of the observed radiation-associatedtumour risk occurs for tumours that are benign. Overall,
exposure during childhood appears to be more effective intumour induction than adult exposure, but the data on adultexposures are fairly sparse, and the most recent study ofsurvivors of the atomic bombings demonstrated ERRs forneurilemmoma following exposure at all ages. Little is
known about other factors that modify risk. The association
between the risk of benign tumours, particularly meningioma and neurilemmoma, and radiation exposure appears
to be substantially stronger than the risk of malignant
tumours. Additional data are needed to better characterize
the dose response for CNS tumours of various histologicaltypes, especially for glioblastoma.

U. Thyroid cancer

1. General background415. Thyroid cancer is one of the less common forms ofcancer, and cases constitute somewhat less than 2% of allcancers [P19]. Unlike most cancers, the thyroid cancer incidence rate is relatively high before age 40 years, increases
comparatively slowly with age, and is about three times
higher in women than men. This predominance among

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females is also observed for benign thyroid tumours.
Incidence (world adjusted) rates for much of the world
range between 1 and 2 cases per 100,000 males and between
2 and 8 per 100,000 females [P19]. Data from many countries suggest that incidence rates are increasing while mortality rates are falling [F16].
416. Papillary, follicular and anaplastic thyroid carcinomas originate from cells derived from the follicular epithelium, and they constitute about 95% of thyroid cancers.
Medullary cancers also arise from epithelial cells, but fromthe calcitonin-producing parafollicular or C-cells. The
degree of malignancy varies widely with histological type,
ranging from the rapidly fatal anaplastic type to the relatively benign papillary type [R42, S87]. Overall 5-year
survival is close to 90%, because papillary carcinoma is
the predominant type (usually over 65% of cases) of
thyroid cancer, whereas anaplastic carcinoma is relatively
rare (generally less than 15% of cases).
417. Ionizing radiation is a well-documented cause of
thyroid cancer. For the most part, radiation-related thyroid
cancers are papillary carcinomas, and their clinical course
is similar to that of other thyroid cancers [S87]. The RR ofthyroid cancer is substantially increased among persons witha history of self-reported benign nodules and goitre, but thecausal role of these benign diseases is unclear. There issome evidence that elevated levels of thyroid-stimulating
hormone, multiparity, miscarriage, artificial menopause,
iodine intake and diet also may be risk factors for thyroid
cancer [R42, S87].

2. Summary of UNSCEAR 2000
418. The UNSCEAR 2000 Report [U2] concluded that the
thyroid gland is highly sensitive to the oncogenic effects ofexternal radiation exposure during childhood and that a
linear dose–response relationship was consistent with the
published data. Age at exposure is an important modifier ofrisk, with a strong tendency for the risk to decrease withincreasing age at exposure.
419. A pooled analysis of studies of external irradiationof the thyroid [R6] allowed a more detailed evaluation of
the dose–response relationship and of modifying factors
than had previously been possible. In the analysis of thefive cohort studies of persons irradiated before age 15 years,
436 thyroid cancers were diagnosed among the exposed
population. The pooled ERR was 7.7 (95% CI: 2.1, 28.7)
Gy–1, and each of the studies in the pooled analysis wasconsistent with a linear dose–response relationship, although
the range of doses varied considerably among studies [R6].
No single study was found to have an undue influence on
the overall estimates of risk.
420. The ERR per unit dose for females was nearly twicethat for males, but the results were not consistent acrossstudies [R6]. Since thyroid cancer naturally occurs two to

three times more frequently among females than males, theabsolute radiation-induced risk was correspondingly higher
for women. Even within the narrow range of ages at exposure, there was strong evidence of a decrease in the ERR
with increasing age at exposure, which suggests that the
thyroid is particularly sensitive to tumour induction at thetime of rapid cell proliferation [W14]. The ERR per unit
dose was highest between 15 and 29 years following childhood exposure, but it remained high for more than 40 years
after exposure [R6]. The latter finding was also reported
from an extended follow-up study of the Stockholm skinhaemangioma cohort in Sweden [L13]. In contrast to thewell-described carcinogenic effects of external exposure inchildhood, data are sparse regarding exposure after age
20 years. To date, there is little evidence of an excess
thyroid cancer risk associated with adult exposure [R6,S14]. Among survivors of the atomic bombings exposed
after age 40 years, the ERR was negative [T1].
421. Elevated risks of thyroid cancer were reported forpatients treated with high-dose radiotherapy for Hodgkin’s
disease [H19, T5], for childhood cancers [B63, D50, H5]
and for bone marrow transplant patients undergoing highdose whole-body radiation [C26]. Few studies, however,described the shape of the dose response or reported quantitative risk estimates.
422. Information about occupational exposure to radiation
and risk of thyroid cancer is limited. Radiation workers generally receive low, fractionated doses to the thyroid gland.
The low doses and the relative rarity of the disease makeincreased risks difficult to observe in most epidemiologicalstudies. A few studies have reported elevated risks [A34,
B64, C46, W29], but they had several methodologicallimitations. Results were based on a small number of cases,
individual dose estimates were not available and multiple
comparisons were tested.
423. The carcinogenic effects of internal exposure to 131I
are less well understood. Most epidemiological studies haveevaluated the risk of thyroid cancer for patients receiving
diagnostic 131I or high-dose 131I treatment for hyperthyroidism or thyroid cancer. Results from these investigations
have provided little evidence of increased risk following awide range of exposure levels, but almost all of the study
patients were adults at the time of exposure, and the studies therefore do not allow strong inferences about the risksof childhood exposure. In addition, although individual thyroid doses have not been calculated, the intention of treatment with 131I for hyperthyroidism is to deliver 60–100 Gyof radiation to destroy thyroid gland function [B53]. Thus
there is a substantially reduced chance of subsequently
developing thyroid cancer. In two [F1, R3] out of the three[F1, H6, R3] cohort studies of hyperthyroid patients treatedwith 131I, small elevated risks of thyroid cancer wereobserved soon after therapy, but no dose response wasdemonstrated. These findings suggest that some of theobserved excess may be due to close medical surveillance
and to the underlying thyroid disease.

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424. Early studies of populations exposed to radiation following the Chernobyl accident indicated that exposure toradioactive iodine, primarily 131I, during childhood waslinked to thyroid cancer development, but the level of risk
was at that time not well quantified [A35, B65, D47, K53,
L94, S86, T40, T44]. The risk appeared to increase withdecreasing age at exposure [A10, K52, P44, W13], andsome data suggested that risks were beginning to stabilize
for individuals who were in their teens at the time of theaccident [K52, T40]. “Ecological studies” of thyroid cancer
risks due to exposure arising from the Chernobyl accident
reported strong associations between childhood exposure to
131I and early development of thyroid cancer [J7, J8, L94,
L95], with EARs and ERRs generally being lower andhigher, respectively, than those observed in studies of external radiation exposure.

3. New or updated studies(a) External low-LET exposures
425. Studies conducted since the 2000 UNSCEAR Report
was issued (table 40) continue to demonstrate clearly astrong association between external low-LET radiation
exposure and increased risk of thyroid cancer. New data onradiation-related thyroid cancer in AHS members, a subgroup of the LSS of survivors of the atomic bombings, were
published recently [I28]. In a population of 3,185 membersof the AHS with DS02 dose estimates and who participatedin a special thyroid screening examination conducted
between 2000 and 2003, the prevalence of malignant thyroid tumours was 2.2%. Almost 60 years after the bombings, a significant linear dose–response relationship was
found (p < 0.001), and the EOR was 1.95 (95% CI: 0.67,
4.92) Sv–1 for a person aged 10 years at the time of exposure. This risk is about one third of those found in the
1958–1987 tumour-registry-based follow-up including themembers of the full LSS cohort who were about the sameage at exposure [T1]. This is partly due to the difference instatistical models used and to the fact that only a small subgroup of the LSS cohort was evaluated in the current report.
The ERR is decreasing somewhat with time since the bombings, and the study shows a small reduction (about 10%) in
risk related to the use of the new dosimetry system [P10].
426. Risk decreased with increasing age at exposure,although the effect of age at exposure was not statisticallysignificant (p = 0.10). The EORs for persons exposed atages 0–9, 10–19 and 20+ years were 3.45 (95% CI: 0.92,
10.51) Sv–1, 1.49 (95% CI: 0.37, 3.74) Sv–1 and 0.25 (95%
CI: –0.29, 1.96) Sv–1, respectively. The major limitation ofthis study is the low participation rate. Of the 11,028 AHS
members alive in 1990, 8,995 were invited to the biennial
AHS examination. Of these only 4,552 actually presented
themselves for the examination, and 4,091 participated in
the special thyroid examination. This resulted in an overall
participation rate of 37% of living AHS members or 46%
of the invited members.

103427. Two studies of X-ray treatment for benign medical
conditions have been published. One study, conducted innorthern Sweden, was of thyroid cancer following X-ray
treatment for benign conditions of the cervical spine (50%females) [D1], and the other was a new follow-up (themedian follow-up time was 39 years) of the New York tinea
capitis study [S68]. The Swedish study is notable because
the patients were adults at the time of radiation treatment.
Out of three series totalling 27,415 patients who weretreated with X-rays, 8,144 had received such treatment tothe cervical spine. For these, the thyroid gland was in the
primary beam. The remaining 19,271 persons who received
X-ray treatment other than to the cervical spine served as
a comparison group.
428. The X-ray series consisted of three treatments given
at 2–3 day intervals, with a skin dose of 100–200 R at eachtreatment: 84% received one treatment series, 13% twoseries, and 3% three or more series. The average thyroid
dose was about 1.0 Gy. For the other 19,000+ members of
the cohort, the thyroid doses were very low. In the wholecohort, 51 thyroid cancers were diagnosed: 22 in the highdose group and 29 in the low-dose group. The ERR was
0.58 Gy–1.
429. In the tinea capitis study in the United States, 2 thyroid cancers occurred among 2,224 irradiated subjects,
whereas none occurred among the 1,380 controls [S68]. The
expected number of thyroid cancers in the irradiated groupwas 2.04, and the mean thyroid dose was 0.06 Gy, resulting in an ERR of –0.67 (95% CI: –29.96, 86.41) Gy–1 [S68],
which is lower than, but not inconsistent with, the Israeli
tinea capitis study [R9], and is exactly the risk obtained in
the pooled analysis of external radiation [R6]. The EAR
based on an earlier follow-up [S14] was 1.5 (90% CI: 0,
9.4) (104 PY Gy)–1, which is consistent with several otherstudies of external radiation exposure in childhood.
430. Because survival for childhood cancer patients has
increased substantially, the risk of developing a second primary cancer has become a more prominent concern. As
noted earlier, the thyroid gland is particularly sensitive toradiation exposure at early ages, and therefore quantifying
the risk of developing thyroid cancer following radiotherapy for childhood cancer is important. Elevated radiationrelated risks of thyroid cancer have been noted among
young patients receiving radiotherapy for Hodgkin’s disease, non-Hodgkin’s lymphoma, neuroblastoma, Wilm’s
tumour, leukaemia, Ewing’s sarcoma and malignancies of
the central nervous system [I27, K7]. Since the UNSCEAR
2000 Report, a number of new studies on secondary thyroid cancer have been published demonstrating that the primary thyroid cancer incidence rate following radiotherapy
for a first childhood malignancy is significantly greater than
expected. The magnitude of the risk estimates, however, isgenerally substantially lower for patients receiving radiotherapy for cancer than those found for people receiving
treatment for benign disease, or for survivors of the atomic
bombings. This appears to be due to flattening out of the

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dose response at doses above several grays, because of thecompeting effect of cell killing, in which the cells available
to transform into malignant cells are, for the most part,
depleted. Little and Wright [L26] have shown that, as theaverage or maximum dose in the medical studies increases,
the ERRs derived in medical studies become smaller relative to those of subgroups of survivors of the atomic bombings of similar age and sex distribution.
431. In Italy, 113 children who underwent bone marrowtransplants between 1981 and 1991 and survived at least
3 years were followed to determine the incidence of subsequent thyroid cancer [C44]. Eight patients developed secondary thyroid cancer between 3 and 16 years after thetransplant. When a multiple regression analysis was performed, there was a suggestion of an association between
increased thyroid cancer risk and radiotherapy doses ofmore than 10 Gy compared with doses of less than 10 Gy(RR = 4.3; 95% CI: 0.67, 7.3). However, the number ofcases was small, and the result did not reach statisticalsignificance.
432. Secondary thyroid cancers occurred in excess following radiotherapy for childhood neuroblastoma in acohort of 544 patients who had survived 5 years and who
were treated in eight centres in France and the United
Kingdom [R47]. Slightly more than 294 (50%) of thepatients were treated between 1948 and 1986 with radiation(214 received both radiotherapy and chemotherapy). The
mean thyroid dose was 3.4 Gy. Among the 5 patients whodeveloped a secondary thyroid cancer, the mean thyroid
dose was 6.7 Gy. However, the dose distribution was
extremely variable, with 3 patients receiving doses of lessthan 1 Gy and 2 patients receiving doses of more than
10 Gy (specifically, 14 and 19 Gy). None of the 5 patientswas treated with chemotherapy. The ERR was 0.50 (95%
CI: <0, 16) Gy–1. The authors noted, however, that the treatment protocols used during the study period have since beenchanged, and the more recent protocols involve less radiotherapy and more intensive chemotherapy, so this finding
may not reflect current practice.
433. In a study of 446 children with childhood malignancies who survived 5 years and who were treated withradiation between 1954 and 1980, 3 subsequent thyroid cancers were diagnosed when only 0.2 were expected, resulting in an RR of 13.7 (95% CI: 2.8, 38) [G20]. No otherinformation about the thyroid cancers was provided in thereport.
434. The largest and most comprehensive study of radiotherapy-related secondary thyroid cancer was publishedrecently [R48, S88]. This nested case-control study included
69 cases of secondary thyroid cancer and 265 controls.
Controls were matched paediatric cancer survivors who did
not have a subsequent thyroid cancer. Both the cases andthe controls were identified from the cohort of over 14,000
5-year survivors enrolled in the Childhood Cancer Survivor
Study, and all had individual thyroid dose estimates. The

first cancers were diagnosed between 1970 and 1986, so
the data on these represent the effects of early treatment
protocols. Radiotherapy was associated with an increase in
the risk of developing a secondary thyroid cancer, and therisk rose with increasing radiation dose up to 29 Gy. Above
30 Gy, a downturn in the RR was seen. No association withchemotherapy was noted.
435. In a paper describing more detailed analyses [R48],
the authors reported an ERR of 0.51 Gy–1 over the whole
range of doses. The linear model, however, was not the best
fitting model. The best fitting model described an ERR of
1.3 (95% CI: 0.4, 4.1) Gy–1 at doses of below 6 Gy, witha significant downturn in the risk above 6 Gy. At 40 Gy,
the ERR per unit dose had decreased by about 95%. At
doses of less than 6 Gy, the risk appeared to decrease withincreasing age at treatment.
436. When taken together, the current research provides
clear evidence of an increase in thyroid cancer risk among
patients treated with high-dose radiotherapy for childhood
cancer; however, the risks per unit dose are smaller thanthose observed for persons receiving lower doses. The exact
shape of the dose–response curve at doses of above 10 Gy,
as well as the role of age at treatment for the first cancer,
type of first cancer and sex, are not yet well characterized.
437. Few studies of thyroid cancer occurring subsequent
to radiotherapy given to adult cancer patients have had adequate information on doses. Using the United States SEER
cancer registry data, Huang et al. [H11] investigated therisk of thyroid cancer for 48,495 women who received
radiotherapy between 1973 and 1993 for breast cancer
during the first four months after diagnosis compared with
146,303 breast cancer cases from the same years who did
not receive radiotherapy during the first four months after
diagnosis. A total of 28 women in the radiotherapy cohortand 112 women in the unexposed cohort subsequently
developed thyroid cancer. Up to 20 years after diagnosis ofbreast cancer, there were no differences in thyroid cancer
risk between the two groups (RR = 1.0; 95% CI: 0.7, 1.5).
However, a subgroup of women who were more likely tohave received higher doses of radiation to the thyroid gland
had an RR of 1.7 (95% CI: 0.9, 3.2).
438. The main limitations of this study were that followup times tended to be short, radiation doses to the thyroid
gland were not known, radiotherapy data were available
only at the time of initial treatment and information on othertreatments was not available.
439. Information on fractionated and low-dose-rate exposures can come from studies of occupational exposure toradiation. However, occupational studies of thyroid cancer
generally are not very informative, because thyroid doses
are rarely available; many occupational groups, e.g. nuclear
workers, are predominately male; and cancer mortality often
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is the thyroid of the young that has been shown to be very
sensitive to radiation, whereas workers are obviously
exposed as adults. Studies that considered thyroid cancer
since the UNSCEAR 2000 Report was published have been
negative on the whole [A22, M4, M34], but have had too
few cases of thyroid cancer to draw any clear conclusions.
440. Thyroid cancer incidence rates were elevated among
Canadian radiation workers, mainly medical workers, compared with national Canadian cancer rates [S8]. Dose
response was not evaluated, because of the few cases withsignificant doses. Similarly, thyroid cancer incidence rates
were higher than expected among United States radiologic
technologists compared with rates for the United States population [S29]. It should be noted, however, that when comparing medical workers with the general population,
presumed better medical diagnosis and reporting among theworkers warrant attention.

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during the study period. Similar to the results describedabove for Estonian and Latvian recovery operation workers, the incidence rate of thyroid cancer was significantly
higher (SIR = 4.33; 95% CI: 3.29, 5.60) among Russianrecovery operation workers compared with rates for theRussian male population, but the risk of thyroid cancer was
not significantly elevated when an internal comparison
based on external dose was performed. The workers’ elevated thyroid cancer incidence rate compared with that for
the general Russian population was likely to be due to more
frequent and comprehensive medical examinations.
(b) External high-LET exposures444. No new studies of external high-LET radiation exposures and thyroid cancer risks have been published since
the UNSCEAR 2000 Report.
(c) Internal low-LET exposures

441. As a consequence of the Chernobyl accident, large
numbers of men from all over the former Soviet Union werebrought in to participate in decontamination and other cleanup activities at the reactor and in the surrounding 30 km
zone. Approximately 600,000 workers (often called“liquidators” or “clean-up workers”) were involved; about
240,000 of them worked during 1986 and 1987. Recovery
operation workers were exposed to varying levels of external gamma and beta radiation, depending on their specificjobs and the time period and duration of their work. Internal
exposure due to radioiodines was minor after the first few
weeks, but a small number of workers who were on-sitesoon after the accident may have been irradiated internally,
resulting in sizeable thyroid doses [U2]. Doses to the thyroid are very uncertain, but the estimated mean thyroid doseis about 0.2 Gy. The majority (about 65%) of workers arelikely to have received doses to the thyroid of less than
0.15 Gy [K10].
442. To date, there is no evidence of a dose response forthyroid cancer incidence among the recovery operation
workers [I10, R11, R49]. A combined cohort of 10,332
Latvian and Estonian recovery operation workers, with amean external whole-body dose of 109 mGy, was followed
until 1998 using national population, mortality and cancer
registries [R49]. Compared with age-, sex- and calendaryear-specific national cancer rates, the recovery operation
workers had a significantly elevated risk of thyroid cancer
based on 3 cases. There was, however, no correlation with
dose, and the workers were under close medical surveillance,suggesting that the enhanced incidence rate of thyroid cancer
seen was related to medical care practices rather than radiation exposures. Given the low doses and older ages at exposure, these negative findings are consistent with data fromthe LSS of survivors of the atomic bombings [T1].
443. Within the framework of large studies of Russian
Chernobyl recovery operation workers [I30], thyroid cancer
incidence between 1986 and 1998 was evaluated among
99,024 workers [I29]. Fifty-eight thyroid cancers occurred

445. Since the UNSCEAR 2000 Report, a large body ofdata on internal low-LET radiation exposure, especially to
131I, has accumulated from studies of situations involving
medical and environmental exposures. New information has
come from studies of radioactive deposits from the Hanford
nuclear weapons plant emissions and from the Chernobyl
accident.
446. Hahn et al. [H1] studied thyroid cancer subsequent
to diagnostic administration of 131I to German patients under
18 years of age. Among the 2,262 patients who received
diagnostic 131I, 74% were females, the median age at firstexamination was 14.9 years, the mean follow-up time was
20.9 years and the average thyroid dose was about 1.0 Gy.
447. In a small subgroup of examined study participants,
2 thyroid cancers were found among the 789 irradiatedpatients, and 3 were found in the unexposed group of 1,118
children who underwent other thyroid diagnostic procedures.
The ERR was –0.14 (95% CI: –0.9, 4.1). Thus there was noevidence of risk associated with administration of 131I.
448. It should be noted that only 20% of the study population were younger than 11 years of age at the time of
131I exposure, and 9% were less than 6 years of age; hence
most of the exposures were received when the subjects wereolder than the most radiosensitive ages for thyroid cancer
induction. Only 35% of the exposed subjects and 41% of
the unexposed subjects participated in the examination programme, and such low participation rates increase the likelihood of selection biases. Additional opportunity forselection biases may have occurred in that several of the
10 participating hospitals had either only exposed patientsor mostly/only unexposed patients, so that institutional
imbalances could have biased the results. Because of thesmall sample size, the study had 80% statistical power todetect RRs on the order of fourfold. Since 80% of the subjects were older than age 10 at exposure, an RR of 4 ormore for a 1 Gy exposure would probably not be expected.

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449. The follow-up to the Swedish study of the long-term
effects of diagnostic 131I administration has been extended
8 years to 26 years on average, and tabulations of thyroid
cancers included those observed more than 2 years after 131I
administration (rather than the 5-year minimum period ofprevious reports) [D42]. For the 1,767 patients who hadreceived previous external radiation to the head and neck,and the 11,015 patients who had been referred because of
suspicion of a thyroid tumour, there were elevated risks,
SIR = 9.8 (95% CI: 6.3, 14.6) and SIR = 3.5 (95% CI: 2.7,4.4), respectively. For the group of 24,010 patients without
external radiation exposure or referral for suspicion of thyroid tumour, the most common reasons for diagnostic
administration of 131I were suspected hyperthyroidism(62%), hypothyroidism (25%) or hypercalcaemia (12%).
For this group, there was no excess risk (SIR = 0.91; 95%
CI: 0.64, 1.26), nor was a dose–response association seen.
However, only 2,367 patients in this group were under age
20, and about 300 under age 10, at the time of exposure to
131I. Among those under age 20 and without prior externalradiation exposure or referral for suspicion of thyroid
tumour, 2 thyroid cancers were observed compared with
2.08 expected (SIR = 0.96; 95% CI: 0.12, 3.46). In interpreting this null result, consideration should be given to thesmall numbers of cases and to the fact that so few were
exposed before age 10, the group for whom the associatedrisk is expected to be the highest.
450. While the data from these studies are informative,
the uncertainties associated with estimating thyroid doses
due to 131I, especially for persons with thyroid abnormalities, reduce the precision of the risk estimates. The nonuniformity of the dose distribution in the thyroid gland following 131I administration results in some areas of tissue
receiving high doses and other areas receiving much lower
doses [N19]. Thus the tumorigenic effects of the exposure
might be lower than would be expected on the basis of theaverage dose. Overall, there is little evidence that radiation
exposure to adult patients treated for hyperthyroidism or
examined with diagnostic levels of 131I or examined to evaluate potential thyroid disease results in a measurablyincreased risk of thyroid cancer. Data regarding risks of
exposure in childhood remain sparse. The three studies ofthe diagnostic use of 131I in Germany, Sweden and theUnited States found that, among 6,659 children examined,with a mean thyroid dose of 0.89 Gy, 9 thyroid cancers
were detected against 8.99 expected [B61].
451. Determining the role of continuous low-dose exposure to radionuclides from living near nuclear plants andwaste sites has been a concern to members of the public inmany countries having nuclear weapons plants or power
plants. These environmental exposures of the thyroid areprimarily due to 131I, but can also be due to short-lived
radioiodines and to some external radiation.
452. The largest evaluation of environmental exposures of
the thyroid is of people living near the Hanford nuclear
weapons site in the United States [D48]. Between 1944 and1957, the Hanford site in Washington state released 20–25
PBq of 131I into the atmosphere during fuel processing. In
total, 5,199 people born between 1940 and 1946 in sevencounties in eastern Washington state were identified forstudy. Ninety-four per cent were located, 4,350 (84%) werealive and 3,441 agreed to participate in the study. Thyroid
doses were estimated for the 3,193 study participants who
had lived near Hanford during the time of the radioiodine
releases. The remaining 248 participants had moved fromthe Hanford area and received little or no exposure. The
131I doses to the thyroid glands of the people who continued to live near Hanford ranged from 0 to 2.84 Gy (median
0.10 Gy), with only a small percentage receiving doses at
the higher end of the range.
453. Nineteen participants were diagnosed with thyroid
cancer and 249 with benign thyroid nodules. No evidenceof a dose–response relationship was found for malignant or
benign nodules, even though the population was exposed atyoung ages. Although there are large uncertainties in thedose estimates [N18], taking these into account does notappear to change the results materially.
454. A recent study compared cancer mortality in four
counties in Washington state (presumably heavily exposed
to 131I from the Hanford plant) with that in five other counties (much less heavily exposed) [B61]. There was noelevation in thyroid cancer mortality in the heavily exposed
counties: the RR was 0.84 (95% CI: 0.56, 1.26), basedon 33 deaths in the highly exposed counties and 76 in thecontrol counties [B61].
455. On 1 March 1954, an unanticipated change in winddirection caused people living on the Marshall Islands tobe exposed to high levels of radioactive fallout from a
United States nuclear weapons test in the Pacific Ocean[C45, R13]. About 80–90% of the dose to the thyroid wasfrom short-lived radioisotopes and gamma radiation, andvery little was from 131I [R13]. Following the accidental
exposure, an elevated risk of thyroid cancer and otherthyroid diseases was linked to the radiation exposure[C45, H25].
456. In a recent evaluation, an international team ofresearchers examined 3,709 Marshall Islanders who wereborn before 1954, using ultrasound and neck palpation.
Thirty thyroid cancers were diagnosed. An additional
27 examinees had had surgery for thyroid cancer in the past.
There was evidence of a weak association between thyroid
cancer prevalence and an increasing surrogate measure of
thyroid dose [T41, T42].
457. From 1949 to 1962, the former Soviet Union conducted over 100 atmospheric nuclear tests at the
Semipalatinsk test site in Kazakhstan [B62, G21, R51].
Local fallout was particularly high from three tests conducted in 1949, 1951 and 1953. Approximately 10,000 persons living near the test site and 40,000 living in the Altai
region in the Russian Federation received more than

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250 mSv effective dose. The first analytical study of thehealth effects on the populations living near Semipalatinsk
was published by Bauer et al. [B58]. They studied solidcancer mortality in a cohort of 19,544 exposed and comparison subjects living near the Semipalatinsk test site, andfound a significant dose response for all solid cancers andseveral specific cancer sites. However, they did not reporton thyroid cancer.
458. Following the 1986 accident at Chernobyl, about
5 million people living in Belarus and in extensive areas in
Ukraine and the Russian Federation were exposed toradioactive materials. Persons living in the contaminatedareas of the three countries received external exposure fromradionuclides deposited on the ground and internal exposure from ingesting milk and leafy green vegetables. The
principal component of dose to the thyroid gland was fromthe atmospheric releases of 131I, although there was also
very limited exposure to shorter-lived radioisotopes, e.g.
132I, 133I and 135I [U2].
459. Four years after the Chernobyl accident, a substantial increase in childhood thyroid cancer in the contaminatedregions of Belarus, Ukraine and the Russian Federation wasobserved [H13, M11]. Although the exact number of thyroid cancers diagnosed among persons who were living inthese areas and who were younger than 18 years old at thetime of the accident is not known, at least 4,000 werereported between 1992 and 2000. Because thyroid cancer isfrequently indolent, efficiency and uniformity of ascertainment are crucial to establishing unbiased estimates of risks.
Variations in the efficiency of screening may have a roleto play in explaining some of the excess incidence, although
the magnitude of the excess leaves little doubt that muchof it is associated with radiation exposures resulting fromthe accident [U2]. However, variations in screening efficiency over time could bias inference of trends in excess
risk with age and time. A recent study showed that, whereas
official screening programmes contributed little to theobserved increase in the thyroid cancer incidence rate in theaffected countries, other factors, such as the introduction ofultrasound examinations, increased attention to thyroid diseases during normal medical examinations and improved
reporting, increased the apparent underlying incidence inBelarus and in the more highly contaminated regions of
Ukraine from 1988 to 1999 by a factor of 3 [J11]; in theother parts of Ukraine, the corresponding factor wasassessed to be 2.
460. Since the UNSCEAR 2000 Report, a number of newstudies have been conducted [C2, D49, H52, J9, K11, T43],
and a few have reported quantitative risk estimates for thyroid cancer related to 131I exposure. A small populationbased case-control study conducted in Bryansk, RussianFederation, included 26 cases diagnosed between 1991 and1997 and twice the number of controls [D49]. Cases andcontrols were younger than 19 years of age at the time of
the accident, and individual thyroid doses due to 131I werereconstructed for all study subjects. A strong dose response107

was demonstrated (p < 0.01), but because of the small study
size, little other information was available.
461. Cardis et al. [C2] recently reported on a larger population-based case-control study that included 276 cases and1,300 matched controls from Gomel and Mogilev in Belarus
and from Bryansk, Kaluga, Orel and Tula in the RussianFederation. Cases were diagnosed between 1992 and 1998.
Individual thyroid doses due to 131I, external radiation, andintake of other short-lived and long-lived radioiodines werereconstructed. A strong association between thyroid cancer
risks and childhood exposure to 131I and to all radioiodines
was observed. Based on a linear dose–response model, theERR ranged from 5.5 (95% CI: 2.2, 8.8) Gy–1 to 8.4 (95%
CI: 4.1, 17.3) Gy–1, depending on the statistical model used.
Of particular interest is the finding that, depending onwhether dose due to all exposures, due to all iodine isotopes or due to 131I alone was evaluated, the risk estimatesremained virtually unchanged. The ERR per unit dose wasthree times greater in areas where dietary iodine was deficient than in regions with sufficient dietary iodine. The
modifying effect of dietary iodine levels was noted also by
Shakhtarin and colleagues [S4], who reported a twofold riskof childhood thyroid cancer in iodine-deficient areas ofBryansk, Russian Federation, compared with that in iodinesufficient areas. While the Cardis et al. [C2] study has significantly added to what is known about 131I, more
information is still needed about the role of iodine deficiency, the effects of age at exposure and for each sex, andthe pattern of risk over time. The results of the study could
be biased by large uncertainties in the dose estimates, which
are based on retrospective determination of consumption
rates and assumptions about the contamination of theingested food. In particular, as discussed by the authors,such uncertainties in the dose estimates could account for
at least part of the marked saturation of the dose response
above 2 Gy [C2].
462. Risk estimates for radiation-related thyroid cancer
have been published from “ecological studies” [H52, J7, J8,
J9, L95, S86]. While these studies have provided important
information about risks from radiation exposure due to theChernobyl accident, they have inherent methodologicalproblems [G13, P15] that need to be considered when interpreting their results. In the most recent “ecological study”
of thyroid cancer risk following childhood exposure to radiation due to the Chernobyl accident, thyroid cancer cases
and thyroid dose data for 426 settlements in Belarus and608 settlements in Ukraine were analysed [J9]. Thyroid
doses were based on 166 012 individual dose estimates forpeople who had direct measurements of 131I activity. There
were 1,089 thyroid cancers observed between 1990 and 2001
in the cohort of people born between 1968 and April 1986.
The estimated linear coefficient of the EAR was 2.66 (95%
CI: 2.19, 3.13) (104 PY Gy)–1 and the quadratic coefficient
was –0.145 (95% CI: –0.171, –0.119) (104 PY)–1 Gy–2. The
linear coefficient of the ERR was 18.9 (95% CI: 11.1, 26.7)
Gy–1 and the quadratic coefficient was –1.03 (95% CI:–1.46, –0.60) Gy–2. The EAR was higher for females than

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males, decreased with age at exposure and increased withattained age. The ERR was higher for males than females
and decreased with age at exposure and attained age.
463. The results from this and earlier “ecological studies”
differ from the Cardis et al. [C2] case-control study andstudies on the effects of external radiation exposure [R6].
The estimates of the EAR in the “ecological studies” areabout half that reported from the pooled analysis of external low-LET radiation [R6] (table 40). Estimates of the
ERR, on the other hand, are considerably larger than theestimate from the pooled analysis or from the most recent
Chernobyl case-control study [C2].
464. The link between thyroid cancer risks and exposureof adults to radioiodine from the Chernobyl accident has
not been studied extensively, but when adult patients
received similar doses of 131I from diagnostic examinations,little evidence of an association was seen [D42], suggesting that the effects of radiation exposure due to the accident would be small. The thyroid cancer incidence rate
among Russians born in the contaminated region of Bryansk
between 1917 and 1971, i.e. who were between the ages of
15 and 69 years at the time of the accident, was elevatedcompared with rates in the general population for the samesex and for similar ages and calendar year periods [I31]. As
in several other studies of persons exposed to radioactive
contamination resulting from the Chernobyl accident, theincreased thyroid cancer rates compared with rates in thegeneral population appear to be due to heightened medical
surveillance rather than to the radiation exposure. Indeed,
when internal comparisons were made, the ERR was –0.9
(95% CI: –2.4, 0.8).
465. To date, there have been few reports of increased
risk of thyroid cancer after in utero exposure [H13]. Thisis an area for which data are clearly lacking and for which
efforts should be made to carefully collect more data.
(d) Internal high-LET exposures466. No new studies of internal high-LET radiation exposures and thyroid cancer risks have been published since
the UNSCEAR 2000 Report.

4. Summary
467. The thyroid gland is highly susceptible to the carcinogenic effects of external radiation exposure duringchildhood. Age at exposure is an important modifier of risk,
and a very strong trend of decreasing risk with increasing
age at exposure is observed in most studies. Although thyroid cancer naturally occurs more frequently among women,
the role of sex in determining radiation risk is unclear. The
BEIR VII Committee [C37] estimates the lifetime risks ofthyroid cancer at 0.32% Gy–1 for men and at 1.6% Gy–1 for
women. Among people exposed during childhood, elevatedrisks persist throughout life, but some data suggest that the

ERR begins to decline at about 20 years after exposure. The
carcinogenic effects from 131I doses are less well understood. Most epidemiological studies of medical exposures
have shown little risk following a wide range of dose levels;
however, most of these studies were of adult exposures. A
follow-up study of persons who lived near the Hanford
nuclear facility in the United States when they were children provides no evidence of an association between 131I
doses and thyroid cancer risk. In contrast, results from studies of people exposed as a result of the Chernobyl accident
demonstrate that exposure to radioactive iodine during earlychildhood is significantly linked with the risk of thyroid
cancer development. The risk appears to be modified by theamount of stable iodine in the diet. Similar to the data on
external low-LET radiation exposure, the data from the
Chernobyl accident studies suggest that risk decreases withincreasing age at exposure. The effect of sex is not consistent in all studies. In the last few years, information about
131I exposures has improved; however, the thyroid cancer
risk from 131I exposure is still not adequately quantified.

V. Non-Hodgkin’s lymphoma

1. General background468. Non-Hodgkin’s lymphoma (NHL) is a collection of
distinct disease entities that are malignant expansions oflymphocytes. The lymphomas that make up this grouping
can generally be separated into those with B-cell or T-celllineage. The precise definition of NHL has varied over time;a classification that is widely used is the Revised
European–American Lymphomas classification [H42].
469. Annual age-standardized world incidence rates for
NHL range from about 3 to about 25 per 100,000 persons,with rates tending to be highest in North America and somewhat lower in African and Asian countries. However, since
the diagnosis of this tumour is inconsistent among registries,
international comparisons of NHL rates can be misleading[P19]. Rates of NHL have increased in many countries overthe past few decades, particularly for older ages [B28, H39],
with no indication that rates have peaked. In part thisincrease is likely to be due to changes in the definition of
NHL and to improved ascertainment, although these factors
are unlikely to explain all of the apparent increases [H39].
Chronic lymphocytic leukaemia, which had been regarded
as a distinct entity, is now thought to be a variety of NHL[J12]. Epidemiological studies have shown associations
with chronic immunosuppression, for example, amongtransplant recipients and other patients who received
immunosuppressive therapy [H43, K33]. However, suchfactors may explain only a small percentage of the temporal increase in NHL rates [Z7]. Associations with certain
viruses, such as Epstein–Barr virus (EBV) [M37] and HIV[S55], have also been identified. Some studies suggest elevated risks for people employed in agriculture, particularly

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those working with pesticides (e.g. [C17]), although otherstudies have not shown such a link (e.g. [W21]). No workplace exposures have been conclusively identified as causesof NHL [B4, C1]. The role of the immune system in
relation to NHL is discussed further in annex D to the2006 UNSCEAR Report, “Effects of ionizing radiation on
the immune system”.

2. Summary of UNSCEAR 2000
470. The results from the studies considered were mixed,with many of the studies having failed to show a statistically significant association with radiation exposure. The
LSS of survivors of the atomic bombings falls into this category, although Preston et al. [P4] reported some evidenceof an increasing dose response for males (p = 0.04) but not
for females, among whom, if anything, the trend was negative. The latter findings might appear to contradict those forthe cervical cancer patients, where there was borderline evidence of a positive dose response; however, among exposed
patients, there was little indication of an increasing risk withincreasing dose [B8]. Furthermore, studies of women treatedfor benign gynaecological disorders [D7, I1] have not suggested associations with radiation. Comparison of the LSS
findings for males with those findings for the ankylosing
spondylitis patients might be informative, given that most ofthese patients were male. Weiss et al. [W8] reported that
NHL mortality among spondylitis patients was raised significantly compared with national rates (RR = 1.73; 95% CI:
1.23, 2.36), and that this elevated risk appeared to disappear
beyond 25 years after exposure; however, no dose–response
analysis was performed. In another study of a mostly malepopulation, Cardis et al. [C3] did not find an association
between NHL risks and external radiation exposure among
nuclear industry workers, although the precision of the study
was limited by the generally low doses. The same limitation
affected a study of patients undergoing diagnostic X-ray
procedures [B17], which also did not show an association
when based on a two-year lag time; however, this study used
numbers of X-ray procedures as a surrogate of exposure
rather than actual doses.
471. In summary, results from studies of NHL risk amonggroups exposed to external low-LET radiation were mixed.
Studies of the survivors of the atomic bombings as a whole
did not show an association, although there was some evidence of a trend of increased incidence with dose among
males (but not females). Findings from other studies werevariable, with no clear consistency. Overall, there was little
evidence of an association between the risk of NHL andexternal low-LET radiation exposure.
472. There was limited information on NHL risk in relation to internal low- or high-LET radiation exposure. The
general absence of analyses in relation to the level of exposure, and the limited statistical precision of one such analysis that was conducted, hindered interpretation of theavailable data.

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3. New or updated studies(a) External low-LET exposures
473. At present, there are no new data on NHL for thesurvivors of the atomic bombings. However, there are some
new findings from studies of other groups exposed to external radiation. Among patients in the United States treatedwith radiation for peptic ulcer [C4], the mortality rate for
NHL was raised relative to national rates (see table 41).
However, there was a suggestion that the NHL mortality
rate was also raised among patients who did not receive
radiotherapy. Overall, the evidence for an increased risk topatients receiving radiotherapy compared with that to otherpeptic ulcer patients was weak. A few other studies of medically exposed groups (e.g. [M35, R4]) have suggested elevated risks of NHL relative to unexposed comparisongroups. However, the small numbers of cases observed
imply that the statistical precision of these results is low.
Furthermore, in instances where this has been examined,
there have been at most very weak indications of any trendof increasing risk with increasing dose [R4].
474. A few recent studies of radiation workers have provided extra information on NHL risk in relation to occupational radiation exposure (see table 41). One of thesestudies was a population-based case-control study conducted for parts of the United States that involved 1,056
NHL cases, of whom 114 reported occupational exposure
to radiation [E10]. The study showed no elevated risk associated with reporting having ever been occupationally
exposed to radiation (RR = 0.90; 95% CI: 0.74, 1.10), nor
any trend in risk with either estimated cumulative dose or
duration of exposure. Although a reasonably large numberof cases, ascertained from population-based cancer registries, and pathological reviews are notable strengths of thisstudy, it is limited by the lack of objective measures of radiation exposure and by the low doses likely to have been
received by exposed workers (mean dose ≈ 0.015 Gy, lowLET radiation). Another study examined cancer incidence
in a group of about 191,000 workers included in the
Canadian National Dose Registry [S8]. Again this was
based on a reasonably large number of NHL cases, identified from cancer registry data, although in this instanceinformation on radiation exposure was obtained in an objective manner. While NHL incidence in this group of workers was substantially less than expected from national rates,
the central estimate of the trend in risk with dose within
the cohort was positive, although with a very wide confidence interval (90% CI for ERR = (<0, 31.8) Sv–1). Rogelet al. [R54] reported no excess mortality due to NHL compared with French national mortality rates among radiation
workers of Électricité de France (5 observed versus
5.6 expected deaths; SMR = 0.89; 90% CI: 0.35, 1.88).
475. Other studies have provided generally little additional information on the risk of NHL in relation to occupational radiation exposure. An updated follow-up of maleradiologists in the United Kingdom indicated an excess rate

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of mortality due to NHL relative to social-class-specificnational rates, but based on only small numbers (9 observed,
3.74 expected) [B2]. In contrast, a study of United Statesradiologic technologists, based on a much larger number ofdeaths, showed that the rate of mortality due to NHL wasclose to that expected from national rates, for both males
and females [M31]. An analysis of NHL incidence in thesame cohort did not show any association either with thenumber of years worked as a radiologic technologist or withthe year of starting this work [L11]. The lack of dosimetric data is a limitation of these last two studies.
(b) External high-LET exposures
476. While various studies have been conducted of cancer
risks among aircrew who have been exposed externally toboth high- and low-LET radiation, results have not always
been reported specifically for NHL. Some studies havereported elevated risks for male cabin attendants; for example, the rate of mortality due to NHL in a cohort study conducted in eight European countries was twice that expectedfrom national rates [Z4]. However, large excesses of AIDSrelated mortality seen among the same workers indicate that
HIV/AIDS is the explanation for the findings in relation toNHL. A similar analysis conducted of male cockpit crewfrom nine European countries indicated that the rate of mortality due to NHL was less than expected from national rates(SMR = 0.71; 95% CI: 0.42, 1.15) [B23].
(c) Internal low-LET exposures
477. There is no new information that would materiallyaffect the previous assessment. Findings from earlier studies are summarized in table 41.
(d) Internal high-LET exposures
478. A difficulty in interpreting the literature has been thesmall number of occasions on which findings have beenpresented specifically for NHL, as opposed to those for all
lymphomas or all lymphopoietic and/or haematopoietic neoplasms together. It would appear that larger disease groupings have often been chosen for presentation because of thevery small numbers of cases involved. For example, Travis
et al. [T30] presented findings for NHL among Thorotrastexposed patients in Denmark and Sweden, but not for asmaller cohort of patients in the United States. In the former
instance, while the SIR for NHL was greater than 1, only
four cases of NHL were observed among the Danish andSwedish Thorotrast patients, and rates in this group wereconsistent both with national rates and with those in a comparison group [T30].
479. A large population-based case-control study of childhood cancer in the United Kingdom found that, if anything,radon concentrations in the homes of NHL cases may havebeen lower than those in the homes of control children[U16]. However, the similarity in findings seen acrossa range of childhood cancer types in this study suggeststhat differences in participation rates both between casesand controls and by level of deprivation might have led tosome bias.

4. Summary
480. Findings from recent studies do not change theassessment made by the Committee in its 2000 Report.
The results from studies of NHL risk among groups
exposed to external low-LET radiation are mixed, withlittle evidence of an association overall. There is still
limited information on NHL risk in relation to either highLET radiation (external or internal) exposure or internal
low-LET radiation exposure, and interpretation of theavailable data is difficult.

W. Hodgkin’s disease

1. General background481. About 62,000 cases of Hodgkin’s disease (HD) arediagnosed annually worldwide, and the disease causes about
25,000 deaths per year [F14]. HD is distinguished fromother lymphomas mainly by the presence of giant
Reed–Sternberg cells. Overall rates of the disease have notchanged greatly in recent decades; rates have increased inadolescents and young adults in a number of populations
but have decreased at older ages [C27]. Mortality rates havedecreased sharply in most countries, reflecting mainly
improved treatment [C27]. At younger ages, disease rates
in Asian populations tend to be much lower than those inEuropean and North American populations; at older ages,
they are about half the rates in Europe and North America[C27]. For example, annual age-standardized world incidence rates for HD are generally less than 0.5 per 100,000
persons for most Chinese registries, whereas rates exceed
3.5 per 100,000 for certain North American registries [P19].
There is substantial evidence for a viral aetiology or cofactors for HD. Particularly suspect is EBV. About 50% ofcases of HD are EBV-seropositive in Western developed
countries and 90% in developing countries [T36]. Elevated
EBV titres have been demonstrated in pre-disease sera,
compatible with a causal role for EBV [M51]. An elevatedrisk of HD has been shown among those with HIV, especially around the time of AIDS onset, suggesting an association with immunosuppression [S49]. Other studies of
immunosuppression or immunodeficiency have shown
mixed results. Elevated HD risk has been found among allogeneic bone marrow transplant patients but not generally
among renal transplant patients, while there is a suggestionof an elevated risk among primary immunodeficiency
patients [S49]. Several studies have documented a familial
risk for HD, and a study of identical versus fraternal twins
demonstrated a strong genetic component to HD risk.
However, probably only around 5% of HD cases are

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attributable to a genetic risk [C27, S49]. Lifestyle factors(e.g. smoking, alcohol consumption and diet) appear to playlittle role in the aetiology of HD, while early childbirth may
be protective for women [C27].

2. Summary of UNSCEAR 2000
482. The UNSCEAR 2000 Report indicated that there
were few studies that had evaluated dose–response associations for HD. The LSS data on HD incidence did not show
a statistically significant dose–response relationship, but the
number of cases was relatively small, so the statisticalpower was low [P4] (see table 42). Studies of people treatedwith external X- or gamma radiation for benign gynaecological disorders and studies of people occupationally
exposed to radiation were also null for HD risk, but again
there were limitations in the data because of the small
number of cases and/or low radiation doses. Two studies ofpeople undergoing internal low-LET irradiation, namely a
Swedish [H6] and a United States [R3] study of 131I treatment for hyperthyroidism, had small numbers of HD cases
and failed to show an association of risk with radiation
exposure. Finally, two studies of Thorotrast patients [A5,V4] and one of miners exposed to radon [D10] also had
small numbers of HD cases and failed to show a radiation
effect. It was concluded that the available data did not indicate an association between the risk of HD and radiation
exposure, either for external or for internal exposure, butthat the data were very limited.

3. New or updated studies(a) External low-LET exposures
483. The additional information considered by theCommittee here includes that from an earlier report of acohort study of patients receiving radiotherapy for cervicalcancer [K1], for whom the mean dose to the bone marrow(used as a surrogate for lymphopoietic tissue) was about
7 Gy. Fifteen cases of HD were observed in this cohort, butthere was no indication of excess risk (table 42). The parallel case-control study also exhibited no excess risk: there
were 14 HD cases and 27 controls, with an RR of 0.63
(90% CI: 0.2, 2.6) [B8]. A cohort of patients treated with
X-rays for benign diseases of the locomotor system [D2]
had a mean dose to lymphopoietic tissue of 390 mGy, andthere were 17 cases of HD and 21 deaths from HD (mortality was observed for a longer time than tumour cases);
analyses did not show statistically significant associationsof either HD risk or HD mortality risk with dose.
484. Various studies of radiation workers have reported
on HD incidence or mortality rates since the UNSCEAR
2000 Report (table 42). The largest of these, the Canadian
National Dose Registry [S8], reported a statistically nonsignificant positive dose–response relationship for HD incidence, based on 79 HD cases and a mean dose of 66 mSv.

111

Other studies with good dosimetry included the Springfields
uranium workers in the United Kingdom (10 HD cases;
mean dose 21 mSv) [M5], the United Kingdom NRRW (17deaths from HD; mean dose 31 mSv) [M12] and the Los
Alamos National Laboratory workers in the United States(10 deaths from HD; mean dose approximately 16 mSv)
[W6]. Two more occupational studies with limited dosecharacterization include that of United States radiologic
technologists [M31], which had 34 deaths from HD, andthe study of the early (1943–1947) workers at the Oak
Ridge National Laboratory in the United States, which had
18 deaths from HD [F2]. None of the occupational studies
cited here showed statistically significant associations
between radiation exposure and risk of HD, but a limitation is that the dose levels were low.
(b) Internal low-LET exposures
485. There is no new information that would materiallyaffect the previous assessment.
(c) Internal high-LET exposures486. The only substantive new study is that of a group of
Danish, Swedish and United States patients who received
the diagnostic contrast medium Thorotrast, and a companion group who received a non-radioactive contrast medium[T30]. There were single cases of HD in both the exposed
and the control groups among the Danish and Swedish
patients, who were followed for cancer incidence, representing an RR of 1.5 (95% CI: 0.1, 81.8) [T30]. Amongthe United States patients, who were followed for mortality, there were 1 and 0 deaths from HD in the Thorotrastexposed and the control group, respectively, representing anominal RR of ∞ (95% CI: 0.1, ∞) [T30].

4. Summary
487. There continues to be no clear indication of anexcess risk of HD associated with radiation exposure, but
the data are very sparse, and most of the data sets lackdose–response analyses.

X. Multiple myeloma

1. General background488. Multiple myeloma is one of a group of plasma cellmalignancies that are characterized by the presence ofelevated numbers of plasma cells in the bone marrow and,very often, elevated levels of monoclonal protein in serumand urine [H33]. Plasma cell malignancies include:
Waldenstrom’s macroglobulinaemia, in which there is production of IgM; multiple myeloma, in which there is production of IgA, IgD, IgE, IgG or light chains; and the heavy

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chain diseases, characterized by production of heavy chains(gamma, mu, delta) [H33, O7]. There is evidence that themalignant transformation causing multiple myeloma occurs
at the early B-cell or lymphoid stem cell lineage [H33].
Multiple myeloma is a difficult disease to diagnose [K30];
in particular, detection of light chains requires electrophoresis or immunofixation, relatively expensive methods [H33]. Perhaps because of the limited availability ofserum protein electrophoresis, the reported diagnosis ofmultiple myeloma varies widely by country [H33, P19], andthe annual age-standardized world incidence rate varies
from about 1 per 100,000 persons in China to more than
8 per 100,000 in parts of the United States [H33, P19]. It
is more common among men than women and is particularly rare at young ages [C38]. Black people in the UnitedStates or the United Kingdom seem to be at particularlyhigh risk, and Asians have relatively low risk [H33, P19].
Incidence rates have been increasing during the past few
decades in various countries [H33]. While part of thisincrease may be due to earlier incompleteness in ascertainment, there have been increases in regions with well-established and high-quality registries [H33]. In particular, inMalmö, Sweden, the incidence rate for men increased by
60% between 1950 and 1979, although little change wasseen for women over this period [T23]. Even larger
increases have been reported in parts of the United Statesover the period 1947–1975, although not after 1975 [D25].
Multiple myeloma has been associated with autoimmune
diseases, in particular rheumatoid arthritis, in a number ofstudies [H33]. There is weak evidence linking incidence ofmultiple myeloma to exposure to a number of physicalagents, including asbestos, benzene and pesticides [H33].
There is little evidence of familial risk factors [H33].
Further details on the epidemiology are to be found in reference [H33].

2. Summary of UNSCEAR 2000
489. Of particular note is the discrepancy between thefindings for incidence and mortality rates among the LSS
cohort of survivors of the atomic bombings. The mostrecent mortality follow-up study [P1] showed a statistically significant association between multiple myeloma
risk and radiation dose. However, LSS data on myeloma
incidence yield a much lower estimate for the trend in risk
with dose, and are consistent with there being no effect ofdose [P4]. The authors of the cancer incidence report notedthat the mortality findings appeared to be heavily dependent on the inclusion of questionable diagnoses and on bothsecond primary cancers and cases in people who hadreceived more than 4 Gy that were excluded from thedisease incidence analysis [P4]. In view of the care taken
to review the myeloma diagnoses in the incidence analysis, it seems reasonable to place greater weight on thesefindings.
490. There were similar discrepancies between analysesof the mortality and incidence data for other cohorts. For

example, in a Swedish study of persons irradiated forbenign lesions of the locomotor system, an elevated risk ofmortality from multiple myeloma was observed in relation
to national mortality rates, but there was no analogous
increase in rates of the disease itself [D2]. In general, thestudies tending to show significantly elevated risks, such as
the metropathia haemorrhagica study of Darby et al. [D7]and the ankylosing spondylitis study of Weiss et al. [W8],tend to be of cancer mortality, whereas studies of cancer
incidence, such as the diagnostic X-ray study of Boice etal. [B17] and the IRSCC [B8, B11], find no elevation inrisk. This suggests that the classification of multiple
myeloma on death certificates may have been conducteddifferentially according to whether there was a known past
radiation exposure, although it is difficult to be certain.
Given the generally better quality of diagnoses recorded indisease incidence data, the findings from the survivors of
the atomic bombings, in particular, would suggest that there
is little evidence of an association of risk with low-LET
radiation exposure.
491. There are a few studies of persons exposed to internal high-LET radiation that suggest an association of therisk of multiple myeloma with radiation dose, but thesestudies are generally based on very small numbers of cases.

3. New or updated studies
492. Table 43 summarizes the radiation risk estimatesderived from epidemiological studies of incidence and mortality rates of multiple myeloma.
(a) External low-LET exposures
493. The analysis of cancer incidence in relation to occupational dose in the Canadian National Dose Registry has
documented a decreased SIR, of statistical significance, formultiple myeloma of 0.68 (90% CI: 0.49, 0.93) [S8]. The
trend with dose of multiple myeloma incidence within thiscohort is not reported, and is presumably not statisticallysignificant. However, as with the parallel analysis of themortality data associated with this cohort [A8], concerns
have been expressed about the reliability of record linkage,a possible source of bias [G16].
494. Analysis of cancer mortality in relation to occupational dose for a group of Japanese nuclear workers has
documented an increased but not statistically significant
SMR, for multiple myeloma of 1.12 (95% CI: 0.69, 1.74)[I11]. The trend with dose of multiple myeloma within
this cohort is not statistically significant, but the numerical value of the ERR (and confidence intervals) is notreported [I11].
495. Wing et al. [W7] have analysed multiple myeloma
mortality for four United States nuclear sites: Hanford, Los
Alamos National Laboratory, Oak Ridge National
Laboratory and Savannah River. Trends of increasing

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multiple myeloma mortality with whole-body dose wererecorded, but were not statistically significant, with values
of ERR of 0.66 (90% CI: –2.35, 3.67) Sv–1, assuming doses
were lagged by 10 years [W7]. Wing et al. went on to
analyse trends of multiple myeloma mortality above certain
critical ages, and found that above the age of 40 (also above
45 and 50) years of age the trends of risk with dose becamemuch larger and generally statistically significant. For
example, considering mortality above the age of 40, Wing
et al. obtained values of ERR of 5.64 (90% CI: 0.61, 10.67)
Sv–1, assuming doses were lagged by 10 years [W7].
However, the values of attained age limit used (40, 45 and50 years) are not chosen a priori. Therefore Wing et al. areeffectively fitting another parameter, and if this is taken into
account, it substantially reduces the nominal statisticalsignificance of the results. The largest χ2 value calculatedby Wing et al. is 5.43, and P[χ22 > 5.43] = 0.07, so that
there is no statistically significant effect in this study.
(b) Internal low-LET exposures496. There is no new information that would materiallyaffect the previous assessment.
(c) Internal high-LET exposures497. The only substantive new study is of a group of
Danish, Swedish and United States patients who received
the diagnostic contrast medium Thorotrast, and a companion group who received a non-radioactive contrast medium[T30]. There were 5 cases of multiple myeloma in the
exposed group and 2 cases in the control group, representing an RR of 3.7 (95% CI: 0.5, 30.9) [T30].

4. Summary
498. As for the UNSCEAR 2000 Report [U2], there
remains only weak evidence that multiple myeloma is
inducible by ionizing radiation. Several studies indicate atrend of increasing risk of mortality due to multiple
myeloma with external low-LET radiation exposure.
However, such trends are not generally apparent in studies
of myeloma incidence, even in groups such as the survivorsof the atomic bombings where the parallel study of diseasemortality points to increased risk. This apparent inconsistency suggests differential classification of myeloma on
death certificates depending on whether there was known
previous radiation exposure. At least in the LSS this isthought possible [P1]. The generally better quality of diagnostic information for the disease incidence data, and in particular the negative findings of the LSS incidence study,
would suggest that there is little evidence of an associationof risk with low-LET radiation exposure.
499. There continues to be limited information for internal low- and high-LET radiation exposures. Although somestudies indicate elevated risk, they are based on only small
numbers of cases.

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Y. Leukaemia

1. General background500. Although one of the rarer cancers, leukaemia is ofparticular interest because there is substantial information,
both epidemiological and experimental, on increased risk ofthis disease due to ionizing radiation exposure. In terms ofthe general epidemiology relating to leukaemia, the variation in rates between different populations is not as large
as that for most solid tumours [U2]. For example, the annual
age-standardized world incidence rate of lymphoidleukaemia varies between about 1 per 100,000 persons and6 per 100,000 persons for most parts of Asia, Europe andNorth America, and a similar range is exhibited for myeloid
leukaemia [P19]. In considering trends and aetiological factors, it is important to take account of the various subtypesof leukaemia and their different age-specific rates. Modern
classifications of leukaemia and other lymphatic andhaematopoietic malignancies (e.g. [B33]) are based on cytogenetic and molecular principles that do not always coincide with the International Classification of Diseases. Threemain subtypes will be considered here: acute lymphoblastic leukaemia (ALL), which is a leukaemia of precursor
cells of either B-cell or T-cell origin; acute myeloid
leukaemia (AML), whose lineage and subtype are generally
defined according to the French–American–British (FAB)
system [B33]; and chronic myeloid leukaemia (CML),
whose predominant haematological feature is an elevatedwhite cell count in the peripheral blood and which is characterized cytogenetically by the Philadelphia chromosome[L58]. Reference will also be made to chronic lymphatic
leukaemia (CLL), which has a B-cell or a T-cell lineage[L58]. CLL is now thought to be a variety of non-Hodgkin’s
lymphoma [J12].
501. Most cases of childhood leukaemia are ALL,
whereas CML and CLL make up a high percentage of casesin adulthood. In the case of childhood ALL, the most striking and consistent trend among different countries since1950 has been the decline in mortality rates [K32], reflecting the introduction of effective chemotherapy and cranialradiotherapy. Childhood ALL incidence rates, in contrast,
have been fairly constant or have perhaps shown a small
increase over the same period [D28]. While over 200 genes
have been associated with chromosomal translocations, todate only MLL, TEL and AML1 have been linked withchildhood leukaemia. There is increasing evidence to support the theory that gene rearrangements such as these may
originate in utero [L57]. Apart from ionizing radiation exposure, risk factors for childhood ALL include exposure toalkylating chemotherapeutic agents and genetic factors suchas Down’s syndrome. Exposure to pesticides has been
hypothesized as being a risk factor for childhood leukaemia[D51, M1, Z10], but this has not been confirmed. Greaves[G18] suggested that the increase in rates during the past
century would be consistent with many acute lymphoblastic leukaemias in children being due to delayed exposure to

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childhood infections. Kinlen suggested, however, that a specific infective agent (or agents) underlies childhood
leukaemias, as is true for several animal leukaemias [K32].
In a recent review, McNally and Eden [M36] suggested thatsome supportive evidence for an infectious aetiology is provided by analyses of space–time clustering and seasonalvariation in the appearance of childhood leukaemia.
502. For adult leukaemia, rates at ages 75–84 years haveincreased in several countries since 1950 [K32]. These
trends are consistent with improvements in cancer registration and in the details of death certification. Ionizing radiation, benzene and cytotoxic agents are known causes ofleukaemias in adults; there is also some evidence that cigarette smoking is a risk factor, particularly for myeloid
leukaemia [K32]. Rates of leukaemia also appear to beraised among patients with ataxia-telangiectasia (e.g. [O9]).

Excesses of childhood leukaemia were reported aroundsome nuclear installations in the United Kingdom, but generally not in other countries; these excesses are based on
small numbers of cases and have not been explained on thebasis of radioactive releases from the installations. Doserelated increases in leukaemia risk have been seen among
patients with large exposures to high-LET radiation arisingfrom injections of the diagnostic X-ray contrast medium
Thorotrast. There was less evidence for elevated risks
among patients injected with 224Ra, and little or no evidence
for increased risks in studies of radium dial workers or studies with individual assessments of radon exposure, either inmines or in homes.

3. New or updated studies(a) External low-LET exposures

2. Summary of UNSCEAR 2000
503. There is a substantial amount of information on therisks of leukaemia due to radiation exposure. This reflects
the high relative increase in risk compared with other cancer
types and the temporal pattern in risk, with many of theexcess leukaemias occurring within about the first two
decades following exposure, particularly among those irradiated at young ages. There are some differences between
the LSS of survivors of the atomic bombings and some large
studies of medically exposed groups in estimates of boththe magnitude of the radiation risk and the shape of thedose response for external low-LET radiation exposure.
These findings may reflect differences between studies in
the uniformity of exposure of the bone marrow and in the
degree of fractionation and protraction of exposure, as well
as differences in the pattern of risk for different leukaemia
subtypes. There is clear evidence of non-linearity in thedose response for leukaemia, which has a slope that
decreases at lower doses.
504. A study of radiation workers in three countries suggested an elevated leukaemia risk, although the results werecompatible with a range of values. Case-control studies ofprenatal X-ray exposures indicated an increased risk ofleukaemia in childhood due to in utero irradiation, although
the absence of a dose-related increase in the sparse corresponding data for survivors of the atomic bombings added
uncertainty to the magnitude of the risk. Epidemiologicalevidence does not suggest that irradiation prior to conception gives rise to a materially increased risk of childhood
leukaemia.
505. The data available on internal exposures to low-LET
radiation did not indicate elevated risks of leukaemia; thismay well reflect the low statistical precision associated withstudies involving generally small radiation doses. There wasno convincing evidence of an increased risk of leukaemia
due to environmental exposures associated with theChernobyl accident, although investigations were continuing.

506. There have been no new findings on leukaemia incidence for the survivors of the atomic bombings since the
UNSCEAR 2000 Report. However, Preston et al. [P10]have reported findings from a follow-up of mortality to theend of 2000, based on the new DS02 dosimetry. The trends
in the EAR of leukaemia with age at exposure and timesince exposure are similar to those from the previous analysis of leukaemia mortality [P1]. In particular, the EAR
decreased sharply with increasing time since exposure forthose exposed in childhood, but varied little with time since
exposure for those exposed in adulthood. The excess
number of deaths due to leukaemia up to the end of 2000
among the cohort of 86,955 survivors was estimated to be
93 [P10]. This compares with an estimate of 87 excess
leukaemia deaths based on follow-up to the end of 1990
[P1], indicating that the elevated risk has been low in recent
years. The shape of the dose–response relationship is virtually unchanged using the new dosimetry system. In particular, using a linear–quadratic dose–response model, theestimated ratio of the quadratic coefficient to the linear
coefficient is 0.89 (90% CI: 0.2, 6.0) Sv–1, which is very
similar to the corresponding estimate based on the DS86
dosimetry. In addition, relative to values based on DS86,
the values of the risks at low doses estimated using alinear–quadratic model are reduced by about 8% as a consequence of the change in dosimetry [P10].
507. Owing to the low prevalence of CLL in Japan, thestudy of survivors of the atomic bombings provides little
information on whether the risk of CLL might be related
to radiation exposure. In a recent review, Richardson et al.
[R37] suggested that the epidemiological evidence linking
CLL risks and external radiation exposure is weak, but thatepidemiological findings are consistent with an elevated
CLL risk “after a latency and morbidity period that spansseveral decades”. However, various studies considered in
the UNSCEAR 2000 Report [U2] that show raised risks ofleukaemia other than CLL in relation to external low-LET
radiation exposures—for example studies of patients treatedfor cervical cancer [B5, K1], breast cancer [C9], cancer of

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the uterine corpus [C8] and benign gynaecological disorders [D7, I1]—do not show such associations for CLL, evenfor latency periods of greater than 30 years [K1]. In
addition, while there was a weak suggestion of raised rates
of mortality due to CLL among irradiated ankylosing
spondylitis patients when compared with national rates,
there was also a weak suggestion of a similar increase
among non-irradiated patients [W2]. More recently, Shore
et al. [S68] found some evidence of a raised risk ofleukaemia among irradiated tinea capitis patients in the
United States, which, although based on small numbers, wasconfined solely to leukaemia other than CLL. In addition,
analyses of occupationally exposed workers that haveshown raised risks for leukaemia other than CLL (e.g. workers at the Mayak plant in the Russian Federation [S28] andradiologic technologists in the United States [L11]; seebelow) have, in contrast, not shown associations for CLL
risks.
508. A few recent analyses of medically exposed groups
have provided extra information on leukaemia risks. For
example, Travis et al. [T24] found a trend of increasing riskof leukaemia with dose to the active bone marrow among
patients treated for testicular cancer. Some other studies,such as those of patients treated for peptic ulcer [C4] or forcancer, e.g. [J1, R36], provide some indication of raised
leukaemia risks, but the small numbers involved and thelack of dosimetric data do not allow detailed inferences on
the relationship between risk and dose. A case-control study
in Canada reported a raised risk of childhood ALL among
those who had two or more post-natal diagnostic X-ray
exposures (RR = 1.61; 95% CI: 1.13, 2.28), and it was suggested that this risk might be modified by variants in repair
genes [I16]. However, a case-control study in the UnitedStates found that, after excluding exposures within the previous two years, there was generally no association between
post-natal diagnostic X-ray exposures and the risk of childhood ALL [S67]. A limitation of both of these studies wastheir reliance on maternal reporting of diagnostic X-ray
examinations.
509. Further analyses have been conducted on the risk ofchildhood leukaemia in relation to in utero exposure. A
large case-control study in the United States reported an RR
of 1.2 (95% CI: 0.8, 1.7) for childhood ALL in relation to
in utero pelvimetric diagnostic X-ray exposure [S67].
However, as mentioned earlier, this study relied solely on
mothers’ reports of diagnostic X-ray exposures. In contrast,
a population-based national study conducted in Sweden successfully ascertained the history of prenatal X-ray examinations using medical records [N4]. In this study, the RR
for childhood leukaemia in relation to obstetric prenatal Xray exposures was 1.11 (95% CI: 0.83, 1.47), and there was
no indication of an increasing risk with increasing numbersof X-rays. In comparing these findings with those from earlier studies, it should be borne in mind that most of the children in these two recent studies were born in the 1970s or
1980s. It is likely that the dose to the foetus per obstetricexamination was lower in this period than in previous

115

decades, although there is no direct information on this topic
from these studies. Furthermore, the frequency of obstetric
X-ray examinations appears to be lower than in earlier
decades; indeed, it was found in the United States study
that the proportion of mothers undergoing pelvimetry wasless than 3% after 1980 [S67]. When additionally statistical uncertainties are taken into account, the above findings
are consistent not only with the absence of a raised risk but
also with the RRs of the order of 1.4 reported from earlier
studies of obstetric X-rays, such as the Oxford Survey ofChildhood Cancers (OSCC), conducted during a period
when both the frequency of such examinations and the associated doses per examination were higher. In a recent
review, Wakeford and Little noted that, once account istaken of various sources of uncertainties, findings from the
OSCC and from the cohort of survivors of the atomic bombings who were exposed in utero are consistent; the findings
support a causal link between in utero irradiation andincreased risk of childhood cancer, although quantificationof this risk at low doses is difficult [W23]. Paragraph 79
includes additional discussion concerning the scientificdebate on the nature of the association between prenatal
X-ray exposures and childhood cancer.
510. Further findings have been reported in recent years
from studies of workers exposed to radiation occupationally. Of these, the largest has been a study of mortality
among over 400,000 nuclear industry workers from 15
countries [C41]. Many of the workers in this study had been
included in earlier, smaller studies. However, this newer
study focused on those workers whose radiation doses werepredominantly from higher-energy photons. Since many
workers with potential doses from neutrons or from internal radiation exposure also had relatively high external
doses, their exclusion from the analysis meant that its statistical power was not as great as might have been expectedfrom the studies of individual components (e.g. [M12]), oreven as great as for the previous three-country study [C3].
The estimated ERR per unit dose from the 15-country study
was similar to that estimated for the survivors of the atomic
bombings and from some other studies of radiation workers; however, the estimate of risk was not statistically significant, and the values of the 95% confidence interval
ranged from less than zero up to about five times the estimate for low doses derived from the study of the survivorsof the atomic bombings (ERR = 1.93 (95% CI: <0, 8.47)
Sv–1. There was little change in this value when the lag
period was increased from 2 to 10 years. There was also
no indication of a decrease in the ERR per unit dose withtime since exposure, although the power of this analysis
was limited. Analyses that excluded workers included inearlier studies gave results similar to those from the fullanalysis [C41].
511. Leukaemia incidence has been studied for about
191,000 persons included in the Canadian National Dose
Registry [S8]. While the incidence rate of leukaemia otherthan CLL was significantly lower than expected fromnational rates (SIR = 0.71; 90% CI: 0.58, 0.86), there was

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some indication of a trend of increasing risk with increasing cumulative dose, although the 90% confidence interval
for the ERR per unit dose was very wide and included zero(see table 44). The estimate of the ERR per unit dose wasconsistent with that obtained from earlier large studies, suchas that of the United Kingdom NRRW [M12] and that of
the combined analysis of nuclear workers from Canada, theUnited Kingdom and the United States [C3], as well as thesubsequent 15-country worker study [C41], which included
data on about 39,000 nuclear workers from the Canadian
National Dose Registry. However, interpretation of findings
from the Canadian dose registry is complicated by the factthat the estimated ERR per unit dose for leukaemia wassimilar to that observed for all other cancers combined, in
contrast to the pattern seen in other large occupational studies (see table 13) and in other studies of radiation-exposedgroups [G16]. An analysis based on a subgroup of the workers recorded in the Canadian National Dose Registry,
namely those employed in the nuclear power industry, gavea higher estimate of the ERR per unit dose for leukaemia
other than CLL (i.e. 52.5), but with a very wide confidence
interval (90% CI: 0.205, 291) and based on only 18 deaths
in total [Z6].
512. In an updated analysis of mortality among nuclear
industry workers in Japan [I14], the number of leukaemias
observed in a prospective follow-up of around 120,000
workers followed for an average of 4.5 years was limited;the estimated ERR per unit dose was consistent with a wide
range of values, including estimates from other studies andvalues less than zero (see table 44). A study of an expandedcohort of workers at the Portsmouth naval shipyard in the
United States followed to the end of 1996 showed that theleukaemia mortality rate among workers monitored for radiation exposure may have been slightly less than that
expected from national rates, but there was some suggestion of a trend of increasing risk with increasing cumulative dose [S56, Y10]. However, confidence limits for theestimated trend were wide, reflecting the fairly small total
number of deaths in this study (see table 44). The analysis
described in reference [Y10] took account of the potential
impact of exposure to solvents, although this did not appear
to be a confounding factor. However, this analysis did not
differentiate between CLL and other types of leukaemia. A
small update to an earlier case-control analysis of leukaemia
among Chernobyl recovery operation workers [I6] found nostatistically significant association with dose, although thenumbers of cases were small and the findings were very
imprecise [K3].
513. Reference was made earlier in this annex to an analysis of mortality in relation to external gamma dose among
about 21,500 workers at the Mayak nuclear complex in the
Russian Federation [S28]. In contrast to studies of recent
radiation workers, the range of doses received by theseworkers was very wide (with an average external dose of
0.8 Gy, low-LET). This analysis provided strong evidenceof a trend of increasing risk of leukaemia other than CLL
with increasing dose. After being adjusted for a surrogate

measure of the exposure to plutonium, the data were consistent with a linear trend of increasing risk with externaldose, although there were weak indications of a concave
upward dose response. There was strong evidence that the
RR was highest within 3–5 years of exposure (ERR of 6.9(90% CI: 2.9, 15) Gy–1) and was lower subsequently (ERR
of 0.45 (90% CI: 0.1, 1.1) Gy–1, in line with the temporal
pattern seen in some other studies of radiation-exposedgroups. Of the 66 observed deaths due to leukaemia otherthan CLL during the follow-up period, it was estimated that
40% might be associated with occupational exposure toexternal gamma radiation [S28].
514. Aside from those included in the Canadian National
Dose Registry discussed earlier [S8], several analyses haveappeared recently involving medical X-ray workers andradiologic technologists. In the study involving medical
radiologic technologists in the United States, data on mortality due to ALL, AML and CML (hereafter collectively
called non-CLL leukaemia) were examined in more detailfor those who had completed an initial questionnaire survey,
which permitted the investigators to control for other disease risk factors [M31]. The results showed that neither thelength of work as a radiation technologist nor the year offirst radiologic certification was associated with the risk of
non-CLL leukaemia. However, the risk of non-CLL
leukaemia rose with increasing length of work prior to 1950
(p = 0.05 for trend). The latter finding is of note since thelevels of radiation exposure were higher prior to 1950 thanin more recent years. Similar findings arose from an analysis of non-CLL leukaemia incidence in the same cohort[L11]. Raised rates of leukaemia incidence have been
observed among Chinese medical X-ray workers employed
before 1970, but there was less evidence for an excess risk
relative to other medical specialists for workers employed
between 1970 and 1980 (RRs of 2.4 and 1.7, respectively)[W3]. For these X-ray workers, the RR of leukaemia washighest for those who started their work at under 20–25
years of age and peaked within 5–14 years of the start ofradiation work. In addition, there was some indication of araised risk of leukaemia mortality among United Kingdom
radiologists who first registered before 1955, although thenumbers of cases were small [B2]. These findings areindicative of an effect associated with radiation exposuresthat were larger in earlier than in later calendar periods.
However, the general lack of dosimetric data makes it difficult to quantify these risks.
515. Although not included in the UNSCEAR 2000
Report [U2], the 1997 study of Artalejo et al. [A32]reported a slight deficit of leukaemia mortality among
workers for the Spanish Nuclear Energy Board; the SMR
was 0.70 (95% CI: 0.19, 1.80), based on only 4 leukaemia
deaths, of which 1 was among the 27% of the cohort who
had been miners and may have been exposed to alpha radiation [A32]. Rogel et al. [R54] reported mortality due tonon-CLL leukaemia close to the values expected from
French national mortality rates among radiation workers
of Électricité de France (5 observed deaths versus

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7.2 expected; SMR = 0.70; 90% CI: 0.27, 1.46); there wasa positive but not statistically significant trend of risk ofmortality due to non-CLL leukaemia with dose (ERR = 6.8(90% CI: –8.4, 62.2) Sv–1).
516. Further information has become available on the riskof leukaemia for young people in relation to their exposure
to gamma radiation from natural sources. A large case-control study in the United Kingdom did not show any association between childhood leukaemia risks and gamma doserate, as measured in the dwelling occupied for at least six
months in the period immediately before diagnosis [U17].
Further details of this study are given in table 15.
Notwithstanding the large number of subjects in this study
and the collection of individual dosimetric data, the study’s
statistical precision is limited both by the low mean gamma
dose rate (i.e. 0.843 mGy per year) and by the relatively
narrow range of dose rates (from less than 0.1 to about
2 mGy per year). A national study in Sweden provided aweak suggestion of a trend of increasing risk of ALL at agesof less than 20 years with gamma radiation exposure arising from living in dwellings built from uranium-containing
alum shale concrete [A24]. However, the statistical precisionof these findings is low, reflecting in part the low doses
received and the lack of detailed dosimetry for dwellings notknown to have been built from alum shale concrete, which
may have led to some misclassification of the exposures.

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protracted internal exposures (mainly due to 90Sr) and external exposures as a consequence of releases from the Mayakcomplex. Krestinina et al. [K50] conducted a study ofleukaemia mortality based on a cohort of about 30,000
people born before 1950 who lived near the river sometime
between 1950 and 1960. As of the start of 2000, about half
the cohort was known to have died, and the cause of death
was known in 85% of these instances. Although it wasstated that about 16% of residents were lost to follow-up,
the date of migration from the study area was known inmany cases, and this allowed a more accurate determination of the number of person-years at risk. Krestinina et al.
[K50] estimated that the ERR (low-LET) was 4.2 (95% CI:
1.2, 13) Gy–1 for all leukaemias and 6.5 (95% CI: 1.8, 24)
Gy–1 for non-CLL leukaemia. However, they stressed caution in interpreting these values because of uncertainties inthe dose estimates. In particular, this risk analysis incorporated “individualized” dose estimates—summed over internal and external exposures—that used age-dependent
parameters and detailed residential histories, but did nottake account of the precise location of individual residences
within villages or of detailed lifestyle patterns. This is likely
to give rise to Berkson measurement errors in the doses.
These measurement errors may not have biased the estimates of any dose–response relationship, but would imply
that confidence intervals for estimated trends in risk withdose are too narrow. Further work to improve the dosimetry and the follow-up of this population is in progress.

(b) External high-LET exposures517. Various studies have been conducted of leukaemia
risks among aircrew exposed externally to both high-LET
and low-LET radiation. As with studies of other types of
exposure, caution needs to be attached to findings fromsmall studies, and more weight should be given to welldesigned large analyses. For example, Gundestrup andStorm [G22] drew attention to an excess incidence of AML
in a cohort of Danish jet cockpit crew, albeit based on only
3 cases. A similar result had been reported previously fora study involving Canadian pilots [B32]. However, this finding was not replicated in a subsequent analysis based on alarger cohort of airline pilots in five Nordic countries [P21].
Analyses of leukaemia mortality in larger cohorts of aircrew from a wider range of European countries have generally provided little evidence of raised leukaemia risks
relative to national rates with duration of employment (formore than 44,000 cabin crew [Z4]) or with estimated cumulative dose (for around 19,000 male pilots [L48]). However,even in large analyses such as these, the numbers ofleukaemias have been small, so making inferences is difficult. Furthermore, when dose–response analyses have been
conducted, the high- and low-LET components of dose havenot been separated [L48].
(c) Internal low-LET exposures518. Two recent studies have considered leukaemia rates
among people who lived near the Techa River in theSouthern Urals in the Russian Federation, and who received

519. The other recent analysis of the Techa River population was a case-control study of leukaemia incidence,nested within essentially the same cohort as above [O13].
Leukaemia cases arising within the study region were identified from medical records of the leading haematologicalclinic in that area. Controls were selected randomly fromthe cohort and individually matched to the cases on the basisof the individual’s age at the time of diagnosis, the individual’s sex, and whether or not they moved into the area
after the period of peak exposures. The dose estimates usedin this analysis pre-dated those used by Krestinina et al.
[K50]. However, the findings were broadly similar. The
EOR (low-LET), based on both internal and external exposures, was 3.5 (95% CI: 1.5, 8.1) Gy–1 for all leukaemiasand 4.6 (95% CI: 1.7, 12.3) Gy–1 for non-CLL leukaemia[O13]. Based solely on the cumulative internal dose at thetime of diagnosis, the EOR (low-LET) for non-CLL
leukaemia was little changed: specifically it was 5.4 (95%
CI: 1.1, 27.2) Gy–1. Adjustment for level of education, occupation and any history of tumours had little impact on theseresults. There was a weak suggestion that the estimated risk
per unit dose might have been greater for persons younger
than 26 years of age at around the time of peak exposuresand for those diagnosed before 1970, but these findings
were not statistically significant. There were a somewhat
larger number of cases (83) than in the recent mortality data
[K50] (49 non-CLL and 12 CLL), although only 50 of thesewere of known cell type, and 20 of these cases were CLL.
Nevertheless, it should be recognized that both analyses are
based on essentially the same cohort. Consequently, precise

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quantification of risks is difficult for the same reasons as
those mentioned earlier, particularly owing to the uncertainties in dosimetry.
520. Some other studies have examined the incidence ofleukaemia in children in relation to radiation exposures arising from the Chernobyl accident in 1986. An updated
follow-up of childhood leukaemia in Belarus continued toshow no increase in rates [G19], as did an analysis in the
Bryansk region of the Russian Federation [I32], whereas ananalysis in Ukraine indicated a raised risk among children
born in 1986 [N5]. Further afield, an analysis of childhood
leukaemia in Hungary did not show a statistically significant increase in relation to the accident [T46] and, while it
had been suggested that infant leukaemia was increased in
Scotland and Wales as a result of the accident [B6], a wider
analysis of data from the United Kingdom did not confirm
an association [C28]. These analyses were “ecological studies”, which did not take account of individual exposures. In
contrast, a case-control study of leukaemia in young people
has been conducted in Ukraine in which individual doses
were estimated [N6]. This study indicated a raised risk
among those with doses due to the accident of 10 mSv or
more relative to those with doses of less than 2 mSv (RR= 2.5; 95% CI: 1.1, 5.4). However, a key limitation was thelow proportion of eligible subjects who were included in
the study, therefore raising the possibility of bias. A larger
case-control study of leukaemia in young people was conducted in parts of Belarus, the Russian Federation andUkraine [D52]. Only a small subset of the cases in this study
was included in the earlier study [N6], while participation
rates appeared to be higher in the three-country study. There
was a statistically significant trend in leukaemia risk withestimated bone marrow dose, but interpretation of this finding was complicated by differences in the estimated dose
response between the three countries [D52]. In particular,
most of the evidence for a raised risk came from Ukraine,even though the mean dose for controls here was lower thanthe corresponding values for the regions of Belarus and theRussian Federation that were included in the study.
Furthermore, all of these mean doses were low; the highest value was 11.74 mSv for the regions studied in Belarus.
521. There is little new information on leukaemia risks
for those who might have received environmental exposuresin adulthood as a consequence of the Chernobyl accident.
An “ecological study” in northern Sweden did not show aclear excess of leukaemia [T47], although some aspects ofthe methodology (e.g. the exclusion of deaths when calculating disease rates during the 1986–1987 reference period)
were questionable. Given these and the “ecological” designof the study, which is known to be susceptible to bias [L68,
L69], little weight should be attached to the null findingsof this study.
522. Further studies have been conducted in recent years
around nuclear installations in other countries. A study ofchildhood leukaemia cases around the 29 nuclear installations in France found no evidence of a generally increased

risk [W24]. While there was a weak suggestion of a raised
rate of the incidence of ALL at ages younger than 10 years
within 10 km of the La Hague reprocessing plant in France
during 1978–1998, this finding was based on only 4 cases.
Furthermore, an assessment based on a radioecological study
conducted around this plant estimated that the expected
number of radiation-induced leukaemias in young people
due to releases from local nuclear installations would be lessthan 0.002 [L70, R50]. Likewise, there was little evidenceof excess risk around French nuclear sites when using a geographical zoning based on gaseous discharge dose estimates[E13]. A study in the United Kingdom found no excess ofchildhood leukaemia during the period 1969–1993 aroundnuclear power plants and, aside from the raised rates previously reported around sites such as Sellafield and Dounreay,
there was generally no new evidence of excesses aroundother nuclear sites [C7]. A study in Japan indicated a raised
rate of leukaemia mortality summed over all ages in municipalities that contained a nuclear installation; however, there
was no increase among young people [Y9]. An updated
analysis of mortality rates around the Three Mile Island
nuclear power plant in the United States did not indicate
clear patterns in leukaemia risks [T45]. Overall, while there
are a few nuclear installations around which raised
leukaemia risks have previously been observed, there is very
little evidence of raised rates around nuclear sites generally[L56]. This is not surprising in view of the very low radiation exposures of those living near most sites.
523. With regard to environmental exposures due toatmospheric nuclear weapons testing, Abylkassimova et al.
[A23] gave brief details of a leukaemia case-control study
conducted in Kazakhstan. This study was nested within acohort of about 10,000 residents of settlements that were
downwind from the Semipalatinsk nuclear test site. The risk
among those with estimated doses of more than 2 Sv wasabout twice that among those whose doses were less than
0.5 Sv. However, this RR value was very imprecise, andthe associated 95% confidence interval included 1, reflecting the small total number of cases of non-CLL leukaemia(i.e. 22). A study in French Polynesia reported higher rates
of childhood leukaemia in the period 1985–1989 when compared with the period 1990–1995, although over the fullstudy period of 1985–1995, rates were similar to those
expected among New Zealand Maoris and natives of Hawaii[C5]. These data were not analysed specifically in relation
to atmospheric nuclear weapons testing at the Mururoa andFangataufa atolls. Extended follow-up of United Kingdom
participants in the United Kingdom atmospheric nuclear
weapons test programme provided some evidence of araised risk of non-CLL leukaemia relative to a control
group, although this might have been a chance finding inview of the low mortality observed in the controls relative
to national rates [M35]. In a study of United States military personnel who took part in nuclear weapons tests in
Nevada or the Pacific in the 1950s, mortality due toleukaemia was less than that expected from national rates,
while the RR compared with a control group was slightly
greater than, but consistent with, unity [I17].

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524. A study of patients in France, Italy and Sweden who
were treated with 131I for thyroid cancer has investigated
the subsequent risk of various types of second cancers,
including leukaemia [R38]. This study indicated a trend ofincreasing leukaemia risk with the cumulative 131I activity
administered during the period two or more years previously. External irradiation as part of the treatment for thyroid cancer did not appear to influence this relationship.
However, although this combined analysis has greater
statistical power than the earlier studies conducted in eachof the three countries [D18, D38, H2], detailed inferences
about the relationship between administered activity andrisk are not possible, because of the small total number ofleukaemias (specifically 18). In addition, the risk of nonCLL leukaemia was not evaluated separately.
(d) Internal high-LET exposures
525. A combined analysis of patients in Denmark andSweden who were injected with Thorotrast [T30] shows asubstantial excess incidence rate of non-CLL leukaemia relative to both national rates and rates in an unexposed groupof patients (see table 44). Leukaemia excesses have also
been seen in recent analyses of patients injected withThorotrast in the United States [T30] and Portugal [D27],
although these were based on smaller numbers. Travis et al.
[T30] noted that leukaemias were diagnosed throughout themore than 50 years of follow-up for the Danish and Swedish
patients, which the authors considered to be due to the continual radiation exposure rather than an effect of the timesince exposure. This analysis provided some suggestion ofa higher incidence of CLL among irradiated than non-irradiated patients (with 6 and 1 cases observed, respectively,in similarly sized groups). However, this difference was notstatistically significant and also appeared to be lower inmagnitude than the corresponding difference for non-CLL
leukaemia [T30]. In the Portuguese study, none of theleukaemias among irradiated patients was CLL, although
the small numbers make inferences difficult [D27]. On thebasis of earlier findings from studies on patients receiving
Thorotrast and on survivors of the atomic bombings [U2],Harrison and Muirhead [H40] suggested that the relative
biological effectiveness of alpha radiation might be around
2–3 times that of external low-LET radiation for the caseof leukaemia, which would fit with associated animal data.
However, Travis et al. [T30] noted that risk estimates basedon Thorotrast data are subject to uncertainty, particularly
with regard to dosimetry.
526. In a review published in 2001 of studies of radon
exposure and leukaemia risks, Laurier et al. [L54] drewattention to the differences between findings from “ecological studies” and those from case-control studies involving
individual assessments of exposures. This point, which was
highlighted in the UNSCEAR 2000 Report [U2], has been
reinforced by results from more recent studies of radon
exposure and leukaemia risks. On the basis of data for 348
geographical units in France, Evrard et al. [E11] reported
a trend of increasing risk of childhood acute leukaemia

119

averaged over each of these areas with the average indoor
radon concentration. This trend was of borderline statisticalsignificance for all acute leukaemias (p = 0.053), but wasmost apparent for AML (p = 0.004) rather than for ALL(p = 0.49). This conclusion was not modified by taking into
account exposure to terrestrial and cosmic radiation [E1].
Attention has been drawn previously to the difficulties arising in interpreting such correlation studies, and greater
weight would generally be placed on cohort and casecontrol studies [U2]. For example, a large case-control study
in the United Kingdom found that, if anything, radon
concentrations in the homes of childhood leukaemia cases
may have been lower than those in the homes of thechildren in the control group [U16]. However, the similarity
in findings seen across a range of childhood cancer types
in this study suggests that differences in participation rates
both between cases and controls and by level of deprivation might have led to some bias. Another large casecontrol study in the United Kingdom, this time focusing on
incidence of acute leukaemia in adults, found no association with radon concentration as measured in the home
occupied at the time of diagnosis [L55].
527. Recent reviews have considered the health risks[T31, T32], including leukaemia risks, in relation to exposure to uranium. These reviews have considered findings
from studies of occupational exposures arising, for example, from the processing, manufacturing and milling of uranium. Studies of uranium miners have also been considered.
In general, these studies have not indicated elevated risksof leukaemia in relation to uranium exposure. The Royal
Society report [T32] concluded that any extra risk of deathfrom leukaemia as a result of exposure to depleted uranium
would be substantially lower than that from lung cancer,
and that any raised leukaemia risk to persons exposed todepleted uranium is likely to be too small to be detectable.
However, epidemiological studies of uranium exposures islimited by difficulties in assessing individual doses and in
separating any effect due to radiation from that due to thechemical toxicity of uranium, as well as by the limited precision of individual studies and by the healthy worker effect[T31, T32]. For example, a recent study involving a cohortof uranium mill workers in the United States indicated that,
if anything, leukaemia mortality was less than that expectedfrom local rates (5 observed versus 6.51 expected), but the
study was based on very small numbers [P25]. Studies ofenvironmental exposures have also been conducted. In particular, a study of uranium and other natural radionuclides
in drinking water in Finland did not indicate an association
with leukaemia incidence, based on a total of 35 cases[A25]. Also, studies of populations living around some sites
in the United States involved in uranium processing, manufacturing and milling did not show raised leukaemia risks[B29, B30, B31].
528. Of the roughly 21,500 workers at the Mayak plantin the Russian Federation who were studied by Shilnikova
et al. [S28], 25% had been monitored for their exposure
to plutonium. Although detailed estimates of plutonium

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exposures were not available, analysis based on a surrogate
measure of plutonium exposure did not indicate an association with rates of mortality due to non-CLL leukaemia[S28]. In a small study of radiation workers in the UnitedStates, Ritz et al. [R1] reported some weak evidence of atrend of increasing rates of mortality due to haematopoietic
and lymphopoietic cancers (of which most were leukaemias)
with internal dose, based on low- and high-LET radiation
exposure from a mixture of radionuclides. However, notonly was this finding based on only 10 deaths, but also thedose estimates were specific to the lung rather than the bonemarrow. Other recent studies of radiation workers exposedinternally to high-LET radiation have not reported results
for leukaemia and/or they lacked detailed measures of
exposure (e.g. [W22]).

4. Summary
529. New findings for leukaemia mortality in the cohortof Japanese survivors of the atomic bombings based on an
extended follow-up show similar age and time patterns inradiation risks to those seen previously in this group.
Furthermore, the use of the new DS02 dosimetry system
has little impact either on the level of risk estimated for thiscohort or on the evidence for a curvilinear dose–response
relationship, such that the excess risk per unit dose
decreases with decreasing dose.
530. A few recent studies have provided extra information on leukaemia risks among groups exposed for medical
reasons. However, the studies of this type that werereviewed in the UNSCEAR 2000 Report and are also considered in Section II of this annex are generally more
informative. In particular, these studies and also those ofoccupational exposures provide far stronger evidence of an
association between non-CLL leukaemia risks and radiation
exposure than is the case for CLL risks. Moreover, in viewof the clinical and aetiological links between CLL and lymphomas, the conclusions reached elsewhere in this annex
concerning radiation exposure and lymphoma risk should
also apply to CLL risk.
531. New analyses of radiologists, radiologic technologists and other X-ray workers have confirmed higher risksof leukaemia among those exposed many years ago, whenoccupational doses are likely to have been higher than thosereceived in recent years. In contrast, there is little evidence
of increased risks for people receiving X-ray exposuresmore recently. However, more detailed inferences are precluded by the general lack of individual dosimetric data forthese groups. Follow-up of workers at the Mayak plant in
the Russian Federation who received a wide range of external and internal doses over a protracted period shows araised risk of leukaemia in relation to external gamma dose,
but not in relation to a measure of plutonium exposure.
While precise quantification of the level of risk for theseworkers is difficult, the findings appear to be consistent withthose from the studies of the Japanese survivors of theatomic bombings. Other recent analyses of radiation workers, including a large study based on data for workers in
15 countries, considered groups whose cumulative doses
tended to be much lower than those of Mayak workers. The
findings from these studies are largely consistent withextrapolation from the atomic bombing survivor data, but
because of their generally low statistical precision, thesestudies are also consistent with a range of risks both lowerand higher than this.
532. Several analyses have been conducted recently of
aircrew exposed to external high-LET and low-LET radiation. In general, these studies have tended not to showraised risks of leukaemia. However, even analyses based onlarge cohorts have been limited by the relatively small numbers of leukaemias involved, as well as by the low doses
received and by the general lack of individual dose
estimates.
533. New information on leukaemia risks for groups
exposed to internal low-LET radiation, as well as to external low-LET radiation, has become available from studies
in the former Soviet Union. Cohort and case-control studies of Techa River residents have indicated dose-related
trends in leukaemia risk that are reasonably consistent withestimates from studies of the Japanese survivors of theatomic bombings, but which are still somewhat uncertain.
At much lower doses, a recent case-control study in Belarus,
the Russian Federation and Ukraine of exposures due to theChernobyl accident reported a dose-related increase inleukaemia in young people, but heterogeneity in the findings between the countries makes interpretation difficult.
Recent studies of people living around nuclear installationsin other countries have generally not shown raised risks,
while findings for groups exposed as a consequence of
atmospheric nuclear weapons testing have been mixed andgenerally do not provide strong evidence of an increased
risk of leukaemia. A combined analysis of patients fromthree countries who were treated for thyroid cancer indicates a trend of increasing leukaemia risk with cumulative
intake of 131I, but the number of cases studied was small.
534. Further data on patients injected with Thorotrast continue to show raised risks of leukaemia associated with thistype of exposure. Comparison of these and earlier findings
with those for the Japanese survivors of the atomic bombings provide some indication that, in this instance, therelative biological effectiveness of alpha radiation forleukaemia induction might be around 2–3. However, thisestimate is subject to various sources of uncertainty, particularly relating to Thorotrast dosimetry.
535. Studies of radon exposure and leukaemia risks published since the UNSCEAR 2000 Report have continued toprovide differing findings, according to whether they arebased on an “ecological design” or on the collection of individual exposure information as part of case-control orcohort studies. While some of the case-control studies have
had methodological limitations, the lack of any indication

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from these and earlier case-control and cohort studies of atrend of increasing leukaemia risk with increasing individually assessed radon exposures is notable. In view of thegenerally low doses to the bone marrow arising from exposure to radon in dwellings, it is unlikely that risks of theorder predicted from current radiation risk estimates forleukaemia could have been observed.

121536. Studies of groups exposed to uranium or plutonium
have generally provided little indication, if any, of raised
leukaemia risks. Many of these studies have been limitedby the relatively small numbers of cases and a general lackof detailed dosimetric data. However, it would appear that
any increase in leukaemia associated with these exposureswould be very small.

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IV. LIFETIME RISK FOR TOTAL CANCER•

A. Methods and assumptions of calculations
537. As noted in the Introduction to this annex and as further discussed in both section I.G and appendix B, theCommittee has evaluated four commonly used measures of
population cancer risk, derived from risk models fitted to
the LSS mortality data, using the latest DS02 dosimetry andfollow-up [P10]. Lifetime population cancer risks have been
calculated for China, Japan, Puerto Rico, the United States
and the United Kingdom. Mortality risk estimates are presented for solid cancers and leukaemia separately, thesebeing the only malignant disease end points yet available
for analysis in the latest version of the LSS mortality datausing the updated DS02 dosimetry [P10]. The Committee
has also evaluated risks of cancer for oesophageal, stomach, colon, liver, lung, bone, non-melanoma skin, female
breast, urinary bladder, central nervous system, thyroid andall other solid cancers in the latest version of the LSS incidence data using the updated DS02 dosimetry [P48]. There
were 100 or more cases for all these cancer sites with the
exception of bone cancer, and a statistically significant (2sided p < 0.05) dose response (see appendix A). Although
there were only 19 cases of bone cancer, risks have nevertheless been assessed. The results of fitting models to themortality rate and cancer incidence rate data using classical likelihood-based methods (with adjustment for dosimetric error) are presented; these methods are described in moredetail in appendices C and D. Models have also been fitted
to the DS02 mortality data using Bayesian methods, as outlined in appendix E. As discussed in section I.D, the main
advantage of the Bayesian approach is that dosimetric andother uncertainties are better reflected in the variability ofthe model parameters. The analysis employs the two-stepmethod recently used to evaluate the effects of dose uncertainties on model parameters and to propagate these into
uncertainties in population cancer risk estimates [B18, L17].

••

1. Risk models
539. As in the previous UNSCEAR reports [U2, U4], theCommittee’s risk estimates are based on recent data fromthe follow-up of the LSS of the survivors of the atomic
bombings in Japan. The recent analysis by Preston et al.
[P10] of LSS mortality data based on mortality follow-up
from October 1950 to December 2000 is employed, as wellas the latest analysis of the solid cancer incidence data
based on follow-up from January 1958 to December 1998
[P48]. The Committee’s analysis of the LSS data is thefirst to use the recently revised DS02 dosimetry [C13]. As
noted in the Introduction, for some time it was thoughtthat the neutron dose estimates for the survivors of thebombing of Hiroshima using the previous (DS86) dosimetry were systematic underestimates, particularly for survivors from beyond 1000 m from the hypocentre [R20,
S39]. This led to substantial multinational efforts todevelop a new dose assessment system, the DS02 dosimetry [C13, R12]. Recent analysis of all the data, including
those on fast-neutron activation products, suggests that
there are no appreciable systematic errors in the DS86 estimates of neutron doses for survivors of the bombing ofHiroshima [C13, R12, S41]. The DS02 dosimetry differs
slightly from the DS86 system, for both neutron andgamma doses, by amounts generally of no more than 20%
in the range up to 1,500 m from the two hypocentres,
where survivors received the greatest doses [C13, R12].
Analyses of the Radiation Effects Research Foundation(RERF) epidemiological data using the new dosimetry
indicate that cancer risk estimates might decrease by about
8% as a result, with no appreciable change in the shapeof the dose response or in the age and time patterns ofexcess risk [P10].

538. Risks are calculated at three test doses, Dt, of 0.01
Sv, 0.1 Sv and 1 Sv. It is implicitly assumed that thesedoses are whole-body doses, uniformly irradiating thetissues under consideration. The results depend on thefollowing factors, which are discussed briefly below:•••

The cause-specific incidence and mortality rates andthe age structure of the population to which the rates
are applied;
The methods used to transfer estimates of excess
cancer risk based on models for one population toanother population;
The method used to allow for dose fractionation or
dose-rate effects.

The exposed population for which risk estimates aredeveloped, and the models used to describe theexcess risks to this population;
The models used to describe risk at low doses;
The method used to extend the excess risk models
beyond the period of observation of the populationfrom which these models were developed;

540. The cancer risk models that are fitted to this data set
for the purposes for deriving population risk estimates weredeveloped specifically for the Committee. Radiation risks
are often described by models for cause-specific death rates
or “hazard functions”. The hazard function, h(a), for mor123

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tality at age a is defined as the probability of dying in ashort interval [a,a + δ] divided by the probability of surviving up to age a and the length of the interval δ, in thelimit that δ → 0, or more formally,h(a) = limδ ↓0+

P[time of death ∈[a,a + δ )]
δ ⋅ P[time of death ≥ a]

Similar definitions for the hazard function can be derived
for deaths from some specific cause, or indeed for theoccurrence of any specific type of event, e.g. the occurrenceof cancer. Quite often the hazard function, h(a), will depend
on variables other than only age, for example sex, s,
calendar period, y, and exogenous exposures, for instanceto a dose of ionizing radiation D delivered at age e, so that
one may write the hazard function as h = h(a,y,s,D,e).
541. In modelling the effect of some exposure, in particular that to ionizing radiation, it is usual to consider the difference between the instantaneous cancer death rate, or
hazard function, when there has been exposure, h(a,y,s,D,e),and what the instantaneous death rate, or hazard function,
would have been without that exposure, h0(a,y,s,e) =h(a,y,s,0,e), the “baseline” hazard function. This differenceis the excess absolute risk (EAR):

h0 (a,( ay,, s,+ πh1( a⋅1, s=y,female
EARy,e,s,c)D,=e)exp[π= h( a0 ,⋅1y,c =sNagasaki, D, e) −s, 0, e)(7)

An essential element of such models is the associated modelfor the baseline hazard function, which is often of simple
parametric form, for example:

h0 (a, y, s, e, c) = exp[π 0 ⋅1c = Nagasaki + π 1 ⋅1s= female+ π 2 ⋅ ln[a] + π 3 ⋅[ln[a]]2 + π 4 ⋅ e](8)

where c refers to the city of residence at the time of thebombings (Hiroshima or Nagasaki), s is the sex, a is attained
age, e is age at exposure, and π0,π1,π2,π3,π4 are the model
parameters (which are often determined by fitting to thedata).
542. Another commonly used measure is the excess relative risk (ERR), which is given by the EAR divided by thebaseline hazard:

ERR(a, y, s, D, e) = EAR(a, y, s, D,e) / h(a, y, s,0, e)= [h(a, y, s, D,e) − h(a, y, s, 0,e)](9)/ h(a, y, s, 0,e)
Again, an essential element in the specification of suchmodels is the baseline hazard function, h0(a,s) = h(a,y,s,0,e),
which is again often assumed to have a simple parametricform, for example along the lines of expression (8).
543. Corresponding to these methods for decomposing thehazard function are two much used models of radiation-induced cancer risk. Until the late 1980s, two fairly simple
models for describing radiation-induced cancer risks wereused by the Committee [U6] and by other national and international committees, such as the BEIR committee [C33]
and the ICRP [I11]. These are empirical models, which do
not depend on assumptions about specific mechanisms ofcarcinogenesis. The first is the “time-constant absolute (oradditive) risk projection model”, which assumes that, aftersome “latent period”, the annual excess cancer risk is constant. This results in the cancer rate following exposure toa dose of radiation being given by:h0(a,s) + F(D)(10)

where h0(a,s) is the baseline cancer hazard function in theabsence of exposure to radiation, i.e. the underlying cancer
rate at age a and for sex s. F(D) is the function describing
the dose dependency of the cancer risk, which is often of
the linear–quadratic form F(D) = α ⋅ D + β ⋅ D2. In the
UNSCEAR 1988 Report [U6], a model of this form wasused for describing the risk of leukaemia. The second modelis the “time-constant relative (or multiplicative) risk projection model”, which assumes that, after some latent period
following an exposure to radiation, the annual cancer rate
rises in a manner proportional to the underlying annual
cancer risk. This results in the cancer rate following exposure to a dose D of radiation being given by:h0(a,s) ⋅ [1 + F(D)](11)

where again F(D) is the function determining the dose
dependency of the cancer risk, which again is often of theform F(D) = α ⋅ D + β ⋅ D2.
544. In the UNSCEAR 1988 Report [U6], a model of thisform (with linear dose response) was used for modelling
solid cancer risks. Until the late 1980s, both models wereused for the purposes of estimating cancer risks. Largely asa result of extra years of follow-up of the survivors of theatomic bombings, it became clear that the RR model fitted
most solid cancer data much better than the absolute risk
model. For this reason, the ICRP [I11] and most other scientific committees [C35] tend to use the RR model rather
than the absolute risk model for projecting solid cancer risks
to the end of life.
545. While the RR model is the most useful for the purpose of modelling cancer risks, it is the absolute risk thatis often of most interest to an exposed individual or population. This is readily derived from the calculated RR when
the baseline risk is known.
546. It is well known that, for all cancer subtypes (including leukaemia), the ERR diminishes with increasing age at
exposure [L51, L52, U2]. For those irradiated in childhood,there is evidence of a reduction in the ERR of solid cancer
25 or more years after exposure [L16, L53, P9, T1].
Therefore, even for solid cancers, various factors have tobe employed to adjust the ERR. For many solid cancers, a

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER“generalized excess relative risk model” is commonly used,
in which the cancer rate at t years after exposure, for sex
s, following exposure at age e to a dose D of radiation is
given by:125

generalized ERR and EAR models. For solid cancer mortality, the following generalized ERR model was used, inwhich the cancer mortality rate for age a, age at exposuree, city c, sex s and “true” colon dose D is given by:h0h(a,e,c, s) ⋅ ⎡1⎡ + α ⋅ D ⋅ exp[κ 12⋅1s = female + κ 2 ⋅ ln[ e]]⎤⎦ ⋅ ln[ a − e] + κ
0 ( a, e, c, s ) ⋅⎣⎣1 + (α ⋅ D + β ⋅ D ) ⋅ exp[κ1 ⋅ 1s= female + κ
2
3
2
(12)
[1 + ERR(D,t,
e,
s)]

(14)
h0 (a, e, c, s) ⋅ 1 + (α ⋅ D + β ⋅ D ) ⋅ exp[κ1 ⋅1s= female + κ2 ⋅ ln[ a − e] + κ3 ⋅ ln[ a ]]⎤⎦

h0 (a, s) ⋅[1 + F(D) ⋅φ(t,e, s)] = h0 (a, s) ⋅where as before h0(a,s) is the baseline cancer rate, a = t +e is the age at observation (attained age) of the person andF(D) is the function determining the dose dependency ofthe cancer risk, which is often of the form F(D) = α ⋅ D +β ⋅ D2. The expression φ(t,e,s) describes the adjustment to
the ERR, F(D), as a function of time since exposure t, age
at exposure e and sex s.

This is a generalized ERR model that is linear in dose andthat incorporates adjustment to the ERR for sex, s, attained
age, a, and time since exposure, a – e. For purposes of comparison with models previously fitted by the Committee, thefollowing generalized ERR model was also used, in which
the cancer mortality rate is given by:

547. For leukaemia, neither the time-constant EAR model
nor the time-constant ERR model fits well. For reasons
largely of ease of interpretation, Preston et al. [P4] present
most of their analyses of the LSS leukaemia incidence data
set using a “generalized excess absolute risk model”, fromwhich the cancer rate t years after exposure, for sex s,following exposure at age e to a dose D of radiation is
given by:h0 (a,e, c, s) ⋅ ⎡⎣1 + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ 1 ⋅1s = female+ κ 2 ⋅ ln[e]]⎤⎦(15)

This is a generalized ERR model that is linear in dose, andthat incorporates adjustment to the ERR for sex, s, and age
at exposure, e.
550. A generalized EAR model was also fitted in which
the mortality rate is given by:

h0 (a, s) + F(D) ⋅ψ (t, e, s) = h0 (a, s) + EAR(D,t,e, s)(13)
The expression ψ (t,e,s) describes the adjustment to theEAR, F(D), as a function of time since exposure t, age at
exposure e and for sex s. As above, very frequently alinear–quadratic form, F(D) = α ⋅ D + β ⋅ D2, is assumed
for the dose response.
548. Given appropriate forms of the adjusting or modifying functions φ (t,e,s) and ψ (t,e,s) of the relative andabsolute risk, respectively, equivalently good fits to theleukaemia incidence data set were achieved using bothgeneralized ERR and generalized EAR models [P4]. It is
to some extent arbitrary which of these two models isused. However, models with equivalent fits to the data canyield somewhat different estimates of population cancer
risks. The reason for this is that about half the LSS cohort
are still alive [P10], so that population risk estimations
based on this data set (and used by many scientific committees [C33, C35, I11, U2, U4, U6]) crucially depend on
extrapolating the current mortality and incidence followup of this group to the end of life. Uncertainties due torisk projection are greatest for solid cancers, because theradiation-associated excess risk as seen by the LSS is still
increasing [P9, P10]. For leukaemia, the excess risk isdecreasing over time [P4], and most models used predictvery few radiation-associated leukaemia deaths or cases in
the future.
549. In modelling solid cancer and leukaemia mortality
for the latest follow-up of mortality of the survivors of
the atomic bombings [P10], the Committee has usedh0 (a,e, c, s) + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ1 ⋅ ln[a − e]+ κ 2 ⋅ ln[a]](16)This is a generalized EAR model that is linear–quadratic indose, and that incorporates adjustment to the EAR for
attained age, a, and time since exposure, a – e. The parameters associated with the fits of these two models to the LSS
DS02 solid cancer mortality data [P10] are given in table
45. The associated analysis of statistical deviance is given
in tables D1 and D2 in appendix D. Table D17 in appendix D gives details of the specific form of the baseline rate,h0(a,e,c,s), used in model fitting.
551. Likewise, for leukaemia mortality the following generalized ERR model was used, in which the leukaemia mortality rate for age a, age at exposure e, city c, sex s and“true” colon dose D is given by:

h0 (a,e, c, s) ⋅ ⎡⎣1 + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ 1 ⋅ ln[a]]⎤⎦(17)This is a generalized ERR model that is linear–quadratic indose, and that incorporates adjustment to the ERR for
attained age, a. The Committee also fitted a generalized
EAR model in which the leukaemia mortality rate isgiven by:h0 (a,e, c, s) + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ 1 ⋅1s = female+ κ 2 ⋅ ln[a − e]](18)

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This is a generalized EAR model that is linear–quadratic indose, and that incorporates adjustment to the absolute risk
for sex, s, and time since exposure, a – e. The parameters
associated with the fits of these two models to the LSS
DS02 leukaemia mortality data [P10] are given in table 46.
The associated analysis of deviance is given in tables D3
and D4 in appendix D. Table D17 in appendix D gives
details of the specific form of the baseline rate, h0(a,e,c,s),
used in model fitting.
552. In modelling the incidence of specific types of solidcancer for the latest follow-up of the survivors of the atomic
bombings [P48], the Committee again used generalized
ERR and EAR models. For solid cancer incidence, the following generalized ERR model was used, in which thecancer rate for age a, age at exposure e, city c, sex s and“true” colon dose D is given by:⎡1 + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[γ ⋅ D] ⋅h0 (a,e, c, s) ⋅ ⎢⎣ exp[κ 1 ⋅1s = female + κ 2 ⋅ ln[a − e]⎤⎥+ κ 3 ⋅ ln[a] + κ 4 ⋅ ln[e]]⎦(19)This is a generalized ERR model that is linear–quadratic–exponential in dose, and that incorporates adjustment
to the ERR for sex, s, attained age, a, time since exposure,a – e, and age at exposure, e. For specific solid cancer subtypes, various coefficients are set to zero. In particular, forall cancers except non-melanoma skin cancer, the cell sterilization parameter, γ, is set to zero.
553. Likewise, the following generalized EAR model wasused, in which the cancer rate for age a, age at exposure e,city c, sex s and “true” colon dose D is given by:

h0 (a,e, c, s) + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[γ ⋅ D] ⋅exp[κ 1 ⋅1s = female + κ 2 ⋅ ln[a − e] + κ 3 ⋅ ln[a]+ κ 4 ⋅ ln[e]](20)This is a generalized EAR model that is linear–quadratic–exponential in dose, and that incorporates
adjustment to the EAR for sex, s, attained age, a, timesince exposure, a – e, and age at exposure, e. Again, for
specific solid cancer subtypes, various coefficients are set
to zero. In particular, for all cancers except non-melanoma
skin cancer, the cell sterilization parameter, γ, is set tozero. The parameters associated with the fits of thesemodels to the DS02 cancer incidence data [P48] are given
in tables 47–58. The associated analyses of deviance are
given in tables D5–D16 in appendix D. Table D17 gives
details of the specific forms of the underlying rate,h0(a,e,c,s), used in model fitting to data on each solidcancer type.

2. Low-dose response, fractionationand dose-rate effects
554. As noted above, it has been customary to model thedose–response function F(D) that appears in expressions(10)–(13) in fits to biological [U5] and epidemiological[U2] data by the linear–quadratic expression:

F(D) = α ⋅ D + β ⋅ D 2(21)While this formulation can be drawn from knowledge ofchromosome repair (e.g. [K54]), on a more heuristic basis,
it represents the second-order Taylor series expansion of thedose response. There is significant curvilinearity in the dose
response for leukaemia in the LSS [L29, L33, L34, L35,
L37, P11], although for solid cancers, apart from nonmelanoma skin cancer [L30, T1] and bone cancer [R27,
T26], there has until recently generally been little evidence
for anything other than a linear dose–response relationshipfor the Japanese cohort [L29, L33, L34, L35, L37, P11,
P12] or for any other group [U2]. This issue is discussed
at greater length in section I.J. However, the most recent
follow-up of the survivors of the atomic bombings exhibits
a pronounced and statistically significant upward curvaturein the low dose (less than 2 Sv) region [P10], as will be
discussed at greater length below.
555. It should be noted that, as well as differences in theeffectiveness (per unit dose) relating to the total dose
received, there are also possible variations in effectivenessas a result of dose fractionation (i.e. the splitting of a givendose into a number of smaller doses suitably separated intime) and dose rate [U5]. This is not surprising from aradiobiological point of view. If a given dose is administered at progressively lower dose rates (i.e. giving the same
total dose over longer periods of time), or is split into many
fractions, the biological system has time to repair thedamage, so that the total damage induced will be less [U5].
Therefore, although for cancers other than leukaemia there
is generally little justification for assuming anything otherthan a linear dose–response relationship, i.e. β = 0 in Eq.
(21), it may nevertheless be justifiable to employ a doseand dose-rate effectiveness factor (DDREF) other than 1.
(The DDREF is the factor by which one divides risks forhigh-dose and high-dose-rate exposures to obtain risks for
low-dose and low-dose-rate exposures.) The ICRP [I11]
recommended that a DDREF of 2 be used together withlinear dose–response models for all cancer sites, largely on
the basis of observations from various epidemiological data
sets. The UNSCEAR 1993 Report [U5] recommended thata DDREF of no more than 3 be used in conjunction withthese linear models. The BEIR VII Committee [C37] estimated what it termed an “LSS DDREF” to be 1.5 (95% CI:
1.1, 2.3) on the basis of estimates of curvature derived fromdata from animal experiments and from the latest LSS solidcancer incidence data. The BEIR VII Committee also conducted a detailed review of the experimental literature, anddocumented substantial DDREF values that had been foundfor chromosome aberrations and cell mutation (for

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example at the HPRT locus), and for carcinogenesis in animals [C37]. DDREF values in excess of 2 were seen formany cellular systems; most of the animal cancer studies—
the experimental end point nearest to cancer in humans—
yield “[DDREF] estimates on the order of 2 to 6, with mostvalues in the range 4–5” [C37]. The BEIR committee statedthat their analysis was sensitive to the particular studies theychose to include and, perhaps more importantly, that the
DDREF should not be mistakenly thought of as a universal low-dose correction factor. There is further discussionof the DDREF in section I.J.
556. Another form to represent dose response, perhaps
less commonly used, slightly generalizes Eq. (21):F(D) = (α ⋅ D + β ⋅ D 2 ) ⋅ exp(γ ⋅ D)

(22)This has been employed in fits to biological data [U5] andto epidemiological data [B5, L29, L30, L31, T21, W2]. Theα ⋅ D + β ⋅ D2 component represents the effect of (carcinogenic) mutation induction, while the exp(γ ⋅ D) term represents the effect of cell sterilization or killing. In general,the cell sterilization coefficient γ is less than zero.
Essentially this expresses the idea that there is a competingmechanism due to cell killing, which is more effective at
higher radiation doses. A dead cell cannot proliferate andbecome the focus of a malignant clone. Variant forms ofthe cell-sterilization term exp(γ ⋅ D) that incorporate higher
powers of dose D, i.e. exp(γ ⋅ Dk) for k > 1, are sometimes
employed [L30, U5].
557. Although it is generally assumed that protraction ofradiation dose results in a reduction of effect (i.e.
DDREF > 1), largely as a result of the extra time that protraction allows for cellular repair processes to operate, there
are biological mechanisms that could increase the effectwhen dose is protracted (i.e. DDREF < 1). Bystander
effects, whereby cells that are not directly exposed to radiation exhibit adverse biological effects, have been observedin a number of experimental systems in vitro and in vivo
[M49, M61]. The bystander effect implies that the dose
response after broad-beam irradiation could be highly concave at low doses because of saturation of the bystander
effect at high doses. This would mean that linear extrapolation from data for high-dose exposures would lead to substantial underestimates of effects at low doses. Recently,
Brenner et al. [B25] proposed a model for the bystander
effect based on the oncogenic transformation data of Sawant
et al. [S43] and Miller et al. [M41] for in vitro exposure of
C3H 10T1⁄2 cells to alpha particles. Brenner et al. [B25] discussed evidence from experimental systems consistent withconcluding that the linear extrapolation of high-dose effects
to low doses underestimates oncogenic transformation rates
by a factor of between 60 and 3,000. However, Little andWakeford [L46] assessed the ratio of the lung cancer riskfor persons exposed to low (residential) doses of radon
daughters to that for persons (underground miners) exposed
to high doses of radon daughters; the ratio lay in the range
2–4 (95% CI: <1, ~14). This implies that low-dose-rate lung127

cancer risks associated with alpha particle exposure are notseriously underestimated by extrapolation from the highdose miner data; it also implies that the bystander effectobserved in the C3H 10T1⁄2 cell system cannot play a largepart in the process of lung carcinogenesis in humans due
to radon exposure [L46]. The bystander effect and other“non-targeted” effects are discussed at greater length inannex C of the UNSCEAR 2006 Report, “Non-targeted anddelayed effects of exposure to ionizing radiation”.
558. As noted above, in the latest follow-up of the survivors of the atomic bombings there has emerged evidenceof a statistically significant (p < 0.05) upward curvature in
the dose response for solid cancer mortality in the low dose
range (colon dose less than 2 Sv) [P10, W20], although thisis not observed over the full dose range (0–4 Sv). Similarfindings have not as yet been observed in the solid cancer
incidence data [P12, T1], so caution is advised in interpretation of this finding. As shown in appendix D, in general
there are only weak indications of curvature in the dose
response for particular solid cancer sites in the latest cancer
incidence data [P48], with the possible exception of bone
cancer and non-melanoma skin cancer. Nevertheless, it isimportant to explore the implications of this curvature in
the low-dose response for solid cancer risk estimates. For
this reason, models (14) and (16) were separately fitted tothe mortality data [P10], assuming both a purely linear
dose–response relationship (with the quadratic coefficient,β, set to zero) and a linear–quadratic dose response.
559. For leukaemia in the low dose range (bone marrowdose less than 2 Sv), comparison of the linear–quadratic andpurely quadratic models suggests that the linear term does
not statistically significantly improve the fit of the pure
quadratic model (p > 0.50), although the linear–quadraticmodel fits statistically significantly better than the purely
linear model (p = 0.003). This suggests that in this low dose
region, a purely quadratic dose response may best describe
the leukaemia induction curve. For solid cancers, similarfindings have not as yet been observed in the incidence data
[L29, P4], so caution is advised in interpretation of thisfinding. Nevertheless, it is important to explore the implications of this curvature in the low-dose leukaemia response
for cancer risk estimates. For this reason, models of theform of Eqs (17) and (18)—assuming both a purely quadratic dose response (with the linear coefficient, α, set tozero) and a linear–quadratic dose response—were separately
fitted to the mortality data [P10].
560. As discussed in section I.D, measurement error can
substantially alter the shape of the dose–response relationship and hence the derived population risk estimates [T17].
The problem of dosimetric error for the RERF data has been
investigated by Jablon [J3], Gilbert [G17], and subsequently
in a series of papers by Pierce et al. [P2, P11, P16] andLittle et al. [B18, L17, L29, L32, L33, L35, L37, L49].
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ments, using the regression calibration methodology developed by Pierce et al. [P2, P11, P16] and Little et al. [L29,L32, L33, L35, L37, L49]. Jablon [J3] investigated the errors
in the dosimetry for the survivors of the atomic bombingsand found that the errors were most likely to be log-normally distributed, with a geometric standard deviation (GSD)of about 30%. The analyses of this report employ the “central” estimate of 35% for GSD. This is the same central estimate as used by Pierce et al. [P2] and assumed by Little etal. [L29, L32, L33, L35, L37, L49]. Details on the methods
for fitting the extended Weibull distribution to the LSS
mortality data are given in appendix C.

3. Projection methods
561. In the UNSCEAR 2000 Report [U2], some use has
been made of generalized ERR models for solid cancer
incorporating adjustment for attained age and sex, and also
such models with adjustment for age at exposure and sex.
However, it is clear from the data on solid cancer incidence[L16, L21, T1], as also from the latest data on mortality[P10], that these models are not optimal. Detailed comparison of models with various sorts of adjustment (all combinations of logarithmic adjustment for attained age, age at
exposure, time since exposure, sex and city) in the latest
follow-up of the solid cancer mortality data [P10] suggested
that, as indicated by the form of model (14) above, the optimal generalized ERR model was one with adjustment forsex, time since exposure and attained age. Among generalized EAR models for solid cancer mortality with these sorts
of adjustment (all combinations of logarithmic adjustment
for attained age, age at exposure, time since exposure, sex
and city), as indicated by the form of model (16) above,again the optimal model was one with adjustment for thetime since exposure and attained age. There was little tochoose between the fits of these two classes of model (generalized ERR and generalized EAR). This annex therefore
uses both models to project cancer risk over time. For purposes of comparison with the risk models used previously[U2], the risks calculated using model (15), with adjustment
to the ERR for age at exposure and sex, are also presented.
The mortality risks for these three models are presented intables 59–62. In table 72, summary risk values from table
59 are presented together with various other recent estimations of population cancer mortality risks. Mortality risks
estimated using Bayesian Markov Chain Monte Carlo(MCMC) methods are given in tables 63 and 64.
562. In previous UNSCEAR reports, a variety of methods were used to project leukaemia risk over time, including a time-constant EAR model for the UNSCEAR 1988
Report [U6] and the generalized EAR models developed by
Preston et al. [P4] from the LSS incidence data for the
UNSCEAR 1994 [U4] and 2000 [U2] Reports. As noted
above, the EAR for leukaemia is generally declining overtime, so projection of risk is not such an issue as for solidcancers. Detailed comparison of models with various sorts
of adjustment (all combinations of logarithmic adjustment

for attained age, age at exposure, time since exposure, sex
and city) in the latest follow-up of the leukaemia mortality
data [P10] suggested that, as indicated by the form of model(17) above, the optimal generalized ERR model was one
with adjustment for attained age. Although the optimal generalized ERR model is one with logarithmic adjustment for
attained age, a model with adjustment for time since exposure and age at exposure fitted nearly as well. However,
the risks predicted by these two models are close, so forsimplicity, the risk values presented here are only those calculated using the model with adjustment for attained age.
Among generalized EAR models for leukaemia mortality
with these sorts of adjustment (all combinations of logarithmic adjustment for attained age, age at exposure, timesince exposure, sex and city), the optimal model was one
with adjustment for sex and time since exposure, as indicated by the form of model (18) above. There was little tochoose between the fits of these two classes of model (generalized ERR and generalized EAR). Therefore both models
have been used to project cancer risk over time. Mortality
risks for these two models are presented in tables 65–67.
In table 72, summary risk values from table 65 are presented together with various other recent estimations of
population mortality risks due to leukaemia. Mortality risks
estimated using Bayesian MCMC methods are given intables 68 and 69.
563. In the UNSCEAR 2000 Report [U2], similar models
were employed for projection of the risk for solid cancer
incidence as of the risk for solid cancer mortality. In particular, generalized ERR models with adjustment for powers
of attained age or powers of age at exposure were used in
the UNSCEAR 2000 Report [U2]. In the current report, ageneral framework for risk projection is used for the generalized ERR and EAR models expressed in Eqs (19) and (20).
The details of the particular ERR and EAR models used foreach cancer site are given in tables 47–58. Appendix D gives
more details on the model fitting and the detailed justification of the form of each model (see particularly tables
D5–D16). Tables 70 and 71 present risk estimates for solidcancer incidence calculated using the generalized ERR andEAR models separately for each of the five populations considered (China, Japan, Puerto Rico, the United States andthe United Kingdom). Table 73 presents summary risk
values from table 70, together with various other recent estimations of population risks for solid cancer.
564. As detailed in appendix B, the four measures of population risk relevant to mortality were estimated, namely:
excess cancer deaths (ECD), risk of exposure-induced death(REID), years of life lost (YLL) per unit dose, and yearsof life lost per radiation-induced cancer death (YLLRIC).
For cancer incidence, the measure of risk expressed as
exposure-induced cancer incidence (REIC) is used. Persons
are assumed capable of surviving in principle up to the ageof yT (121 years here), at which point they are assumed todie instantaneously (i.e. the population is truncated at thatage). It was assumed that there are no excess solid cancer
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129

excess leukaemia deaths in the first 2 years after exposure.
Otherwise the temporal expression of risk, in particular the
projection of risk to the end of life, is as predicted by thefitted models expressed as Eqs (14)–(20).

population being considered are given (from published tabulations, such as [O8, O12, P19, S38, S83, W38]) by λ(a,s),
then, when using the generalized ERR model (14), thecancer rate following a dose D incurred at age e will be:

4. Populations, mortality rates and cancer incidenceλ(a, s) ⋅ ⎡⎣1 + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ 1 ⋅1s= female

565. Risks are calculated separately for populations
having the population structure, cancer incidence and mortality rates of current Chinese, Japanese, Puerto Rican,
United States and United Kingdom populations. For China,
Japan, Puerto Rico and the United States, the mortality rates
and population structure were derived from a database maintained by the World Health Organization (WHO) [W38].
These correspond to a 1999 Chinese population (a combined
urban and rural sample), a 1994 Japanese population, a 1992
Puerto Rican population and a 1998 United States population. For the United Kingdom, mortality rates of the 2003
England and Wales population were used [O8]. Current
cancer incidence rates were tabulated from reference [P19]for China (1993–1997), Japan (1993–1997) and Puerto Rico
(1992–1993) (using rates from the Shanghai registry for
China and from the Osaka registry for Japan). For theUnited States, rates for 2002 from the nine SEER registries(Atlanta, Connecticut, Detroit, Hawaii, Iowa, New Mexico,
San Francisco–Oakland, Seattle–Puget Sound and Utah)
were used [S83]; and for non-melanoma skin cancer, rates(for basal and squamous cell carcinoma) for eight areas in
1977–1978 were used [S38]. For the United Kingdom, thecancer incidence rates for England in 2001 were employed[O12]. For the purposes of calculating cancer mortality risks
in the United Kingdom population, “solid cancer” is defined
to be any cause of death with an International Classificationof Diseases (10th revision) (ICD10) code of C00–C80 or
C97; “leukaemia” is defined as any cause of death with
ICD10 code C91–C95 excluding C91.1, i.e. all leukaemias
excluding CLL. CLL is excluded from the calculations ofleukaemia risk here because there is little evidence that it
is radiogenic [U2]. Similar definitions, in some cases basedon ICD9 codes, were used for the other populations. The
populations are assumed to be in equilibrium prior to radiation exposure, an assumption commonly made in suchcalculations [B18, L15, L16, L17]. All high-dose-rate risks
are evaluated using models expressed by Eqs (14)–(20)fitted to the various LSS mortality and cancer incidence data
sets [P10, P48].

5. Transfer of risk estimates between populations
566. Risks of cancer and cancer mortality were transferredby means appropriate for each of the two sorts of model(generalized ERR and generalized EAR). Therefore, forgeneralized ERR models (time-, age- and sex-specific),
ERR was assumed to be invariant between populations,whereas for generalized EAR models (time-, age- and sexspecific), EAR was assumed to be invariant. So, for example, if the age- and sex-specific solid cancer rates for the+ κ 2 ⋅ ln[a −e] + κ 3 ⋅ ln[a]]⎤⎦(23)

whereas if the generalized EAR model (16) is being used,the cancer rate is:λ(a, s) + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ 1 ⋅ ln[a − e]+ κ 2 ⋅ ln[a]](24)where again the underlying cancer or cancer mortality rate
λ(a,s) is estimated from the published tabulations [O8, O12,
P19, S38, S83, W38].

B. Lifetime risk estimates
567. Table 59 presents the risks for various models fitted
to the solid cancer mortality data. Risks are calculated
assuming a number of test doses—0.01, 0.1 or 1.0 Sv. Thereis not much variation in any of the risk measures by test
dose for the linear models, but as would be expected forthe linear–quadratic models, which exhibit upward curvature (table 59), risks (for all measures except YLLRIC) aresomewhat less, by about 20% at low doses (0.01 Sv) compared with high doses (1.0 Sv). For the linear models ingeneral, the reverse effect is observed, whereby risks perunit dose are slightly higher (by about 5%) at lower test
doses (0.01 Sv) compared with higher test doses (1.0 Sv).
This is a consequence of the saturation of the solid cancer
induction curve as a function of dose.
568. Most measures of risk (all except YLLRIC) that are
estimated for the generalized ERR model, which assumes
only variation of ERR with age at exposure (as used in previous UNSCEAR risk evaluations [U2]), are somewhat
higher than risks estimated for the other four models. For
example, for the United Kingdom population, this model
predicts a low-dose (test dose = 0.1 Sv) REID of 11.5%
Sv–1, compared with REID in the range 4.5–7.4% Sv–1 forthe other four models. This is because this model assumes
that the RR is constant over time to the end of life, whereas
the other four models predict an ERR that will decrease
with increasing follow-up (from now onwards), particularly
for the groups for which this assumption is most critical,
namely those exposed in childhood. In general, most measures of risks are fairly similar for generalized EAR models
and generalized ERR models, although there is a tendency
for most measures of risk (all except YLLRIC) under thetwo EAR models to be somewhat lower than under any of
the three generalized ERR models. For example, for theUnited Kingdom population, the two generalized EAR

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models predict a value for low-dose (test dose = 0.1 Sv)
REID of 4.5–6.9% Sv–1, compared with REID in the range
5.3–11.5% Sv–1 from the generalized ERR models.
569. Not too much should be made of the magnitude ofvariation of risk estimates between the various models, atleast at high doses. Apart from the age-at-exposure model,
for all populations at a test dose of 0.1 Sv, the estimatedexcess cancer deaths are 3.1–6.4% Sv–1, REID is in therange 3.6–7.7% Sv–1, YLL is in the range 0.5–1.1 years per
sievert and YLLRIC is in the range 13.8–15.2 years.
570. Table 60 shows that, in general, the values for all
four measures of risk for women are higher than for men,irrespective of the models used. For example, for the United
Kingdom, the REID for men is in the range 4.1–8.7% Sv–1,
while for women the REID is in the range 4.9–14.2% Sv–1.
571. Table 61 shows that, in general, the values for all
measures of risk decrease with increasing age at exposure.
For example, for the United Kingdom, the REID for persons exposed under the age of 10 is in the range 8.4–38.3%
Sv–1, but the REID rapidly decreases with age at exposure,so that for those exposed over the age of 70, the REID isin the range 0.5–2.2% Sv–1. This also highlights the substantial uncertainties in relation to risk estimates for those
exposed in childhood, which are greater because, at least in
the LSS cohort, risk estimates for this age group are much
more dependent on extrapolation to the end of life than they
are for those exposed in adulthood. Of those exposed under
the age of 10 in the LSS cohort, 92% are still alive, as are
87% of those aged between 10 and 20 at exposure [P10].
572. Table 62 demonstrates the difference made by useof the latest DS02 dosimetry, by the choice of risk models
and by the period of fit for the risk models. The Committee
has fitted models to data corresponding to the period
1950–2000, the full period of follow-up in the current mortality data [P10], as well as over 1950–1990, corresponding
to the period available for the LSS mortality data [P1] evaluated in the UNSCEAR 2000 Report [U2]. For illustrative
purposes, the Committee considers two linear generalized
ERR risk models: one with adjustment to the ERR for age
at exposure only (corresponding to one of the models usedin the UNSCEAR 2000 Report [U2]); and one with adjustment to the ERR for attained age and time since exposure,which the Committee regards as more nearly optimal forthe current follow-up (see table D1 in appendix D). The
form of both models (if not the fitted parameter values) is
described above, and also in table 45. As can be seen fromtable 62, in general, use of DS02 versus DS86 dosimetry
leads to the REID value decreasing by 9.9–10.8%. For
example, for the model of ERR with adjustment for age andyears since exposure fitted for the period 1950–2000, therisk estimate decreases from 8.2% Sv–1 with DS86 to 7.4%
Sv–1 with DS02, a reduction of 10.8%. Changing the interval over which models are fitted (1950–2000 versus
1950–1990) reduces the value for REID by 2.8–6.9%. For
example, for the model of ERR with adjustment for age andtime since exposure, using DS02 dose estimates and fitting
for the period 1950–1990, the risk value is 7.6% Sv–1, andover 1950–2000, the risk value is 7.4% Sv–1, a reductionof 3.1%. The most substantial difference is made by the
choice of risk model. The newer optimal model, with modification of ERR for age and time since exposure, generally predicts REID values of 35.8–38.3% lower than thosepredicted by the older model (with adjustment of ERR forage at exposure only). For example, using DS02 dose estimates and fitting over the period 1950–2000, the REID
value calculated using the older model (adjusted for age at
exposure) is 11.5% Sv–1, while using the newer model(adjusted for age and years since exposure) it is 7.14% Sv–1,
a reduction of 35.8%.
573. Tables 63 and 64 and figures XV and XVI illustrate
the distribution of risk predicted by the optimal linear–quadratic and linear–quadratic–exponential models fitted to thesolid cancer mortality data using Bayesian techniques. Fora United Kingdom population, using a test dose of 0.1 Sv,
the mean REID value using the linear–quadratic–exponential model is 3.3% (90% CI: –0.6, 7.0) Sv–1. However, whena higher test dose is used, risks increase appreciably for the
linear–quadratic–exponential model: the REID value at 1
Sv is 7.1% (90% CI: 5.6, 8.7) Sv–1. The reason for this canbe seen from table E.1 in appendix E, which shows that thequadratic coefficient, β, is about four times larger than thelinear coefficient, α; the crossover value for the dose atwhich the linear and quadratic terms are of equal magnitude is 0.24 Sv. For a United Kingdom population, using atest dose of 0.1 Sv, the mean REID value using thelinear–quadratic model is 5.4% (90% CI: 3.1, 8.0) Sv–1.
When a higher test dose of 1 Sv is used, the REID value
increases to 6.7% (90% CI: 5.3, 8.1) Sv–1, a figure verymuch in line with that predicted by the linear–quadratic–exponential model. The generally lower risk values produced by the linear–quadratic–exponential model (at least
at low test doses) is perhaps remarkable, and is a result of
the incorporation of an exponential term representing cellsterilization, exp[γ ⋅ D], in the dose response, as detailed inAppendix E. Although the value for the cell sterilization
coefficient, γ, is not statistically significant, its effect on thelinear and quadratic coefficients is profound, resulting in
the linear term becoming smaller (and generally not statistically significant) and the quadratic term becoming muchlarger (and generally statistically significant) (see table E.1).
These effects are also observed in the fitting of similar
models by maximum-likelihood techniques, which produce
very similar central estimates of risk.
574. Table 65 presents the risk estimates using various
models fitted to the leukaemia mortality data. As for solidcancers, risks are calculated assuming a number of test
doses—0.01, 0.1 or 1.0 Sv. There is substantial variation
with test dose in the values for all of the risk measures
except YLLRIC; as would be expected, this variation is particularly marked for the purely quadratic models. Even forthe linear–quadratic models, the risk values (for all measures except YLLRIC) are somewhat less, by about a factor

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Figure XV. Distribution of the REID from solid cancer
for various current populations, assuming a test dose,Dt, of 0.1 Sv, and using generalized linear–quadratic–
exponential ERR models fitted by Bayesian MCMC
(models described in appendix E)
Risks are calculated for a population in equilibrium (underlying
mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom
populations) from various models fitted to LSS mortality data
[P10], assuming 35% GSD errors

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of 2 at low doses (0.01 Sv) compared with those at highdoses (1.0 Sv). For all measures of risk except YLLRIC,
risks are generally slightly higher when generalized EAR
models are employed than when generalized ERR models
are employed. However, not too much should be made of
the magnitude of variation of risk between the various

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models, at least at high doses. At a test dose of 1 Sv, whenusing the linear–quadratic models, the values for REID andexcess leukaemia deaths for all five populations are in therange 0.4–1.0% Sv–1, values for YLL are in the range
0.1–0.3 years per sievert and values for YLLRIC are
between 18.8 and 38.8 years.

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Figure XVI. Distribution of the REID from solid cancer
for various current populations, assuming a test dose, Dt,of 0.1 Sv, and using generalized linear–quadratic ERR
models fitted by Bayesian MCMC (models described inappendix E)
Risks are calculated for a population in equilibrium (underlying
mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom
populations) from various models fitted to LSS mortality data
[P10], assuming 35% GSD errors

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575. Table 66 shows that, in general, values for all measures of leukaemia risk (except YLLRIC) are higher for men
than for women, irrespective of the models used. For example, for the United Kingdom, the REID at 0.1 Sv for men
is in the range 0.08–0.58% Sv–1, while for women it is in
the range 0.05–0.35% Sv–1.

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576. Table 67 shows that, in general, values for all measures of leukaemia risk decrease with increasing age at exposure. For example, for the United Kingdom, the REID for
persons exposed under the age of 10 (calculated using thelinear–quadratic models) is in the range 0.70–0.74% Sv–1,
but the REID rapidly decreases with increasing age at

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exposure, so that for those exposed over the age of 70, the
REID is in the range 0.16–0.17% Sv–1.
577. Tables 68 and 69 and figures XVII and XVIII illustrate the distribution of risk predicted by the optimallinear–quadratic and linear–quadratic–exponential models
fitted to the leukaemia mortality data using Bayesian techniques. For a United Kingdom population, using a test doseof 0.1 Sv, the mean REID value using the linear–quadratic–exponential model is 0.19% (90% CI: –0.27, 0.81)
Sv–1. However, when a higher test dose is used, risks
increase appreciably: the REID value at 1 Sv is 1.28% (90%
CI: 0.85, 1.84) Sv–1. The reason for this can be seen fromtable E.1 in appendix E, which shows that the quadraticcoefficient, β, is positive and the linear coefficient, α, negative and much smaller in absolute value. The crossovervalue for the dose at which the linear and quadratic terms
are of equal magnitude is about 0.02 Sv. For a United
Kingdom population, using a test dose of 0.1 Sv, the meanvalue for REID using the linear–quadratic model is 0.58%(90% CI: 0.13, 1.15) Sv–1. When a higher test dose of 1 Sv
is used, the value for REID increases to 1.14% (90% CI:
0.74, 1.73) Sv–1, a figure very much in line with that predicted by the linear–quadratic–exponential model. The
slightly lower risk values produced by the linear–quadratic–exponential model (at least at low test doses) is perhaps remarkable, and is a result of the incorporation of an
exponential cell sterilization term, exp[γ ⋅ D], in the dose
response, as detailed in appendix E. Although the value forthe cell sterilization coefficient, γ, is not statistically significant, its effect on the linear and quadratic coefficientsis profound, resulting in the linear term becoming smaller,
indeed even negative (but generally not statistically significantly different from zero) and the quadratic term becoming much larger (and generally statistically significant) (seetable E.1). These effects are also observed in the fitting ofsimilar models using maximum-likelihood techniques,
which produce very similar central estimates of risk.
578. Crucial to determining which of these sets ofBayesian risk estimates is best—those using the linear–quadratic–exponential or those employing the linear–quadratic models—is not straightforward, and it is not simply astatistical question. One justification for fitting a linear–quadratic–exponential model is that it allows more flexibility in the shape of the dose response. Because there are indications of a reduction in cancer risk at high doses in boththe solid cancer and the leukaemia dose response in the LSS
data (see figures VII and IX), arguably this flexibility is necessary. Both models are plausible from a radiobiologicalpoint of view, and the estimates derived for the cell sterilization term, δ, of –0.41 Sv–1 for solid cancers and –0.47
Sv–1 for leukaemia (appendix E, table E1) are not inconsistent with experimentally derived “inactivation” coefficients.
For a variety of fibroblastic and other human cell lines, theserange from –1.72 to –0.30 Gy–1, with a median of –0.65 Gy–1
[D54]. Growth-factor-stimulated CD34+ cells (haemopoietic
stem cells) have inactivation coefficients of between –2.44
Gy–1 and –0.45 Gy–1 [Z12]. Although cell sterilization is133

biologically plausible, its effect may be largely negated bycellular repopulation after radiation exposure. Crucial todetermining risk is the balance between repopulation innormal stem cells and in pre-initiated cells [S84]. There areindications of relatively efficient repopulation of cells indamaged tissue for certain solid cancers [S84], although perhaps rather less for leukaemia [L91].
579. Table 70 presents risks of solid cancer (REIC) for
the five populations being considered. Risks are calculated
assuming a number of test doses—0.01, 0.1 or 1.0 Sv. For
most cancer sites, there is not much variation in any of therisk measures by test dose. The only exceptions to this arefor the sites that one would expect, i.e. bone cancer andnon-melanoma skin cancer, for both of which non-linear
dose–response relationships are assumed (see tables 52 and53). For these two sites, the risks per unit dose strongly
increase with increasing test dose. On aggregate, values of
REIC per unit dose do not vary much with test dose. For
example, for the United Kingdom, the values for REIC
range between 15.7 and 23.1% per sievert for the generalized ERR models, and between 10.8 and 11.8% Sv–1 forthe generalized EAR models, for test doses between 0.01
Sv and 1.0 Sv. The value for non-melanoma skin cancer
accounts for the somewhat larger risk values for the United
Kingdom and United States than for the other populations
at high test doses (1 Sv). At low to moderate doses (0.01
Sv, 0.1 Sv), it contributes much less (a consequence of thequadratic–exponential dose response assumed), and indeed
for these dose levels, the United Kingdom and United Statesrisk values are on aggregate much more in line with thosefor the other three populations.
580. The choice of risk model (generalized ERR versus
generalized EAR) has somewhat greater impact dependingon the cancer sites and population considered. For theUnited States and the United Kingdom, REICs can vary byan order of magnitude or more for sites such as stomach
cancer and non-melanoma skin cancer (table 70). However,
the aggregate REIC does not vary by as much as this. The
variation is most substantial for the United States and theUnited Kingdom, where, as indicated above, the aggregate
REIC value may differ by a factor of 2 for the two sets ofmodels. For other populations, the REIC values predictedby the two sets of models (generalized ERR and generalized EAR) are generally within 20% of each other.
581. Table 71 shows that, in general, for all five populations, the aggregate REICs for women are higher than formen, irrespective of the models used. These differences
between the REIC for each sex are most marked for theUnited States and the United Kingdom. For example, for theUnited Kingdom, the REIC for men is in the range 8.6–12.9%
Sv–1, while for women it is in the range 14.8–20.8% Sv–1.
However, for certain solid cancer sites and models, thereverse situation is true. For example, risk values forstomach cancer using the generalized ERR model are higher
for men than for women in all five populations, althoughusing the generalized EAR models the reverse is the case.

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Figure XVII. Distribution of the REID from leukaemia
for various current populations, assuming a test dose,Dt, of 0.1 Sv, and using generalized linear–quadratic–
exponential ERR models fitted by Bayesian MCMC
(models described in appendix E)
Risks are calculated for a population in equilibrium (underlying
mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom
populations) from various models fitted to LSS mortality data
[P10], assuming 35% GSD errors

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582. Table 72 presents the mortality risks calculated here
against some previous estimates of risk, for all four measures of risk employed. As can be seen, the solid cancer
risk estimates (particularly excess cancer deaths, REID)
are generally somewhat lower, by factors of up to 2, compared with some previous estimates; this is true irrespective of the population considered or the assumed test dose.

United Kingdom

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For example, for a United Kingdom population, this report
estimates a value for REID at 0.1 Sv of 3.3–5.4%
Sv–1 (depending on the projection/transfer model used),
whereas the UNSCEAR 2000 Report [U2] estimateda value for a similar population of 7.9–14.4% Sv–1
(again depending on the projection/transfer model used)(table 72).

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Figure XVIII. Distribution of the REID from leukaemia forvarious current populations, assuming a test dose, Dt, of
0.1 Sv, and using generalized linear–quadratic ERR
models fitted by Bayesian MCMC (models described inappendix E)
Risks are calculated for a population in equilibrium (underlying
mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom
populations) from various models fitted to LSS mortality data
[P10], assuming 35% GSD errors

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583. For a United States population, the Committee estimates a value for REID at 0.1 Sv of 3.0–5.0% Sv–1
(depending on the projection/transfer model used), whereas
the recent BEIR VII report estimates a value at this doseof 7.4% (95% CI: 3.7, 15.0) Sv–1 [C37] (table 72). A possible reason for this slight discrepancy is that BEIR VII
assumed an adjustment to the ERR and EAR that was

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proportional to a power of attained age and an exponentialfunction of min (age at exposure, 30), i.e. the variation of
ERR or EAR with exposure age disappears above age 30(see appendix D for more details). As shown in appendix
D, there is no strong evidence from the LSS data for sucha discontinuity in adjustment for age at exposure. The
result of assuming such a variation of ERR or EAR would

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be to inflate risks for those exposed above this age.
However, one should not overemphasize this discrepancy
in view of the other uncertainties, as implied by the uncertainty interval for the BEIR estimate, as well as by theuncertainty interval for the Bayesian risk calculations performed here.
584. Leukaemia risk estimates are more similar, althougheven in this case the risks estimated here tend to be smaller
than previous estimates, by 20–30% (but in some cases bymuch more than this). For example, for a United Kingdom
population, this report estimates a value for REID at 1.0 Svof 0.8–0.9% Sv–1 (depending on the projection/transfer
model used), whereas the UNSCEAR 2000 Report estimated a value for a similar population and dose of 1.0%
Sv–1 (table 72). For a United States population, this report
estimates a value for REID at 0.1 Sv of 0.2–0.7% Sv–1,
whereas the recent BEIR VII report estimates a value at thisdose of 0.6 % Sv–1 [C37] (table 72).
585. Table 73 presents the risks of solid cancer (REIC)
calculated here against some previous estimates of risk. As
can be seen, aggregate values for solid cancer REIC are
generally similar to those previously estimated, although
there is a substantial spread in the risk estimates, depending on the projection/transfer model used. For example, fora United Kingdom population, this report estimates anaggregate value for REIC at 1.0 Sv of 10.8–23.1% Sv–1
(depending on the projection/transfer model used), whereas
the UNSCEAR 2000 Report estimated an aggregate valueof 17.0–19.3% Sv–1 (again depending on the projection/transfer model used). For a United States population, thisreport estimates an aggregate value for REIC at 0.1 Sv of
11.6–24.1% Sv–1 (depending on the projection/transfer
model used), whereas the recent BEIR VII report estimatesa value at this dose of 16.9–18.6% Sv–1 (depending on the
projection/transfer model used) [C37] (tables 70 and 73). It
may be thought remarkable that risks of solid cancer are
not vastly dissimilar from those assessed in the UNSCEAR
2000 Report [U2], in contrast to the much lower mortality
risks compared with those previously derived. However, as
shown by the analysis of table 62, a major part of the reduction in the risk estimates for solid cancer mortality is driven
by the use of different optimal risk models, with a muchsmaller part of the reduction due to alterations in the interval for follow-up and to changes in dosimetry. Models forsolid cancer incidence are generally of a different form frommodels for solid cancer mortality, and much more heterogeneous, as can be seen from tables 45 and 47–58, so it is
not unexpected that they should produce changes in risk
values of the same magnitude.
586. The risk estimates derived here using linear models
are nominally exposure risks for high dose rates, and takeno account of possible effects due to dose rate or fractionation. As discussed in section I.J (see also table 8), a DDREF
of about 2 may be applied to obtain cancer risks at low
doses and low dose rates. However, those models in whicha linear–quadratic or more general (linear–quadratic–
exponential) dose response is assumed (in particular theBayesian MCMC model fits) implicitly take account of
extrapolation of dose (if not dose rate), so that to someextent they take account of DDREF. As can be seen fromtables 59, 63 and 64, these models predict solid cancer mortality risks (REID) per unit dose at high dose (1 Sv) thatare between 20% and 185% larger than those at low doses(0.01 Sv). Likewise it is seen from tables 65, 68 and 69
that the leukaemia mortality risks (REID) per unit dose athigh dose (1 Sv) are at least 100% larger than those at low
doses (0.01 Sv).

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CONCLUSIONS
587. Since the Committee’s assessment of the risks ofradiation-induced cancer in the UNSCEAR 2000 Report[U2], more information has become available from epidemiological studies of radiation-exposed groups. There
have been substantive updates to the follow-up of the survivors of the atomic bombings at Hiroshima and Nagasaki,
both for solid cancer morbidity [P48] and for all cancer
mortality [P10]; both of these reports incorporate therecently revised (DS02) dose estimates [R12]. The latest
mortality analysis [P10] extended follow-up of the LSS
cohort another 10 years, to the end of 2000, from the 1990
follow-up available in the previous report [P1]. As of
December 2000, 45% of the cohort of 86,611 survivors werestill alive. Out of the 10,127 deaths due to solid cancer,
some 479 would be estimated to be associated with the radiation exposure incurred from either bomb detonation; andsome 93 leukaemia deaths out of 296 would be estimatedto be associated with radiation exposure [P10]. Analyses of
the RERF epidemiological data using the new DS02
dosimetry indicate that values for cancer risk factors might
decrease by about 8% as a result, with no appreciablechange in the shape of the dose response or in the age–timepatterns of excess risk [P10]. The reanalysis of the solidcancer incidence data using the DS02 dosimetry [P48]extends the follow-up to 1998 from 1994 (the year to whichdata had previously been followed up [P12]), resulting in a
total of 18,645 cases of cancer, 13,454 of these within 10
km of either hypocentre at the time of the bombings andwith a DS02 dose estimate. It is sometimes forgotten that,
despite the high doses received by some survivors (in excess
of 4 Sv), this is fundamentally a moderate dose cohort, forwhich the average colon dose is about 0.21 Sv.

with linear or linear–quadratic dose trends over a wide rangeof doses. However, analyses restricted solely to low doses
are complicated by: the limitations of statistical precision;
the potential for misleading findings owing to any small,undetected biases; and the effects of performing multiple
tests of statistical significance when attempting to establish
a minimum dose at which elevated risks can be detected.
Longer follow-up of large groups such as the survivors of
the atomic bombings should hopefully provide more information at low doses. However, epidemiology alone will not
be able to resolve the issue of whether there are dose thresholds for risk. In particular, the inability to detect increased
risk at very low doses using epidemiological methods does
not mean that the underlying cancer risks are not elevated.
However, the high-dose radiotherapy studies of patients
indicate that, for some cancers, e.g. bone, connective tissue,
rectum, uterus and small intestine, any risks at doses of
below several grays, if they exist, are small.
590. New findings have also been published from analyses of fractionated or chronic low-dose exposure to lowLET radiation, in particular the IARC 15-country nuclear
worker study [C41] (although the statistical precision ofthese studies is low in comparison with the results from thesurvivors of the atomic bombings, exposed at high dose
rates). There have also been major new analyses of the
Techa River [K49, K50] and Semipalatinsk [B58] data sets.
As noted in section II.E, there are concerns about bias inall three studies, which may explain why solid cancer risks
are substantially elevated in comparison with those seen in
the LSS cohort, although at least for the 15-country andTecha River studies, the confidence intervals for the risk
estimates are wide [C41, K50] and overlap with findings
from the studies of the survivors of the atomic bombings.
However, these studies are potentially informative aboutrisks following chronic exposure to moderate doses, oncethe various problems can be resolved. Further work to
improve dosimetry and follow-up in all three cohorts would
improve the interpretation of the studies’ findings.

588. Both these studies and further follow-up of patients
who were medically exposed to radiation have provided
additional data on cancer risks at long times after irradiation, particularly for those exposed at young ages. However,there are still uncertainties in the projection of risks fromthe current follow-up periods until the end of life, giventhat most of the people who were irradiated at young ages
are still alive. For example, 92% of those exposed under
the age of 10 in the LSS are still alive, as are 87% of those
aged between 10 and 20 at exposure [P10].

591. Particular attention has been paid in this report to risks
for specific cancer sites. Again, the information that has
become available in recent years has helped in the examination of risks. Risks have been assessed for cancer of the salivary gland, oesophagus, stomach, small intestine (including
duodenum), colon, rectum, liver, pancreas, lung, bone andconnective tissue, female breast, uterus, ovary, prostate, urinary bladder, kidney, brain and central nervous system, andthyroid, as well as for cutaneous melanoma, non-melanoma
skin cancer, non-Hodgkin’s lymphoma, Hodgkin’s disease,589. The increased statistical precision associated with thelonger follow-up and the resulting larger number of cancer
cases observed in the above studies have also been usefulin the examination of dose–response relationships, particularly at lower doses. For example, the most recent data forthe survivors of the atomic bombings are largely consistent137

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UNSCEAR 2006 REPORT: VOLUME I

multiple myeloma and leukaemia. Of these, cancers of thesalivary gland, small intestine, rectum, pancreas, uterus, ovary
and kidney, as well as cutaneous melanoma, were not considered in the UNSCEAR 2000 Report [U2]. There are still
problems in characterizing risks for some cancer sites, owing
to the low statistical precision associated with relatively small
numbers of estimated excess cases. This can limit, for example, the ability to estimate trends in risk in relation to factors
such as age at exposure, time since exposure and sex.
Furthermore, data are sometimes lacking or have not been
published in a format that is detailed enough to allow anassessment of how risks vary between populations. An exception is breast cancer, where a comparison of data on the survivors of the atomic bombings and on women with medical
exposures in North America indicates an absolute transfer ofrisks between populations. For some other sites, such as thestomach, there are indications that a multiplicative transfer
between populations would be appropriate, although the evidence is generally not strong. There are some cancer sites forwhich there is little evidence for an association with radiation (e.g. chronic lymphocytic leukaemia, pancreatic cancer,
prostate cancer, cervical cancer, testicular cancer, uterine
cancer, non-Hodgkin’s lymphoma, Hodgkin’s disease andmultiple myeloma), and others where excess risks have only
been seen following very high (radiotherapeutic) doses (e.g.
cancers of the small intestine, rectum, uterus and kidney).
While the risk evaluations for lymphomas are affected by thesmall numbers of cases in several studies, these results should
be contrasted with the clear relation found in many populations between radiation and the risk of leukaemia (excluding
CLL), which is also a rare disease. Despite the statisticalproblems posed by considering particular cancer sites, there
are indications of differences in the shape of dose response;in particular, the more substantial upward curvature in thedose response for bone cancer, non-melanoma skin cancer
and leukaemia should be noted.
592. The results presented in tables 59–73 illustrate thesensitivity of the lifetime risk estimates to variations inunderlying rates. These findings suggest that this variability can lead to differences that are comparable with the variations associated with the transfer method or method of risk
projection. Issues of uncertainty in lifetime risk estimates
are discussed in more detail in Report No. 126 of the
National Council on Radiation Protection andMeasurements [N17] and in the recent BEIR VII report[C37]. The variability in these projections highlights the difficulty of choosing a single value to represent the lifetime
risk of radiation-induced cancer. Furthermore, uncertainties
in estimates of risk for specific types of cancer are generally greater than for all cancers combined.
593. Despite these difficulties, risk estimates are of considerable value for use in characterizing the health impact
of exposure of a population to radiation. In the UNSCEAR
2000 Report [U2], models with variation of relative risk
according to age at exposure or attained age were emphasized for risk projection purposes. With the increased followup, it has become clear that those models do not fit well.

The preferred models for solid cancer mortality imply that
relative or absolute excess risk is proportional to a productof powers of the time since exposure and attained age, withlinear, linear–quadratic or linear–quadratic–exponential dose
response. The preferred models for leukaemia mortality
imply that relative excess risk is proportional to a power of
the attained age, and that absolute excess risk is proportional
to a power of the time since exposure, with in both cases a
linear–quadratic or linear–quadratic–exponential dose
response. When these models are applied to any of five specific populations (China, Japan, Puerto Rico, United Statesor United Kingdom) of all ages, the lifetime risk of exposure-induced death due to all solid cancers combined following an acute dose of 0.1 Sv is estimated to be about
3.6–7.7% Sv–1 averaged over both sexes, and at 1 Sv therisk is about 4.3–7.2% Sv–1. When Bayesian models areused, the range of mean risks is 2.3–5.4% Sv–1 following
an acute dose of 0.1 Sv, and at 1 Sv the mean risk range is
4.6–7.1% Sv–1. Leukaemia mortality risks at a dose of 0.1
Sv are estimated to be about 0.3–0.5% Sv–1 averaged overboth sexes, and at 1 Sv the risk is 0.6–1.0% Sv–1. When
Bayesian models are used, the range of mean risks is
0.2–0.7% Sv–1 following an acute dose of 0.1 Sv, and at 1
Sv the mean risk range is 1.1–1.5% Sv–1. The calculationsin this report show that these values can vary for different
populations and with different risk models. These cancer risk
estimates are somewhat lower, although not much lower,
than those previously estimated by UNSCEAR [U2], as wellas those previously estimated by other bodies, e.g. [C35,C37]. A reduction of about 10% in the solid cancer risk estimate may be due to the new atomic bombings dosimetry,
and a relatively small reduction of 3–7% may be due toincreased follow-up [P10]. However, there is a relatively
large reduction of 35–40% due to the different risk projection and transfer models used. The statistical uncertainties
in the above estimates may be of the order of a factor of
2 higher, and the lower bounds include zero. These estimates,
particularly those based on linear–quadratic or linear–quadratic–exponential models, implicitly adjust for extrapolation to low doses, so that no extra adjustment for chronic
exposure (i.e. application of a DDREF) is needed. Values of
DDREF of about 2, recommended by others [I11], areconsistent with the dose protraction effects predicted by thesemodels and with a large body of epidemiological and experimental data. Lifetime solid cancer risk estimates for those
exposed as children might be factors of 2–3 times higher thanthe estimates for the general population. For certain cancer
sites (e.g. thyroid and breast), the variation of risk with age
at exposure would be expected to be greater than implied bythis. Continued follow-up of existing irradiated cohorts will
be important in determining lifetime risks. The experience ofstudies of the survivors of the atomic bombings is consistent
with a linear dose response for the risk of all solid cancers
combined; therefore, as a first approximation, linear extrapolation of the estimates of risk following an acute dose of
1 Sv can be used for estimating solid cancer risks at lower
doses. For specific types of solid cancer, the risks estimatedin this annex are broadly similar to those presented in the
UNSCEAR 2000 Report [U2].

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ACKNOWLEDGEMENTS
594. The Committee gratefully acknowledges the considerable help (particularly in providing and reviewing thematerial for section III on site-specific cancer risks) received

by the principal consultant, M. Little, from J. Boice,
E. Gilbert, D. Hoel, P. Inskip, C. Land, J. Lubin, C. Muirhead,
D. Preston, E. Ron, R. Shore, L. Travis and R. Wakeford.

139

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TABLES
Table 1 Lung cancer risks associated with cigarette smoking and radiation exposure for the survivors of the atomic
bombings in Japan [P17]
Relative risk due to cigarette smoking a

Relative risk due to radiation exposure b

1–15 cigarettes per day

16–25 cigarettes per day>25 cigarettes per day

1 Sv

4.9

8

13.3

2.2

a Relative risk adjusted for radiation exposure, age at exposure 30, attained age 60–70.
b Relative risk adjusted for smoking, attained age 60–70.

Table 2 Illustrative scenarios showing the impact of dose level on the width of the confidence interval
The scenarios assume two groups (one exposed, one unexposed) and an ERR of 1 Gy–1
Scenario

Dose
(Gy)

Total numberof cancer cases

Proportion of personyears in exposedgroup (%)

ExposedUnexposed

A

1

50

50

33

17

0.94 (0.10, 2.56)

B

1

100

50

67

33

1.03 (0.35, 2.12)

C

1

200

50

133

67

0.99 (0.49, 1.68)

D

1

400

50

267

133

1.01 (0.63, 1.48)

E

1

800

50

533

267

1.00 (0.73, 1.32)

F

0.05

50

50

26

24

1.67 (–7.58, 17.95)

Cancers expected in each group a

Estimated b ERR(95% CI) c (Gy–1)

G

0.05

100

50

51

49

0.82 (–5.95, 10.86)

H

0.05

200

50

102

98

0.82 (–4.23, 7.49)

I

0.05

400

50

205

195

1.03 (–2.72, 5.59)

J

0.05

800

50

410

390

1.03 (–1.70, 4.16)

K

1

50

10

9

41

0.98 (–0.10, 2.88)

L

1

100

10

18

82

0.98 (0.15, 2.21)

M

1

200

10

36

164

0.98 (0.36, 1.80)

N

1

400

10

73

327

1.01 (0.55, 1.57)

O

1

800

10

145

655

0.99 (0.66, 1.38)

P

0.05

50

10

5

45

0.00 (–13.07, 25.77)

Q

0.05

100

10

10

90

0.00 (–10.24, 16.55)

R

0.05

200

10

21

179

1.12 (–6.95, 12.38)

S

0.05

400

10

42

358

1.12 (–4.88, 8.71)

T

0.05

800

10

84

716

1.12 (–3.27, 6.30)

a Assumed to be distributed according to the underlying ERR and to the
distribution of person-years, rounded to the nearest whole number.

b Maximum-likelihood value.
c Profile-likelihood-based confidence intervals.

141

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Table 3 Probabilities of various numbers of statistically significant results occurring by chance when various numbersof independent comparisons are madeNumber ofstatisticallysignificant results(at p = 0.05)

5

10

15

20

30

40

50

100

1

20.4%

31.5%

36.6%

37.7%

33.9%

27.1%

20.2%

3.1%

2

2.1%

7.5%

13.5%

18.9%

25.9%

27.8%

26.1%

8.1%

3

0.1%

1.0%

3.1%

6.0%

12.7%

18.5%

22.0%

14.0%

4

0.0%

0.1%

0.5%

1.3%

4.5%

9.0%

13.6%

17.8%

5

0.0%

0.0%

0.1%

0.2%

1.2%

3.4%

6.6%

18.0%

6

–

0.0%

0.0%

0.0%

0.3%

1.0%

2.6%

15.0%

Probability of
1 or more

22.6%

40.1%

53.7%

64.2%

78.5%

87.1%

92.3%

99.4%

Probability of
2 or more

2.3%

8.6%

17.1%

26.4%

44.6%

60.1%

72.1%

96.3%

Number of independent comparisons or tests madeTable 4 Excess relative risk of a second cancer occurring, according to whether or not the patient has anothercondition (bilateral retinoblastoma, a cancer-prone disorder) [L9]Second cancer

Study

Bone

[T10]

Brain

[L24]

First condition

ERR estimate (95% CI)(Gy–1)First cancer
Non-retinoblastoma

0.08 (0.02, 0.26)

Retinoblastoma

0.05 (0.00, 0.27)

Total

0.08 (0.03, 0.18)

Cancer-prone disorderNo

0.28 (0.05, 1.40)

Yes–0.01 (–0.04, 0.08) a

Total

0.19 (0.03, 0.85)

a Wald-based CI (likelihood bounds did not converge).

Table 5 Multiplier of the risk of a second cancer occurring after treatment with radiotherapy for a first cancer,
according to whether the first cancer is heritable or non-heritable retinoblastoma [L9, W11]First cancer

Treatment

Observed

Expected

Observed/Expected

Multiplier of radiosensitivity in heritableradioblastoma group = θNon-heritableretinoblastoma

UnirradiatedIrradiated

6
3

4.48
1.11

1.3 (0.5, 2.9)
2.7 (0.6, 7.9)

n.a.

Heritable
retinoblastoma

UnirradiatedIrradiated

10
180

1.37
4.91

7.3 (3.5, 13.4)
36.7 (31.6, 42.5)

1.62 (0.70, >10 000)

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143

Table 6 Criteria for defining the low dose range for assessing cancer risks due to low-LET radiation exposure
SourceBasis of estimation

Upper value of low dose range (mGy)

UNSCEAR 1993 Report [U5]Linear term dominant in fits to LSS data

200

UNSCEAR 2000 Report [U2]Linear term dominant in fits to LSS data

200

UNSCEAR 2000 Report [U2]Linear term dominant in fits to peripheral blood
lymphocyte chromosome aberration data

20–40

UNSCEAR 2000 Report [U2]Microdosimetric analysis of multitrack coincidences

0.8

BEIR VII report [C37]–100This reportLinear term dominant in fits to LSS data

100

Table 7 Criteria for defining the range for low dose rates for assessing cancer risks due to low-LET radiation exposure
SourceBasis of estimation

Upper value of range of low
dose rate (mGy/min)

UNSCEAR 1986 Report [U7]Data from dose-rate studies with experimental animals

0.05

UNSCEAR 1993 Report [U5]

Data from dose-rate studies with experimental animals and other biophysical data

0.1 a

UNSCEAR 2000 Report [U2]

Data from dose-rate studies with experimental animals

0.06

UNSCEAR 2000 Report [U2]Microdosimetric analysis of multitrack coincidences, based on lifetime exposure of celland no repair

10–8

UNSCEAR 2000 Report [U2]Microdosimetric analysis of multitrack coincidences, assuming DNA repair

10–3

BEIR VII report [C37]

–

0.01

a Averaged over about an hour.

Table 8 Values of dose and dose-rate effectiveness factors (DDREFs) used to assess cancer risks due to low-LET
radiation exposure
SourceICRP [I11]
UNSCEAR 1993 Report [U5]

Basis of estimation
Mainly LSS and other epidemiological data

2

Data from dose-rate studies with experimental
animals and other biophysical data

<3

LSS leukaemia mortality data
Pierce and Vaeth [P11]
LSS solid cancer mortality data
LSS leukaemia incidence data fitted to 0–4 Gy
Little and Muirhead [L37]

LSS leukaemia incidence data fitted to 0–2 Gy
LSS solid cancer incidence data fitted to 0–4 Gy
LSS solid cancer incidence data fitted to 0–2 Gy

BEIR VII report [C37]

DDREF (95% CI)

Estimates of curvature from selected data on
tumours and lifespan shortening in experimental
animals, data on chromosomal aberrations in human
lymphocytes, and LSS solid cancer incidence data

1.8 (1.0, 6.0) a
1.2 (<1, 3.4) a
2.47 (1.24, >1 000) a
1.73 (<1, 147.67) a
1.06 (<1, 1.62) a
1.21 (<1, 2.45) a

1.5 (1.1, 2.3)

a Low-dose extrapolation factor, representing the ratio of the linear dose coefficient in the fit of a linear model and the linear dose coefficient in the fit of a linear–quadratic model.

Nature of exposure

End point

Age at exposure(years)Follow-up(years)Average dose(and range) (Sv)Casesor deaths

LSS casesor deaths

ERR estimate(Sv –1)LSS ERR estimate(Sv –1)

5–61(mean 27)0.75
(0.00–8.98)

19

364

0.38 (<0, 0.60)

1.85 (1.14, 2.75)a, e[D4]

Repeated chest fluoroscopy(90 kVp X-rays) in many low-dose
fractions (each of about 10 mGy)

Mortality<24–>38(mean 33)

0–50
(mean 25)

0.84
(0.0–>8)69936–0.16 (–0.32, 0.08) b

0.59 (0.33, 0.91)c, e[G6]

Mostly 200–250 kVp X-rays in asmall number of moderate-dose
fractions

Mortality<35–>55(mean 49)

1–51
(mean 21.5)

1.17(0–1.17)

162

750

0.60 (0.17, 1.20)

0.69 (0.37, 1.09)d[H7]

Repeated chest fluoroscopy(90 kVp X-rays) in many low-dose
fractions(each of about 10 mGy)

Mortality<10–>50(mean 28)

10–57
(mean 37)

1.02
(0–24.2)

1 178

936

0.00 (–0.06, 0.07)

0.59 (0.33, 0.91)c, e

a Calculation based on lung dose, females.
b Calculation incorporates adjustment to underlying rate for age at exposure (<30 versus >30).
c Calculation based on lung dose.
d Calculation based on lung dose, age at exposure >30 years.
e LSS and radiation therapy ERR statistically inconsistent (p < 0.001).

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170–175 kVp therapeutic X-rays;
small number of high-dose-rate
fractions

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Study

144

Table 9 Excess relative risks of lung cancer in moderate- and low-dose-rate radiation therapy studies and in matched subsets of the survivors of the atomic bombingsin Japan [L20]
Subsets are matched for sex, age at exposure and years of follow-up; values with 95% CI

UNSCEAR REPORT-PART 2.qxp

Table 10 Excess relative risks of breast cancer in moderate- and low-dose-rate radiation therapy studies and in matched subsets of the survivors of the atomic
bombings in Japan [L20]
Subsets are matched for sex, age at exposure and years of follow-up; ERR estimates with 95% CI
Follow-up(years)Average dose(and range) (Sv)Cases ordeaths

LSS casesor deaths

ERR estimate(Sv –1)LSS ERR estimate(Sv –1)[S30]

Repeated chest fluoroscopy (90 kVp
X-rays) in many low-dose fractions(each of about 10 mGy)

Cancerincidence<20–>60<10–>40(mean 30)

0.27
(0–2.74)89

330–0.00 (–0.43, 0.94)

0.90 (0.47, 1.48)a[G6]

Mostly 200–250 kVp X-rays in small
number of moderate-dose fractions

Cancermortality<35–>55(mean 49)

1–51
(mean 21.5)

Unknown(0–0.17)16

100

6.07 (–3.70, 39.26)

0.74 (0.08, 1.87)b[H9]

Repeated chest fluoroscopy (90 kVp
X-rays) in many low-dose fractions(each of about 10 mGy)

Cancermortality<10–>50(mean 26)

5–57
(mean 39)

0.89
(0–18.40)

688

151

0.90 (0.55, 1.39)c

1.56 (0.41, 3.53)c[D17]

Multiple low-dose (<10 mGy) X-rays

Cancermortality

0–19(mean 10.1)

0–>70(mean 40.1)

0.11(0.00–1.70)

7767

2.7 (–0.2, 9.3)d2.62 (1.09, 5.31)ea Calculation based on breast dose, age at exposure >20 years.
b Calculation based on breast dose, age at exposure >30 years.
c Modelled ERR adjusted for age at exposure 15 years.
d Calculation based on women who received at least one radiographic examination.
e Calculation based on breast dose, age at exposure <20 years.

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Nature of exposure

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Study

145

StudyNature of exposure

Mean breast dose(and range) (Sv)

Cases

Person-years
of follow-up

ERR (95% CI)(Sv –1)

EAR (95% CI)(104 PY Sv) –1

High-dose-rate studies

1 182 306

2.10 (1.6, 2.8)a11.6 (7.3, 17)b

Rochester thymus irradiation

80–250 kVp therapeutic X-rays; small
number of high-dose, high-dose-rate
fractions

0.7 (0.02–7.5)

34

59 2220.74 (0.4, 1.2)a30 (7.7, 71)c

Acute post-partum mastitis

175–250 kVp therapeutic X-rays; small
number of high-dose, high-dose-rate
fractions

3.8 (0.6–14)

114

35 5850.56 (0.3, 0.9)d18.8 (8.1, 37)b

Low-dose-rate studies
Gothenburg and Stockholm
haemangioma

External, mainly protracted lowdose-rate gamma rays from 226 Ra
applicators

0.37 (0.02–35)e226

415 8770.34 (0.1, 0.7)d20 (6, 124)c

Massachusetts TB
fluoroscopy cohorts

Repeated chest fluoroscopy(90 kVp X-rays) in many low-dose
fractions (each of about 10 mGy)

0.8 (0.02–6)f

211

90 0260.74 (0.4, 1.2)a

5.7 (0.7, 16)ba Risk estimate based on relative risk model with adjustment for attained age, adjusted to age 50 years, takenfrom Preston et al. [P3].
b Risk estimate based on absolute risk model with adjustment for age at exposure and attained age, adjusted toage at exposure 25 years, attained age 50 years, taken from Preston et al. [P3].
c Risk estimate based on absolute risk model with adjustment for attained age, adjusted to attained age 50 years,
taken from Preston et al. [P3].

d Risk estimate based on unadjusted relative risk model, taken from Preston et al. [P3].
e Total average dose derived from individual averages in each subcohort (Gothenburg, Stockholm) weighted bynumbers of women in each subcohort.
f Total average dose derived from individual averages in each subcohort (adult, childhood) weighted by numbersof women in each subcohort.

Page 146707

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0.3 (0.02–5)

10/7/08

Single acute exposure, mixed wholebody gamma and neutron irradiation,
predominantly high-energy (>1 MeV )UNSCEAR 2006 REPORT: VOLUME I

Survivors of the atomic
bombings

UNSCEAR REPORT-PART 2.qxp

146

Table 11 Risk estimates for radiation-induced breast cancer [P3]
From epidemiological studies where the mean breast dose was acute/high-dose, or the doses were fractionated or protracted

UNSCEAR REPORT-PART 2.qxp

Table 12 Excess relative risks of leukaemia in moderate- and low-dose-rate radiation therapy studies and in matched subsets of the survivors of the atomic bombingsin Japan [L20]
Subsets are matched for sex, age at exposure and years of follow-up; ERR estimates with 95% CI
Follow-up(years)Average dose(and range) (Sv)aCases ordeaths

LSS casesor deaths b

ERR estimate(Sv –1)aLSS ERR
estimate(Sv –1)b[A13]

Thorotrast exposure (protracted
moderate-dose-rate exposure overmany years)

Cancer incidence

1–73(mean 37.4)

0–50(median 21.0)

26.8(0–171.4)

23

192

0.56 (>0, 5.50)c

5.24 (3.58, 7.55)d[H12]131I exposures (mean<2 treatments), protracted overa few days

Cancer incidence

1–75(mean 47)

2–37
(mean 21)0.01
(0.01–2.22)

130

192

–1.04 (–3.44, 3.64)

5.24 (3.58, 7.55)d, k[D2]

Small number (3–6) of moderate
doses of X-rays

Cancer incidence<20–>70(mean 53)

0–>19.6(mean 19.6)

0.39(<0.06–>1.04)

61 e

91

0.70 (–0.43, 3.48)e

6.49 (3.76, 10.99)f, l[I1]

Mixture of brachytherapy (gamma
rays from 226Ra applicators), radium,200 kVp X-rays in small number offractions (usually <10)

Cancer mortality

13–89(mean 46.5)

0–59.9(mean 24.9)

1.19(0–11)43 e

97

2.1 (0.19, 9.49)e

3.62 (1.91, 6.29)g[G6]

Mostly 200–250 kVp X-rays in small
number of moderate-dose fractions

Cancer mortality<35–>55(mean 49)

1–51
(mean 21.5)

1.55(0–1.55)

11 h

136

1.13 (–0.19, 6.45)h3.14 (1.81, 5.07)i[L6]

External, mainly protracted lowdose-rate gamma rays from 226Ra
applicators

Cancer mortality

0–1.5(mean 0.5)

0–>65
(mean 38.6)

0.13(<0.01–4.6)

20

49

2.12 (–0.70, 10.18)

14.16 (7.02, 29.12) j, ka Unless otherwise stated, all doses and risks are in terms of bone marrow dose.
b In all analyses of risks in the LSS incidence data, the three main radiogenic leukaemia subtypes (acute
myeloid leukaemia, acute lymphoblastic leukaemia, chronic myeloid leukaemia) are analysed together,using bone marrow dose.
c 95% CIs are Wald-based (likelihood bounds did not converge).
d Calculation based on full cohort.
e Acute leukaemia and chronic myeloid leukaemia.

f Calculation based on age at exposure >20 years, time since exposure <30 years.
g Calculation based on females, age at exposure >15 years.
h Leukaemia excluding chronic lymphoblastic leukaemia.
i Calculation based on age at exposure >30 years.
j Calculation based on age at exposure <15 years.
k LSS and radiation therapy ERR statistically inconsistent (p < 0.05).
l LSS and radiation therapy ERR statistically inconsistent (p < 0.01).

Page 147

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Nature of exposure

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Study

147

Second NRRW analysis [M12]2.55 (–0.03, 7.16)

First NRRW analysis [K27]
IARC 15-country study [C41]4.28 (0.40, 13.6)
1.93 (<0, 8.47)b

0.17 (–0.26, 0.70)

0.09 (–0.27, 0.52)

0.41 (–0.17, 1.15)
0.97 (0.14, 1.97)b

0.56 (–0.14, 1.48)a

0.47 (–0.12, 1.20)

IARC 3-country study [C3]2.18 (0.13, 5.7)

n.a.

n.a.

n.a.

1.59 (0.03, 3.82)d–0.07 (–0.39, 0.30)
0.25 (0.13, 0.37)e

0.26 (0.12, 0.41)f–0.02 (–0.34, 0.35)
0.31 (0.20, 0.44)eAtomic bombing survivor data [P9, P10]c

Estimated ratio of risk coefficients fromthe second NRRW analysis [M12] andatomic bombing survivor data [P9, P10]g

1.60 (<0, 5.27)

0.35 (<0, 2.10)

0.67 (<0, 2.74)

0.30 (<0, 1.67)

Estimated ratio of risk coefficients fromIARC 15-country study [C41] and atomic
bombing survivor data [P9, P10]g

1.21 (<0, 5.85)b

3.93 (<0, 8.62)b

n.a.

n.a.

Estimated ratio of risk coefficients fromIARC 3-country study [C3] and atomic
bombing survivor data [P9, P10]g

1.37 (<0, 4.31)<0 (<0, 1.22)

n.a.

n.a.

a Also excluding pleural cancer.
b 95% CI.
c Japanese male atomic bombing survivors, aged between 20 and 60 at exposure, excluding survivors with
>4 Gy shielded kerma, fitted to data of Preston et al. [P9, P10].
d Based on fitting a model of the format of BEIR [C35] to the data of Preston et al. [P10]. Values given are
relevant to exposure at age >20 years, follow-up time 2–25 years and low doses.

e Based on fitting a time-constant relative risk model with a linear dose response to the data of Preston
et al. [P10].
f Based on fitting a time-constant relative risk model with a linear dose response to the data of Preston
et al. [P9].
g The upper (respectively lower) 90%/95% confidence limit is estimated from the length of the upper(respectively lower) part of the CI for the ERR for the relevant data sets in the upper part of the table.

Page 148

0.09 (–0.28, 0.52)

3:19 pm

All malignant neoplasms

UNSCEAR 2006 REPORT: VOLUME I

All malignant neoplasms excluding
leukaemia and lung cancer

10/7/08

All malignant neoplasms
excluding leukaemia

UNSCEAR REPORT-PART 2.qxp

Leukaemia excluding chronic
lymphoblastic leukaemia

148

Table 13 Comparison of estimates (and 90% CI) of ERR per unit dose (Sv–1) in the United Kingdom NRRW [K27, M12], the IARC 3-country study [C3], the IARC
15-country study [C41] and data on the survivors of the atomic bombings in Japan, adapted from references [C3, C41, M12]

UNSCEAR REPORT-PART 2.qxp

Table 14 Excess relative risk (and 95% CI) per unit dose (Sv–1) as a function of dose range fitted to the DS02 LSS cancer mortality and incidence data
The lowest dose range with a statistically significant trend (lower 97.5 centile for ERR > 0) is highlighted in boldface for each sitea
Colondose range(Sv)

0–0.040–0.100–0.150–0.175
0–0.200–0.25
0–0.300–0.500–0.750–1.000–1.25
0–1.500–1.750–2.000–2.500–3.00All solid
cancerb

Oesophageal
cancer(ICD9 150)d

Stomachcancer(ICD9 151)

Colon cancer(ICD9 153)

Liver cancer(ICD9 155)

Lung cancer(ICD9 162)

1.45(–4.14, 7.38)
0.61(–1.94, 3.33)
0.45(–1.14, 2.14)
0.58(–0.62, 1.87)
0.52(–0.44, 1.55)
0.11(–0.66, 0.93)
0.48(–0.18, 1.18)
0.28(–0.27, 0.87)
0.53(0.02, 1.07)
0.41(–0.01, 0.86)
0.53(0.18, 0.91)
0.36(0.13, 0.60)
0.31(0.14, 0.48)
0.40(0.26, 0.54)
0.41(0.29, 0.54)
0.39(0.28, 0.50)
0.46(0.36, 0.57)
0.46(0.37, 0.57)
0.46(0.37, 0.56)
0.47(0.38, 0.56)–12.01(–36.51, 24.25)
2.34(–10.40, 21.39)–6.68(–12.77, 2.88)–3.44(–8.08, 3.82)–2.49(–6.22, 3.28)–0.09(–3.60, 5.12)–0.55(–3.48, 3.79)–1.65(–3.93, 1.80)–0.22(–2.48, 3.14)
0.82(–1.29, 3.88)
0.88(–0.94, 3.51)
1.37(–0.02, 3.29)
2.29(1.03, 4.00)
2.45(1.31, 3.99)
3.16(1.99, 4.72)
3.27(2.13, 4.76)
3.45(2.32, 4.94)
3.56(2.43, 5.03)
4.02(2.86, 5.55)
3.96(2.81, 5.47)–0.71(–5.69, 4.53)–2.13(–4.37, 0.22)–0.53(–1.94, 0.96)–0.34(–1.40, 0.78)
0.26(–0.60, 1.16)
0.22(–0.48, 0.95)
0.36(–0.23, 0.98)
0.29(–0.21, 0.81)
0.43(–0.03, 0.90)
0.58(0.20, 0.98)
0.69(0.37, 1.03)
0.52(0.31, 0.74)
0.47(0.32, 0.63)
0.55(0.42, 0.69)
0.59(0.48, 0.71)
0.57(0.46, 0.67)
0.59(0.49, 0.69)
0.60(0.50, 0.70)
0.61(0.53, 0.70)
0.61(0.53, 0.70)

0.47(–29.33, 42.57)–7.14(–18.81, 10.26)–5.24(–12.08, 4.95)–2.48(–7.93, 5.50)
0.07(–4.69, 6.93)–0.58(–4.37, 4.86)
0.15(–3.21, 4.91)–0.05(–2.91, 4.00)
0.13(–2.47, 3.82)–0.89(–2.83, 1.94)
0.10(–1.65, 2.59)
0.32(–0.86, 1.97)–0.05(–0.84, 1.09)
0.22(–0.44, 1.16)
0.31(–0.27, 1.13)
0.09(–0.40, 0.79)
0.11(–0.34, 0.77)
0.33(–0.13, 1.00)
0.57(0.11, 1.22)
0.55(0.11, 1.17)

6.22(–3.41, 16.82)–1.15(–5.31, 3.46)–1.26(–3.78, 1.52)–0.23(–2.15, 1.88)–0.54(–2.03, 1.10)–0.55(–1.75, 0.77)–0.14(–1.15, 0.98)–0.17(–1.03, 0.77)–0.43(–1.19, 0.40)
0.10(–0.54, 0.80)
0.19(–0.34, 0.78)
0.22(–0.14, 0.60)
0.12(–0.13, 0.40)
0.25(0.04, 0.48)
0.30(0.12, 0.50)
0.33(0.16, 0.51)
0.34(0.19, 0.52)
0.35(0.19, 0.51)
0.33(0.19, 0.48)
0.37(0.24, 0.51)

0.06(–16.15, 19.35)–4.62(–11.40, 3.58)–2.76(–6.97, 2.30)–0.85(–4.14, 3.09)
0.03(–2.67, 3.25)
0.96(–1.33, 3.68)
0.36(–1.55, 2.62)
0.84(–0.82, 2.80)
0.91(–0.60, 2.69)
1.49(0.17, 3.05)
1.24(0.13, 2.55)
0.81(0.09, 1.65)
0.53(0.02, 1.13)
0.48(0.07, 0.97)
0.52(0.16, 0.95)
0.63(0.29, 1.02)
0.54(0.23, 0.91)
0.49(0.19, 0.83)
0.56(0.29, 0.88)
0.58(0.32, 0.88)–8.05(–22.02, 8.50)–7.08(–13.00, 0.09)–2.97(–6.76, 1.56)–3.33(–6.04, –0.07)–1.07(–3.35, 1.66)–0.87(–2.74, 1.37)–0.26(–1.91, 1.70)–0.49(–1.88, 1.17)
0.36(–0.97, 1.94)–0.09(–1.17, 1.19)
0.19(–0.72, 1.28)
0.60(–0.05, 1.35)
0.51(0.04, 1.06)
0.63(0.23, 1.10)
0.44(0.11, 0.84)
0.35(0.06, 0.70)
0.46(0.18, 0.80)
0.50(0.22, 0.81)
0.45(0.21, 0.74)
0.41(0.19, 0.68)–0.75–20.91(–14.81, 15.86) (<–20.91, 153.10)
2.63–19.66(–4.02, 10.55) (<–19.66, 20.23)
2.36–14.08(–1.85, 7.33)(<–14.08, 6.66)
0.99–10.12(–2.12, 4.65)(<–10.12, 4.77)
1.55
–7.81
(–0.98, 4.53)(<–7.805, 3.90)
0.46
–5.56
(–1.51, 2.78)(<–5.56, 4.11)
0.66
–5.41
(–1.00, 2.62)(<–5.413, 2.48)
0.30
–3.95
(–1.09, 1.92)(<–3.952, 2.95)
0.87
–3.50
(–0.43, 2.40)(<–3.499, 2.62)
1.06
–2.79
(–0.04, 2.35)(<–2.793, 2.25)
1.33
–2.31
(0.38, 2.44)(<–2.313, 2.00)
0.56
–1.30
(–0.02, 1.24)(<–1.296, 1.54)
0.44
–1.09
(0.02, 0.93)(<–1.09, 1.01)
0.61
–0.78
(0.25, 1.02)(<–0.7806, 0.92)
0.71
–0.19
(0.38, 1.08)(<–0.1942, 2.46)
0.65
–0.28
(0.37, 0.99)(<–0.2784, 2.11)
0.80
0.48
(0.52, 1.13)(<0, 3.51)
0.78
1.12
(0.51, 1.10)(–0.18, 4.72)
0.74
1.64
(0.49, 1.03)(0.10, 5.58)
0.68
1.55
(0.45, 0.95)(0.08, 5.31)

b Using colon dose.
c Using red bone marrow dose.
d Using stomach dose.
e Using skeletal dose.
f Using brain dose.

Non-melanoma
skin cancer(ICD9 173)

Femalebreast cancer(ICD9 174)

Urinary bladder
CNS cancer
cancer(ICD9 191–192)f(ICD9 188)

–18.96(<–18.96, 6.08)
1.13(–8.85, 15.67)
0.41(–5.87, 9.52)
1.00(–3.91, 8.11)
2.19(–2.02, 8.24)
3.23(–0.47, 8.49)
1.20(–1.64, 5.23)
1.00(–1.36, 4.34)–0.13(–2.06, 2.65)–0.55(–1.99, 1.59)
0.25(–1.03, 2.11)–0.01(–0.78, 1.11)
0.21(–0.37, 1.05)
0.48(–0.06, 1.24)
0.63(0.13, 1.32)
0.93(0.41, 1.63)
1.13(0.59, 1.85)
1.18(0.65, 1.90)
1.31(0.79, 2.01)
1.33(0.82, 2.00)–5.47(–20.86, 13.51)–2.97(–9.95, 5.81)–4.61(–8.59, 0.44)–2.90(–5.89, 0.88)–1.50(–3.96, 1.60)–0.43(–2.54, 2.19)–0.11(–1.91, 2.13)
0.36(–1.21, 2.30)
1.35(–0.18, 3.22)
1.55(0.22, 3.17)
1.30(0.17, 2.66)
1.65(0.84, 2.62)
1.46(0.85, 2.19)
1.61(1.07, 2.25)
1.65(1.15, 2.25)
1.52(1.06, 2.06)
1.50(1.07, 2.02)
1.43(1.01, 1.92)
1.49(1.09, 1.95)
1.50(1.12, 1.95)

11.76–25.589.49(–19.21, 54.61) (<–25.58, 3.59) (–15.81, 43.78)
10.14
–8.81
–11.08(–5.18, 31.56)(–17.57, 5.01) (<–11.08, 1.28)
5.62
–4.01
2.77
(–3.75, 18.61)(–10.17, 5.33)(–4.27, 12.53)
3.10
0.14
0.70
(–3.74, 12.58)(–5.32, 8.12)(–4.44, 7.76)
1.46
2.30
3.83
(–3.72, 8.66)(–2.63, 9.38)(–0.84, 10.17)
0.67
1.56
3.57
(–3.38, 6.30)(–2.49, 7.36)(–0.33, 8.83)
0.35
0.50
2.23
(–2.98, 4.97)(–2.76, 5.18)(–0.93, 6.48)
0.50
–0.12
1.79
(–2.38, 4.46)(–2.73, 3.69)(–0.87, 5.36)
2.63
–0.35
0.90
(–0.30, 6.61)(–2.62, 2.98)(–1.38, 3.95)
1.92
0.07
1.12
(–0.49, 5.17)(–1.85, 2.87)(–0.79, 3.70)
1.44
1.59
2.23
(–0.55, 4.12)(–0.32, 4.29)(0.44, 4.61)
1.51
0.05
2.18
(0.13, 3.35)(–1.00, 1.57)(0.89, 3.84)
1.61
0.71
2.21
(0.52, 3.02)(–0.15, 1.92)(1.18, 3.53)
1.08
0.98
1.86
(0.24, 2.19)(0.20, 2.07)(1.00, 2.96)
1.19
0.90
2.05
(0.43, 2.18)(0.19, 1.86)(1.24, 3.09)
1.09
0.86
1.77
(0.41, 1.98)(0.22, 1.74)(1.05, 2.69)
1.05
0.87
1.54
(0.41, 1.89)(0.26, 1.72)(0.87, 2.39)
0.90
0.77
1.60
(0.30, 1.69)(0.19, 1.56)(0.95, 2.43)
0.78
0.55
1.84
(0.24, 1.48)(0.04, 1.24)(1.20, 2.65)
0.85
0.54
1.65
(0.33, 1.53)(0.06, 1.19)(1.06, 2.40)

Thyroid cancer(ICD9 193)

149

a Relative risks and profile-likelihood CIs obtained by fitting a linear relative risk model, stratifying on city, sex,
age at exposure and attained age, using data from Preston et al. [P10]. All analyses use the relevant organdose (except where indicated), adjusted for dosimetric errors (assumed 35% GSD), neutron RBE of 10. Thosesurvivors “not-in-city” (>10 km from either hypocentre) were excluded from the incidence data; survivors withshielded kerma dose >4 Gy were excluded from the mortality data.

Bone cancer(ICD9 170)ePage 149

0–0.125

Leukaemiac

3:19 pm

0–0.08All solid
cancerb

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

0–0.06

DS02 incidence

10/7/08

0–0.02

DS02 mortality

Population studied
Study

Follow-up(years)Type of study
Characteristics

National origin

Total
person-years a

Type of exposure

Type of dosimetry

Cancers studied b

EXTERNAL HIGH-DOSE-RATE EXPOSURES

Japan

5–45

2 812 863
(32.5)

Gamma and neutronradiation from nuclear
explosions

Individual estimatesderived from detailed
shielding histories

Leukaemia*, tongue, pharynx,
oesophagus*, stomach*, colon*,
rectum, liver*, gallbladder, pancreas,
nose, larynx, lung*, bone, skin, female
breast*, cervix uteri and uterus, ovary*,
prostate, bladder, kidney, brain, other
CNS, lymphoma, myeloma*

LSS [P9]

Mortality

49 114 exposed persons(≥5 mSv)
37 458 unexposed persons
Age: 0–>90

Japan

5–52

3 062 046
(35.4)

Gamma and neutronradiation from nuclear
explosions

Individual estimatesderived from detailed
shielding histories

Total solid cancer, oesophagus,
stomach, colon, rectum, liver,gallbladder, pancreas, lung, breast,
uterus, ovary, prostate, bladder, othersolid tumours

LSS [P4, T1]

Incidence

37 270 exposed persons e
42 702 unexposed persons
55.5% females
Age: 0–>90 (26.8)

Japan

13–42 f

1 950 567 g(24.4)

Gamma and neutronradiation from nuclear
explosions

Individual estimatesderived from detailed
shielding histories

Leukaemia*, non-Hodgkin’s lymphoma*,myeloma, oral cavity, salivary gland*,oesophagus, stomach*, colon*, rectum,
liver*, gallbladder, pancreas, lung*,
female breast*, non-melanoma skin*,uterus, ovary*, prostate, bladder*, CNS,
thyroid*

Survivors of theatomic bombings(in utero) [D14, Y1]

Mortality/
incidence

1 078 exposed persons h
2 211 unexposed persons
50.7% females
Exposure: in utero

Japan

5–47

n.a.i

Maternal exposure to
gamma and neutronradiation at high dose
rate

Estimated dose touterus of mother

Leukaemia, all solid cancers

Radiotherapy, including
external beam andintracavity applicationand experimental
reconstruction

Data on typical rangeof estimates for
specific organs
and phantom
measurements

Oral cavity, salivary gland, oesophagus*,stomach, small intestine*, colon,
rectum*, liver, gallbladder, pancreas*,lung*, breast, uterus, other genital*,kidney, bladder, melanoma, other skin,brain, thyroid, bone, connective tissue,
leukaemia (non-CLL)*, Myeloma,
lymphoma

Treatment of malignant disease
Cervical cancer
cohort [B11]

Incidence

82 616 exposed women
99 424 unexposed women
Age: <30–>70 (26.8)

CanadaDenmark
Finland
NorwaySlovenia
SwedenUnited Kingdom
United States

1–>30

1 278 950
(7.0)

Page 150

50 113 exposed persons c
36 459 unexposed persons
55.5% females
Age: 0–>90 (28.4)d3:19 pm

Mortality

UNSCEAR 2006 REPORT: VOLUME I

LSS [P1]

10/7/08

Exposure to atomic bombings

UNSCEAR REPORT-PART 2.qxp

150

Table 15 Cohort and case-control epidemiological studies of the carcinogenic effects of exposures to low-LET radiation
Table is expanded from table 2 in annex I of the UNSCEAR 2000 Report [U2]

Type of study

Population studied
Characteristics

National origin

Total
person-years a

Type of exposure

Type of dosimetry

Cancers studied bRadiotherapy, externalbeam or brachytherapy

Data on typical rangeof estimates for specificorgans and phantom
measurements

Oesophagus, stomach, small intestine,colon, rectum, liver, pancreas, larynx,
lung, breast, uterine corpus, vagina,
vulva, ovary, kidney, bladder, thyroid,bone, connective tissue, non-Hodgkin’s
lymphoma, Hodgkin’s disease, multiple
myeloma, leukaemia (non-CLL), total

Denmark
Finland
NorwaySwedenConnecticut
and Iowa(UnitedStates) SEER

1–>30

532 740(10.4)

Lung cancer following
breast cancer [I7]Case -control
61 cases
120 controls from acohort of 27 106 women

38 exposed women
143 unexposed women
Age: 35–72 (50)

United States

10–46(18 years
per case)n.a.

Radiotherapy

Individual doses fromtherapy records andexperimental
measurements

Lung cancer

Lung cancer following
breast cancer [Z8]Cohort(111 lung cancers
occurring in the ipsilateralbreast ≥10 years afterradiotherapy only)

28 038 exposed women
166 943 unexposed women

United States(SEER)

6 months to>15 years(only thosewith ≥10 years
of follow-up)

n.a.

External radiotherapy

Not performed (usedassessment given inreference [I7])

Ipsilateral lung cancer

Cervical cancer casecontrol [B5, B7, B8]

Case-control
4 188 cases
6 880 controls

10 286 exposed women
782 unexposed women
Age: <30–>70 (26.8)

Austria
CanadaCzech Rep.
Denmark
FinlandFranceGermany
Iceland
Italy
NorwaySlovenia
SwedenUnited Kingdom
United States

0–>30(7.0 years
per case)n.a.

Radiotherapy, including
external beam andintracavity applicationand experimental
reconstruction

Individual doses fromtherapy records

Stomach*, pancreas, small intestine,colon, rectum*, breast, uterine corpus*,
vagina*, ovary, vulva, bladder*, bone,connective tissue, leukaemia (nonCLL)*, myeloma, lymphoma, thyroid

Contralateral breast
cancer [B10]

Case-control
655 cases
1 189 controls from acohort of 41 109 women

449 exposed women
1 395 unexposed women
Age: <45–>60 (51)

United States

7–55(~13 years
per case)n.a.

Radiotherapy

Individual doses
from therapy records
and experimental
measurements

Contralateral breast among womenless than 45 years old at exposure*,contralateral breast in older womenContralateral breast
cancer [S20]Case-control
529 cases
529 controls from acohort of 56 540 women

157 exposed women
901 unexposed women
Age: <45–>60 (51)Denmark

12–47(~16 years
per case)n.a.

Radiotherapy

Individual doses
from therapy records
and experimental
measurements

Contralateral breast

Page 151

49 828 exposed women
16 713 unexposed women
Age: <40–>60

3:19 pm

Incidence

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Cervical cancer
cohort [K1]10/7/08

Follow-up(years)UNSCEAR REPORT-PART 2.qxp

Study

151

Radiotherapy

Total absorbed energy
from radiotherapy,
and location of
sarcoma in relation to
the treatment region

Soft-tissue sarcoma

110 exposed women
244 unexposed women
Age: <50–>70 (61)

United States<12(~5 years
per case)n.a.

Adjuvant radiotherapy

Individual doses
from therapy records
and experimental
measurements

Acute non-lymphoblastic leukaemia
and myelodysplastic syndrome*,
chronic myelogenous leukaemia, acutelymphoblastic leukaemia

Case-control
218 cases
775 controls froma cohort of 110 000
women

612 exposed women
351 unexposed women
30 women with unknown
exposure status
Age: <55–>75 (62)CanadaDenmark
Finland
NorwayUnited States

1–50

n.a.

Radiotherapy

Individual doses
from therapy records
and experimental
measurements

Leukaemia*

Leukaemia following
testicular cancer[T24]

Case-control
36 cases
106 controls from acohort of 18 567 men

93 exposed men
49 unexposed men
Age: <30–>50

CanadaDenmark
FinlandNetherlands
SwedenUnited States

1–>15

n.a.

Radiotherapy

Individual doses
from therapy records
and experimental
measurements

Leukaemia (other than CLL)*

Lung cancer following
Hodgkin’s disease(seven cancer
registries) [T3]

Nested case-control
222 cases
444 controls

150 exposed cases
256 exposed controls
Age: <30–>55

Ontario(Canada)Denmark
FinlandNetherlands
SwedenConnecticut andIowa (UnitedStates)

1–>20

n.a.

Radiotherapy (andchemotherapy forsome)

Individual treatment
information andexperimental
measurements

Lung cancer

Lung cancer
following Hodgkin’s
disease [K9]Case-control
98 cases259 controls

303 exposed persons
54 unexposed persons
15% female

CanadaDenmark
FinlandFranceNorwaySlovenia
United Kingdom

1–>10

n.a.

Radiotherapy

Individual doses
from therapy records
and experimental
measurements

Lung cancer

Leukaemia following
breast cancer [C9]Case-control
90 cases
264 controls froma cohort of 82 700
women

Leukaemia following
cancer of the uterine
corpus [C8]Cancers studied b

Page 152n.a.

310 exposed women
86 unexposed women
32 women with unknown
exposure status
Age: 29–86 (59)Type of dosimetry

3:19 pm

1–35(10 years
per case)

Case-control
107 cases
321 controls from acohort of 122 991
womenType of exposure

10/7/08

SwedenSoft-tissue sarcoma
following breast cancer[K18]

National origin

UNSCEAR REPORT-PART 2.qxp

Total
person-years a

Characteristics

UNSCEAR 2006 REPORT: VOLUME I

Follow-up(years)Type of study

152

Population studied
StudyTotalperson-years a

Case-control
30 cases
82 controls from acohort of 1 939 patients

101 exposed persons
11 unexposed persons
4% female
Age: <45–55 (49.4)

Netherlands

1–23

n.a.

Radiotherapy

Individual doses
from therapy records
and experimental
measurements

Lung cancer*

Breast cancer
following Hodgkin’s
disease [H20]

Incidence/mortality

855 exposed women
30 unexposed women
Age: 4–81 (28)

United States

0–29

8 832
(10)

Radiotherapy

Individual doses fromtherapy records

Breast cancer*

Breast cancer
following Hodgkin’s
disease [V8]

Case-control
48 cases
175 matched controls
from a cohort offemale patients

650 exposed women
Age: <40 at radiotherapy

Netherlands

Median17.8(5 to >25)

n.a.

Radiotherapy (pluschemotherapy forsome)

Individual dosereconstruction fromtherapy records

Breast cancer

Breast cancer
following Hodgkin’s
disease [T25]

Case-control
105 cases
266 matched controls
from a cohort offemale patients

3 817 exposed women
Age: ≤30 at radiotherapy

CanadaDenmark
FinlandNetherlands
SwedenUnited StatesMedian18.0(7 to 30)n.a.

Radiotherapy(plus chemotherapy
for 35%)

Individual dosereconstruction to thespecific breast locationfrom therapy records

Breast cancer

Leukaemia following
Hodgkin’s disease[K20]

Case-control
163 cases
455 controls
from a cohort of
29 552 patients

36% exposed
35% females
Age: (40)CanadaDenmark
FinlandFranceGermany
Italy
Netherlands
NorwaySlovenia
United Kingdom

1–>10

n.a.

Radiotherapy

Individual doses
from therapy records
and experimental
measurements

Leukaemia (non-CLL)

Leukaemia following
non-Hodgkin’s
lymphoma [T6]

Case-control
35 cases
140 controls from acohort of 11 386
women

123 exposed persons
52 unexposed persons
Age: <50–70

CanadaNetherlands
SwedenUnited States

2–25(7.6 years
per case)n.a.

Radiotherapy

Individual doses
from therapy records
and experimental
measurements

Leukaemia

Leukaemia following
non-Hodgkin’s
lymphoma [T15]

Incidence

61 exposed persons
50% females
Age: 18–70 (49.5)

United States

2–22

590
(9.7)

Total-body irradiation

Individual doses
from therapy records
and experimental
measurements

Acute non-lymphoblastic leukaemia*,
all solid cancers

Lung cancer following
Hodgkin’s disease[V2]

Type of exposure

Type of dosimetry

Cancers studied b

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Page 153

Follow-up(years)

3:19 pm

National origin

10/7/08

Characteristics

Type of study

UNSCEAR REPORT-PART 2.qxp

Population studied
Study

153

Totalperson-years a

CanadaFrance
Italy
Netherlands
United Kingdom
United States

5–48

3 109 exposed persons
1 291 unexposed persons
45% females
Age: 0–16 (7)FranceUnited Kingdom

Case-control
59 cases
220 controls, largely
within a cohort of
13 175 members

208 exposed persons
71 unexposed persons
Age: 0–14

Leukaemia afterchildhood cancer[H21]

Case-control
26 cases
96 controls

Retinoblastoma[W11]Type of dosimetry

Cancers studied b

50 609(5.5)

Adjuvant radiotherapy

Individual doses
from therapy records
and experimental
measurements

Thyroid*, leukaemia, bone sarcoma*

3–48

66 000(15)

External radiotherapy

Individual doses
from therapy records
and experimental
measurements

All solid cancers combined*, breast*,
bone*, soft-tissue sarcoma*, thyroid*,
brain*

United Kingdom

3–>20

n.a.

External radiotherapy

Individual doses fromtherapy records
and experimental
measurements

Bone cancer

88 exposed persons
34 unexposed persons
Age: 0–14

United Kingdom

1–43

n.a.

External radiotherapy

Individual doses
from therapy records
and experimental
measurements

Leukaemia

Incidence

962 exposed persons
642 unexposed persons
47% females
Age: 0–17

United States

1–>60

n.a.
(median 20)

External radiotherapy

Individual doses
from therapy records
and experimental
measurements

Soft-tissue sarcoma*, bone andsoft-tissue sarcoma*, all othercancers

Thyroid cancer
following childhood
cancer [D20]

Incidence

2 827 exposed persons

FranceUnited Kingdom

3–29

n.a.

External radiotherapy

Individual doses
from therapy records
and experimental
measurements

Thyroid cancer*

Childhood Hodgkin’s
disease [B16]

Incidence

1 380 persons
8% unexposed
35% female
Age: 1–16 (median 11)

CanadaFrance
Italy
United Kingdom
United States

0–37(median 11.4)

15 660(11.3)

Radiotherapy

Individual doses fromtherapy records andexperimental measurements

Leukaemia*, non-Hodgkin’s
lymphoma*, breast*, thyroid*,
other solid cancers*

Childhood cancers[T5, T7, T10]

Childhood cancers[D16, D19]

Incidence

Bone cancer afterchildhood cancer [H27]Page 154112 exposed persons
388 unexposed persons
45% females
Age: 0–18 (7)3:19 pm

Case-control
23 thyroid cancers
with 89 controls,
25 leukaemia with90 controls,
64 bone cancers
with 209 controls
from a cohortof 9 170 members

UNSCEAR 2006 REPORT: VOLUME I

National origin

10/7/08

Type of exposure

Characteristics

UNSCEAR REPORT-PART 2.qxp

Follow-up(years)Type of study

154

Population studied
StudyType of study

Characteristics

Follow-up(years)National origin

Total
person-years a

Type of exposure

UNSCEAR REPORT-PART 2.qxp

Population studied
Study

Cancers studied b

Type of dosimetry

Treatment of benign disease
1–67

406 355(39)

Radiotherapy

Individual organ
doses from therapy
records and phantom
measurements

Thyroid*, breast*, leukaemia, allother sites

Childhood skinhaemangioma:
Gothenburg[K14, K15, L4, L12]

Incidence

11 914 exposed persons
88% aged <1 year

Sweden0–69

370 517(31.1)

Radiotherapy

Individual organ
doses from therapy
records and phantom
measurements

Thyroid*, other endocrine glands*,
CNS*, all other sites

Benign lesions inlocomotor system[D2, J2]

Incidence/mortality

20 024 exposed persons
49% females
Age: <20–>70 (53)SwedenUp to 38

Incidence:
493 400 (24.6)
Mortality:
392 900 (19.6)

X-ray therapy

Individual red bonemarrow doses fromtherapy records andphantom measurements

Leukaemia*, non-Hodgkin’s lymphoma,
Hodgkin’s disease, multiple myeloma

Ankylosing spondylitis[W2, W8] kMortality

13 914 exposed persons
16.5% females
Age: <20–>60

United Kingdom

1–57

245 413(17.6)

X-ray therapy

Individual doses forleukaemia cases and a
1-in-15 sample of the
population

Leukaemia*, other neoplasms*(except colon)

Tinea capitis[R5, R9, R16, R17]

Incidence/mortality

10 834 exposed persons
16 226 unexposed persons
50% females
Age: <1–15 (7.1)

Israel26–38

686 210(25.3)

X-ray induced epilation

Individual doses fromphantom measurements
based on institution
and age

Incidence: thyroid*, skin*, brain*,salivary gland*, breast
Mortality: head and neck*,leukaemia*

Tinea capitis:
New York[S7, S15, S22, S68]

Incidence

2 224 exposed persons
1 380 unexposed persons
12.8% females
Age: <1–19 (7.7)

United States
24% AfricanAmerican

10–>50125 357(35)

X-ray induced epilation

Representative doses
based on standard
treatment

Thyroid*, skin*, brain*, leukaemia,
salivary gland

Acute postpartum mastitis:
New York [S5, S22]

Incidence

571 exposed women
993 unexposed women
Age: 14–>40 (27.8)

United States

20–35

38 784(25.1)

X-ray therapy

Individual doses fromtherapy records

Breast*Thymic irradiation:
Rochester [H10, H26,
S18, S22]

Incidence

2 657 exposed persons
4 833 unexposed persons
42% females
Age: 0–1

United States

23–>50237 048(31.6)

X-ray therapy

Individual doses fromtherapy records

Thyroid*, breast*, skinTonsil irradiation[S17, S21, S22, S74]

Incidence

2 634 exposed persons l
40.7% females
Age: 0–15 (4.3)

United States

0–50

88 101(33)

X-ray therapy

Individual doses fromtherapy records andphantom measurements

Skin*, thyroid*, benign parathyroid*,
salivary gland*, neural tumours*

Page 155

Sweden3:19 pm

14 351 exposed persons j
67% females
Age: 0–1.5 (0.5)

10/7/08

Incidence/mortality

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Childhood skinhaemangioma:
Stockholm[K15, L6, L7, L10,
L12, L13]

155

Characteristics

National origin

Follow-up(years)Total
person-years a

Type of exposure

Cancers studied b

Type of dosimetryn.a.

11 000(26.4)

Radiotherapy

Individual doses fromtherapy records

Thyroid*

Benign breast disease[M3, M8, M17]

Incidence

1 216 exposed women
1 874 unexposed women
Age: 10–>85

Sweden

5–60

56 900(18)

X-ray therapy

Individual doses fromtherapy records andphantom measurements

Breast*, all other sites

Metropathia
haemorrhagica[D7]mMortality

2 067 exposed women
Age: 35–60

United Kingdom

5–>30

53 144

X-ray therapy

Individual doses fromtherapy records andphantom measurements

Pelvic sites*, leukaemia*, multiple
myeloma*, lymphoma, all other sites

Benign gynaecologicaldisorders [I1, I4]

Mortality

4 153 exposed women
Age: 13–88 (46.6)

United States

0–60

109 910(26.5)

Intrauterine 226Ra

Individual doses fromtherapy records andphantom measurements

Leukaemia*, other haematolymphopoietic cancers, uterus*, bladder*,rectum*, other genital*, colon, bone
(in pelvis), liver and gallbladder,stomach, kidney, pancreas*

Lymphoid hyperplasia
screening [P5]

Incidence/prevalence

1 195 exposed persons
1 063 unexposed persons
40% females
Age: 0–17 (6.9)

United States

12–44

66 000(29)

X-ray therapy

Individual doses fromtherapy records andphantom measurements

Thyroid nodular disease*

Peptic ulcer [C4, G6]

Mortality

1 859 exposed persons
1 860 unexposed persons
19.8% females
Age: <35–>55 (49)

United States
6% non-white

20–61

92 979(25.0)

X-ray therapy

Individual doses
from therapy records
and experimental
measurements

Stomach*, colon, pancreas*, lung*,
leukaemia*, female breast,
oesophagus, liver, bladder, prostate,kidney, thyroid, non-Hodgkin’s
lymphoma, myeloma, pancreas

Diagnostic examinations
TB fluoroscopy:
Massachusetts [B3,S22]

Incidence

2 367 exposed women
2 427 unexposed women
Age: 12–50 (26)

United States

0–>50

54 609(11.4)

Multiple X-ray chest
fluoroscopies

Individual exposuresfrom medical records
and doses from phantom
measurements andcomputer simulations

Breast*, skin

TB fluoroscopy:
Massachusetts [D4]Mortality

6 285 exposed persons
7 100 unexposed persons
49% females
Age: 12–50 (26)

United States

0–>50

331 206(24.7)

Multiple X-ray chest
fluoroscopies

Individual exposuresfrom medical records
and doses from phantom
measurements andcomputer simulations

Breast*, oesophagus*, lung,leukaemia

Page 156

United States

3:19 pm

416 exposed persons
Age: (7.1)

10/7/08

Incidence

UNSCEAR 2006 REPORT: VOLUME I

Tonsil, thymus oracne irradiation [D9]UNSCEAR REPORT-PART 2.qxp

Type of study

156

Population studied
StudyType of study

Characteristics

National origin

Follow-up(years)Total
person-years a

Type of exposure

Type of dosimetry

UNSCEAR REPORT-PART 2.qxp

Population studied
Study

Cancers studied b

0–57

1 608 491
(25.1)

Multiple X-ray chest
fluoroscopies

Individual exposuresfrom medical records
and doses fromphantom measurements

Lung, breast*

Diagnostic X-rays(United States
health plans) [B17]

Case-control
565 leukaemia
318 non-Hodgkin’s
lymphoma
208 multiple myeloma
1 390 controls

2 203 exposed persons
278 unexposed persons
39% females
Age: 15–>50

United States

n.a.

n.a.

Diagnostic X-rays

Average dose basedon number and typeof procedures andestimated doses frompublished literature

Leukaemia, non-Hodgkin’s lymphoma,
multiple myeloma

Medical anddental X-rays:
Los Angeles [P7]Case-control
408 cases
408 controls

62% females

United States

2–64

n.a.

Medical and dental
diagnostic X-rays

Average dose basedon number and typeof procedures andestimated doses frompublished literature

Parotid gland*

Diagnostic X-rays:
Los Angeles [P6]Case-control
130 cases
130 controls

39% females

United States

3–20

n.a.

Diagnostic X-rays

Average dose basedon number and typeof procedures andestimated doses frompublished literature

Chronic myeloid leukaemia*

Diagnostic X-rays [I9]Case-control
484 cases
484 controls

736 exposed persons
232 unexposed persons
77% females
Age: <20–>60

Sweden

5–>50

n.a.

Diagnostic X-rays

Average dose basedon number and typeof procedures andestimated doses frompublished literature

Thyroid

Scoliosis [D17]Mortality

4 822 exposed women
644 unexposed women
Age: <3–≥10 (10.6)

United States

3–>60

218 976(40.1)

Diagnostic X-rays

Average dose based on
number of treatments
and estimated doses
from published literature

Breast*Number of exposureswith a model for dose
per exposure

Leukaemia*, all solid tumours*

EXTERNAL LOW-DOSE OR LOW-DOSE-RATE EXPOSURES
Prenatal exposures
Oxford Survey ofChildhood Cancers[B12, M18, S11]

Case-control
14 491 cases
14 491 controls

3 797 exposed persons
25 185 unexposed persons
56% females
Exposure: in utero

United Kingdom

16(max.)

n.a.

Maternal X-rays
during pregnancy

Page 157

Canada3:19 pm

25 007 exposed persons
39 165 unexposed persons
50% females
Age: <20–>35 (28)

10/7/08

Mortality

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

TB fluoroscopy[H7, H9]157

Population studied
Characteristics

National origin

Follow-up(years)Total
person-yearsa

Type of exposure

Type of dosimetry

Cancers studied b

20(max.)

n.a.

Maternal X-rays during
pregnancy

Number of exposures

Leukaemia*, solid tumours

Childhood acutelymphoblastic
leukaemia [S67]Case-control
1 811 cases
1 966 controls

273 exposed persons
3 504 unexposed persons
45.3% females
Exposure: in utero

United States

15(max.)

n.a.

Maternal X-rays during
pregnancy

Number of exposures

Acute lymphoblastic leukaemia

Sweden [N4]Case-control
624 cases
624 controls

234 exposed persons
1 014 unexposed persons
48.2% females
Exposure: in utero

Sweden16(max.)

n.a.

Maternal X-rays during
pregnancy

Number and type of
X-rays, plus trimesterand calendar period of
exposure, abstracted
blindly from medical
records

All leukaemia, lymphoblastic leukaemia,
myeloid leukaemia

Occupational exposures
15-country nuclear
worker study [C41]

Mortality

407 391 workers
10% females

Australia
Belgium
CanadaFinlandFranceHungaryJapan
Korea (Rep. of)
Lithuania
Slovakia
Spain
SwedenSwitzerland
United Kingdom
United States

Up to 47
(but variedby country)

5 192 710
(12.7)

Nuclear power plants,
fuel cycle, defence,
weapons production
and research facilities

Recorded exposures toexternal radiation

Leukaemia, all other cancers combined*

Nuclear workers inJapan [I14]

Mortality

175 939 men

Japan

Up to 12(but upto >24
since first
exposure)~1 390 000(7.9)

Nuclear power plants,
fuel processing andresearch facilities

Recorded exposures toexternal radiation

Leukaemia, all other tumours, oral/
pharynx, oesophagus, stomach, colon,
rectum, liver, gallbladder, pancreas, lung,
prostate, bladder, kidney/other urinary,brain/CNS, non-Hodgkin’s lymphoma,
multiple myeloma

Nuclear workers
in Canada, United
Kingdom and UnitedStates [C3]nMortality

95 673 workers
15% females

CanadaUnited Kingdom
United States

Up to 43

2 124 526
(22.2)

Nuclear power plants,
fuel processing andresearch facilities

Recorded exposures toexternal radiation

Leukaemia, all other cancers

Page 158

United States

3:19 pm

1 506 exposed persons
14 130 unexposed persons
49.2% females
Exposure: in utero

10/7/08

Case-control
1 342 cases
14 292 controls

UNSCEAR 2006 REPORT: VOLUME I

Northeastern UnitedStates childhood
cancers [M16]

UNSCEAR REPORT-PART 2.qxp

Type of study

158

Study

Type of study

Population studied
Characteristics

National origin

Follow-up(years)Total
person-years a

Type of exposure

Type of dosimetry

Cancers studied b

Up to 47

2 063 300
(16.5)

Nuclear power plants,
fuel cycle, defence,
weapons production
and research facilities

Recorded exposures
to external radiation

Leukaemia, all other cancers

Sellafield [C10, D11] p

Mortality/incidence

10 028 monitored workers
3 711 other workers
19% females

United Kingdom

Up to 40260 000 q(26)

Fuel processing andreactor operation

Recorded exposures
to external radiation

Leukaemia, all other cancers

United Kingdom Atomic
Energy Authority[A22, C10, F8] pMortality/incidence

26 395 monitored workers
24 972 other workers
29% females

United Kingdom

Up to 51

1 371 153
(26.7)

Nuclear and reactor
research and fuelprocessing

Recorded exposures
to external radiation

Leukaemia, all other cancers

United Kingdom
Atomic Weapons
Establishment[B14, C10] p

Mortality

9 389 monitored workers
12 463 other workers
9% females

United Kingdom

Up to 37

216 000 q(23)

Weapons research

Recorded exposures
to external radiation

Leukaemia, all other cancers

Chapelcross workers[B15, M6]

Mortality/incidence

2 209 monitored workers
419 other workers
14% females

United Kingdom

Up to 41

63 967(24.3)

Reactor operation

Recorded exposures
to external radiation

Buccal cavity and pharynx, prostate,
all cancers combined

Capenhurst uranium
facility [M4]

Mortality/incidence

3 244 radiation workers
9 296 other workers
3% females

United Kingdom

Up to 46

61 190(18.9)Uranium enrichment
plant

Recorded exposures
to external radiation(alpha dose notassessed)

Leukaemia, all other cancers, stomach,colon, rectum, lung, pleura, melanoma,prostate, bladder, brain, non-Hodgkin’s
lymphoma, all lymphohaematopoietic
cancers

Springfields uranium
workers [M5]

Mortality/incidence

13 960 radiation workers
5 489 other workers
4% females

United Kingdom

Up to 50

341 813(24.5)Uranium production
facility

Recorded exposures
to external radiation(alpha dose notassessed)

Leukaemia, all other cancers, mouth/
pharynx, oesophagus, stomach, colon,
liver, pancreas, larynx, lung, pleura, bone,connective tissue, melanoma, breast,
uterus, ovary, prostate, testis, bladder,kidney, brain, thyroid, non-Hodgkin’s
lymphoma, Hodgkin’s disease, myeloma

Canadian National Dose
Registry [A8] r

Mortality

206 620 monitored workers
49% females

CanadaUp to 37

2 861 093
(13.8)

Dental, medical,
industrial and nuclear
power

Recorded exposures
to external radiation

Leukaemia, all other cancers

Canadian National Dose
Registry [S8]Incidence

191 333 monitored workers
50% females

CanadaUp to 38

2 667 903
(13.9)

Dental, medical,
industrial and nuclear
power

Recorded exposures
to external radiation

Leukaemia, all other cancers

Atomic Energy ofCanada Ltd. [C3, G9] sMortality

11 355 monitored workers
24% females

CanadaUp to 30

198 210(17.5)

Nuclear and reactor
research and related
technologies

Recorded exposures
to external radiation

Leukaemia, all other cancers

Page 159

United Kingdom

3:19 pm

124 743 monitored workers
9% females

10/7/08

Mortality

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

NRRW[M12] oUNSCEAR REPORT-PART 2.qxp

Study

159

Population studied
Characteristics

National origin

Follow-up(years)Total
person-years a

Type of exposure

Type of dosimetry

Cancers studied b

Up to 39

89 946(15.9)

Research, inspection of
nuclear facilities, open
pit mining

Recorded exposures
to external radiation(alpha dose notassessed)

Total cancer, bone, lung, liver, stomach,
nervous system

Hanford[G8, G10] t

Mortality

32 643 monitored workers
24% females

United States

Up to 43633 511(19.4)

Nuclear fuel cycle andresearch

Recorded exposures
to external radiation

Leukaemia, all other cancers

Oak Ridge X-10 and
Y-12 plants [F5]Mortality

28 347 men

United States(white)Up to 40n.a.

Nuclear fuel cycle andresearch

Recorded exposures
to external radiation

Leukaemia, all other cancers

Rocky Flats[G8, W12]

Mortality

5 952 men

United States(white)Up to 32

81 237(13.6)

Nuclear fuel cycle andresearch

Recorded exposures
to external radiation

Leukaemia, all other cancers

Portsmouth Naval
Shipyard [S56, Y10]

Mortality

13 468 monitored workers
24 385 other workers
13% females

United States

Up to 47303 892 cc(22.6)

Work on overhaulingand building nuclear
submarines

Recorded exposures
to external radiation

Leukaemia, oesophagus, pancreas,
pharynx, larynx, lung, kidney, bladderand other urinary organs

Rocketdyne/Atomics
International [R15]

Mortality

4 563 monitored workers
6% females

United States

Up to 45

118 749(26)

Nuclear research andproduction facility

Recorded exposures
to external radiation

Leukaemia, all other cancers

Mound facility[W5]

MortalityMales
3 229 monitored workers
953 other workers

United States(white)Up to 33

78 600(18.8)

Nuclear research andproduction facility

Recorded exposures
to external radiation

Leukaemia, all other cancers

5 rem study[F3]

Mortality/incidenceMales
2 392 workers with≥50 mSv in a year

United States(white)Up to 4269 000(20)

Department of Energy
facilities or nuclear
shipyards

Recorded exposures
to external radiation

Leukaemia, digestive organs, colon, lung,
lymphopoietic, all cancers combined

Multiple myeloma(Hanford, Oak Ridge,
Savannah River,
Los Alamos) [W7]Case-control
98 cases
391 controls

11% females
5% African-American

United States

n.a.

n.a.

Four Department ofEnergy facilities

Recorded exposures
to external radiation;
indications of monitoring for radionuclides

Multiple myeloma

Non-Hodgkin’s
lymphoma (Atlanta,
Connecticut, Detroit,
Iowa, Kansas, Miami,
San Francisco, Seattle)[E10]

Case-control
1 056 cases
1 860 controls

Males
342 with reported
occupational exposure
2 574 unexposed

United States

n.a.

n.a.

Various occupations

Self-reported
occupational history,
plus job exposure
matrix

Non-Hodgkin’s lymphoma

Chernobyl clean-up
workers (cohort)[I5, I8] u

Incidence

114 504 male workers
Age: <20–≥61

RussianFederation

0–9

797 781(7.0)

Emergency andrecovery work in thevicinity of Chernobyl

Assessed externalradiation dose

Digestive*, respiratory, thyroid, all solid
tumours combined, leukaemia*

Chernobyl clean-up
workers (leukaemia
case-control) [K3]Case-control
41 cases
162 controls from acohort of 162 684 men

Males
Age: <20–>55

RussianFederation

2–9

n.a.

Emergency andrecovery work in thevicinity of Chernobyl

Assessed externalradiation dose

All leukaemia, non-CLL leukaemia

Page 160

Spain

3:19 pm

5 657
17% females

10/7/08

Mortality

UNSCEAR 2006 REPORT: VOLUME I

Spanish Nuclear
Energy Board [A32]

UNSCEAR REPORT-PART 2.qxp

Type of study

160

Study

Type of study

Population studied
Characteristics

National origin

Follow-up(years)Total
person-years a

Type of exposure

Cancers studied b

Type of dosimetry

0–7

30 643(6.5)

Emergency and recovery work in the vicinityof Chernobyl

Recorded radiation
doses

Thyroid, all other sites

Mayak workers [S28]Mortality

21 557 workers
25% femalesv

RussianFederation

0–50

720 000

Nuclear fuel cycle andresearch

Recorded exposures
to external radiation

Lung, liver and skeletal (combined)*,other solid cancers*, leukaemia*

Mayak workers:
stomach cancer study[Z3]

Case-control
157 cases
346 controls

40 persons with external
doses of above 3 Gy
463 with lower doses
10% females

RussianFederation

Up to 37

n.a.

Nuclear fuel cycle andresearch

Recorded exposures
to external radiationand measurementsof plutonium

Stomach*Medical radiologic
technologists[M10, M31, S29]

Mortality/incidence

146 02290 30573% females
95% Caucasian American

United States

Up to 72

Approx.
3 900 000
(26.7)

Medical diagnostic
X-ray

Time and durationof radiation work

Total cancer, buccal/pharynx, oesophagus, stomach, colon, rectum, liver,pancreas, larynx, lung, skin, breast,
cervix, uterus, prostate, bladder, kidney,
brain/CNS, thyroid, non-Hodgkin’s lymphoma, multiple myeloma, leukaemia

Radiological
technologists[A3]

Mortality

9 179 radiological
technologists(2 300 with recorded doses)

Japan

Up to 28

n.a.

Radiology

Recorded exposures
to external radiation

All cancers combined*, oesophagus,
stomach, colorectal, lungRadiotherapy staff[A6]

Incidence

4 151 persons
Age: <20–≥50

Denmark

Up to 32

49 553(11.9)

Work in radiotherapy
departments

Recorded exposures
to external radiation

Leukaemia, prostate*, all other cancers

Nuclear workers[R54]

Mortality

22 395 monitored persons
3.4% females

FranceUp to 33
(average
11.7)

261 418

Reactor operation

Recorded exposures
to external radiation

Leukaemia, all other cancers

Continuous backgroundradiation

Individual estimates,
both direct (TLD
measurements) andindirect (environmental
measurements andoccupancy patterns)

Leukaemia, all other sites

Gamma radiation

Measurements indwelling occupied forsix months or moreprior to diagnosis

Leukaemia, non-Hodgkin’s lymphoma,
Hodgkin’s disease

Natural sources of radiation
Mortality

89 694 persons inhigh-background area
35 385 persons in control
area
50% females
All ages

China

Childhood Cancer
Study [U17]Case-control
Approx. 800 leukaemia
cases, 160 nonHodgkin’s lymphoma
cases, 70 Hodgkin’s
disease cases

Similar proportions ofmales and females
Age at diagnosis: 0–14
Mean annual absorbed dosefor controls: 0.843 mGy

United Kingdom

Up to 17

1 698 350
(13.6)

n.a.

n.a.

Page 161

Estonia

3:19 pm

4 742 men
Age: <30–>60

10/7/08

Mortality/incidence

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Chernobyl clean-up
workers[I10, R11, T13]

Yangjiang[A11, S23, T12, T14,
T16, Z2] w

UNSCEAR REPORT-PART 2.qxp

Study

161

Population studied
Characteristics

National origin

Follow-up(years)Total
person-years a

Type of exposure

Type of dosimetry

Cancers studied b

Case-control
312 cases
1 418 controls

Age at diagnosis: 0–19
% females unknown

Swedenn.a.

n.a.

Gamma radiation

Measurements outsidedwellings known tohave been built fromalum shale concrete

Acute lymphoblastic leukaemia

Central Italy[F7]

Case-control
44 cases
211 controls

Males
Age at diagnosis: 35–80 (68)
76% with gamma dose rate
above 300 nGy/h

Italy

10

n.a.

Gamma radiation
Radon

Measurements in lastdwellings occupied
and characteristics ofdwellings

Acute myeloid leukaemia
10/7/08

Sweden[A24]

UNSCEAR REPORT-PART 2.qxp

Type of study

162

Study

3:19 pm

INTERNAL LOW-DOSE-RATE EXPOSURES
Medical exposures
36 792 exposed persons
80% females
Age: 1–75 (43)

Sweden5–39

885 618(26.1)

Diagnostic 131I

Individual values ofactivity administered;organ dose estimates
for thyroid

Thyroid, leukaemia, all other sites

131
I hyperthyroidism[H6, H24] yIncidence/mortality

10 522 exposed persons
82% females
Age: 13–70

Sweden

1–26

139 018(13.6)

Treatment ofhyperthyroidism

Average administeredactivity (multiple
treatments)

Stomach*, kidney*, brain*, all othersites zThyrotoxicosis patients[D12, R3, S24] aa

Incidence/mortality

23 020 exposed persons
12 573 unexposed persons
79% females
Age: <10–80

United States

0–45

738 831(20.8)

Treatment ofhyperthyroidism

Individual values ofactivity administered;organ dose estimates

Buccal cavity, oesophagus, stomach,colorectal, liver, pancreas, larynx, lung*,
breast*, uterus, ovary, prostate, bladder,kidney*, brain and other CNS tumours,
thyroid*, lymphoma, myeloma, leukaemia

131
I hyperthyroidism[F1]

Incidence/mortality

7 417 exposed persons
83% females
Age: ≤49–≥70 (57)

United Kingdom

1–≥20

72 073(9.7)

Treatment ofhyperthyroidism

Individual values ofactivity administered

Thyroid*, bladder, uterus, small bowel*,all other sites

131
I thyroid cancer[H2]

Incidence

834 exposed persons
1 121 unexposed persons
75% females
Age: 5–75 (48)

Sweden

2–34

25 830(13.2)

Treatment of thyroid
cancerIndividual values ofactivity administered

Leukaemia, salivary gland*, kidney*,
all other sites

Therapeutic 131I
[D18]

Incidence

846 persons withtherapeutic exposures
501 persons with diagnostic
exposures
274 unexposed persons
79% females
Age: 5–89 (40)France2–37

14 615(10)

Diagnostic andtherapeutic 131I
exposures for thyroid
cancer patients

Individual values ofactivity administeredand organ dose
estimates

Colon, leukaemia, all other sites

Page 162

Incidence

UNSCEAR 2006 REPORT: VOLUME I

Diagnostic 131I
[D42, H8, H12, H14] x

131
I thyroid cancer
patients [R38]

Type of study
Incidence

Population studied
Characteristics

France
Italy
SwedenFollow-up(years)Total
person-years a

Type of exposure

Type of dosimetry

Cancers studied b

2–55

n.a.
(13)

Treatment of thyroid
cancerIndividual values of 131I
activity administered

All solid cancers*, soft tissue and bone*,colorectal*, breast, leukaemia*

Environmental exposures
Up to 50865 812Internal and external
exposures to radioactive
waste discharged
by nuclear weapons
production plant

Dose reconstruction
based on environmental
measurements of
gamma dose rate andwhole-body counting

Leukaemia*, all solid cancers otherthan bone*

Extended Techa River
cohort: leukaemia
case-control study[O13]

Case-control
83 cases
415 controls

59% females
Age: 9–83 (54.3) dd

RussianFederation(ethnic
Russians
and Tartars/
Bashkirs)

Up to 47

n.a.

Internal and external
exposures to radioactive
waste discharged
by nuclear weapons
production plant

Dose reconstruction
based on environmental
measurements of
gamma dose rate andwhole-body counting

Leukaemia*

Chernobyl-related
exposure in Belarus,
Russian Federationand Ukraine [D52]

Case-control
421 cases
835 controls

44% females
Age at exposure: in utero
and 0–5
Age at diagnosis: 0–19

Belarus,
RussianFederationand Ukraine

Up to 14n.a.

Internal and external
exposure in areas
contaminated by the
Chernobyl accident

Dose reconstruction
based on environmental
measurements andmodelling of external
and internal doses

Leukaemia*

Chernobyl-related
exposure in Ukraine[N6]

Case-control
98 cases
151 controls

44% females
Age: 0–20

UkraineUp to 11n.a.

Internal and external
exposure in areas
contaminated by the
Chernobyl accident

Dose reconstruction
based on environmental measurements andmodelling of external
and internal doses

Leukaemia*

Chernobyl-related
exposure in Belarus[A10]

Case-control
107 cases
214 controls

52% females
Age: 0–16

Belarus

Up to 6n.a.

Internal exposure toradioactive iodine inareas contaminated by
the Chernobyl accident

131
I dose estimatedfrom ground depositionof 137Cs and 131I, fromcontemporary thyroid
radiation measurements, and fromquestionnaires andinterviews

Thyroid*

Semipalatinsk:
leukaemia case-control
study [A23]

Case-control
22 cases, 132 controls
from a cohort of~10 000 persons

All ages, both sexes

Kazakhstan

Up to 49

n.a.

Short-lived
radionuclides from
nuclear weapons
tests

Based on residence
histories and age at
exposure

Non-CLL leukaemia

Page 163

RussianFederation(ethnic
Russians
and Tartars/
Bashkirs)

3:19 pm

29 873 residents~60% females
Age: <20–>60

163

Mortality

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Extended Techa
River Cohort[K49, K50]

10/7/08

6 676 patients
4 225 treated with 131I
1 194 treated with externalbeam radiotherapy(9% received both typesof treatment)

National origin

UNSCEAR REPORT-PART 2.qxp

StudyPopulation studied
Characteristics

National origin

Follow-up(years)Total
person-years a

Type of exposure

Type of dosimetry

Cancers studied b

2 273 exposed persons
55% females
Age: 5–>60

MarshallIslands

29–31

n.a.

Short-lived
radionuclides from
nuclear explosion

Estimated average
dose; distance wasalso used as asurrogate

Thyroid

Utah 131I fallout:
thyroid disease[K19]

Prevalence

2 473 persons

United States

12–17
and
32–33 bbn.a.

Fallout from nuclear
weapons tests

Based on residence
histories and fallout
deposition records

Thyroid

Utah 131I fallout[S2]

Case-control

92 persons with bonemarrow doses of 6 mGyor more6 415 persons with lower
doses

United States

Up to 30

n.a.

Fallout from nuclear
weapons tests

Based on residence
histories and fallout
deposition records

Leukaemia

Nuclear fuel cycle andresearch

Urine measurements
and whole-bodymonitoring

Prostate*

3:19 pm

Prevalence

Males
Age at diagnosis:<65–>75
14% of subjects with
documented internal
exposure

United Kingdom

n.a.

a Mean per person in parentheses.
b An asterisk denotes sites for which statistically significant excesses are reported in the exposed group(cohort studies) or for which a higher proportion of the cases were exposed to radiation (case-control studies).
c Exposed to more than 0.005 Sv weighted colon dose.
d Age at exposure, mean in parentheses.
e Exposed to more than 0.01 Sv weighted colon dose.
f 5–42 years for leukaemia and lymphomas [P4].
g Based on the follow-up for solid cancer [T1].
h Figures quoted are for the mortality study [D14]. Exposure denotes doses of above 0.01 Sv.
i Not available.
j Figures quoted in reference [L10].
k Figures quoted are for the leukaemia study [W2].
l Figures quoted in reference [S21].
m Significance tests based on 5-year survivors (2 years for leukaemia).
n Includes workers in studies [B14, C10, D11, F8, G8, G9, G10, W12, W16].
o Includes workers in studies [B14, B15, C10, D11, F8].

n.a.

p Figures quoted are from reference [C10].
q Values for monitored workers only.
r Includes workers in study [G9].
s Figures quoted are from reference [C3].
t Figures quoted are from reference [G8].
u Figures quoted are from reference [I8].
v Figures quoted are from reference [K16].
w Figures quoted are from reference [T14, T16].
x Figures quoted are for the thyroid cancer study [H14].
y Figures quoted are for the incidence study [H6].
z Significance tests based on 10-year survivors.
aa Figures quoted are from reference [R3].
bb Periods of thyroid examinations, relative to the peak fallout in 1953 [K19].
cc Values for monitored workers only [Y10].
dd Values for controls.

Page 164

Case-control
136 cases
404 controls

UNSCEAR 2006 REPORT: VOLUME I

Occupational exposures
United Kingdom Atomic
Energy Authority:
prostate cancer [R14]10/7/08

Marshall Islands
fallout[H25, R13]

UNSCEAR REPORT-PART 2.qxp

Type of study

164

Study

UNSCEAR REPORT-PART 3.qxp

Table 16 Cohort and case-control epidemiological studies of carcinogenic effects of exposures to high-LET radiation
Study

Type of study

Population studied
Characteristics

National origin

Follow-up(years)Total
person-years a

Type of exposure

Type of dosimetry

Cancers studied b

Medical exposures
0–54

23 400(28.8) d

Injection with 224Ra

Internal dosimetriccalculations based onamount injected

Bone*, breast*, connective tissue*,liver*, kidney*, thyroid*, ovary,leukaemia, pancreas, uterus, prostate,
bladder*, stomach, colon, lung224
Ra ankylosing
spondylitis patients[W9, W15]

Incidence

1 577 exposed persons
1 462 unexposed persons

Germany

0–51

63 500(20.8)

Injection with 224Ra

Information on amountinjected

Bone and connective tissue, leukaemia*,
non-Hodgkin’s lymphoma, Hodgkin’s
disease, stomach, liver, lung, urinary
system, female breast

Cohort with cerebral
angiography[T4, T30]

Mortality

1 736 with Thorotrast
1 407 with non-radioactive
agent
45% females
Age: <20–>60 (33.9)

Denmark (45%)
Sweden (29%)
United States
(26%)

2–>50

37 542(26.6)

Thorotrast

Volume of Thorotrast
injected (available on
80% of patients)
length of exposure

Total cancer, leukaemia, lung, pancreas,kidneyCohort with cerebral
angiography [N1]

Incidence

432 with Thorotrast
44% females
Age: <20–>40 (34)

Sweden

1–>40

7 284
(34)

Thorotrast

Injected volume of
Thorotrast (availableon 55%; number of
injections used forremainder); mean
injected volume:
3–52 mL (15.5)

Total cancer, stomach, small intestine,colon, rectum, liver, pancreas,
respiratory, uterine corpus, ovary,
prostate, kidney, bladder, skin (nonmelanoma), brain/CNS, thyroid,
connective tissue, sarcoma, leukaemia

Thorotrast patients[V1, V4]

Incidence

2 326 exposed persons
1 890 unexposed persons
26% females

Germany

3–>50

n.a. eInjection withThorotrast

Hospital records ofamounts injected;
computerized
tomographymeasurements ofsome patients;
X-ray films

Liver*, extrahepatic bile ducts*,
gallbladder, myeloid leukaemia*,pancreas*, myelodysplastic syndrome*,
non-Hodgkin’s lymphoma*,
plasmacytoma, larynx, bone sarcoma,lung, mesothelioma*, Hodgkin’s disease,
lymphoblastic leukaemia, kidney,
bladder, prostate, adrenal, brain,
gastrointestinal tract

Cohort with mainly
cerebral angiography[D27]

Mortality

1 096 with Thorotrast
1 014 with non-radioactive
agent
38% females
Age: <20–79

Portugal

0–>50(22.2)

13 283(for >5
years after
exposure)

Thorotrast

Volume of Thorotrast
injected (available for
92% of the exposed
patients)

Liver*, lung, bone, breast, brain,leukaemia*, all lymphoblastic andhaematopoetic*

Early Thorotrast
patients [M14, M19]

Mortality

262 exposed persons
1 630 unexposed persons
Age: 20–39

Japan

18–68

n.a.

Injection withThorotrast

Amount injected

Liver*, lung, bone sarcoma, leukaemia*

Page 165

Germany

3:37 pm

899 exposed persons
31% females
24% aged c ≤20 years

10/7/08

Incidence

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER224
Ra TB and ankylosing
spondylitis patients[H53, N2, N3, S79]

165

Mortality

Population studied
Characteristics
150 exposed persons
Age: 15–39

National origin

Follow-up(years)Total
person-years a

Japan

34–65

n.a.

Type of exposure

Cancers studied b

Type of dosimetry

Injection withThorotrast

Amount injected

Liver*, lung, leukaemia*

Occupational exposures: radium
2 543 females

United States

0–69.5119 020

Ingestion of 226Ra and228
Ra

Body burdens of about
1 500 women assessedby measurement of
gamma rays and/or
exhaled radon, used
for calculation ofsystemic intake andskeletal dose

Bone sarcoma*, paranasal sinuses andmastoid air cells*, stomach, colon,
rectum, liver, lung, breast*, pancreas,
brain and other CNS tumours, leukaemia,
multiple myeloma

Radium luminizers[B54, B55]

Mortality

1 203 females

United Kingdom

47 (max.)

44 883Work with radium

Some measurementsof body burdens
Assessments ofexternal doses

Breast, leukaemia, osteosarcoma,
all cancers combined

Plutonium, radiochemicalor reactor work

Bioassays forplutonium andrecorded dose due
to external exposures

Lung

3:37 pm

Incidence/mortality

10/7/08

Radium luminizers[C11, S12, S16, S25]

UNSCEAR REPORT-PART 3.qxp

Later Thorotrast
patients [K48, M14]

Type of study

166

Study

Mortality

1 669 men employed
between 1948 and 1958,
with plutonium bioassays
2 172 reactors workers
exposed only to gamma
rays

RussianFederation

Up to 46(39.8)

25 727
plutonium
exposed;85 151
gamma
exposedMayak plutonium
workers: liver cancer
study [G2]Mortality

2 207 with detectable
plutonium body burden
31% females

RussianFederation

Up to 49

n.a.

Plutonium or radiochemical work

Bioassays forplutonium andrecorded dose due
to external exposure

Liver

Sellafield plutonium
workers [O1]

Incidence/mortality

5 203 plutonium workers
4 609 of whom had
plutonium dose assessed
5 179 other radiation
workers
4 003 non-radiation workers
19% females

United Kingdom

Up to 46
for mortality(29);
Up to 40 for
incidence

415 432(29)

Nuclear fuel cycle andresearch

Measurement of
plutonium in urine,
recorded exposures
to external radiation

Stomach, colon, pancreas, lung , pleura,breast, prostate, bladder, brain and other
CNS tumours, ill-defined and secondary,non-Hodgkin’s lymphoma, leukaemia

Rocky Flats workers[W12]

Mortality

5 413 males with external
United States
and/or plutonium exposures

Up to 28

52 772(9.7)

Nuclear fuel cycle andresearch

Measurement of
plutonium in urine,
recorded exposures
to external radiation

Buccal cavity and pharynx, oesophagus,
stomach, colon, rectum, liver andgallbladder, pancreas, larynx, lung, bone,skin, prostate, bladder, kidney, brain
and other CNS tumours, thyroid, nonHodgkin’s lymphoma, leukaemia, otherlymphoblastic, benign and unspecified*Page 166

Mayak workers as
plutonium or radiochemical workers: lungcancer study [K8]UNSCEAR 2006 REPORT: VOLUME I

Occupational exposures: plutonium

Type of study

Population studied
Characteristics

National origin

Follow-up(years)Total
person-years a

Type of exposure

Type of dosimetry

UNSCEAR REPORT-PART 3.qxp

Study

Cancers studied b

Up to 50n.a.

Nuclear fuel cycle andresearch

Classification of jobs
according to potential
for plutonium
exposure, number ofyears in such jobs

All cancers combined, digestive, lung,brain, lymphoma

Los Alamos workers[W6]

Mortality

3 775 males with plutonium
body burdens of 74 Bq or
more
11 952 males with lower
body burdens

United States

Up to 47456 637(29)

Nuclear fuel cycle andresearch

Measurement of
plutonium in urine,
recorded exposures
to external radiation

Oral, stomach, colon, rectum, pancreas,
lung, bone, prostate, bladder, kidney,
brain and other CNS tumours, alllymphoblastic/haematopoietic cancers

Occupational exposures: others (excluding radon in mines)
Three industry
workforces [C40]

Mortality

17 605 workers monitored
for radionuclide exposure
23 156 other radiation
workers
8% females

United Kingdom

Up to 43

1 020 000
(25)

Nuclear fuel cycle andresearch

Data on monitoring forplutonium, tritium andother radionuclides

Lung, pleura, skin, uterus, prostate,
multiple myeloma, leukaemia, othercancers

Oak Ridge,
Y-12 workers [C6]MortalityMales
3 490 workers with internal
exposure monitoring data
3 291 other workers
Age at entry: 16–64

United States

0–33

133 535(19.7)

Nuclear fuel cycle andresearch

Urine measurements
and whole-bodymonitoring of internallydeposited uranium

Lung, brain and other CNS

Mound workers[W36]

Mortality

4 402 males

United States(white)Up to 40

104 326(23.7)

Nuclear fuel cycle andresearch

Measurement ofpolonium in urine

Oral, oesophagus, stomach, colon,
rectum, liver, pancreas, lung, bone, skin,prostate, bladder, kidney, brain and other
CNS tumours, thyroid, non-Hodgkin’s
lymphoma, Hodgkin’s disease, leukaemia

Fernald workers[D43, R43]

Mortality

4 014 males
Age at entry: (30.4)

United States

0–49

124 177(30.9)

Nuclear fuel cycle andresearch

Measurement of
uranium, thorium andradium compounds
in urine, plus environmental area sampling;
recorded exposures toexternal radiation

Buccal cavity and pharynx, oesophagus,
stomach, colon, rectum, liver, pancreas,
larynx, lung, bone, skin, prostate, testis,
bladder, kidney, eye, brain and other
CNS tumours, thyroid, non-Hodgkin’s
lymphoma, Hodgkin’s disease, leukaemia

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3 065 workers in jobs withroutine potential forplutonium exposure
8 266 workers in jobs with
non-routine or limited
potential for plutonium
exposure
15 058 workers in jobs
with minimal potential forplutonium exposure
25% females

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Mortality

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Hanford workers
[W22]

167

National origin

Follow-up(years)Total
person-years a

17 929 males
Age at entry: (median 25)

United States

Up to 44

Cancers studied b

Type of dosimetry

545 867(30.4)

Exposures in mining
and chemical processingof phosphate ores

Assessment of
cumulative exposures
to alpha and gamma
radiation based on job
histories

Lung

Mortality

Mill workers [P25]

Mortality

Rocketdyne/Atomics
International [R1]

Mortality

2 297 workers
3% females
Age at entry: (34.5)

United States

Up to 45

58 837(25.6)

Nuclear research anddevelopment

Measurement of
uranium, mixed fission
products, strontium,
caesium and plutonium
in urine and faeces,plus in vivo wholebody and lung counts;
recorded exposures toexternal radiation

All cancers combined, all haematopoietic
and lymphopoietic cancers, lung, upper
aerodigestive tract cancers, bladder andkidney, prostate

Iron and steel workers[L86]

MortalityMales
5 985 exposed
2 849 unexposed

China

Up to 17111 286(12.6)

Thorium-containing
dust in an iron andsteel company

Assessment of lungdoses due to inhalation

Lung, leukaemia*

United States

Mortality

5 002
All males

Czech Republic

Up to 48(25.5)

127 397

Radon

Measurement
data plus dosereconstruction

Lung

Uranium miners[R39]

Mortality

4 134 exposed
All males

FranceUp to 49
(26.2)~108 000

Radon

Dose reconstruction
before 1956, exposure
records from 1956 on

Lung

Exposures to radon in residences
Iowa case-control
study [F6, F12]

Incidence413
100% females

United States

n.a.

n.a.

Radon

Four 1-year alpha track
detectors per home
plus regional outdoor
measurements

Lung

Cohort study [T38]

Mortality

11 800 exposed
Female % n.a.

Czech Republic(49.4)

582 751

Radon

Direct measurements
in homes; village
means

Lung

Acute lymphoblastic
leukaemia study[L85]

Case-control
505 cases
443 controls

48% females
Age at diagnosis: 0–14
10% with time-weighted
average radon concentrations above 148 Bq/m3

United States

n.a.

n.a.

Radon in homes

Track-etch detector
measurements inhomes occupied bysubjects

Acute lymphoblastic leukaemia

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Uranium miners[T33]

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Occupational exposures: radon in mines

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Florida phosphateworkers [C39]10/7/08

Type of exposure

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Characteristics

Type of study

168

Population studied
Study

Characteristics

National origin

Follow-up(years)Type of study

Total
person-years a

Type of exposure

Type of dosimetry

Cancers studied b

n.a.

n.a.

Radon in homes

Track-etch detector
measurements inhomes occupied bysubjects at time ofdiagnosis

Acute lymphoblastic leukaemia

West German
childhood cancer [K47]

Case-control
82 leukaemia cases
82 solid tumour cases
209 controls

Age at diagnosis: 0–14
Mean time-weighted
average radon concentration 27 Bq/m 3

Germany

n.a.

n.a.

Radon in homes

Track-etch detector
measurements in homes
occupied by subjects
for at least one year

Leukaemia, solid tumours

Childhood Cancer
Study [U16]

Case-control
805 leukaemia cases(1 306 controls)
166 non-Hodgkin’s
lymphoma cases(265 controls),
72 Hodgkin’s disease
cases (136 controls)

Similar proportions of males
and females
Age at diagnosis: 0–14
2.8% of controls with radon
concentrations of
100 Bq/m3 or more

United Kingdom

n.a.

n.a.

Radon in homes

Measurements inhomes occupied forsix months or more
Characteristics ofhomes

Leukaemia, non-Hodgkin’s lymphoma,
Hodgkin’s disease

Adult acute leukaemia[L55]

Case-control
578 cases
983 controls

Age at diagnosis: 16–69
5% with radon concentrationsof 100 Bq/m3 or more

United Kingdom

n.a.

n.a.

Radon in homes

Track-etch detector
measurements in homes
occupied by subjects
at time of diagnosis

Acute leukaemia

Central Italy [F7]Case-control
44 cases
211 controls

Males
Age at diagnosis: 35–80 (68)75% with radon concentrations above 100 Bq/m3

Italy

10

n.a.

Radon and gamma
radiation in homes

Measurements inlast homes occupied
and characteristics ofhomes

Acute myeloid leukaemia

Case-control [B41]

Case-control
486 cases
984 controls

Cases (under age 75) andcontrols selected from five
university hospitals
43% of cases and 40% of
controls with time-weighted
average radon concentrationsof 100 Bq/m3 or more

Francen.a.

n.a.

Radon in homes

Radon measured ineach home occupied
for at least 1 year inlast 5–30 years

Lung

Measurements ofdrinking water from
drilled wells

Leukaemia

Uranium and other radionuclides in drinking water
Case-cohort [A25]

Case-cohort
35 cases
Sample of 274 persons
from larger cohort

41% females

Finlandn.a.

a Mean per person in parentheses.
b An asterisk denotes sites for which statistically significant excesses are reported in the exposed group(cohort studies) or for which a higher proportion of the cases were exposed to radiation (case-control studies).

n.a.

Uranium, 226Ra andradon in drinking waterc Age at first exposure, mean in parentheses.
d Figures quoted are for 812 persons with complete information [N3].
e Not available.

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51% females
Age at diagnosis: 0–17
Mean time-weighted average radon concentration
53 Bq/m 3 (14% above
100 Bq/m3)

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Case-control
173 cases
254 controls

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Acute myeloid
leukaemia study [S80]

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Population studied
Study

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Table 17 Strengths and limitations of major cohort and case-control epidemiological studies of carcinogenic effects of
exposures to low-LET radiation
Study

Strengths

Limitations

EXTERNAL HIGH-DOSE-RATE EXPOSURES
Exposures to atomic bombings
LSS [P1, P4, P9, P10,
P48, T1]

Large population of all ages and both sexes, not selected because of
disease or occupation
Wide range of doses
Comprehensive individual dosimetry
Survivors followed prospectively for up to 45 years
Complete mortality ascertainment
Cancer incidence ascertainment

Acute, high-dose-rate exposure that provides no direct information on
effects of chronic low-dose-rate exposure
Restriction to 5-year survivors for mortality (13 years for incidence)
Possible contribution of neutrons somewhat uncertain
Possible effects of thermal or mechanical injury and conditions
following the bombings uncertain

Survivors of atomic
bombings (in utero)[D14, Y1]

Not selected for exposure
Reasonably accurate estimate of doses
Mortality follow-up relatively complete
Follow-up into adulthood

Small numbers of exposed individuals and cases
Cancer case determination may not be complete
Mechanical and thermal effects may have influenced results

Treatment of malignant disease
Cervical cancer cohort[B5, B7, B11]

Large-scale incidence study based on tumour registry records
Long-term follow-up
Relatively complete ascertainment of cancers
Unexposed comparison patients

Very large doses to some organs result in cell killing and tissue
damagePotential misclassification of metastatic disease for some organs
Potential misclassification of exposure
No individual dosimetry
Characteristics of patients with cervical cancer differ from general
population

Cervical cancer
case-control [B8]Comprehensive individual dosimetry for many organs
Dose–response analyses
Other strengths as above [B5]As above [B5], except that problems with individual dosimetry andcomparison with general population now removed
Small number of unexposed cases
Partial-body and partial-organ dosimetry complex

Lung cancer following
breast cancer [I7]

Individual estimates of radiation dose to different segments of
the lungs
Large number of unirradiated patients
Most patients did not receive chemotherapy
Substantial proportion of patients with over 20 years of follow-up

Small number of lung cancers
Lack of data on individual smoking habits
Potential inaccuracies in partial-body dosimetry

Contralateral breast
cancer [B10, S20]

Large numbers of cases within population-based tumour registries
Individual radiation dosimetry
Wide range of doses

Limited number of young womenPossibility of overmatching, resulting in some concordance of
exposure between cases and controls
Possible misclassification of metastases or recurrence

Soft-tissue sarcoma
following breast cancer[K18]

Cases identified from a population-based tumour registry

Analyses based on estimates of energy imparted from radiotherapy(i.e. product of the mass of the patient and the absorbed dose),rather than organ dose

Leukaemia following
breast cancer [C9]Comprehensive individual dosimetry for bone marrow compartments
Comprehensive ascertainment of treatment information to separate
chemotherapy risk
Dose–response analyses

Very large high-dose partial-body exposure to chest wall, probably
resulting in cell-killing

Leukaemia following
cancer of the uterine
corpus [C8]Large number of cases within population-based cancer registries
Comprehensive individual dosimetry for bone marrow compartments
Attempt to adjust for chemotherapy
Large unirradiated comparison group
Dose–response analyses covering doses below 1.5 Gy as well as
above 10 Gy

Effects of cell-killing at high doses
Potential inaccuracies in partial-body dosimetry

Leukaemia following
testicular cancer [T24]

Cases within population-based cancer registries
Comprehensive individual dosimetry for bone marrow compartments
Attempt to adjust for chemotherapy
Dose–response analyses

Small number of leukaemias available to analyse the effects of age
at exposure, time since exposure and interaction withchemotherapy

Lung cancer following
Hodgkin’s disease(international) [K9]Individual estimates of radiation dose to the affected lung
Some data on individual smoking habits
Detailed information on chemotherapy
Relatively large number of cases

Smoking data limited, and reported more fully for cases than forcontrols
Follow-up period generally less than 10 years

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

StudyStrengths

Limitations

Lung cancer following
Hodgkin’s disease(Netherlands) [V2]

Individual estimates of radiation dose to the area of the lung where
the tumour developed
Individual data on smoking habits
Extensive data on doses from chemotherapy

Small number of cases
Limited follow-up (median 10 years)
Few females

Breast cancer following
Hodgkin’s disease [H20]

Individual assessment of doses
Analysis by age at exposure

Small number of cases
Limited follow-up
Mostly very high doses (>40 Gy)

Leukaemia following
Hodgkin’s disease(international) [K20]

Individual radiation dosimetry
Detailed information on chemotherapy

Follow-up period generally less than 10 years

Leukaemia following
non-Hodgkin’s
lymphoma(international) [T6]

Comprehensive individual dosimetry for bone marrow compartments
Detailed information on chemotherapy

Small number of cases
No dose–response analysis, other than separation into two groups

Leukaemia following
non-Hodgkin’s
lymphoma (UnitedStates) [T15]

Individual dosimetry for bone marrow
Detailed information on chemotherapy

Very small cohort, few cases
No comparison group of unexposed patientsChildhood cancers(international)[T5, T7, T10]

Comprehensive individual dosimetry to estimate organ doses
Attempt to adjust for drug exposure
Dose–response analyses

Only high-dose exposures
Potential for some overmatching since hospital-based
Complete dosimetry not always available

Childhood cancers(France, United
Kingdom) [D16, D19]

Incidence follow-up
Doses from radiotherapy and chemotherapy estimatedIndividual dose estimates generally not used in analyses
Lack of external comparison group
Small numbers for specific types of cancers

Bone cancer andleukaemia after childhood cancers (United
Kingdom) [H21, H27]

Cancer case follow-up
Individual dosimetry
Information available on chemotherapy

Most of the findings concern doses of 5–10 Gy or more

Retinoblastoma [W11]Long-term cancer case follow-up
Individual dose estimates for bone and soft-tissue sarcoma sites
Wide range of doses

Little information on chemotherapy
Most of the findings concern doses of 5 Gy or more

Thyroid cancer following
childhood cancers [D20]Cancer case follow-up
Individual organ dose estimates
Wide range of thyroid doses

Lack of external comparison groupChildhood Hodgkin’s
disease [B16]

Cohort of persons exposed at young ages to high radiation doses
Individual dosimetry
Information available on chemotherapy doses

Small number of cases
No formal modelling of dose response or of chemotherapy effects

Treatment of benign disease
Childhood skin haemangioma [K15, L4, L6, L7,
L10, L12, L13]

Long-term and complete follow-up
Comprehensive individual dosimetry for many organs
Incidence ascertained
Protracted exposure to radium plaques

Relatively small numbers of specific cancers

Benign lesions in locomotor system [D2, J2]

Long-term and complete follow-up
Individual dose estimates
Incidence and mortality ascertained

Uncertainties in computing individual doses to sites, based upon asample of records

Ankylosing spondylitis[W2, W8]

Large number of exposed patients
Long-term and complete mortality follow-up
Detailed dosimetry for leukaemia cases and sample of cohort
Small unexposed group evaluated for general reassurance thatleukaemia risk was unrelated to underlying disease

Comparisons with general population
Underlying disease related to colon cancer and possibly otherconditions
Individual dose estimates available only for leukaemia cases and a
1-in-15 sample of the population

Israel tinea capitis[R5, R9, R16, R17]

Large number of exposed patients
Two control groups
Ascertainment of cancer cases from hospital records and tumour
registry
Individual dosimetry for many organs

Dosimetry for some sites (e.g. thyroid) uncertain, owing to possible
patient movement or uncertainty in tumour location
Limited dose range

171

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StudyStrengths

Limitations

New York tinea capitis[S7, S15, S22, S68]

Relatively good dose ascertainment for skin and other cancers

Small number of cancers
Few females

New York post-partum
mastitis [S5, S22]

Individual estimates of breast dose from medical records
Breast cancer incidence ascertained
Dose–response analyses

All exposed women were parous, but comparison women were not(380 unexposed and sisters of both exposed and unexposed)
Inflamed and lactating breast might modify radiation effectRochester thymic
irradiation[H10, H26, S18, S22]

Individual dosimetry for thyroid and some other sites
Sibling control groupLong follow-up
Fractionation effects could be evaluatedDose–response analyses

Radiation treatment fields for newborns varied, and dosimetry
uncertain for some sites
Adjustment in analysis for sibship size uncertain
Questionnaire follow-up may have resulted in underascertainmentof casesTonsil irradiation[S17, S21, S22]

Individual dosimetry for thyroid and some other sites
Long follow-up
Large numbers of cases for certain sites
Dose–responses analyses

Effect of screening on ascertainment of thyroid cancer and nodules
No unexposed control group

Tonsil, thymus oracne irradiation [D9]Long period between exposure and examination
Prospective as well as retrospective follow-up

Possible screening effect
Small cohort
No unexposed control group

Swedish benign breastdisease [M3, M8, M17]

Incidence study with long-term follow-up
Individual dosimetry for many organs
Fractionated exposure
Unexposed control group

Lack of data on potential confounding factors
Small numbers for most cancer types other than breast

Benign gynaecologicaldisorders [D7, I1, I4]

Large number of exposed women
Unexposed women with benign gynaecological disorders
Very long mortality follow-up
Individual dosimetryProtracted exposures to radium implants (10–24 hours)
Dose–response analyses

Uncertainty in proportion of active bone marrow exposed
Small numbers of specific types of cancer
Misclassification of certain cancers on death certificates(e.g. pancreas)

Lymphoid hyperplasia
screening [P5]

Individual dosimetry
Comparison of questionnaire and clinical examination results
Comparison group treated by surgery for the same condition

Apparent bias in questionnaire data, owing to self-selectionof subjects
Clinical examinations provide data on prevalence rather thanincidence
Study of thyroid nodules; cancer cases not confirmed

Peptic ulcer [C4, G6]

Individual dosimetry
Unexposed patients with peptic ulcer
Exceptionally long follow-up (>50 years)
Some risk factor information available in records

Standardized radiotherapy precluded dose–response analyses
Non-homogeneous dose distribution within organs, such that simple
averaging may be misleading
Metastatic spread of stomach cancer probably misclassified as liverand pancreatic cancer on death certificates
Possible selection of somewhat unfit patients for radiotherapy rather
than surgery

Diagnostic examinations
TB fluoroscopy(Massachusetts)[B3, D4, S22]

Incidence study with long-term follow-up (50 years)
Individual dosimetry based on patient records and measurements
Unexposed TB patients
Fractionated exposures occurred over many years
Dose–response analyses

Uncertainty in dose estimates related to fluoroscopic exposure timeand patient orientation
Questionnaire response probably underascertained cancers
Debilitating effect of TB may have modified radiation effect for somesites, e.g. lungDiagnostic X-rays(United States healthplans) [B17]

Information on diagnostic X-rays abstracted from medical records
Surveillance bias unlikely, since cases and controls were at equal riskfor having X-ray procedures recorded and malignancy diagnosed

Potential for ascertainment bias, for example through early diagnosis
of a malignancy
Analyses based on number of X-ray procedures rather than actualdoses

TB fluoroscopy (Canada)[H7, H9]Large number of patients
Unexposed TB comparison group
Individual dosimetry for lung and female breast
Fractionated exposures occurred over many years
Dose–response analyses

Mortality limits comparisons with breast cancer incidence series, e.g.
time response
Uncertainties in dosimetry limit precise quantification of risk
Different dose responses for female breast cancer between one
sanatorium and the rest of Canada may indicate errors in dosimetry,differential ascertainment or differences in biological response

Diagnostic medical
and dental X-rays(Los Angeles) [P6, P7]

Dosimetry attempted on the basis of number and type of
examinations

No available records of X-rays
Potential for recall bias in dose assessment
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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

StudyStrengths

Limitations

Diagnostic X-rays(Sweden) [I9]Information on diagnostic X-rays over many years abstracted frommedical records

Analyses based on number and type of X-ray procedures rather thanactual doses

Scoliosis [D17]Adolescence possibly a vulnerable age for exposure
Dosimetry undertaken on the basis of number of films and breast
exposure
Dose–response analysis

Comparison with general population potentially misleading, sincescoliosis associated with several breast cancer risk factors(e.g. nulliparity)
Dose estimates may be subject to bias as well as random error

173

EXTERNAL LOW-DOSE OR LOW-DOSE-RATE EXPOSURES
Prenatal exposures
Oxford Survey ofChildhood Cancers[B12, M18, S11]

Very large numbers
Comprehensive evaluation of potential confounding
Early concerns over response bias and selection bias resolved

Uncertainty in foetal dose from obstetric X-ray examinations
Similar relative risks for leukaemia and other cancers may point topossible residual confounding

Northeastern UnitedStates childhood
cancers [M16]

Large numbers
Reliance on obstetric records

Uncertainty in foetal dose

United States childhood
acute lymphoblastic
leukaemia [S67]Large numbers of cancer cases
Information collected on potential confounding factors

Uncertainty in foetal dose – likely to be lower than in previous
decades
Not possible to validate exposure data using medical records

Swedish childhood
leukaemia [N4]Population-based design with cancer cases
Reliance on medical records, which were ascertained for mostpotential study subjects

Uncertainty in foetal dose
Number of cases smaller than in some other studies

Occupational exposures
Nuclear workers

Often large numbers
Personal dosimetry
Low-dose fractionated exposures
Could provide useful information in future

Low doses make clear demonstration of radiation effect difficult
Possibly confounding influence of chemical and other toxic exposures
in workplace
Healthy worker effectMortality follow-up
Lifestyle factors (e.g. smoking histories) generally not available

United States nonHodgkin’s lymphoma
case-control [E10]Large number of cases, identified from population-based cancer
registries
Pathological review of cases
Low-dose fractionated exposures
Information on potential confounding factors

Reliance on self-reported occupational exposures
Low doses make clear demonstration of radiation effect difficult

Chernobyl clean-up
workers

Often large numbers
Low-dose fractionated exposures
Could provide useful information in future

Difficulties in assessing individual exposures
Possible differences in cancer ascertainment relative to the general
population
Short period of follow-up so far

Mayak workers[S28, Z3]

Wide range of exposures
Individual measurements of external gamma dose and plutonium
body burden
Individual information on potential confounding factors in stomach
cancer study

Possible uncertainties in assessment of exposures
Further details of ascertainment of stomach cancer cases andcontrols desirable

Medical workers

Often large numbers
Low-dose fractionated exposures over long periods

General lack of information on individual doses precludes usefulness
to dateNatural sources of radiation
Yangjiang [A11, S23,
T12, T14, T16, Z2]

Large cohorts in high-background and control areas
Stable population
Extensive dosimetry for region
Assessment of potential confounding factors

Mortality follow-up
Small numbers for some cancer types
Low doses

United Kingdom
Childhood Cancer Study[U17]

Large numbers of cases ascertained within a population-based study
Individual measurements of domestic gamma radiation dose rates

Gamma radiation dose rates generally low and did not vary greatly

Sweden [A24]

Cancer cases within population-based registry

Possible misclassification of exposures, owing to absence ofmeasurements for dwellings not known to have been built fromalum shale concrete
Low doses

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Study
Central Italy [F7]Strengths

Limitations

Individual measurements of domestic gamma radiation and radon

Small number of cases
Mortality data only
Measurements only in last home
Low doses

INTERNAL LOW-DOSE-RATE EXPOSURES
Medical exposures
131

Swedish I thyroid
cancer [H6, H24]

Large numbers
Nearly complete cancer case ascertainment
Administered activities of 131I known

Comparison with general population
Dose–response not based on organ doses
High-dose cell-killing probably reduced possible thyroid effectPatients selected for treatment

Diagnostic 131I
[H8, H12, H14]

Large numbers
Unbiased and nearly complete ascertainment of cancers throughlinkage with cancer registry
Administered activities of 131I known for each patient
Organ doses to the thyroid computed with some precision
Dose–response analyses for thyroid cancer and leukaemia, basedon wide range of doses
Low-dose-rate exposure

Comparison with general population only, except for thyroid cancer
and leukaemia
Reason for some examinations related to high detection of thyroid
cancers, i.e. suspicion of thyroid tumour was often correct
Doses to organs other than thyroid very low
Population under surveillance

United States
thyrotoxicosis patients[D12, R3, S24]

Large numbers of patients treated with 131I
Large unexposed comparison groups
Comprehensive follow-up effort
Administered activities of 131I known

Individual doses computed only for certain organs
Mortality follow-up
Few patients irradiated at young ages
Possibility of selection bias by treatment

Thyroid cancer patients[D18, H2, R38]

Cancer case follow-up
Administered activities of 131I known
Unexposed group

Individual doses not computed
Small numbers for some specific cancer types
Few patients irradiated at young ages
Possibility of selection bias by treatment

French therapeutic 131I
[D18]

Cancer case follow-up
Administered activities of 131I known
Exclusion of patients who received external radiotherapy
Unexposed group

Individual doses not computed
Small numbers for specific cancer types
Few patients irradiated at young ages
Possibility of selection bias by treatment

Environmental exposures
Techa River population[K4, K13, K49, K50, O2]

Large numbers with relatively long follow-up
Wide range of estimated doses
Unselected population; attempted use of local population rates forcomparison
Possible to examine ethnic differences in cancer risk
Potential for future

Dosimetry difficult and not individual
Mixture of internal and external exposures complicates dosimetry
Follow-up and cancer case ascertainment uncertain
Contribution of chemical exposures not evaluatedChernobyl-related
exposure[A10, D52, N6]

Large numbers exposed
Wide range of thyroid doses within the states of the former
Soviet Union

Mixture of radioiodines and availability of data make dose estimationdifficult, particularly for individuals
Possible differences in cancer ascertainment relative to the general
population
Fairly short period of follow-up so far
Generally low doses to bone marrow
Low participation rates in Ukrainian leukaemia study [N6]

Marshall Islands fallout[H25, R13]

Population unselected for exposure
Comprehensive long-term medical follow-up
Individual dosimetry attempted

Mixture of radioiodines and gamma radiation precludes accurate
dose estimation
Surgery and hormonal therapy probably influenced subsequent
occurrence of thyroid neoplasms
Small numbers

Utah 131I fallout: thyroid
disease [K19]

Comprehensive dosimetry attemptedProtracted exposures at low rate

Possible recall bias in consumption data used for risk estimation
Possible underascertainment of disease in low-dose subjects
Small number of thyroid cancers

Utah 131I fallout [S2]Comprehensive dosimetry attempted
Large number of leukaemia deaths
Protracted exposures at low rate

Uncertainty in estimating bone marrow doses
Estimated cumulative doses lower than from natural backgroundradiation
Occupational exposures

United Kingdom Atomic
Energy Authority:
prostate cancer [R14]

Information abstracted for study subjects on socio-demographic
factors, exposures to radionuclides, external doses and othersubstances in the workplace
Cases and controls selected from an existing cohort

Exposures to some radionuclides tended to be simultaneous, making
it difficult to study them individually

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175

Table 18 Strengths and limitations of major cohort and case-control epidemiological studies of carcinogenic effects of
exposures to high-LET radiation
Study

Strengths

Limitations
Treatment for benign disease224

Ra patients

Large number of excess bone cancers
Long-term follow-up
Substantial proportion of patients treated in childhood or adolescence

Uncertainties in organ doses for individual patients
Other aspects of treatment may be relevant (e.g. X-rays)
Comparison group constructed only recently for the Spiess study[S79]

Diagnostic examinations
Thorotrast patients

Large number of excess cancers
Long-term follow-up

Uncertainties in organ doses for individual patients
Chemical attributes of Thorotrast might influence risks
Occupational exposures

Radium luminizers

Protracted exposures from 226Ra
Large numbers of excess cancers in United States study

Potential inaccuracies in estimating radium intakes
Distribution of radium in bone may be non-uniform
External irradiation may be relevant for breast cancers

Mayak workers

Wide range of exposures
Individual measurements of plutonium body burden and external
gamma dose
Information on smoking and other potential confounding factors in
the lung cancer case-control study

Possible uncertainties in assessment of exposures
Further details of the ascertainment of subjects in the lung cancer
case-control study [T9] would be desirable

United Kingdom andUnited States nuclear
workers

Individual measurements of plutonium body burden or other internallydeposited radionuclides, and external gamma dose

General lack of information on smoking and other potential
non-radiation confounding factors
Possible uncertainties in assessment of internal exposures

Florida phosphateworkers [C39]

Relatively large number of person-years
Assessment of exposures to other agents (e.g. silica and acid mists)

Not possible to obtain direct quantitative estimates of exposure levels
Absence of data on smoking habits for lung cancer analysis

Chinese iron and steel
workers [L86]

Assessments made of lung doses due to inhalation of thorium
Information available on smoking habits

Lung doses generally low
Small numbers of deaths for specific cancer types

Radon-exposed
underground miners

Large numbers
Protracted exposures over several years
Wide range of cumulative exposures
Exposure–response analyses

Uncertainties in assessment of early exposures (e.g. [R8, W10, X2],
but applies to other studies considered in reference [L8])
Possible modifying effect of other types of exposure (e.g. arsenic)
Smoking histories limited or not available
Environmental exposures

Residential radon

Large numbers in most studies
Protracted exposures over many years
Individual data on radon and smoking

Uncertainties in assessing exposures (measurement error, mobility
between dwellings, structural changes to dwellings)
Radon concentrations low for many subjects

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Table 19 Risk estimates for cancer incidence and mortality from studies of radiation exposure: total solid cancers (orall cancers apart from leukaemia when noted)
The number of observed and expected cases as well as the mean dose and number of person-years for cohort studies are computed
throughout this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more(weighted colon dose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv) –1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

3 433

3 192.2 d

0.21

436 1800.43 (0.35, 0.53)

14.57 (10.68, 18.88)

Females

4 418

3 836.0 d

0.20

729 6070.81 (0.71, 0.92)

31.52 (27.40, 35.83)<20 years

2 120

1 758.3 d

0.21

586 255

1.00 (0.86, 1.15)

20.65 (17.26, 24.25)

20–40 years

3 093

2 832.8 d

0.21

378 204

0.50 (0.39, 0.61)

29.67 (23.47, 36.24)>40 years

2 638

2 472.9 d

0.19

201 3290.36 (0.25, 0.48)

37.28 (25.23, 50.11)

Time since exposure 12–15 years

389

348.3 d0.21

119 774

0.44 (0.16, 0.78)

9.68 (5.42, 15.16)

15–30 years

2 492

2 218.5 d

0.21

514 582

0.58 (0.46, 0.71)

16.87 (13.39, 20.63)

4 970

4 476.9 d

0.20

531 432

0.64 (0.55, 0.73)

46.84 (40.69, 53.24)

7 851

7 036.4

0.21

1 165 787

0.62 (0.55, 0.69)

24.54 (21.53, 27.68)

2 030

n.a.

0.066 4

2 667 903

2.3 (1.1, 3.9) g

n.a.
n.a.

Age at exposure

Males>30 years
All
Canadian National Dose Registry [S8] b
Capenhurst uranium facility,
United Kingdom [M4] b

177

215.83

0.098 540 933–0.67 (<–1.72, 4.32) i

Springfields uranium workers,
United Kingdom [M5] b901

1 115.79

0.022 8

190 795

1.77 (–0.06, 4.02) in.a.

United Kingdom Chapelcross workers [M6] b

131

149.440.083 639 2101.28 (–0.38, 3.79) in.a.

Mortality
LSS [P10]
Sex

2 711

2 564.2 d

0.20

682 048

0.34 (0.24, 0.45)

2.74 (1.20, 4.67)

Females

3 090

2 745.7 d

0.19

1 075 919

0.65 (0.52,0.78)

7.10 (5.19, 9.17)<20 years

1 185

998.6 d0.20

916 8300.80 (0.62,1.00)

3.42 (2.09, 4.93)

20–40 years

2 138

1 968.4 d

0.20

520 263

0.49 (0.36, 0.63)

9.50 (6.13, 13.21)>40 years

2 478

2 353.6 d

0.18

320 873

0.28 (0.17, 0.41)

17.14 (10.01, 24.84)

Time since exposure 12–15 years

762

719.2 d0.20

465 730

0.26 (0.07, 0.48)

0.92 (0.04, 2.16)

15–30 years

1 625

1 480.3 d

0.20

586 804

0.44 (0.29, 0.60)

4.48 (2.60, 6.68)

3 414

3 116.9 d

0.19705 432

0.54 (0.44, 0.65)

17.95 (14.48, 21.63)

5 801

5 313.2 d

0.20

1 757 966

0.48 (0.40, 0.57)

5.16 (3.80, 6.63)

Nuclear workers in Canada, United Kingdom
and United States [C3] b

3 830

n.a.

0.040 2

2 124 526

–0.07 (–0.39, 0.30)

n.a.

United Kingdom NRRW [M12] b

3 020

n.a.

0.030 5

2 063 300

0.09 (–0.28, 0.52)

n.a.
n.a.

Age at exposure

Males>30 years
All

Nuclear power industry workers in the
United States [H44]

368

564.3

0.026

698 041

0.51 (–2.01, 4.64) c

Extended Techa River cohort [K50]

1 842

n.a.

0.03 e865 8120.92 (0.2, 1.7) c, e

70.5 (25, 118) c, e, f

IARC 15-country nuclear worker study [C41]

6 519

n.a.

0.019 45 192 710

0.97 (0.14, 1.97) cn.a.

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Study

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

177

Average excess
relative riska at 1 Sv

Average excess absoluteriska (104 PY Sv)–1

0.81 (0.46, 1.33)c, hn.a.

INTERNAL LOW-LET EXPOSURES
Mortality
Semipalatinsk study [B58]

889

n.a.

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for other studies unless otherwise stated.
b All cancers except leukaemia.
c 95% CI in parentheses.
d All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
e Based on stomach dose, which is predominantly (75%) due to external
exposure [K50].

0.63

582 750f Estimated at age 70.
g Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
h Based on a dose–response analysis, restricted to the exposed group only.
i Males only.

Table 20 Risk estimates for cancer incidence and mortality from studies of radiation exposure: salivary gland cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS of cancer incidence the exposed group included survivors with organ (brain) doses of
0.005 Sv or more. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative riska at 1 Sv

Average excess absoluteriska (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]e
Sex

Males

17

7.1b

0.26

436 180

4.50 (1.32, 12.68)<0 (<0, 105.57)

Females

6

6.9 b

0.24

729 608

0.95 (<0, 4.09)<0 (<0, 46.27)<20 years

13

3.2 b

0.25

586 255

11.12 (3.40, 43.32)<0 (<0, 64.40)

20–40 years

5

7.8 b

0.26

378 204<0 (<0, 0.46)<0 (<0, 0.05)>40 years

5

3.8 b

0.24

201 330

1.39 (<0, 8.30)<0 (<0, 63.69)

12–15 years

4

2.2 b

0.26

119 774

1.91 (<0, 25.28)<0 (<0, 81.44)

15–30 years

7

5.6 b

0.25

514 582

1.42 (0.01, 5.76)<0 (<0, 55.01)

12

6.6 b

0.24

531 433

3.81 (0.99, 10.65)<0 (<0, 85.38)

23

14.4 b0.25

1 165 788

2.55 (0.87, 5.72)<0 (<0, 73.21)

LSS [L83]
Mucoepidermoid carcinoma

11

n.a.

0.20 c2 124 057 d

8.30 (2.56, 29.6) c

0.21 (0.10, 0.37) c

Other malignant neoplasm

20

n.a.

0.20 c2 124 057 d

1.36 (–0.01, 4.73) c

0.12 (0.01, 0.28) c

Warthin’s tumour

12

n.a.

0.20 c2 124 057 d

3.05 (0.58, 10.3) c

0.10 (0.01, 0.25) c

Other benign neoplasm

52

n.a.

0.20 c2 124 057 d

0.30 (–0.10, 1.18) c

0.08 (<0, 0.26) c

68

n.a.

4.2

n.a.

19.6 (0.16, ∞) f

n.a.
n.a.
n.a.

Age at exposure

Time since exposure>30 years
All

Childhood benign head and neck tumour
cohort [S74]
Benign tumours
Malignant tumours

22

n.a.

4.2

n.a.

–0.06 (–∞, 4.0) f

All tumours

90

n.a.

4.2

n.a.

0.82 (0.04, ∞) f

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for other studies.
b All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
c Calculated using shielded kerma dose.

d Calculated using all survivors excluding the not-in-city group and those withunknown dose.
e Calculated using brain dose.
f 95% CI.

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Table 21 Risk estimates for cancer incidence and mortality from studies of radiation exposure: oesophageal cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted
stomach dose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48] i
120110.2 j0.22

436 180

0.48 (0.09, 1.00)<0 (<0, 1112.5)

Females32

29.9 j0.21

729 6080.70 (<0, 2.28)

0.02 (<0, 0.45)<20 years40

29.3 j0.22

586 255

1.34 (0.44, 2.82)<0 (<0, 262.07)

20–40 years

44

42.3 j0.21

378 204<0 (<0, 0.76)<0 (<0, 501.33)>40 years

68

70.1 j0.20

201 330

0.33 (<0, 1.06)

1.90 (0.46, 3.93)

12–15 years

9

6.4 j

0.22

119 774

0.90 (<0, 5.21)<0 (<0, 282.42)

15–30 years

57

56.2 j0.22

514 582

0.59 (<0, 1.51)

0.32 (0.04, 0.83)

8677.4 j0.21

531 433

0.45 (0.03, 1.08)

4.72 (3.64, 5.98)

152

140.2 j0.21

1 165 788

0.51 (0.14, 0.99)

0.19 (<0, 0.53)

Cervical cancer cohort [B11] c

12

11.0

0.35

178 243

0.26 (–1.1, 1.3) l

0.16 (–0.6, 1.3) l

Springfields uranium workers,
United Kingdom [M5]

20

26.65

0.022 8

190 795–1.96 (<–2.00, 5.95) n

n.a.

Sex

Age at exposure

Time since exposure

Males>30 years
AllMortality
LSS [P9]Sex

Age at exposure

Time since exposure

128

118.8 j0.19

666 869

0.55 (0.09, 1.17)

0.25 (0.01, 0.82)

Females

43

33.7 j0.18

1 061 687

1.40 (0.20, 3.37)<0 (<0, 151.21)<20 years36

24.3 j0.19

885 656

1.38 (0.18, 3.60)<0 (<0, 141.53)

20–40 years

52

41.8 j0.19

514 9030.59 (<0, 1.93)<0 (<0, 353.89)>40 years

83

86.5 j0.18

327 997

0.60 (0.05, 1.37)

1.95 (0.72, 3.60)

12–15 years

33

27.4 j0.18

504 1121.30 (0.16, 3.24)<0 (<0, <0)

15–30 years

57

55.5 j0.19

592 956

0.81 (0.09, 1.92)<0 (<0, 373.73)>30 years

81

69.9 j0.19

631 488

0.40 (<0, 1.23)<0 (<0, 493.70)

171153.0 j0.19

1 728 556

0.69 (0.24, 1.28)<0 (<0, 386.68)
0.23 (0.1, 0.3) e, l

Males

All
Ankylosing spondylitis [W8] d

74

38

5.55

287 095

0.17 (0.09, 0.25) lMetropathia haemorrhagica [D7]

9

9 .27

0.05

47 144–0.58 (–11.2, 16.8) b, l

–1.15 (–22.0, 33.0) b, l

Nuclear workers in Canada, United Kingdom
and United States [C3]

104

n.a.

0.04

2 124 526

>0 f

n.a.

United Kingdom NRRW [M12]

120

n.a.

0.040 2

2 124 526

–0.095 (<–1.95, 4.06)

n.a.
n.a.

Los Alamos National Laboratory workers,
United States [W6]

22

27.4

~0.016

251 651>0 h

Nuclear workers in Japan [E3]

25

37.1

0.014

533 168

>0 g

n.a.

Nuclear industry workers in Japan [I14]

100

119.3

0.015

~1 390 000

>0 k

>0 k

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Study

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

179

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

0.18 (–0.09, 0.66) l, mn.a.

INTERNAL LOW-LET EXPOSURES
Mortality
Semipalatinsk study [B58]

317

n.a.

0.63

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies, unless otherwise stated.
b Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
c The values given are for 10-year survivors.
d The values given exclude the period within 5 years of first treatment.
e Dose–response analysis based on the number of treatment courses given.
f Based on a 10-year lag. Trend not statistically significant.
g 90% CI in parentheses derived from published data for the LSS and using
exact Poisson methods for the other studies.

582 750h Positive dose–response trend (p < 0.10).
i Calculated using stomach dose.
j All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
k Statistically significant increasing trend with dose (2-sided p < 0.05, adjusted
for multiple comparisons (Bonferroni method)).
l 95% CI in parentheses.
m Based on a dose–response analysis, restricted to the exposed group only.
n Males only.

Table 22 Risk estimates for cancer incidence and mortality from studies of radiation exposure: stomach cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted
stomach dose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

Age at exposure

Time since exposure

1 084

1 036.6 v

0.22

436 180

0.26 (0.14, 0.42)

2.45 (0.92, 4.49)

Females

1 011

913.2 v

0.21

729 608

0.51 (0.33, 0.72)

4.36 (2.78, 6.15)<20 years435

381.2 v

0.22

586 255

0.56 (0.32, 0.85)

2.74 (1.52, 4.19)

20–40 years

809

750.6 v

0.21

378 204

0.39 (0.22, 0.59)

6.18 (3.42, 9.32)>40 years

851

821.9 v

0.20

201 330

0.23 (0.07, 0.41)

7.99 (2.25, 14.59)

12–15 years

154132.2 v

0.22

119 774

0.37 (<0, 0.92)

2.40 (0.66, 5.21)

15–30 years

796

758.5 v

0.22

514 582

0.31 (0.15, 0.50)

2.71 (1.32, 4.40)

1 145

1 059.6 v

0.21

531 433

0.42 (0.27, 0.58)

6.75 (4.28, 9.48)

2 095

1 951.5 v

0.21

1 165 788

0.37 (0.26, 0.49) b

3.61 (2.42, 4.96)

348

167.3

2

n.a.

0.54 (0.05, 1.5)

n.a.
n.a.

Males

>30 years
All
Cervical cancer case-control [B8] c
Swedish benign breast disease [M3]

14

15.6

0.66

26 493

1.3 (0, 4.4) n

Stockholm skin haemangioma [L10]

5

~6

0.09

406 565<0<0

190 795–1.96 (<–2.00, 9.73) zn.a.

Springfields uranium workers,
United Kingdom [M5]

56

73.90

0.022 8

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Study

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

Mortality
LSS [P9]Sex

Age at exposure

Males

890

867.3 v

0.19

666 869

0.11 (<0, 0.30)

0.32 (<0, 1.33)

Females

780

717.6 v

0.18

1 061 687

0.50 (0.27, 0.75)

1.46 (0.55, 2.56)<20 years

206

176.9 v

0.19

885 656

0.72 (0.29, 1.27)

0.51 (<0, 1.27)

20–40 years

530

488.1 v

0.19

514 9030.42 (0.18, 0.71)

2.78 (1.06, 4.82)

934

918.0 v

0.18

327 997

0.12 (<0, 0.31)

3.46 (<0, 8.03)

12–15 years

368

356.4 v

0.18

504 1120.17 (<0, 0.48)

0.17 (<0, 1.25)

15–30 years

623

604.9 v

0.19

592 956

0.22 (0.02, 0.46)

0.62 (<0, 1.76)

679

615.7 v

0.19

631 488

0.46 (0.23, 0.73)

3.89 (2.19, 5.83)

1 670

1 585.3 v

0.19

1 728 556

0.28 (0.14, 0.42)

0.94 (0.31, 1.71)

127

128

3.21

287 095–0.004 (–0.05, 0.05) e, nn.a.

1 246 340

–0.27 (–1.37, 2.69) g, nn.a.

>40 years
Time since exposure>30 years
All
Ankylosing spondylitis [W8] d
Yangjiang background radiation [T14, T16]

70

77.8

n.a. fPeptic ulcer [C4]

47

14.7

14.8

41 7790.20 (0.0, 0.73) h, nn.a.

Metropathia haemorrhagica [D7] i

33

26.8

0.23

47 1441.01 (–0.65, 3.17) b, n

5.72 (–3.71,18.0) b, n

Benign gynaecological disorders [I4] j

23

21.8

0.2

71 9580.27 (–4.25, 4.80) k

0.83 (<0, 72.7) b

Nuclear workers in Canada, United
Kingdom and United States [C3]

275

n.a.

0.040 2

2 124 526

<0 l

n.a.

United Kingdom NRRW [M12]

245

294.4

0.030 5

2 063 300

–0.032 (–0.95, 1.49) mn.a.

Canadian National Dose Registry [A8]

70

121.7

0.063

2 861 093

12.5 (<0, 33) z

n.a.
>0 w

Nuclear industry workers in Japan [I14]

428

481.9

0.015

~1 390 000>0 w

Nuclear power industry workers in the
United States [H44]

16

19.7

0.026

698 041

19.5 (–2.23, 141) nn.a.

Japanese radiological technologists [A4]

98

151.1

0.466

270 585

<0

<0

n.a.

1.32 p(0.04, 2.84)

n.a.

INTERNAL LOW-LET EXPOSURES
Incidence
Swedish hyperthyroid patients [H6]

58 o

43.6

0.25 Gy

Mortality
United States thyrotoxicosis patients [R3]

82

78

0.178

385 468

>0 q

n.a.

Semipalatinsk study [B58]

150

n.a.

0.63

582 750

0.95 (0.17, 3.49) n, yn.a.

32 8001.56 r, s

n.a.
n.a.

INTERNAL HIGH-LET EXPOSURES
Incidence224

Ra ankylosing spondylitis patients [W15]

18

12.2

n.a.

224

13

~11

n.a.

25 000~1.2 rDanish Thorotrast patients [A5]

7

6.9

n.a.

19 3651.82 (0.61, 5.66) r, nn.a.

25 4802.7 (1.1, 7.9) r, xn.a.

Ra ankylosing spondylitis patients [N2]Danish and Swedish Thorotrast patients
[T30]

13

10.8

n.a.

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Study

Observed
cases

Expectedcases

Mean dose
(Sv)

181

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

n.a.

0.6 r

n.a.

Mortality
German Thorotrast patients [V3, V4]

30 t

n.a.

20.6 mL u

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies, unless otherwise stated.
b Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
c Based on 5-year survivors. The observed and expected numbers are for both
exposed and unexposed.
d The values given exclude the period within 5 years of first treatment.
e Dose–response analysis based on the number of treatment courses given.
f Mean annual effective dose = 6.4 mSv.
g Based on a 10-year latent period.
h Trend based on the exposed patients only, with doses of 1–10 Gy.
i The values given exclude the period within 5 years of irradiation.
j The observed and expected numbers of cases are for 10-year survivors.
The estimated number of expected cases incorporated an adjustment basedon the Poisson regression model given in reference [I4].
k Wald-type CI.
l Based on a 10-year lag. Trend not statistically significant.
m Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
n 95% CI in parentheses.

o Restricted to the period 10 or more years after treatment.
p Relative risk at 1 Gy.
q No apparent trend with administered activity of 131I, although a significancetest was not performed.
r Risk relative to unexposed controls.
s In the control group, 16 stomach cancers were diagnosed, compared with
16.9 expected.
t Number quoted in an earlier follow-up [V3].
u Amount of Thorotrast administered.
v All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
w Statistically significant increasing trend with dose (2-sided p < 0.05
(unadjusted for multiple comparisons), 2-sided p > 0.2 (adjusted for multiple
comparisons using Bonferroni method)).
x Relative risk and 95% CI (compared with Thorotrast unexposed group), butthere is no statistically significant trend with administered Thorotrast(p = 0.997).
y Based on a dose–response analysis, restricted to the exposed group only.
z Males only.

Table 23 Risk estimates for cancer incidence and mortality from studies of radiation exposure: colon cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted colon
dose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv) –1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

Males

323

274.0 o0.21

436 180

0.85 (0.52, 1.26)

1.41 (0.10, 3.07)

Females348

330.3 o0.20

729 6070.42 (0.14, 0.76)

1.46 (0.69, 2.45)<20 years229

205.2 o0.21

586 255

0.81 (0.46, 1.24)

0.99 (0.31, 1.92)

20–40 years

301

274.0 o0.21

378 204

0.44 (0.14, 0.82)

1.78 (0.56, 3.46)>40 years

141

129.6 o0.19

201 3290.45 (<0, 1.13)

3.11 (0.22, 6.54)

12–15 years

12

7.5 o

0.21

119 774

2.02 (<0, 9.30)<0 (<0, 349.91)

15–30 years

97

77.1 o0.21

514 582

1.24 (0.51, 2.25)

1.14 (0.44, 2.09)>30 years562

520.5 o0.20

531 432

0.52 (0.30, 0.78)

2.95 (1.32, 4.89)

671

603.7 o0.21

1 165 787

0.64 (0.42, 0.90)

1.44 (0.76, 2.27)

Cervical cancer case-control [B8]c

409

409

24

n.a.

0.00 (–0.01, 0.02)

0.01 (–0.09, 0.18)

Swedish metropathia cohort [R26]

12

8.2

0.093

9 289

5.0 (–2.2, 16) k

n.a.
0.11
n.a.

Age at exposure

Time since exposure

All

Stockholm skin haemangioma [L10]

12

~11

0.07

406 565

0.37 dCanadian National Dose Registry [S8]

315

349.4

0.066 2

2 667 903

2.6 (<0, 7.5) m

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Study

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

Capenhurst uranium facility, United
Kingdom [M4]b

14

13.60

0.098 540 933–1.30 (<–1.30, 23.97) nn.a.

Springfields uranium workers, United
Kingdom [M5]

52

71.37

0.022 8

190 795

11.41 (<–6.27, 36.45) nn.a.

United Kingdom Chapelcross workers [M6]

8

9.37

0.083 639 2102.10 (<–2.65, 13.92) nn.a.

Mortality
LSS [P9]
Sex

Males

118

108.4 o0.18

666 689

0.53 (0.04, 1.20)<0 (<0, 707.28)

Females147

145.3 o0.18

1 061 687

0.50 (0.06, 1.09)<0 (<0, 623.23)<20 years51

43.0 o0.18

885 656

1.13 (0.32, 2.34)<0 (<0, 210.88)

20–40 years

115

112.3 o0.18

514 903

0.23 (<0, 0.84)<0 (<0, 966.47)>40 years

99

100.4 o0.17

327 997

0.38 (<0, 1.12)<0 (<0, 1440.1)

12–15 years

11

10.9 o0.18

504 112<0 (<0, 2.85)<0 (<0, 74.98)

15–30 years

64

55.0 o0.18

592 956

1.12 (0.27, 2.41)<0 (<0, 457.15)>30 years

190

190.1 o0.18

631 4880.30 (<0, 0.73)<0 (<0, 1309.2)

265

253.7 o0.18

1 728 556

0.51 (0.17, 0.94)<0 (<0, 656.32)

Benign gynaecological disorders [I4] e

75

46.6

1.3

71 9580.51 (–0.8, 5.61)

3.2 (–0.9, 7.1) b

Metropathia haemorrhagica [D7] f

47

33.0

3.2

47 1440.13 (0.01, 0.26) g, q0.93 (0.11, 1.95) b, g

Peptic ulcer [C4]

36

26.9

10

41 779–0.01 (<–0.01, 0.07) g, k, pn.a.

United Kingdom NRRW [M12]

228

243.4

0.031

2 063 300

–0.71 (–1.36, 0.49) mn.a.

Nuclear power industry workers in the
United States [H44]

36

47.8

0.026

698 041–2.28 (<–2.51, 10.5) gn.a.

5 rem study in the United States [F3]

14

9.86

0.228

69 0001.8 (–1.0, 6.1) l

2.6 (–1.4, 8.6)

270 585

0.62 (–0.2, 1.7) k

0.6 (–0.2, 1.7)

25 4801.5 (0.7, 3.0) g, hn.a.

Age at exposure

Time since exposure

All

Japanese radiological technologists [A4]

35

27.1

0.466

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients
[T30]

16

10.7

n.a.

German Thorotrast patients [V3, V4]

10 i

n.a.

20.6 mL jn.a.

~0.5 hn.a.

United States Thorotrast patients [T30]

5

3.3

n.a.

8 740

∞ (0.5, ∞) g, hn.a.

Mortality

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies.
b Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
c Based on 10-year survivors. The observed and expected numbers cover both
exposed and unexposed persons. The excess absolute risk estimate wascomputed using underlying cancer incidence, estimated using the cervicalcancer cohort study [B11].
d Not statistically significantly different from zero.
e The observed and expected numbers of cases are for 10-year survivors.
The estimated number of expected cases incorporated an adjustment basedon the Poisson regression model given in reference [I4].
f The values given exclude the period within 5 years of irradiation.
g 95% CI in parentheses.

h
i
j
k

l
m
n
o
p
q

Risk relative to unexposed controls.
Number quoted in earlier follow-up [V3].
Amount of Thorotrast administered.
Excess relative risk and excess absolute risk values were calculated fromthe mean dose and the observed and expected cancers (or the relative risk
and confidence interval) reported in the paper.
Includes both small and large intestine.
Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
Males only.
All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
Based on follow-up of 11 or more years after radiotherapy.
Based on a dose–response analysis.

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183

Table 24 Risk estimates for cancer incidence and mortality from studies of radiation exposure: rectal cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted colon
dose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

Males

177

185.2 b0.21

436 180<0 (<0, 0.28)<0 (<0, 0.35)

Females199

169.7 b0.20

729 6070.46 (0.08, 0.97)

0.40 (0.03, 1.11)<20 years

114

117.3 b0.21

586 255

0.16 (<0, 0.60)

0.10 (<0, 0.58)

20–40 years

153

141.3 b0.21

378 204

0.12 (<0, 0.58)<0 (<0, 1.70)>40 years

109

97.1 b0.19

201 329

0.24 (<0, 0.97)

0.64 (<0, 3.44)

12–15 years

11

10.7 b

0.21

119 774<0 (<0, 2.47)

2.44 (1.15, 4.47)

15–30 years

88

84.7 b

0.21

514 582<0 (<0, <0)<0 (<0, 0.12)>30 years277

262.6 b0.20

531 432

0.32 (0.05, 0.66)

0.59 (<0, 2.02)

376

354.6 b0.21

1 165 787

0.18 (<0, 0.46)

0.19 (<0, 0.64)

Canadian National Dose Registry [S8]

145

199.0

0.0662

2 667 903

13.8 (3.7, 33.6) cn.a.

Springfields uranium workers, United
Kingdom [M5]

49

57.62

0.0228

190 795–0.17 (<–3.42, 11.95) fn.a.

Age at exposure

Time since exposure

All

Mortality
LSS [P9]Sex

Age at exposure

Time since exposure

96

98.5 b0.18

666 869<0 (<0, 0.33)<0 (<0, 601.18)

Females127104.7 b0.18

1 061 687

0.95 (0.28, 1.86)<0 (<0, 488.26)<20 years

38

35.9 b0.18

885 656

0.48 (<0, 1.82)<0 (<0, 167.70)

20–40 years

77

68.9 b0.18

514 9030.20 (<0, 1.08)<0 (<0, 590.50)>40 years

10897.3 b

0.17

327 9970.49 (<0, 1.37)

1.11 (<0, 3.23)

12–15 years

31

30.5 b0.18

504 1120.38 (<0, 2.00)<0 (<0, 262.78)

15–30 years

63

62.1 b0.18

592 956<0 (<0, 0.40)<0 (<0, 426.25)

129111.0 b0.18

631 488

0.68 (0.11, 1.47)<0 (<0, 848.25)

223

202.7 b0.18

1 728 556

0.36 (<0, 0.88)<0 (<0, 532.76)

123

155.6

0.031

2 063 300

1.69 (–0.12, 5.01) c

n.a.
0.07 (–0.20, 0.48) g

Males>30 years
All
United Kingdom NRRW [M12]
Metropathia haemorrhagica [D7]

14

12.36

4.9

47 1440.04 (–0.09, 0.16) d

Benign gynaecological disorders [I4]

15

15

3.0

71 9580.03 (–0.14, 0.19)

n.a.

1.8 (0.6, 5.3) en.a.

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients [T30]88.0

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies.
b All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
c Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.

n.a.

25 480d Risk estimate based on a dose–response analysis, with 95% CI.
e Risk relative to unexposed controls, with 95% CI.
f Males only.
g Estimates (with 95% confidence intervals) are based on method described in
the introduction to chapter III of annex I in the UNSCEAR 2000 Report [U2].

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Table 25 Risk estimates for cancer incidence and mortality from studies of radiation exposure: liver cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted liverdose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

393

354.5 o0.23

436 1800.42 (0.18, 0.70)

0.29 (<0, 1.96)

252

253.0 o0.21

729 608

0.39 (0.09, 0.76)

0.52 (0.12, 1.14)<20 years

260

226.3 o0.22

586 255

0.50 (0.21, 0.85)

0.48 (0.09, 1.08)

20–40 years

221

236.8 o0.22

378 204

0.21 (<0, 0.54)

0.72 (<0, 2.02)>40 years

164

144.7 o0.21

201 330

0.61 (0.14, 1.23)

3.03 (0.02, 6.75)

23

21.9 o0.22

119 774

0.54 (<0, 2.18)<0 (<0, 0.92)

15–30 years

129

108.9 o0.22

514 582

0.57 (0.13, 1.19)

0.39 (0.02, 1.08)>30 years

493

477.5 o0.21

531 433

0.37 (0.16, 0.61)

1.23 (0.24, 2.69)

645

607.4 o0.22

1 165 788

0.41 (0.22, 0.63)

0.50 (0.12, 1.06)–0.03 (–0.16, 0.2) c

Males
Females

Age at exposure

Time since exposure

12–15 years

All
Cervical cancer cohort [B11] d

8

8.8

1.50

178 243–0.06 (–0.37, 0.4) c

Swedish benign breast disease [M3]

12

11.3

0.66

26 4930.09 (<0, 1.4) mn.a.

Springfields uranium workers, United
Kingdom [M5]12 i

22.72 i0.022 8

190 795–1.96 (<–2.08, 21.58) i, qn.a.

Mortality
LSS [P9] e
Sex

408

374.7 o0.19

666 869

0.61 (0.33, 0.94)<0 (<0, 0.89)

Females289

283.1 o0.19

1 061 688

0.36 (0.05, 0.74)<0 (<0, 1091.7)<20 years

219

195.7 o0.19

885 656

0.46 (0.13, 0.89)<0 (<0, 0.34)

20–40 years

233

230.5 o0.20

514 903

0.58 (0.23, 1.01)

0.30 (<0, 1.58)>40 years245

232.2 o0.18

327 998

0.45 (0.09, 0.92)

2.00 (<0, 4.59)

12–15 years

97

100.1 o0.19

504 1120.24 (<0, 0.84)<0 (<0, 0.25)

15–30 years

138

125.0 o0.19

592 957

0.68 (0.19, 1.33)

0.08 (<0, 0.91)

462

434.1 o0.19

631 488

0.51 (0.25, 0.81)

0.90 (0.01, 2.21)

697

657.4 o0.19

1 728 557

0.51 (0.30, 0.75)<0 (<0, 0.41)

Ankylosing spondylitis [W8] f

11

13.6

2.13

287 095–0.09 (–0.24, 0.2) cn.a.

Metropathia haemorrhagica [D7] i

2

5.99

0.27

47 144–2.47 (–3.56, 0.78) c, m

–3.13 (–4.52, 0.99) c, m

Peptic ulcer [C4]

11

6.1

4.8

41 779–0.03 (<–0.03, 0.31) b, g, mn.a.

Benign gynaecological disorders [I4] h

9i

16.6

0.21

71 958–2.18(–3.26, 0.3) cn.a.

Yangjiang background radiation [T14, T16]

171

213.8

n.a. j

1 246 340

–0.99 (–1.60, 0.10) k, mn.a.

Nuclear workers in Canada, United Kingdom
and United States [C3]

33

n.a.

0.04

2 124 526

~0

n.a.

Nuclear workers in Japan [E3]

111

128.9

0.014

533 168

>0 l

n.a.

–0.08 (–0.41, 1.00) m, pn.a.

Age at exposure

Time since exposure

Males>30 years
All

INTERNAL LOW-LET EXPOSURES
Mortality
Semipalatinsk study [B58]

60

n.a.

0.63

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Study

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

185

Average relative risk

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish Thorotrast patients [A5]

84

0.7

3.9–6.1 Gyn.a.

194.2 (31.0, 1216) m, nDanish and Swedish Thorotrast patients
[T30]

136

1.3

n.a.

25 480∞ (44.2, ∞) m

Mortality
German Thorotrast patients [V1, V4]

454

3.6

4.9 Gyn.a.

25 Gy –1

Portuguese Thorotrast patients [D21]

104

6.6

26 mL

16 963

5.7 n

Combined Japanese Thorotrast patients
[M14]

143

4

n.a.

10 685n.a.

United States Thorotrast patients [T30]

22

0.9

n.a.

8 740

22.5 (1.8, 464.3) m

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies, unless otherwise stated.
b Based on follow-up of 11 or more years after radiotherapy.
c Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
d Based on 10-year survivors.
e Includes deaths coded as primary liver cancer and liver cancer not specified
as secondary.
f The values given exclude the period within 5 years of first treatment.
g Excess relative risk value was calculated from the mean dose and the relativerisk and confidence interval reported in the paper.
h The estimated number of expected cases incorporated an adjustment basedon the Poisson regression model given in reference [I4].

i
j
k
l
m
n
o

Including gall bladder.
Mean annual effective dose = 6.4 mSv.
Based on a 10-year latent period.
Based on a 10-year lag. Trend not statistically significant.
95% CI in parentheses.
Per 10 mL injected dose.
All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
p Based on a dose–response analysis, restricted to the exposed group only.
q Males only.

Table 26 Risk estimates for cancer incidence and mortality from studies of radiation exposure: pancreatic cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS of cancer incidence and mortality the exposed group included survivors with pancreatic
doses of 0.005 Sv or more. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

99

91.8 c0.21

436 180

0.09 (<0, 0.63)<0 (<0, 0.36)

130119.9 c0.19

729 6070.54 (0.00, 1.26)

0.44 (0.05, 1.04)<20 years38

27.6 c0.20

586 255

1.00 (0.01, 2.71)<0 (<0, 264.97)

20–40 years

94

92.0 c0.20

378 204

0.24 (<0, 0.94)

0.13 (<0, 1.13)>40 years

97

93.6 c0.19

201 3290.07 (<0, 0.67)<0 (<0, 1.43)

10

12.4 c0.20

119 774<0 (<0, <0)<0 (<0, <0)

15–30 years

72

70.0 c0.20

514 582<0 (<0, 0.59)<0 (<0, 0.27)>30 years

147

130.8 c0.20

531 432

0.62 (0.12, 1.28)

1.22 (0.46, 2.27)

229

212.6 c0.20

1 165 787

0.29 (<0, 0.72)

0.22 (<0, 0.63)
n.a.
n.a.

Males
Females

Age at exposure

Time since exposure

12–15 years

All
Canadian National Dose Registry [S8]

76

101.1

0.006 6

2 667 903

6.9 (<0, 27.1) d

Cervical cancer case-control study [B8]

221

n.a.

1.9

n.a.

0.21 (–0.16, 0.89)

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Stockholm skin haemangioma [L10]

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–19

2.7

0.09 f

406 565

25.1 (5.5, 57.7) e

1.7
n.a.

Swedish benign breast disease [M3]

14

11.0

0.37

26 493–0.37 (<0, 0.8) e

Springfields uranium workers, United
Kingdom [M5]

23

31.73

0.022 8

190 795

3.60 (<–12.05, 34.01) hn.a.

Mortality
LSS [P9]Sex

Age at exposure

Males

103

94.5 c0.18

666 869

0.02 (<0, 0.65)<0 (<0, 621.53)

Females134

139.4 c0.17

1 061 681

<0 (<0, 0.41)<0 (<0, 535.39)<20 years

44

38.5 c0.18

885 656

0.56 (<0, 1.82)<0 (<0, 218.32)

96

100.8 c0.18

514 903<0 (<0, 0.41)<0 (<0, 745.72)>40 years

9795.3 c0.16

327 991<0 (<0, 0.28)<0 (<0, 1307.5)

12–15 years

20

23.3 c0.17

504 112<0 (<0, <0)<0 (<0, 153.39)

15–30 years

58

57.5 c0.18

592 956<0 (<0, 0.78)<0 (<0, 410.14)

159

156.5 c0.18

631 482<0 (<0, 0.51)<0 (<0, 1051.6)

237

233.8 c0.18

1 728 550

<0 (<0, 0.33)

0.14 (0.02, 0.35)

72

89.7

0.063

2 861 093 b

7.3 (<0, 19.0)

n.a.

<0 (<0, 18.3)

n.a.

20–40 years

Time since exposure>30 years
All
Canadian National Dose Registry [A8]
Sex
Males

15

25.0

0.063

2 861 093 b

United Kingdom NRRW [M12]126153.860.031

2 063 300

<0 (<0, 2.31)

n.a.

Nuclear power industry workers in the
United States [H44]

18

29.0

0.026

698 041–9.38 (<–2.5, 89.7) en.a.

Metropathia haemorrhagica [D7]

9

13.57

0.29

47 144–1.16 (–2.41, 0.90) j–3.34 (–6.95, 2.58) j

Peptic ulcer [C4]

37

13.4

13.5

41 7790.04 (0.00, 0.08) e, in.a.

Benign gynaecological disorders [I4]

37

24.7

0.16

71 9580.14 (–2.76, 28.84)

n.a.

25 4803.8 (1.3, 12.3) e, g

n.a.

8 740

0.9 (0.1, 4.4) e, gn.a.

Females

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients
[T30]

11

4.6

n.a.

Mortality
United States Thorotrast patients [T30]31.6

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies.
b Person-years of follow-up for males and females.
c All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
d Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.

n.a.

e 95% CI in parentheses.
f Stomach dose.
g Relative risk in Thorotrast-exposed group compared with control group.
h Males only.
i Trend based on the exposed patients only.
j Estimates (with 95% CI) based on method described in the introduction tochapter III of annex I in the UNSCEAR 2000 Report [U2].

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Table 27 Risk estimates for cancer incidence and mortality from studies of radiation exposure: lung cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted lungdose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv) –1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]Sex

428

408.7 v

0.24

436 1800.32 (0.13, 0.55)

0.57 (0.04, 1.54)

361

269.1 v

0.23

729 608

1.48 (1.04, 1.99)

2.38 (1.37, 3.53)

140

118.1 v

0.23

586 255

0.68 (0.28, 1.20)

0.64 (0.10, 1.38)

20–40 years

316

280.2 v

0.24

378 204

0.65 (0.35, 1.00)

2.65 (1.04, 4.60)>40 years

333

284.5 v

0.22

201 330

0.71 (0.40, 1.09)

9.47 (5.75, 13.78)

12–15 years

18

13.5 v

0.24

119 774

1.41 (0.07, 4.09)

5.49 (0.00, 32.06)

256

207.4 v

0.24

514 582

0.96 (0.57, 1.44)

0.89 (0.21, 1.86)

515

461.8 v

0.23

531 433

0.53 (0.31, 0.78)

3.35 (1.93, 5.02)

789

681.7 v

0.23

1 165 788

0.69 (0.49, 0.92)

1.55 (0.84, 2.37)

Hodgkin’s disease (international) [K9]

79

n.a.

2.2

n.a.

n.a. cn.a.

Hodgkin’s disease (international) [G23, T3,
V2] (5-year lagged dose > 0) a, o

146

n.a.

25 Gy

271 exposed
controls

0.15 (0.06, 0.39)

n.a.

Breast cancer [I7]

17

n.a.

15.2 ddose toipsilateral
lung

n.a.

0.20 (–0.62, 1.03) e, xn.a.

Swedish benign breast disease [M3]

10

11.2

0.75

26 4930.38 (<0, 0.6) x

n.a.

Stockholm skin haemangioma [L10]

11

~9

0.12

406 565

1.4 (n.s.)

0.33

Canadian National Dose Registry [S8]

476

717.1

0.066 2

2 667 903

3.0 (0.5, 6.8) w

n.a.

Capenhurst uranium facility, United Kingdom[M4] b49

58.13

0.098 540 933–1.30 (<–1.30, 9.66) zn.a.

Springfields uranium workers, United
Kingdom [M5]225301.370.022 8

190 795

1.48 (<–2.43, 6.06) z

n.a.

United Kingdom Chapelcross workers [M6]

25

39.32

0.083 639 2100.63 (–1.61, 5.95)z

n.a.

Males
Females

Age at exposure

Time since exposure<20 years

15–30 years>30 years
AllMortality
LSS [P9]Sex

Age at exposure

Males

403

367.7 v

0.20

666 870

0.57 (0.30, 0.89)

0.19 (<0, 0.85)

Females347

272.0 v

0.20

1 061 688

1.28 (0.84, 1.80)<0 (<0, 1269.1)<20 years

117

99.1 v

0.20

885 656

0.94 (0.42, 1.63)

0.11 (<0, 0.56)

20–40 years

314

271.4 v

0.21

514 9030.78 (0.43, 1.19)

0.51 (<0, 1.83)

319

272.3 v

0.19

327 999

0.76 (0.38, 1.23)<0 (<0, 4062.9)

40

35.3 v

0.20

504 1120.72 (<0, 2.16)<0 (<0, 294.01)

15–30 years

221

180.8 v

0.20

592 958

0.90 (0.43, 1.49)

0.24 (<0, 0.97)>30 years489

429.8 v

0.20

631 488

0.71 (0.44, 1.02)

2.56 (1.32, 4.03)

750

640.7 v

0.20

1 728 558

0.84 (0.59, 1.11)

0.37 (0.02, 0.87)>40 years
Time since exposureAll

12–15 years

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Study

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

LSS [P17] (adjusted for smoking, andbased on additive model for smoking andradiation)

357

n.a.

n.a. (similar
to LSS [P9])n.a.

0.9 (S.E. = 0.64)
sex-averagedn.a.

Ankylosing spondylitis [W8] f

563

469

8.9 bb287 095

0.05 (0.002, 0.09) g, xn.a.

Canadian TB fluoroscopy [H7] h

455

473.7

1.02

672 0710.00 (–0.06, 0.07) x

0.00 (–0.4, 0.4) x

Massachusetts TB fluoroscopy [D4]

69

81.8

0.84

169 425–0.19 (<–0.2, 0.04) b–0.90 (<–1.8, 0.2) b

Peptic ulcer [C4]

125

62.8

1.8

41 7790.24 (0.07, 0.44) i, xn.a.

Yangjiang background radiation [T14, T16]

62

76.5

n.a. j

1 246 340

–0.68 (–1.58, 1.67) k, x

n.a.

219

n.a.

1.23 Gy

109 2900.06 (–0.07, 0.20) x

Males

594

n.a.

0.80

485 8620.17 (0.052, 0.32) x, aa

2.4 (0.56, 4.4) x, aa

Females

61

n.a.

0.82

184 6160.32 (<0, 1.3) x, aa

0.43 (<0, 1.6) x, aa

Nuclear workers in Canada, United Kingdom
and United States [C3]

1 238

n.a.

0.04

2 124 526

<0 l

n.a.

United Kingdom NRRW [M12]

921

1 300

0.031

2 063 300

–0.11 (–0.72, 0.72)

n.a.

Canadian National Dose Registry [A8]

386

631.3

0.063

2 861 093

3.6 (0.4, 6.9) z

n.a.
<0

Male Mayak nuclear workers [K34] (external
dose; adjusted for plutonium exposure)
Mayak nuclear workers [G12] (external
dose; adjusted for plutonium exposure)

Nuclear industry workers in Japan [I14]

397

410.9

0.015

~1 390 000<0 l

Nuclear power industry workers in the
United States [H44]

125

210.4

0.026

698 041

0.25 (<–2.51, 8.44) xn.a.

Nuclear power station workers in France
[R54]

23

47.5

0.018

261 418

0.1 (–7.5, 17.4) t

n.a.

1.76 (0.48, 8.83) x, yn.a.

INTERNAL LOW-LET EXPOSURES
Mortality
Semipalatinsk study [B58]

130

n.a.

0.63

582 750

INTERNAL HIGH-LET EXPOSURES (plutonium)Mortality
127

n.a.

0.24 Gy(4.8 Sv)

30 4774.50 (3.15, 6.10) x

n.a.

Males

167

n.a.

0.21 Gy

52 546

4.7 (3.3, 6.7) x, aa

115 (81, 156) x, aa

Females25

n.a.

0.38 Gy

17 47619 (9.5, 39) x, aa

49 (29, 78) x, aa

133

145.8

0.01 Gy(0.19 Sv)

134 817

1.12 m, cc

n.a.

Male Mayak nuclear workers [K34] (dosefrom plutonium; adjusted for external dose)
Mayak nuclear workers [G12] (dose fromplutonium; adjusted for external dose)

Sellafield plutonium workers [O1]

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Study

Observed
cases

Expectedcases

Mean dose

Person-years

189

Average relative risk n

INTERNAL HIGH-LET EXPOSURES (other than radon and plutonium)
Incidence
224

25

35.7

n.a.

32 8001.20 u

224

20

30

n.a.

25 5000.67

Danish Thorotrast patients [A5]21

10.9 o0.18 Gy p

19 3650.7 (0.3, 1.7) q, x

Danish and Swedish Thorotrast patients [T30]

28

13.3

n.a.

25 4801.3 (0.7, 2.2) x

Ra ankylosing spondylitis patients [W15]
Ra ankylosing spondylitis patients [N2]Mortality
Japanese Thorotrast patients, combined
data [M14]

11

n.a.

17 mL r10 6852.0 (1.0, 3.9) x

German Thorotrast patients [V1]

53

n.a.

20.6 mL s

n.a.

0.75

United States Thorotrast patients [T30]

11

5.5

n.a.

8 740

3.3 (0.7, 14) x

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies, unless otherwise stated.
b Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
c Relative risks quoted in Section III.E of Annex I in reference [U2].
d Average dose to both lungs for irradiated controls.
e Wald-type CI; likelihood-based lower confidence bound could not be identified.
f The values given exclude the period within 5 years of first treatment.
g Dose–response analysis based on the number of treatment courses given.
h The values given exclude the period within 10 years of exposure and ages atrisk less than 20 years old.
i Trend based on the exposed patients only.
j Mean annual effective dose = 6.4 mSv.
k Based on a 10-year latent period.
l Trend not statistically significant.
m Relative to other radiation workers at Sellafield; difference is not statisticallysignificant [O1].
n Risk relative to unexposed controls.
o Based on national rates [A5].

p As given in reference [A12].
q Risk relative to unexposed controls, with adjustment for sex, age atangiography and calendar period.
r Mean amount of Thorotrast administered in the first series of Japanesepatients [M19].
s Amount of Thorotrast administered.
t Trend for all respiratory cancers, based on a 10-year latent period.
u Risk relative to unexposed controls, among whom 29 cases were observed,
compared with 49.6 expected [W15].
v All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
w Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
x 95% CI in parentheses.
y Based on a dose–response analysis, restricted to the exposed group only.
z Males only.
aa At attained age 60.
bb Dose to main bronchi used in dose–response analyses.
cc Plutonium workers compared with other radiation workers.

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Table 28 Risk estimates for lung cancer mortality from studies of radon daughter exposure of underground miners
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only
Study

Observed case

Expected cases

Mean exposure(WLM)

Person-years

Average excess relative risk a
at 100 WLM

INTERNAL HIGH-LET EXPOSURES (occupational exposure to radon)
Chinese tin miners [L8, X2] b

936

649

277.4

135 357

0.16 (0.1, 0.2)

West Bohemia uranium miners [T48] c

915

240.8

70.0

261 428

1.6 (1.2, 2.2)

Colorado Plateau uranium miners [H17, L8] a

327

74

807.2

75 0320.42 (0.3, 0.7)

Ontario uranium miners [K12, L8] a

282

221

30.8

319 701

0.89 (0.5, 1.5)

Newfoundland fluorspar miners [L8, M15] d

138

32.1

382.8

48 1890.70 (0.44, 1.14)

Swedish iron miners [L8, R8] a

79

44.7

80.6

32 4520.95 (0.1, 4.1)

New Mexico uranium miners [L8, S19] a

68

23.5

110.3

46 7971.72 (0.6, 6.7)

Beaverlodge uranium miners [H15, H18, L8] a

56

15.4

81.3 e68 0403.25 (1.0, 9.6) f

Port Radium uranium miners [H16, L8] a

39

26.7

242.8

31 4540.19 (0.1, 0.6)

Radium Hill uranium miners [L8, W10] a

32

23.1

7.6

25 5495.06 (1.0, 12.2)

French uranium miners [L8, L92, R39, T8] a

125

83.1

36.5

133 5210.8 (0.3, 1.4) g

Cornish tin miners [H23]

82

n.a.

65

66 9000.045 ha 95% CI in parentheses.
b The values cited are from reference [L8] unless otherwise noted, and except
for the expected number of cases, which has been calculated asO/(1 + 100αD), where O is the number of observed cases, α is the excess
relative risk at 100 WLM and D is the mean exposure in WLM.
c Values cited are based on data from references [T11, T48].

d Values cited are from reference [M15] and include unexposed miners.
e Revised value for persons in nested case-control study [H18].
f Values based on case-control analysis with revised exposure estimates [H18].
g Coefficient based on internal regression, taken from reference [R39].
h Coefficient based on time-weighted cumulative exposure.

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Table 29 Results from analyses of pooled data from case-control studies in China [L61], Europe [D24] andNorth America [K38]
Summary information is based on pooled analyses and may differ slightly from original publications
Mean radon concentration (Bq/m3)

Number of subjects
Study
Cases

Controls

Cases

Controls

EOR a for 100 Bq/m3(95% CI)

Studies in China b
Shenyang [B37]

285

338

122

123

–0.02 (–0.13, 0.43)

Gansu [W27]

768

1 659

232

226

0.18 (0.02, 0.49)

Studies in Europe cAustria [O10]

183

188

267

130

0.46 (n.a. d, >5.00)

Czech Republic [T35]

171

713

528

493

0.19 (–0.00, 2.07)

Finland (nationwide) [A26]

881

1 435

104

103

0.03 (n.a., 0.17)

Finland (south) [R40]

160

328

221

212

0.06 (–0.08, 1.58)

France [B41]

571

1 209

138

131

0.11 (–0.01, 0.41)

Germany (eastern) [W28]

945

1 516

78

74

0.18 (–0.00, 0.56)

Germany (western) [W28]

1 323

2 146

49

51

–0.02 (n.a., 0.36)

Italy [B38]

384

405

113

102

0.10 (–0.18, 1.40)

Spain [B39]

156

235

123

137

–0.11 (n.a., 0.59)

Sweden (nationwide) [P18]

960

2 045

99

94

0.11 (–0.04, 0.46)

Sweden (never-smokers) [L65]

258

487

79

72

0.24 (–0.08, 0.95)

Sweden (Stockholm) [P30]

196

375

131

136

0.12 (–0.14, 1.41)

United Kingdom [D13]

960

3 126

57

54

0.04 (–0.05, 0.22)

Studies in North America b, e
New Jersey [S62]

429

396

27

25

0.56 (–0.22, 2.97)

Winnipeg [L64]

647

693

137

147

0.02 (–0.05, 0.25)

Missouri-I [A27]

530

1 177

62

63

0.01 (n.a., 0.42)

Missouri-II [A9]

477

516

55

56

0.27 (–0.12, 1.53)

Iowa [F12]

412

613

136

121

0.44 (0.05, 1.59)

Connecticut [S66]

726

779

32

33

0.02 (–0.21, 0.51)

Utah, southern Idaho [S66]

441

792

55

58

0.03 (–0.20, 0.55)

Combined studies
China [L61]

1 053

1 997

202

209

0.13 (0.01, 0.36)

Europe [D24, D30]

7 148

14 20810497

0.08 (0.030, 0.16)

North America [K38, K39]

4 081

5 281

74

74

0.11 (0.00, 0.28)

a Estimates of excess odds ratio (EOR) for 100 Bq/m3 based on fitted linear
model for time-weighted radon concentration (x): OR(x) = 1 + β x.
b Study mean concentrations based on residential occupancy 5–30 years prior
to index date.
c Study mean concentrations based on residential occupancy 5–35 years prior
to index date.

d “n.a.” denotes estimate could not be calculated.
e Includes subjects with radon concentration measurements made using alphatrack air monitoring detectors.

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Table 30 Risk estimates for cancer incidence and mortality from studies of radiation exposure: malignant tumours of
the bone and connective tissue
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with organ doses of 0.005 Sv or more (weighted
skeletal dose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

Males

4

4.1 p

0.24

436 180

3.34 (0.90, 9.69)<0 (<0, <0)Females

3

7.6 p

0.23

729 608<0 (<0, <0)<0 (<0, 9.75)

3

2.3 p

0.23

586 255

4.33 (0.90, 16.11)<0 (<0, <0)

20–40 years

2

1.2 p

0.23

378 204

3.16 (<0, 24.05)<0 (<0, 12.66)>40 years

2

8.8 p

0.22

201 330<0 (<0, <0)<0 (<0, <0)

12–15 years

3

5.8 p

0.24

634 356

1.27 (0.07, 4.55)<0 (<0, 10.87)

4

2.9 p

0.23

531 433

2.28 (0.23, 9.32)<0 (<0, 18.77)

7

8.7 p

0.23

1 165 788

1.64 (0.40, 4.31)<0 (<0, 14.36)

Retinoblastoma patients [W11](bone and soft-tissue sarcoma) c

81

16.9

0.0 d

n.a.

0.19 (0.14, 0.32) jn.a.

Childhood radiotherapy (international) [T10]

54

20

27

n.a.

0.06 (0.01, 0.2) bn.a.

United Kingdom childhood cancer [H27](bone) e

49

18.8

10 d

n.a.

0.16 (0.07, 0.37) jn.a.

French breast cancer [R52]

12

1.7

>11.8 q

48 9930.05 (n.a., 1.18) rn.a.

Cervical cancer case-control [B8](connective tissue) f

46

70.8

7

n.a.

–0.05 (–0.11, 0.13)–0.01 (–0.03, 0.03)

Cervical cancer case-control [B8] (bone) f

15

10.4

22

n.a.

0.02 (–0.03, 0.21) b

n.a.

Bone

16

23

0.066 2

2 667 903<0<0Connective tissue

42

46.4

<0

<0

Age at exposure<20 years

Time since exposure

15–30 years>30 years

Canadian National Dose Registry [S8]Mortality
LSS [P9]Sex

Age at exposure

6

7.5 p

0.20

666 869

1.24 (0.03, 4.47)<0 (<0, 24.40)

Females

8

7.0 p

0.20

1 061 688

<0 (<0, 3.15)<0 (<0, 25.43)<20 years

2

2.3 p

0.20

885 656

2.11 (<0, 11.62)<0 (<0, 7.21)

20–40 years

5

1.9 p

0.21

514 9038.26 (0.70, 50.09)<0 (<0, <0)710.3 p0.19

327 998<0 (<0, 0.01)<0 (<0, 35.48)

12–15 years

8

9.2 p

0.20

1 097 069

1.33 (0.05, 4.70)

0.08 (0.01, 0.26)

15–30 years

6

5.2 p

0.20

631 488<0 (<0, 4.20)<0 (<0, 31.08)>30 years

1414.2 p0.20

1 728 557

0.88 (<0, 3.03)<0 (<0, 21.23)
0.097 b

n.a.

Males

>40 years
Time since exposure

Ankylosing spondylitis [W8](bone and connective and soft tissue) g

19

6.3

4.54

287 095

0.44 b

Nuclear workers in Canada, United
Kingdom and United States [C3] (bone)

11

n.a.

0.04

2 124 526

<0 i

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193

Study

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv) –1

Nuclear workers in Canada, United Kingdom
and United States [C3] (connective
tissue)

19

n.a.

0.04

2 124 526

>0 i

n.a.

Oak Ridge National Laboratory workers,
United States, 1943–1947 [F2] (bone)

11

10.4

n.a.

n.a.

n.a.

n.a.

United States radiologic technologists
[M10]

5

13.3

n.a.

~3 900 000<0<0Canadian National Dose Registry [A8]

3s

8.3 s

0.063

2 861 093

–0.9 (–57.5, 55.7) sn.a.

INTERNAL HIGH-LET EXPOSURES
Incidence
224
Ra TB and ankylosing spondylitis
patients [N3] (bone)

55

0.2

30.6 Gy

25 500

n.a.

n.a.

224
Ra ankylosing spondylitis patients[W15] (bone and connective tissue)

4

1.3

~6 Gy

32 800

4.3 k

n.a.

German Thorotrast patients [V4](bone sarcoma)

4

n.a.

20.6 mL ln.a.

~3.3 mn.a.

Mortality
United States radium luminizers [C11, R18,
S12, S13, S16, S25] (bone) n

46<1

8.6 Gy35 819n.a.

~13Portuguese Thorotrast patients [D15](bone)

16n.a.

26.3 mL l

16 9637.08(1.65, 30.3) h, j, on.a.

United States Thorotrast patients [T30]

2

0.1

n.a.

8 740

∞ (0.1, ∞) h, jn.a.

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies, unless otherwise stated.
b Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
c Results are for patients with bone or soft-tissue sarcoma for whom dosimetry
information was available.
d Mean dose for controls of bone cancer cases.
e Results are based on a case-control analysis of bone cancer.
f Based on 1-year survivors. The observed and expected numbers cover both
exposed and unexposed persons. The excess absolute risk for connective
tissue is computed using underlying cancer incidence data derived from thecohort study [B11].
g The values given exclude the period within 5 years of first treatment.
h Risk relative to unexposed controls.
i Based on a 10-year lag. Trend not statistically significantly different from zero.

j 95% CI in parentheses.
k Risk relative to unexposed controls, among whom 1 case was observed
compared with 1.4 expected [W15].
l Amount of Thorotrast administered.
m Crude relative risk, based on one case in the control group. This relative risk
is not significantly different from 1 (p > 0.05) [V4].
n Based on pre-1930 workers with an average skeletal dose greater than zero
[C11].
o Based on 5 deaths in the control group, and excluding the first 5 years afteradministration of Thorotrast [D15].
p All expected numbers calculated by means of fitted relative risk model (4)(with purely quadratic dose response), evaluated at zero dose.
q All cases have at least 11.8 Gy.
r Lower bound did not converge.
s Males only.

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Table 31 Risk estimates for cancer incidence and mortality from studies of radiation exposure: cutaneous malignant
melanoma
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted skindose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

3

3.3 b

0.33

436 183

0.01 (<0, 2.66)<0 (<0, 6.99)

4

4.3 b

0.32

729 610<0 (<0, 0.57)<0 (<0, 1.56)<40 years

3

3.7 b

0.32

964 458<0 (<0, 0.68)<0 (<0, 1.09)>40 years

4

3.8 b

0.31

201 3340.07 (<0, 2.73)<0 (<0, <0)

12–30 years

4

4.5 b

0.33

634 360<0 (<0, 2.10)<0 (<0, 5.05)

3

3.0 b

0.31

531 433<0 (<0, 0.96)<0 (<0, 2.18)

7

7.4 b

0.32

1 165 793

<0 (<0, 0.74)<0 (<0, 0.03)

222

191.3

0.006 6

2 667 903

4.3 (<0, 19.6) c

n.a.
n.a.

Males
Females

Age at exposure

Time since exposure>30 years
All
Canadian National Dose Registry [S8]Capenhurst uranium facility, United
Kingdom [M4] b

35

30.22

0.098 540 933–1.30 (<–1.30, 10.51) e

Springfields uranium workers, United
Kingdom [M5]161153.01

0.022 8

190 795

4.38 (–0.21, 11.78) en.a.

United Kingdom Chapelcross workers [M6]29 d21.56 d0.083 639 2100.15 (<–2.23, 6.43) en.a.

Mortality
LSS [P9]Sex

Age at exposure

Time since exposure

3

1.2 b

0.28

666 872

1.91 (<0, 15.25)

0.03 (<0, 0.13)

Females

4

6.0 b

0.28

1 061 688

<0 (<0, <0)<0 (<0, 5.84)<40 years

3

3.0 b

0.28

1 400 559

0.66 (<0, 4.11)<0 (<0, 1.13)>40 years

4

3.1 b

0.27

328 000<0 (<0, 0.58)

0.36 (0.14, 2.32)

3

3.2 b

0.28

1 097 072

<0 (<0, 0.40)<0 (<0, <0)

4

3.0 b

0.28

631 488

0.66 (<0, 4.11)<0 (<0, 11.93)

7

5.4 b

0.28

1 728 560

0.30 (<0, 2.10)<0 (<0, 6.25)

21

n.a.

0.006 6

2 861 093

44.9 (–67.1, 156.8)

n.a.

–0.1 (–1340.0, 1339.0)n.a.

0.4 (0.1, 2.1) f, g

n.a.

Males

12–30 years>30 years

All
Canadian National Dose Registry [A8]
Sex
Males
Females

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients
[T30]

2

2.0

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies.
b All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
c Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.

n.a.

25 480d Melanoma and other skin cancers.
e Males only.
f Risk relative to unexposed controls.
g 95% CI in parentheses.

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Table 32 Risk estimates for cancer incidence and mortality from studies of radiation exposure: non-melanoma skin cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted skindose) for incidence. For case-control studies, the observed number of cases covers both exposed and unexposed persons. The studies
listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

Age at exposure

Males

66

45.6 b0.33

436 183

1.27 (0.65, 2.17)

1.23 (0.65, 1.96)

Females101

78.1 b

0.32

729 6101.37 (0.81, 2.12)

1.07 (0.68, 1.56)<20 years

41

14.0 b0.32

586 255

5.69 (3.16, 10.27)<0 (<0, 150.01)

67

52.6 b0.33

378 204

0.90 (0.38, 1.66)

0.98 (0.43, 1.72)>40 years

10

8.7 b

0.33

119 776<0 (<0, 1.73)

0.38 (0.04, 1.42)

12–15 years

36

29.1 b

0.33

514 584

0.90 (0.20, 2.08)

0.42 (0.16, 0.84)

15–30 years

121

86.9 b0.31

531 433

1.53 (1.00, 2.24)

2.31 (1.62, 3.14)

167

123.7 b0.32

1 165 793

1.33 (0.89, 1.88)

1.12 (0.79, 1.52)

20–40 years

Time since exposure>30 years

Childhood exposures
Israel tinea capitis [L42, R16] c

41

21.7

6.8

662 950

0.70 (0.35, 1.32)

1.31 (0.94, 1.77) d

New York tinea capitis (whites) [S7] c

124

37.7

4.3 e

125 357

0.6 (0.3, 1.1) f

1.9 (0.5, 3.3) f

Rochester thymic irradiation [H26, L42]

14

4.2

2.3

87 000 g

1.05 (0.50, 1.84)

15.9 (7.5, 27.9) d

Tonsil irradiation [L42, S17]

63

45.0

3.8

96 000 g

0.11 (0.04, 0.19)

10.2 (3.3, 18.3) d

Adult exposures
Cervical cancer cohort [B11, L42]

88

100

10

342 786 j<0 (<0, 0.01)<0 (<0, 0.6) h

Massachusetts TB fluoroscopy [D6, L42]

80

75.3

9.6

122 000 g

0.007 (0, 0.03)

0.9 (<0, 4.5) h

New York mastitis [L42]

14

10.7

2.6

14 000 g

0.12 (<0, 0.38)

60 (<0, 193.5) h

1.3 (0.6, 2.8) f, in.a.

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients
[T30]

14

9.5

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies.
b All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
c All estimates are for basal cell carcinoma.
d Risks normalized to 3 000 cm2 of UVR-exposed skin (as in reference [L42]).
e Average dose to the scalp and the margin around the scalp in exposed group.

n.a.

25 480f 95% CI in parentheses.
g Person-years estimated from data presented by Shore [S22].
h Risks normalized to 15 000 cm2 of UVR-unexposed skin (as in reference[L42]).
i Risk relative to unexposed controls.
j Five or more years of follow-up.

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Table 33 Risk estimates for cancer incidence and mortality from studies of radiation exposure: female breast cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted
breast dose) for incidence or mortality. For case-control studies, the observed number of cases covers both exposed and unexposed
persons. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Age at exposure

246

166.7 q0.26

315 537

1.89 (1.38, 2.50)

8.78 (6.54, 11.28)

222

170.8 q0.26

287 982

1.31 (0.86, 1.87)

6.97 (4.71, 9.54)>40 years5956.6 q0.23

126 0900.62 (0.04, 1.51)

2.49 (0.02, 5.82)

12–15 years

15

12.7 q0.26

72 5661.45 (0.09, 4.18)<0 (<0, 3.01)

15–30 years

153

99.0 q0.26

318 513

1.94 (1.30, 2.77)

6.07 (4.29, 8.09)

359

282.3 q0.25

338 529

1.30 (0.94, 1.73)

11.08 (8.36, 14.05)

527

393.0 q

0.26

729 608

1.49 (1.17, 1.85)

7.55 (6.08, 9.14)

Pooled analysis: eight cohorts [P3] m

829

509

0.17–5.8 forvarious studies

839 9070.86 (0.7, 1.04) n

13.4 (9.5, 17) n

Massachusetts TB fluoroscopy [B3]

142

107.6

0.79

54 6000.40 (0.2, 0.7) b

7.98 (3.6, 13) b

New York acute post-partum mastitis [S5]

54

20.8

3.7

9 800

0.43 (0.3, 0.6) b

9.14 (6.0, 13) b

Swedish benign breast disease [M8, M17]

115

28.8

8.46

37 4000.35 (0.3, 0.4) b

2.72 (2.2, 3.3) b

Cervical cancer case-control [B7] c953 d

1 083.0

0.31

n.a.

–0.2 (<–0.2, 0.3)<–0.3 (<–0.3, 0.2) b

91 e

82.6

0.31

n.a.

0.33 (<–0.2, 5.8)

n.a.

Denmark [S20]

529

508.7

2.51

n.a.

0.02 (<–0.1, 0.2) bn.a.

United States [B10]

655

550.4

2.82

n.a.

0.07 (<–0.1, 0.2) bn.a.

Rochester thymic irradiation H10] f

22

7.8

0.76

38 2002.39 (1.2, 4.0) b

4.89 (2.4, 8.1) b

Childhood skin haemangioma [L12] f

245

204

0.33

600 000

0.35 (0.18, 0.59) r

1.44 (0.78, 2.28) r

French–United Kingdom childhood cancer
[G29]

16

n.a.

5.06

~29 000

0.13 (<0, 0.75)

n.a.

Hodgkin’s disease (Stanford) [H20]

25

6.1

44.0

100 057

0.07 (0.04, 0.11) b

0.04 (0.03, 0.07) b

Hodgkin’s disease (Netherlands) [V8]

48

n.a.

25.2

Mean
follow-up
18.7 years

0.06 (0.01, 0.13)

n.a.

Hodgkin’s disease (international) [T25]

105

n.a.

25.1

Mean
follow-up
18.0 years

0.15 (0.04, 0.73)(radiotherapy alone)n.a.

Canadian National Dose Registry [S8]

544

584

0.017 5

n.a.

<0

<0

Employed before 1970

29

21.64

0.551

357 753

0.62 (–0.16, 1.6)

0.37 (–0.09, 1.0)

Employed only 1970–1980

17

12.79

0.082

337 133

4.0 (–2.4, 13) o

1.5 (–0.9, 5.0)

<20 years
20–40 years

Time since exposure>30 years
All

Without ovaries
Contralateral breast

Chinese medical X-ray workers [W3]

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Study

Observed
cases

Expectedcases

Mean dose
(Sv)

197

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

Mortality
LSS [P9]
Age at exposure<20 years

66

38.7 q0.23

469 884

2.94 (1.63, 4.86)<0 (<0, 437.60)

20–40 years

70

54.5 q0.23

391 356

1.01 (0.31, 2.06)<0 (<0, 556.08)

34

39.6 q0.21

200 448<0 (<0, 0.99)<0 (<0, 622.72)

12–15 years

28

25.2 q0.22

301 146

0.04 (<0, 1.61)<0 (<0, 279.27)

15–30 years

50

38.2 q0.23

365 465

1.33 (0.46, 2.68)

1.13 (0.30, 2.23)>30 years

92

68.7 q0.23

395 077

1.82 (0.98, 2.98)

3.28 (1.97, 4.83)

170

131.5 q0.23

1 061 688

1.39 (0.83, 2.10)<0 (<0, 513.45)

Scoliosis patients [D17] f

70

35.7

0.11

184 508

5.4 (1.2, 14.1) r

12.9 (4.0, 21.0) r

Ankylosing spondylitis [W8] g

42

39.3

0.59

n.a.

0.08 (–0.30, 0.65) h, r

n.a.
3.16 (1.97, 4.78) j, r

>40 years
Time since exposure

All

Canadian TB fluoroscopy [H9]

349

237

0.89

411 706

0.90 (0.55, 1.39) i, r

Peptic ulcer [C4]

14

7.7

0.2

41 7790.10 (<0, 10.40) o, r, un.a.

Nuclear workers in Canada, United Kingdom
and United States [C3]

84

n.a.

0.04

n.a.

>0 k

n.a.

United Kingdom NRRW [M12]

25

39.1

0.006

~192 000

0.12 (<–1.95, 40.5) pn.a.

1.09 (–0.05, 15.8) r, sn.a.

INTERNAL LOW-LET EXPOSURES
Mortality
Semipalatinsk study [B58]

61

n.a.

0.63

582 750

INTERNAL HIGH-LET EXPOSURES
Incidence
224
Ra TB and ankylosing spondylitis patients
[N2]

28

8

~0.1 Gy l

n.a.

0.9

n.a.

Danish and Swedish Thorotrast patients
[T30]

27

10.0

n.a.

12 247

1.6 (0.9, 2.8) r, t

n.a.

4 613

0.9 (0.3, 7.2) r, tn.a.

Mortality
United States Thorotrast patients [T30]62.9

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies, unless otherwise stated.
b Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
c Excess absolute risk among cervical cancer patients is computed using
underlying cancer incidence data derived from the cohort study [B11].
d Based on 5-year survivors.
e Based on 10-year survivors.
f Population exposed as children.
g The values given exclude the period within 5 years of first treatment.
h Dose–response analysis based on the number of treatment courses given.
i Including a factor to allow for differences between Nova Scotia and other
Canadian provinces. Values apply to exposure at age 15 years.
j Including a factor to allow for differences between Nova Scotia and other
Canadian provinces. Values apply for 20 years following exposure at age
15 years.
k Based on a 10-year lag. Trend not statistically significant.
l High-LET breast dose from 224Ra.

n.a.

m Cohorts are: LSS of the survivors of the atomic bombings in Japan, two
Massachusetts multiple fluoroscopy cohorts, and New York mastitis,
Rochester thymus, Swedish benign breast disease, Gothenburg haemangioma
and Stockholm haemangioma studies.
n Risk estimate for exposure at age 25 years, except for the infant exposurecohorts where risk was modelled for 0.5 years of age at exposure.
o Excess relative risk and excess absolute risk values were calculated from themean dose and the observed and expected cancers (or the relative risk andconfidence interval) reported in the paper.
p Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
q All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
r 95% CI in parentheses.
s Based on a dose–response analysis, restricted to the exposed group only.
t Risk relative to unexposed controls.
u Based on follow-up of 11 or more years after radiotherapy.

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Table 34 Risk estimates for cancer incidence and mortality from studies of radiation exposure: uterine cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted
uterine dose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Age at exposure

130

120.8 b0.20

315 537

0.38 (<0, 0.90)

0.75 (<0, 2.59)

230

227.4 b0.19

287 982<0 (<0, 0.33)

0.20 (<0, 2.85)>40 years

144

142.7 b0.17

126 089<0 (<0, 0.41)

0.09 (<0, 4.88)

12–15 years

45

52.8 b0.20

72 566<0 (<0, 0.28)<0 (<0, 1.61)

15–30 years

243

231.5 b0.20

318 513<0 (<0, 0.09)<0 (<0, 0.67)

216

205.8 b0.19

338 5280.53 (0.17, 0.99)

2.86 (0.83, 5.31)

504

490.2 b0.19

729 6070.10 (<0, 0.32)

0.09 (<0, 1.48)<45 years

130

n.a.

166 d

n.a.

0.002 3 e, fn.a.

45–54 years

100

166

0.004 855–64 years

60

158

0.000 8≥65 years

23

170

0.000 0

1–<5 years

19

5–<10 years

66

169

0.002 410–<15 years

85

165

–0.002 0≥15 years

143

163

0.030 7<20 years
20–40 years

Time since exposure>30 years
All
Cervical cancer [B8]Age at treatment cTime since treatment

n.a.

168

n.a.

–0.004 5

n.a.

Mortality
LSS [P9]<20 years40

34.2 b0.18

469 884

0.42 (<0, 1.68)<0 (<0, 333.82)

20–40 years

133

122.6 b0.18

391 356

0.17 (<0, 0.77)<0 (<0, 1.61)

148

138.9 b0.16

200 441<0 (<0, 0.51)<0 (<0, 3.53)

5–15 years

96

82.6 b0.17

301 1460.31 (<0, 1.23)<0 (<0, 0.26)

15–30 years

115

109.3 b0.18

365 464<0 (<0, <0)<0 (<0, 0.07)>30 years

110103.7 b0.18

395 071

0.52 (0.01, 1.24)<0 (<0, 1229.1)

321

295.5 b0.17

1 061 681

0.09 (<0, 0.44)<0 (<0, 0.33)

All uterus

105

57.2

32.0 d109 9110.006 g0.14 hCervix

10

16.4

32.0

109 911–0.01 e–0.02

All uterus

25

17.73

5.2 d

47 1440.09 (–0.02, 0.19) g, j

0.30 (–0.07, 0.78) h, j

Cervix

12

9.20

5.2

47 1440.06 (–0.06, 0.25) e, j

0.11 (–0.12, 0.48) h, j

Age at exposure>40 years
Time since exposure

All
Benign gynaecological disorders [I4]Metropathia [D7]

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199

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

All uterus

13

13.57

4.94 d61 619–0.01 e–0.02 hCervix

3

8.33

4.94

61 619

–0.13

–0.18

Other uterus

10

5.24

4.94

61 1190.18

0.16

Spondylitis [W8]INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients [T30]
Uterine cervix

6

6.0

n.a.

12 2470.6 (0.2, 1.8) i

n.a.

Uterine corpus

5

4.5

n.a.

12 2470.6 (0.2, 1.8) in.a.

a 90% CI derived from fitting models (4) and (5) for the LSS, and derived frompublished data for the other studies.
b All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
c Data are for uterine corpus cancer.
d Dose in grays.

e Calculated as [RR – 1] divided by the mean dose.
f Reference group includes women with uterine dose of <100 Gy.
g Slope of linear dose response.
h Calculated as [observed – expected] × 104 divided by [PY × mean dose].
i Risk relative to unexposed controls, with 95% CI in parentheses.
j 95% CI in parentheses.

Table 35 Risk estimates for cancer incidence and mortality from studies of radiation exposure: ovarian cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted
ovarian dose) for incidence or mortality. For case-control studies, the observed number of cases covers both exposed and unexposed
persons. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]<20 years29

27.1 d0.20

315 537

1.16 (0.15, 2.86)

0.71 (0.09, 1.72)

20–40 years

4546.1 d0.19

287 982<0 (<0, 0.71)<0 (<0, 0.71)>40 years29

24.6 d0.17

126 089

1.73 (0.20, 4.45)

3.24 (0.45, 7.21)

4

5.1 d

0.19

72 566<0 (<0, 0.04)<0 (<0, 0.71)

15–30 years

35

32.1 d0.19

318 513

1.47 (0.37, 3.26)

1.04 (0.21, 2.30)>30 years

64

63.0 d0.19

338 528

0.23 (<0, 1.11)

0.54 (<0, 1.92)

103

98.6 d0.19

729 6070.61 (0.08, 1.35)

0.59 (0.07, 1.34)

Cervical cancer case-control [B8]

309

n.a.

32.1

n.a.

0.01 (–0.02, 0.14)

0.05 (–0.08, 0.60)

Stockholm skin haemangioma [L10]

15

n.a.

0.05

406 565

0.62

0.33

Age at exposure

Time since exposure

12–15 years

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Study

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

Mortality
LSS [P9]<20 years20

17.7 d0.18

469 884

1.53 (0.19, 4.06)<0 (<0, 185.15)

20–40 years

34

29.7 d0.18

391 356

0.92 (<0, 2.65)<0 (<0, 386.99)>40 years31

22.6 d0.16

200 447

1.33 (<0, 4.25)<0 (<0, 717.59)

13

14.8 d0.17

301 146<0 (<0, 9171.6)<0 (<0, 187.74)

15–30 years

26

19.5 d0.18

365 464

2.65 (0.78, 6.00)<0 (<0, 298.86)>30 years

46

38.6 d0.18

395 077

0.88 (<0, 2.41)<0 (<0, 513.47)

All

85

70.3 d0.18

1 061 687

1.18 (0.39, 2.31)<0 (<0, 348.40)

226

Ra for uterine bleeding [I4]: mortality b

37

23

2.3

109 9110.41 (–0.69, 1.51)

n.a.

United Kingdom X-ray for uterine bleeding
[D7]

18

15.6

5.3

47 1440.02 (–0. 08, 0.12) c

0.10 (–0.20, 0.51)

United Kingdom NRRW [M12]

10

11.6

0.006

~192 000

82.8 (<–1.95, 2583)

n.a.

4.3 (1.1, 24.3) en.a.

Age at exposure

Time since exposure

5–15 years

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients
[T30]

9

4.5

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies.
b Data for “genital organs other than uterus”.
c Excess relative risk (and 95% CI) was derived from dose–response analysis;
excess absolute risk (and 95% CI) was calculated from the observed andn.a.

12 247

expected cancers and the mean dose and person years of follow-up reported
in the paper.
d All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
e Risk relative to unexposed controls, with 95% CI in parentheses.

Table 36 Risk estimates for cancer incidence and mortality from studies of radiation exposure: prostate cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted
testicular dose) for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Age at exposure

18

19.1 k

0.22

270 718

0.12 (<0, 1.38)<0 (<0, 0.42)

5960.2 k

0.26

90 2220.03 (<0, 0.70)<0 (<0, 1.84)>40 years

7978.5 k

0.23

75 2400.11 (<0, 0.70)<0 (<0, 2.96)

12–15 years

4

5.4 k

0.24

47 208<0 (<0, 1.14)<0 (<0, 325.76)

15–30 years

44

48.2 k

0.24

196 069<0 (<0, 0.31)<0 (<0, 0.38)

108103.4 k0.22

192 9030.36 (<0, 0.93)<0 (<0, 2207.9)

156

157.3 k0.23

436 1800.12 (<0, 0.51)<0 (<0, 0.38)

232

279

0.115

n.a.

0.1 (<0, 3.5) jn.a.

<20 years
20–40 years

Time since exposure>30 years
All
Canadian National Dose Registry [S8]

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Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

Capenhurst uranium facility, United
Kingdom [M4]

9

16.72

0.098 540 933–1.31 (<–1.31, 12.76)

n.a.

Springfields uranium workers,
United Kingdom [M5]

69

89.79

0.022 8

190 795

0.41 (<–2.90, 9.27)

n.a.

Study

Mortality
LSS [P9]
Age at exposure

Time since exposure

5

9.3 k

0.19

415 772<0 (<0, >10 000)<0 (<0, 45.63)

20–40 years

18

19.2 k

0.22

123 547<0 (<0, 1.10)<0 (<0, 473.90)>40 years30

28.9 k

0.20

127 550

1.01 (0.01, 2.78)<0 (<0, 910.84)

12–15 years

4

5.0 k

0.20

202 966<0 (<0, 0.38)<0 (<0, 59.26)

10

15.2 k

0.20

227 492<0 (<0, 1.33)<0 (<0, 184.91)

39

36.3 k

0.20

236 4110.69 (<0, 0.97)<0 (<0, 623.82)

53

54.9 k

0.20

666 869

0.40 (<0, 1.31)<0 (<0, 298.22)

88

64.7

2.18

n.a.

0.14 (0.02, 0.28) c, e

n.a.
n.a.

<20 years

15–30 years>30 years
All
Ankylosing spondylitis [W8] b
Peptic ulcer [C4]

30

24.2

0.1

41 779–1.60 (<–1.60, 4.50) e, h, l

Nuclear workers in Canada, United Kingdom
and United States [C3]

256

n.a.

0.04

n.a.

<0 d

n.a.

United Kingdom NRRW [M12]

211

214.6

0.033

~1 871 000

0.29 (–1.13, 2.95)

n.a.

Nuclear power industry workers in the
United States [H44]

14

23.2

0.026

698 041–2.50 (<–2.51, 26.4) en.a.

Oak Ridge National Laboratory workers,
United States, 1943–1947 [F2]

150

142.0

n.a.

n.a.

n.a.

n.a.

Oak Ridge X-10 and Y-12 plants [F5]

77

n.a.

0.013

n.a.

2.06 (<0, 24.6)

n.a.

Los Alamos National Laboratory workers,
United States [W6]

53

79.0

~0.016

251 651

<0 i

n.a.

INTERNAL HIGH-LET EXPOSURES
Incidence
224
Ra TB and ankylosing spondylitis patients
[N2]

16

~12

n.a.

n.a.

~1.3 gn.a.

Danish and Swedish Thorotrast patients
[T30]

14

10.0

n.a.

13 2334.5 (1.6, 16.3) e, gn.a.

Mortality
German Thorotrast patients [V4]

21

n.a.

20.6 mL fn.a.

~0.9 gn.a.

United States Thorotrast patients [T30]

1

1.4

n.a.

4 127

0.2 (0.0, 5.1) e, gn.a.

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies, unless otherwise stated.
b The values given exclude the period within 5 years of first treatment.
c Dose–response analysis based on the number of treatment courses given.
d Based on a 10-year lag. One-sided p-value for increasing trend = 0.953,
based on a normal approximation.
e 95% CI in parentheses.
f Amount of Thorotrast administered (mL).
g Risk relative to unexposed controls.

h Excess relative risk and excess absolute risk values were calculated from themean dose and the observed and expected cancers (or the relative risk andconfidence interval) reported in the paper.
i Dose response was in the negative direction.
j Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
k All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
l Based on follow-up of 11 or more years after radiotherapy.

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Table 37 Risk estimates for cancer incidence and mortality from studies of radiation exposure: cancer of the urinary bladder
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with organ doses of 0.005 Sv or more (weighted
bladder dose (incidence data), weighted urinary tract dose (mortality data)) for incidence or mortality. The studies listed are those forwhich quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
132

118.3 k0.22

436 180

0.63 (0.17, 1.25)

0.47 (<0, 1.60)

9059.0 k

0.20

729 6071.74 (0.71, 3.22)

0.52 (0.12, 1.13)<20 years

48

38.6 k

0.21

586 255

1.00 (0.16, 2.32)<0 (<0, 0.46)

20–40 years

80

61.3 k

0.21

378 204

0.95 (0.23, 2.01)

0.69 (<0, 1.89)>40 years

94

79.2 k

0.19

201 3290.78 (0.14, 1.70)

2.28 (0.21, 5.01)

9

9.6 k

0.21

119 774<0 (<0, 1.06)<0 (<0, 292.76)

15–30 years

66

52.8 k

0.21

514 582

0.98 (0.17, 2.20)

0.50 (<0, 1.19)>30 years

147

116.4 k0.20

531 432

1.00 (0.44, 1.74)

1.28 (0.33, 2.50)

222

178.1 k0.21

1 165 787

0.92 (0.46, 1.50)

0.51 (0.14, 1.02)

Cervical cancer case-control [B8] c

273

65.8

45

n.a.

0.07 (0.02, 0.17)

0.12 (0.04, 0.3)

Canadian National Dose Registry – males
only [S8]

139

183

0.115

n.a.

1.4 (<0, 8.2) jn.a.

Capenhurst uranium facility, United
Kingdom [M4] b

14

14.57

0.098 540 93310.33 (<0, 57.24) d, mn.a.

Springfields uranium workers, United
Kingdom [M5]

57

75.15

0.022 8

190 795

2.68 (<–4.11, 14.50) d, m

n.a.

Sex

Males
Females

Age at exposure

Time since exposure

12–15 years

All

Mortality
LSS [P9]
Males

55

43.6 k

0.19

666 869

1.03 (0.07, 2.53)<0 (<0, 313.73)

Females43

33.6 k

0.18

1 061 687

1.37 (0.15, 3.40)<0 (<0, 170.04)

12

10.9 k

0.19

885 656<0 (<0, 2.28)<0 (<0, 43.45)

20–40 years

24

16.6 k

0.19

514 903

1.52 (<0, 4.72)<0 (<0, 176.04)>40 years

6250.1 k

0.18

327 997

1.36 (0.34, 2.89)<0 (<0, 848.46)

12–15 years

14

12.9 k

0.18

504 112<0 (<0, 2.73)<0 (<0, 118.39)

30

25.7 k

0.19

592 956

0.87 (<0, 2.87)<0 (<0, 203.71)

54

38.7 k

0.19

631 488

1.76 (0.51, 3.73)<0 (<0, 334.58)

98

77.2 k

0.19

1 728 556

1.17 (0.36, 2.30)<0 (<0, 226.53)

Benign gynaecological disorders [I4] e

19

9

6

71 9580.20 (0.08, 0.35)

0.24 (0.1, 0.4) b

Metropathia haemorrhagica [D7] f

20

6.65

5.2

47 1440.40 (0.15, 0.66) l, p0.54 (0.23, 0.99) b, lAnkylosing spondylitis [W8] g

71

46.1

2.18

287 095

0.24 (–0.09, 0.41) h, l

0.39 (0.19, 0.54) b, l

Peptic ulcer [C4]

13

8.8

0.2

41 7792.5 (<0, 17.2) i, l, o

n.a.
n.a.

n.a.

Sex

Age at exposure

Time since exposure<20 years

15–30 years>30 yearsAll

Los Alamos National Laboratory workers,
United States [W6]

18

30.1

~0.016

251 651<0 d

Nuclear industry workers in Japan [I14]

27

23.4

0.015

~1 390 000<0 d

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203

Study

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

Nuclear workers in Canada, United Kingdom
and United States [C3]

104

n.a.

0.04

2 142 526

<0 d

n.a.

United Kingdom NRRW [M12]

110

130.8

0.031

2 063 300

–0.33 (–1.28, 1.61)jj

n.a.

25 4800.8 (0.3, 1.9) l, n

n.a.

8 740

∞ (0.2, ∞) l, nn.a.

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients
[T30]

8

6.7

n.a.

Mortality
United States Thorotrast patients [T30]

3

0.8

n.a.

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies, unless otherwise stated.
b Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
c Based on 10-year survivors. The observed and expected numbers coverboth exposed and unexposed persons. The excess absolute risk estimate
was computed using underlying cancer incidence estimated using the cervicalcancer cohort study [B11].
d Based on a 10-year lag. Trend not statistically significant.
e The observed and expected numbers of cases are for 10-year survivors.
The estimated number of expected cases incorporated an adjustment basedon the Poisson regression model given in reference [I4].
f The values given exclude the period within 5 years of irradiation.
g The values given exclude the period within 5 years of first treatment.

h Dose–response analysis based on the number of treatment courses given.
i Excess relative risk and excess absolute risk values were calculated fromthe mean dose and the observed and expected cancers (or the relative risk
and confidence interval) reported in the paper.
j Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
k All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
l 95% CI in parentheses.
m Males only.
n Risk relative to unexposed controls.
o Based on follow-up of 11 or more years after radiotherapy.
p Risk estimate based on a dose–response analysis.

Table 38 Risk estimates for cancer incidence and mortality from studies of radiation exposure: kidney cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted
bladder dose (incidence data), weighted urinary tract dose (mortality data)) for incidence or mortality. For case-control studies, theobserved number of cases covers both exposed and unexposed persons. The studies listed are those for which quantitative estimatesof risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

Age at exposure

34

40.2 b0.22

436 180<0 (<0, 0.42)

0.18 (0.02, 0.61)

Females36

30.6 b

0.20

729 607

1.04 (0.02, 2.83)<0 (<0, 244.95)<20 years

23

22.7 b

0.21

586 255

0.75 (<0, 2.19)

0.31 (0.08, 0.74)

20–40 years

27

22.8 b

0.21

378 204

0.23 (<0, 1.94)<0 (<0, 304.44)

20

28.9 b0.19

201 329<0 (<0, <0)<0 (<0, <0)

12–15 years

2

4.6 b

0.21

119 774<0 (<0, 0.33)<0 (<0, 92.43)

15–30 years

23

20.6 b

0.21

514 582

0.66 (<0, 2.38)

0.47 (0.13, 0.96)>30 years

45

48.3 b0.20

531 432<0 (<0, 0.71)

0.10 (<0, 0.69)

70

72.1 b0.21

1 165 787

0.16 (<0, 0.78)

0.28 (0.09, 0.58)

70

67

2.0

623 798

0.02 (–0.06, 0.16)

0.02 (–0.10, 0.17)

Males>40 years
Time since exposure

All
Cervical cancer cohort [B11]

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Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

Cervical cancer case-control [B8]

148

n.a.

2.0

n.a.

0.71 (0.03, 2.24)

1.10 (0.05, 3.50)

Springfields uranium workers, United
Kingdom [M5]

14 c

22.31 c190 795

19.85 (<–14.57, 108.30) c, h

n.a.

Study

0.022 8Mortality
LSS [P9]Sex

Age at exposure

Time since exposure

Males

18

23.8 b0.19

666 869<0 (<0, >10 000)<0 (<0, 114.09)

Females21

15.9 b0.18

1 061 687

1.17 (<0, 4.28)<0 (<0, 71.62)

8

7.8 b

0.19

885 656<0 (<0, >10 000)<0 (<0, 39.84)

20–40 years

17

11.4 b

0.19

514 9030.86 (<0, 4.13)<0 (<0, 106.20)>40 years

14

20.3 b0.18

327 997<0 (<0, <0)<0 (<0, 198.97)

12–15 years

4

4.0 b

0.18

504 112<0 (<0, 2.02)<0 (<0, 25.61)

12

11.0 b

0.19

592 956

1.25 (<0, 4.60)<0 (<0, 76.09)

23

22.0 b

0.19

631 488<0 (<0, 1.29)<0 (<0, 150.04)

3936.2 b0.19

1 728 556

0.35 (<0, 1.51)<0 (<0, 88.31)

35

21.6

6.08

378 0140.10 (0.02, 0.20)

0.06 (0.01, 0.12)
0.43 (–1.36, 3.96) k<20 years

15–30 years>30 years
All
Ankylosing spondylitis [W8]Metropathia haemorrhagica [D7]

5

4.19

0.4

47 1440.48 (–1.53, 4.45) k

Peptic ulcer [C4]

7

5.3

14.2

41 7790.12 (<0, 0.97) d, i, jn.a.

Nuclear workers in Canada, United Kingdom
and United States [C3]

34

37.3

0.04

2 142 526

<0

n.a.

United Kingdom NRRW [M12]

67

73.1

0.031

2 063 300<–1.95 (<–1.95, 0.96) f

n.a.
n.a.

Nuclear power industry workers in the
United States [H44]

14

17.7

0.026

698 04148.8 (–1.77, 315) i

Oak Ridge National Laboratory,
United States, X-10 and Y-12 plants [F5]

35

n.a.

0.013

n.a.

2.6 (<0, 10.9)

n.a.

Los Alamos National Laboratory workers,
United States [W6]

17

28.8

~0.016

251 651

>0 e

n.a.

Nuclear industry workers in Japan [I14]

32

37.4

0.015

~1 390 000<0<07.8 (1.7, 15)

27 (6, 52)

25 4805.7 (1.9, 21) g

n.a.

8 740

∞ (0.1, ∞) gn.a.

INTERNAL LOW-LET EXPOSURES
Incidence
Swedish 131I for hyperthyroidism [H6]

66

47.5

0.05

139 018

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients
[T30]

12

4.4

n.a.

Mortality
United States Thorotrast patients [T30]10.6

a 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies.
b All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
c Kidney and ureter.
d Excess relative risk value was calculated from the mean dose and the relativerisk and confidence interval reported in the paper.
e Dose–response trend was in the positive direction but not statisticallysignificant.

n.a.

f Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
g Risk relative to unexposed controls, with 95% CI.
h Males only.
i 95% CI in parentheses.
j Based on follow-up of 11 or more years after radiotherapy.
k Estimates (with 95% CI) based on method described in the introduction tochapter III of annex I in the UNSCEAR 2000 Report [U2].

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Table 39 Risk estimates for cancer incidence and mortality from studies of radiation exposure: brain and central
nervous system tumours
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Sv or more (weighted braindose) for incidence or mortality. For case-control studies, the observed number of cases covers both exposed and unexposed persons.
The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48] dSex

4634.9 e0.26

436 180

1.54 (0.66, 2.87)

1.21 (0.58, 2.03)

91

94.9 e0.24

729 608<0 (<0, 0.46)

0.01 (<0, 0.50)5049.2 e0.25

586 255

0.88 (0.28, 1.78)

0.68 (0.24, 1.28)

20–40 years

48

41.3 e0.26

378 204

0.64 (<0, 1.82)

0.48 (<0, 1.43)>40 years

39

37.7 e0.24

201 330<0 (<0, 0.51)<0 (<0, 0.28)

12–15 years

9

4.7 e

0.26

119 774

2.20 (<0, 11.11)<0 (<0, 226.13)

15–30 years

46

41.6 e0.25

514 582

0.42 (<0, 1.44)<0 (<0, 357.41)

82

80.1 e0.24

531 433

0.57 (0.10, 1.24)

0.96 (0.26, 1.83)

137

126.9 e0.25

1 165 788

0.55 (0.16, 1.07)

0.57 (0.23, 1.01)

228

n.a.

0.26

1 989 297

1.2 (0.6, 2.1) b

n.a.

0.26

0.56 (–0.2, 2.0) bn.a.
0.14 (0.00, 0.45) b

Males
Females

Age at exposure

Time since exposure<20 years>30 yearsAll
LSS [P33]
All nervous system tumours
Glioma43

n.a.

Meningioma

88

n.a.

0.26

0.64 (–0.01, 1.8) b

Schwannoma

55

n.a.

0.26

4.5 (1.9, 9.2) b

0.67 (0.3, 1.1) b

Males

14

n.a.

n.a.

745 157

1.6 (–0.04, 7.1) b

n.a.

Females

74

n.a.

n.a.

1 244 140

0.4 (–0.2, 1.7) bn.a.

975 373

1.3 (0.01, 4.5) bn.a.

645 557

0.5 (–0.05, 2.8) b

n.a.
n.a.

LSS [P33]
Meningioma
Sex

Age at exposure<20 years
20–39 years

n.a.
n.a.

n.a.
n.a.

n.a.
n.a.

≥40 years

n.a.

n.a.

n.a.

358 367

0.3 (<–0.1, 2.0) b

Males

23

n.a.

n.a.

745 157

8.0 (2.7, 21) b

n.a.
n.a.

LSS [P33]
Schwannoma
Sex

n.a.

n.a.

1 244 140

2.3 (0.3, 7.0) b<20 years

n.a.

n.a.

975 373

6.0 (2.1, 14) b

n.a.

20–39 years

n.a.

n.a.

645 557

2.6 (<–0.2, 10) bn.a.

≥40 years

n.a.

n.a.

358 367

3.3 (0.33, 11) bn.a.

283 930

4.9 c

n.a.

n.a.

1.6 c

n.a.

n.a.

5.7 c

n.a.

n.a.

21.4 cn.a.

Females
Age at exposure

Israel tinea capitis [R17]
GliomaMeningioma
Schwannoma

32

60
7
19
22

n.a.

1.5

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Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

Malignant brain tumours

44

n.a.

1.5

1 069 450

1.98 (0.73, 4.69)

0.31 (0.12, 0.53)

Benign meningioma

81

n.a.

1.5

1 069 043

4.63 (2.43, 9.12)

0.48 (0.28, 0.73)

Brain cancer

7

2.34

1.4

125 357

1.1 (0.1, 2.8) bn.a.

All intracranial tumours

16

1.6

1.4

125 357

5.6 (3.0, 9.4) b

n.a.

83

58.0

0.07

913 402

2.7 (1.0, 5.6) b

2.1 (0.3, 4.4) bn.a.

0.19 (0.03, 0.85) b

n.a.
n.a.

Study

Israel tinea capitis [S48]

New York tinea capitis [S68]

Swedish pooled skin haemangioma [K15]Childhood cancer survivors [L24]
All brain tumours

6.2
22

n.a.

Malignant tumours

12

n.a.

n.a.

0.07 (<0, 0.62) b

Benign tumours

10

n.a.

n.a.

n.a.

n.a.

190 795–1.96 (<–2.00, 9.31) bn.a.

Springfields uranium workers, United
Kingdom [M5]

12

18.76

0.022 8Mortality

LSS [P9] d
Sex

9

4.0 e

0.22

666 870

5.87 (1.55, 17.94)<0 (<0, 46.43)

10

8.9 e

0.21

1 061 688

0.78 (<0, 4.62)<0 (<0, 29.00)<20 years

11

4.8 e

0.21

885 656

5.72 (1.56, 17.04)<0 (<0, <0)>20 years

8

7.5 e

0.22

842 9020.77 (<0, 4.88)<0 (<0, 35.70)

5–30 years

4

3.8 e

0.22

1 097 070

<0 (<0, >10 000)<0 (<0, 14.96)

15

9.9 e

0.22

631 488

2.56 (0.54, 6.89)<0 (<0, 72.60)

19

12.2 e0.22

1 728 558

2.86 (0.83, 6.76)<0 (<0, 35.75)
0.27 (0.09, 0.59) c
n.a.

Males
Females

Age at exposure

Time since exposure>30 years
All
Pituitary adenoma (United Kingdom) [B13]

5

0.5

45

3 760

0.20 (0.07, 0.45) c

Nuclear workers in Canada, United Kingdom
and United States [C3]

122

n.a.

0.04

2 142 526<0

United Kingdom NRRW [M12]

111

114.2

0.031

2 063 300

–0.54 (<–1.95, 4.26)

n.a.

Nuclear power industry workers in the
United States [H44]

23

27.0

0.026

698 041–2.50 (<–2.51, 27.1) bn.a.

Canadian National Dose Registry [S8]

105 c

133.2

0.006 6

2 667 903<0<0261 418–4.1 (–9.9, 28.9) hn.a.

1.3 (0.6, 3.7) b, gn.a.

Nuclear power station workers in France[R54] d

16

10.3

0.018

INTERNAL HIGH-LET EXPOSURES
Mortality
United States Thorotrast patients [T30]

21

0.6

a Some risk estimates are based on formal dose–response analyses (for example
for the LSS [P9, P48], derived from fitting models (4) and (5)); others are
simply excess relative risk or absolute risk divided by mean dose. All CIs
shown are 90% CI unless otherwise stated.
b 95% CI.
c Data are for all brain and nervous system tumours combined; estimates basedon method described in the introduction to chapter III of annex I in the
UNSCEAR 2000 Report [U2].

n.a.

8 740

d Data are for all brain and nervous system tumours combined.
e All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
f Males only.
g Risk relative to unexposed controls, with 95% CI.
h Based on a 10-year latent period.

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Table 40 Risk estimates for cancer incidence and mortality from studies of radiation exposure: thyroid cancer
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with organ doses of 0.005 Sv (weighted thyroiddose) or more for incidence or mortality. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P48]
Sex

Age at exposure

Males

48

41.5 l0.26

436 180

0.78 (0.15, 1.77)

1.03 (0.46, 1.79)

Females217

144.1 l0.24

729 608

1.89 (1.28, 2.65)

3.75 (2.73, 4.89)<20 years

10552.3 l0.24

586 255

3.93 (2.57, 5.81)

3.07 (2.14, 4.14)

20–40 years

87

65.5 l0.26

378 204

0.99 (0.34, 1.93)

1.46 (0.49, 2.69)

73

69.0 l0.24

201 330

0.29 (<0, 0.95)

0.86 (<0, 2.84)

12–15 years

21

13.0 l

0.25

119 774

3.24 (1.10, 7.28)

2.85 (1.17, 5.22)

15–30 years

115

84.4 l0.25

514 582

1.35 (0.69, 2.23)

2.04 (1.18, 3.07)>30 years

12988.2 l0.24

531 433

1.61 (0.93, 2.52)

2.31 (1.34, 3.48)

265

186.4 l0.25

1 165 788

1.59 (1.10, 2.19)

2.30 (1.67, 3.02)<20 years

6

0.0

8.20

950

36.5 (16, 72) b

7.7 (3.3, 15) b>20 years

2

0.2

8.20

3 100

1.2 (0.1, 3.7) b

0.7 (0.1, 2.4) b>40 years
Time since exposureAll
TB, adenitis screening [H22, S14]
Age at exposure

Cohort studies of children
Israeli tinea capitis [R9] c

43

10.7

0.1

274 180

34 (23, 47) b

13 (9.0, 18) b

New York tinea capitis [S14, S68]

2

2.04

0.06

78 056–0.3 (–14.0, 37.3) b, kn.a.

Rochester thymic irradiation [S18] e

37

1.5

1.36

85 2049.0 (4.2, 21.7)

2.9 (2.1, 3.9) b

Childhood cancer [T5] f

23

0.4

12.5

50 6094.5 (3.1, 6.4) b

0.4 (0.2, 0.5) b

Stockholm skin haemangioma [L13]

17

7.5

0.26

406 355

4.9 (1.3, 10.2) k

0.9 (0.2, 1.9) k

Gothenburg skin haemangioma [L4]

15

8

0.12

370 5177.5 (0.4, 18.1) k

1.6 (0.09, 3.9) k

Screening studies of children
Lymphoid hyperplasia screening [P5, S14] e, g

13

5.4 b

0.24

34 7005.9 (1.8, 11.8) b

9.1 (2.7, 18.3) b

Thymus adenitis screening [M13, S14]

16

1.1 b

2.9

44 3104.5 (2.7, 7.0) b

1.2 (0.7, 1.8) b

Michael Reese Hospital, tonsils [S21] h

309

110.4

0.6

88 1013.0 (2.6, 3.5) b

37.6 (32, 43) b

Tonsils/thymus/acne screening [D9, S14]

11

0.2 b

4.5

6 800

12.0 (6.6, 20) b

3.5 (2.0, 5.9) bn.a.

7.7 (2.1, 28.7) k

4.4 (1.9, 10.1) k

Pooled analysis of five studies of children
LSS
Israeli tinea capitis
Rochester thymic irradiation
Lymphoid hyperplasia screening
Michael Reese Hospital, tonsils [R6]

436

n.a.

n.a.

Studies of adults
Cervical cancer case-control [B8] d

43

18.8

0.11

n.a.

12.3 (<0, 76) b

6.9 (<0, 39.2) b

Cervical cancer cohort [B11] d, i

16

12

0.11

178 243

2.5 (<0, 6.8) b

0.9 (<0, 2.5) b

Stanford thyroid [H19]

6

0.4

45

17 7000.3 (0.1, 0.7) b

0.07 (0.03, 0.1) b

Canadian National Dose Registry [S8]

129

92.6

0.066 2

2 667 903

5.9 (2.5, 9.9) j

2.1 (0.9, 3.4)

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Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

Employed before 1970

13

6.32

0.551

357 753

1.9 (0.3, 4.4) j

0.3 (0.15, 0.8)

Employed only 1970–1980

1

2.54

0.082

337 133

<0

<0

Study

Chinese medical x-ray workers [W3]INTERNAL LOW-LET EXPOSURES
Incidence
Diagnostic 131I [D42]

36

39.5

0.94

~648 000<0<0Diagnostic 131I [H14]

67

49.6

1.1

653 0930.25 (0, 2.7) p

n.a.

n.a.

4.9 (2.2, 7.5) nn.a.

~19 440 000

18.9 (11.1, 26.7) o

2.66 (2.19, 3.13) o

Russian Federation–Belarus Chernobyl
case-control study [C2]

276

n.a.

0.37, 0.04 m

Ukraine–Belarus Chernobyl cohort study [J9]

1 185

n.a.

n.a.

EXTERNAL LOW-LET EXPOSURES
Mortality
LSS [P9]Sex

Age at exposure

Time since exposure

Males

6

7.4 l

0.22

666 870

0.46 (<0, 2.96)<0 (<0, 24.90)

Females32

29.7 l0.21

1 061 688

<0 (<0, 0.22)<0 (<0, 0.09)

5

3.7 l

0.21

885 656

1.67 (<0, 7.67)<0 (<0, 12.88)

20–40 years

14

14.2 l0.23

514 903<0 (<0, 0.87)<0 (<0, 0.23)>40 years

19

19.7 l0.21

327 999<0 (<0, <0)<0 (<0, 0.01)

12–15 years

4

6.4 l

0.21

504 112<0 (<0, 2.03)<0 (<0, <0)

13

9.3 l

0.22

592 958<0 (<0, 3.17)

0.12 (<0, 0.41)

2121.2 l0.21

631 488<0 (<0, 0.45)<0 (<0, 97.90)

38

37.1 l0.21

1 728 558

<0 (<0, 0.42)<0 (<0, 43.97)<20 years

15–30 years>30 years
Alla 90% CI in parentheses derived from fitting models (4) and (5) for the LSS,
and derived from published data for the other studies, unless otherwise stated.
b Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
c Doses to the thyroid in this study may be much more uncertain than doses
to organs directly in the X-ray beam.
d Expected number of cases computed using excess relative risk estimates
given in reference [S14].
e Known dose. Person-years and expected number of cases estimated fromdata given in reference [S14].
f Based on cohort members with 15 or more years of follow-up andpopulation-expected rates.
g This was a study of nodular disease, and cancer cases were not confirmed.
h Study includes no unexposed controls; estimates of the number of expectedcases were computed using the fitted excess relative risk reported in reference[S21]. Results are based on the new dosimetry described in reference [S21].
The large excess absolute risk in this study illustrates the impact of screening
on thyroid cancer risk estimates. As described in reference [S21], a special
thyroid screening programme in this cohort was initiated in 1974. This
screening led to a large increase in the number of cancer cases detected

i
j

k
l
m
n
o
p

among both cases and controls. The paper describes an analysis in whichallowance was made for the effect of screening. The screening-adjusted
excess absolute risk was estimated as 1.7 (104 PY Gy)–1.
Excludes cases diagnosed during first 10 years of follow-up.
Excess relative risk and excess absolute risk values were calculated from themean dose and the observed and expected cancers (or the relative risk andconfidence interval) reported in the paper.
95% CI in parentheses.
All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
Median doses to all subjects (cases, controls) in Belarus, Russian Federation,
respectively.
Fitted using linear–quadratic model for odds ratio over full dose range,
95% CI.
Linear coefficient of linear–quadratic model fit, 95% CI.
Trend estimate (with 95% CI) is for exposure in childhood and adolescenceamong those referred for diagnosis with 131I without suspicion of thyroid
tumour; the overall trend (among all ages at exposure, with or without
suspicion of thyroid tumour at diagnosis) is not statistically significant (seetable 28 of annex I in the UNSCEAR 2000 Report [U2]).

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Table 41 Risk estimates for cancer incidence and mortality from studies of radiation exposure: non-Hodgkin’s lymphoma
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Gy or more (unweighted
bone marrow dose) for incidence and 0.005 Sv or more (weighted bone marrow dose) for mortality. The studies listed are those forwhich quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv) aPerson-years

Average excess
relative risk b at 1 Sv

Average excess absoluterisk b (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P4]Sex

Age at exposure

Time since exposure

Males

41

n.a.

0.26

412 371

0.44 (–0.16, 1.42)

0.46 (0.04, 1.16)

Females

35

n.a.

0.25

664 481–0.22 (<–0.22, 0.40)

0.00 (<0, 0.28)<20 years

17

n.a.

0.26

478 1080.45 (<0, 2.16)

0.18 (<0, 0.61)

20–40 years

34

n.a.

0.26

346 807–0.12 (<–0.12, 0.73)

0.03 (<0, 0.63)>40 years

25

n.a.

0.24

251 938

0.09 (<0, 1.04)–0.11 (<–0.11, 1.72)

12–15 years

7

n.a.

0.25

369 152

0.33 (<0, 2.14)

0.24 (–0.01, 0.70)

15–30 years

34

n.a.

0.25

436 877

0.33 (<0, 1.44)

0.09 (<0, 0.71)>30 years

35

n.a.

0.25

270 824–0.22 (<–0.22, 0.45)–0.17 (<–0.17, 2.28)

76

n.a.

0.25

1 076 850

0.08 (<0, 0.62)

0.12 (<0, 0.40)
n.a.

All
Cervical cancer case-control [B8] d

94

37.5

7.10

n.a.

0.21 (–0.03, 0.93) c

Benign lesions in locomotor system [D2]

81

80.3

0.39

392 900

0.02 c0.05 cCanadian National Dose Registry [S8]

133

188.3

0.066 2

2 667 903

6.6 (<0, 28.3) p

n.a.
n.a.

United States case-control: occupational
exposure [E10]

114

n.a.

0.015

n.a.

(p = 0.66) h

Springfields uranium workers, United
Kingdom [M5]

20

25.39

0.022 8

190 795

20.62 (<–5.69, 86.62) rn.a.

Mortalityq

LSS [P1]
Sex

Males

74

0.25 (<0, 6.41)

0.18 (<0, 0.81)

Females88–0.06 (<0, 0.22)–0.12 (<0, 0.47)

n.a.

0.01 (<0, 0.42)

0.01 (<0, 0.23)–0.40 c

Total

162

n.a.

0.25

Benign lesions in locomotor system [D2]

50

56.9

0.39

439 400–0.31 cAnkylosing spondylitis [W8] e

37

21.3

4.38

287 095

0.17 c0.77 cBenign gynaecological disorders [I1]

40

42.5

1.19

246 821–0.05 (<–0.2, 0.2) c–0.08 (<–0.3, 0.3) c

Massachusetts TB fluoroscopy [D4]

13 f

13.1

0.09

157 578–0.05 (<–0.2, 6.5) b–0.04 (<–0.2, 5.4) b

Peptic ulcer [C4]

14

7.1

1.6 a

41 7790.65 (<0, 3.28) j, o, t

n.a.
<0

Nuclear workers in Canada, United
Kingdom and United States [C3]

135

n.a.

0.04

2 142 526

<0 g

United Kingdom NRRW [M12]

84

80.2

0.031

2 063 300

0.03 (–1.33, 3.06) p

n.a.
n.a.

<0

Nuclear power industry workers in the
United States [H44]

14

n.a.

0.026

698 04161.3 (–2.51, 313) j

Oak Ridge National Laboratory workers,
United States, 1943–1947 [F2] (lymphosarcoma, reticulosarcoma, ICD8-200)

39

45.8

n.a.

n.a.

<0

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Study

Observed
cases

Expectedcases

Mean dose(Sv) aPerson-years

Average excess
relative risk b at 1 Sv

Average excess absoluterisk b (104 PY Sv)–1

Los Alamos National Laboratory workers,
United States [W6]

46 f

n.a.

~0.016

251 651

>0 g

n.a.

Nuclear industry workers in Japan [I14]

46

57.3

0.015

~1 390 000<0<0Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess relative risk at 1 Sv

Study

INTERNAL LOW-LET EXPOSURES
Mortality
United States thyrotoxicosis [R3] i

Study

74

n.a.

0.042

735 255

0.6 s

Observed
cases

Expectedcases

Mean dose(Gy)

Person-years

Average relative risk

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients
[T30]

4

2.7

n.a.

25 4801.6 (0.3, 11.4) j, k224

2

0.9–1.8

n.a.

n.a.

~2 l

n.a.

~2.5 nRa ankylosing spondylitis patients [W9]Mortality
German Thorotrast patients [V4]

15

n.a.

a Mean dose to red bone marrow.
b 90% CI in parentheses derived from published data for the LSS and for theother studies.
c Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
d Based on 5-year survivors. The observed and expected numbers cover both
exposed and unexposed persons.
e The values given exclude the period within 5 years of first treatment. Mean
dose to bone marrow taken from reference [W2].
f Includes deaths from multiple myeloma.
g Not statistically significantly different from zero.
h p-value from test for trend in risk with dose.
i Some patients from the United Kingdom were included in this analysis [R3].
j 95% CI in parentheses.
k Risk relative to an unexposed control group, in which 3 cases were observed
compared with 3.3 expected [T30].

0.83 m
l
m

n
o

p
q
r
s
t

Risk relative to an unexposed control group, in which 1 case was observed
compared with 1.0–2.3 expected.
Dose to bone marrow (Gy) over 10 years based on estimated mean of
20.8 mL injected Thorotrast derived from hospital records, and using
dosimetry from reference [K42].
Crude relative risk, based on 5 cases in an unexposed control group.
Excess relative risk and excess absolute risk values were calculated from themean dose and the observed and expected cancers (or the relative risk andconfidence interval) reported in the paper.
Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
Includes deaths from Hodgkin’s disease.
Males only.
Non-significant trend with dose (p > 0.5).
Based on follow-up of 11 or more years after radiotherapy.

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Table 42 Risk estimates for cancer incidence and mortality from studies of radiation exposure: Hodgkin’s disease
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Gy or more (unweighted
bone marrow dose) for incidence. The studies listed are those for which quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv) aPerson-years

Average excess
relative risk b at 1 Sv

Average excess absoluterisk b (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P4]

10

9.02

0.23

1 076 500

0.43 (1.6, 3.5)

0.04 (0.1, 0.3)

Cervical cancer cohort [K1]

15

15.5

7

532 740–0.005 (–0.06, 0.08)–0.001 (–0.02, 0.02)

Cervical cancer case-control [B8] c

14

n.a.

7.10

n.a.

n.a.

n.a.

392 900

0.30 (–1.01, 7.38) d, en.a.

2 667 903

64.8 (<0, 591.3) m

n.a.

Benign lesions in locomotor system [D2]Canadian National Dose Registry [S8]

17
79

22.3

0.39

n.a.

0.066 2Mortality

Benign lesions in locomotor system [D2]

21

15.4

0.39

439 400

0.93 f0.33 fMetropathia haemorrhagica [D7]

4

1.21

1.3

47 1441.77 (–0.08, 5.74) e, f

0.45 (–0.02, 1.48) e, f

Ankylosing spondylitis [W8] g

13

7.9

4.38

287 095

0.15 f0.04 fBenign gynaecological disorders [I1]

10

6.6

1.19

246 8210.43 f0.12 fNuclear workers in Canada, United Kingdom
and United States [C3]

43

n.a.

0.040 2

2 124 526

>0 h

n.a.

United Kingdom NRRW [M12]

21

n.a.

0.031

2 063 300<–1.95 (<–1.95, 2.84)

n.a.

Los Alamos National Laboratory workers,
United States [W6]

10

n.a.

~0.016

251 651

>0 i

n.a.

–1.0 pn.a.

INTERNAL LOW-LET EXPOSURES
Mortality
United States thyrotoxicosis [R3] o

12

n.a.

0.042

735 255

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish Thorotrast patients [A5]

1

0.65

n.a.

19 3651.60 (0.06, 40.40) e, jn.a.

Danish and Swedish Thorotrast patients [T30]

1

1.0

n.a.

25 4801.5 (0.1, 81.8) e, nn.a.

Mortality
German Thorotrast patients [V4]

2

n.a.

0.83 k

n.a.

0.8

n.a.

United States Thorotrast patients [T30]

1

0.2

n.a.

8 740

∞ (0.0, ∞) e, nn.a.

a Mean dose to red bone marrow.
b 90% CI in parentheses derived from published data for the LSS and for theother studies.
c Based on 1-year survivors. The observed and expected numbers cover both
exposed and unexposed persons.
d Estimates derived from published data, as given in reference [L20].
e 95% CI in parentheses.
f Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
g The values given exclude the period within 5 years of treatment. Mean doseto bone marrow taken from reference [W2].
h Trend not statistically significantly different from zero.
i Trend statistically significantly different from 0 (0.01 < p < 0.05).

j
k

l
m
n
o
p

Relative risk based on comparison with control group in which 1 caseoccurred with 1.04 expected.
Dose to bone marrow (Gy) over 10 years based on estimated mean of
20.8 mL injected Thorotrast derived from hospital records, and using
dosimetry from reference [K42].
Crude relative risk based on comparison with (unexposed) control group inwhich 2 cases occurred.
Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
Risk relative to unexposed control group.
Some patients from the United Kingdom were included in this analysis [R3].
Non-significant trend with dose (p > 0.5).

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Table 43 Risk estimates for cancer incidence and mortality from studies of radiation exposure: multiple myeloma
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Gy or more (unweighted
bone marrow dose) for incidence and 0.005 Sv or more (weighted bone marrow dose) for mortality. The studies listed are those forwhich quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv) aPerson-years

Average excess
relative risk b at 1 Sv

Average excess absoluterisk b (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P4]Sex

Age at exposure

Males

12

9.2

0.26

412 400

0.17

0.26

Females

18

19.3

0.25

664 500

0.28

0.08

<20 years

4

3.1

0.26

478 100

1.07

0.07

>20 years

26

25.4

0.25

598 800

0.09

0.04

30

28.6

0.25

1 076 900

0.20 (<0, 21.7) c

0.05 (<–0.05, 0.4) c

56

n.a.

7.10

n.a.

–0.10 (<0, 0.23) cn.a.
–0.16 cAll
Cervical cancer case-control [B8] d
Benign lesions in locomotor system [D2]

65

67.5

0.39

392 900–0.09 cSpringfields uranium workers, United
Kingdom [M5]

10

12.36

0.022 8

190 795

7.66 (<–17.18, 109.52) nn.a.

Mortality
LSS [P1]
Sex

Males

16

14

0.23

614 997

1.13 (<0, 6.41)

0.15 (<0, 0.51)

Females

35

31

0.23

972 359

1.16 (0.01, 3.9)

0.19 (0.001, 0.5)

51

45

0.23

1 587 355

1.15 (0.12, 3.27) c

0.17 (0.02, 0.4) c

Benign lesions in locomotor system [D2]

80

63.8

0.39

439 400

0.65 c0.95 cAnkylosing spondylitis [W8] e

22

13.6

4.38

287 095

n.a.

n.a.

Benign gynaecological disorders [I1]

14

12.4

1.19

246 8210.11 (<–0.2, 0.6) c

0.05 (<–0.1, 0.3) c

Peptic ulcer [C4]

4

3.5

1.6

41 779–0.61 (<–0.61, 1.38) f, k, pn.a.
0.90 (0.11, 2.22) c, k

All

Metropathia haemorrhagica [D7] g

9

3.5

1.3

47 144

1.23 (0.15, 3.02) c, k

Nuclear workers in Canada, United
Kingdom and United States [C3]

44

n.a.

0.04

2 142 526

4.2 (0.3, 14.4)

n.a.

United Kingdom NRRW [M12]

35

45.8

0.031

2 063 300

4.1
(0.03, 14.8) m

n.a.

Nuclear industry workers in Japan [I14]

20

17.8

0.015 3

~1 390 000

n.a.

n.a.

United States four-cohort analysis [W7]

98

n.a.

n.a.

n.a.

0.66 (–2.35, 3.67) mn.a.

INTERNAL LOW-LET EXPOSURES
Incidence
Diagnostic 131I [H8]

50

45.9

0.000 19 h

527 056

n.a.

n.a.

131

21

20.0

0.06

139 018

n.a.

n.a.

735 255

11.0 jn.a.

SwedishI hyperthyroid [H6]Mortality
United States thyrotoxicosis [R3]

28 i

n.a.

0.042

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Study

Observed
cases

Expectedcases

Mean dose

Person-years

213

Average relative risk at 1 Sv

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish Thorotrast patients [A5]

4

0.95

n.a.

19 3654.34 (0.85, 31.3) k, lDanish and Swedish Thorotrast patients [T30]

5

1.7

n.a.

25 4803.7 (0.5, 30.9) k, o

8 740

1.8 (0.1, 51.6) k, oMortality
United States Thorotrast patients [T30]

1

0.4

n.a.

a Mean dose to red bone marrow.
b 90% CI in parentheses derived from published data for the LSS and using
exact Poisson methods for the other studies.
c Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
d Based on 1-year survivors. The observed number of cases covers both exposed
and unexposed persons.
e The values given exclude the period within 5 years of first treatment. Mean
dose to bone marrow taken from reference [W2].
f Excess relative risk value was calculated from the mean dose and the relativerisk and confidence interval reported in the paper.
g The values given exclude the period within 5 years of irradiation.

h
i
j
k
l
m
n
o
p

Mean dose to bone marrow given in reference [H12].
Some patients from the United Kingdom were included in this analysis [R3].
Not statistically significantly different from zero (p = 0.3).
95% CI in parentheses.
Risk relative to an unexposed control group, in which 2 cases were observed
compared with 2.1 expected.
Tabulation and analysis with a 10-year lag. Risk estimate based on adose–response analysis.
Males only.
Risk relative to unexposed controls.
Based on follow-up of 11 or more years after radiotherapy.

Table 44 Risk estimates for cancer incidence and mortality from studies of radiation exposure: leukaemia
The number of observed and expected cases as well as the mean dose and person-years for cohort studies are computed throughout
this table for exposed persons only. In the LSS the exposed group included survivors with doses of 0.005 Gy or more (unweighted
bone marrow dose) for incidence and 0.005 Sv or more (weighted bone marrow dose) for mortality. The studies listed are those forwhich quantitative estimates of risk could be madeStudy

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv)–1

EXTERNAL LOW-LET EXPOSURES
Incidence
LSS [P4]Sex

Age at exposure

Time since exposure

Males

71

n.a.

0.26

412 371

4.66 (3.07, 6.88)

4.14 (3.06, 5.39)

Females

70

n.a.

0.25

664 4815.05 (3.24, 7.61)

2.41 (1.71, 3.23)<20 years

46

n.a.

0.26

478 108

8.27 (4.95, 13.66)

2.79 (1.99, 3.74)

20–40 years

46

n.a.

0.26

346 8073.59 (2.01, 5.97)

2.69 (1.70, 3.90)>40 years

49

n.a.

0.24

251 938

3.98 (2.32, 6.45)

4.68 (3.10, 6.57)

12–15 years

57

n.a.

0.25

369 152

13.78 (8.67, 22.24)

5.19 (3.97, 6.60)

15–30 years

51

n.a.

0.25

436 877

4.37 (2.53, 7.16)

2.41 (1.55, 3.45)>30 years

33

n.a.

0.25

270 8240.88 (0.17, 2.02)

1.09 (0.33, 2.19)

141

n.a.

0.25

1 076 850

4.84 (3.59, 6.44)

3.08 (2.47, 3.77)

141

n.a.

7.2

n.a.

0.74 (0.1, 3.8)

0.50 (0.1, 2.6)
n.a.

All
Cervical cancer case-control [B5] b, c
Cancer of the uterine corpus [C8] c, d

118

n.a.

5.4

n.a.

0.10 (<0.0, 0.23) e

Benign lesions in locomotor system [D2]

116

98.5

0.39

392 900

0.70 (–0.43, 3.48) e, f

1.14 g

Hodgkin’s disease [K20] c, h

60

n.a.

n.a.

n.a.

0.24 (0.04, 0.43) e, fn.a.

Breast cancer therapy [C9] i

38

n.a.

7.5

n.a.

0.19 (0.00, 0.6)

0.89 (0.00, 3.0)

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Study

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv) –1

United Kingdom childhood cancers [H21] h

26

n.a.

n.a.

n.a.

0.24 (0.01, 1.28) e, fn.a.

International childhood cancer [T7] j, k

25

n.a.

10

n.a.

0.0 (0.0, 0.004)

n.a.

Chernobyl recovery operation workers in
Russian Federation [K3] l

25

n.a.

0.105

n.a.

15.6 (–24.9, 56.1)

n.a.

Testicular cancer [T24]

22

n.a.

12.6

n.a.

0.37 (0.12, 1.3) e

n.a.
n.a.

Canadian National Dose Registry [S8] c, m

72

101.8

0.066 2

2 667 903

2.7 (<0, 18.8) n, v

— Employed before 1970

33

13.95

0.551

357 753

2.5 (1.2, 4.1) o

1.0 (0.5, 1.6)

— Employed only 1970–1980

11

6.35

0.082

337 133

8.9 (–1.1, 25)

1.7 (–0.2, 4.6)

Chinese medical X-ray workers [W3]Mortality
LSS [P10]Sex

Age at exposure

98

55.4 qq

0.23

682 0484.07 (2.75, 5.84)

3.23 (2.41, 4.18)

Females9152.2 qq

0.22

1 075 920

3.96 (2.57, 5.87)<0 (<0, 291.33)<20 years

68

29.5 qq

0.23

916 830

6.63 (4.21, 10.26)<0 (<0, 271.86)

20–40 years

66

41.9 qq

0.23

520 263

3.07 (1.81, 4.87)

2.39 (1.56, 3.39)

55

35.8 qq

0.21

320 874

3.15 (1.74, 5.24)

3.46 (2.12, 5.09)

12–15 years

58

18.3 qq

0.23

465 730

10.24 (6.34, 16.59)

3.92 (2.90, 5.13)

15–30 years

51

29.4 qq

0.23

586 805

3.82 (2.13, 6.40)

1.87 (1.19, 2.69)>30 years

8061.0 qq

0.22

705 433

1.97 (1.09, 3.18)<0 (<0, 396.31)

189

107.7 qq

0.22

1 757 967

4.02 (3.02, 5.26)

2.31 (1.85, 2.82)
1.14 gMales>40 years
Time since exposure

All
Benign lesions in locomotor system [D2]

115

95.5

0.39

439 400

0.52 gAnkylosing spondylitis [W2] c, p

53

17.0

4.38

245 4130.02 (–0.07, 0.29) e, fn.a.

Benign gynaecological disorders [I1] c

47

27.6

1.19

246 8212.97 (2.2, .0)

1.25 (0.9, 1.7)<–0.2 (<–0.2, 5.1) g

Massachusetts TB fluoroscopy [D4] c

17

18

0.09

157 578<–0.2 (<–0.2, 4.5) g

Israeli tinea capitis [R5] k, r

14

6

0.3

279 901

4.44 (1.7, 8.7) g

0.95 (0.4, 1.9) g

Stockholm skin haemangioma [L6] k

14

~11

0.2

373 5421.6 (–0.6, 5.5) e, sn.a.

Metropathia haemorrhagica [D7] t

12

5.86

1.3

53 1440.74 (–0.11, 1.59) e, tt

0.89 (0.05, 2.19) e, g

Peptic ulcer [C4] c

10

7.1

1.6

41 7790.91 (<0, 4.38) e, o, ss

n.a.
n.a.

IARC 15-country nuclear worker study [C41] c

196

n.a.

0.019 45 192 710

1.93 (<0, 8.47) e

Nuclear workers in Canada, United
Kingdom and United States [C3] c

119

n.a.

0.04

2 142 526

2.18 (0.13, 5.7) un.a.

United Kingdom NRRW [M12] c

89

91.1

0.031

2 063 300

2.55 (–0.03, 7.16) v

n.a.

Nuclear power industry workers in the
United States [H44] c

26

n.a.

0.026

698 041

5.67 (–2.56, 30.4) en.a.

Mayak workers [S28]

66

39.5

0.81

720 000

1.0 (0.5, 2.0)

n.a.

Oak Ridge National Laboratory, UnitedStates, X-10 and Y-12 plants [F5]

50

n.a.

0.013

n.a.

<0 (<0–6.5)

n.a.

Los Alamos National Laboratory workers,
United States [W6]

44

43.6

~0.016

251 651

~0 w

n.a.

Portsmouth shipyard workers, UnitedStates [S56, Y10]

34

38.6

0.020

303 892

10.88 (–0.90, 38.77) e, ll

33.8 (16.8, 50.7) mm

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Study
Nuclear industry workers in Japan [I14] x

215

Observed
cases

Expectedcases

Mean dose(Sv)

Person-years

Average excess
relative risk a at 1 Sv

Average excess absoluterisk a (104 PY Sv) –1

28

30.9

0.015

~540 000

0.01 (–10.0, 10.0)n.a.
0.4 (–0.2, 1.2)

Japanese radiological technologists [A4]

20

15.3

0.466

270 585

0.7 (–0.4, 2.1) o

Nuclear power station workers in France[R54] c

5

7.2

0.018

261 418

6.8 (–8.4, 62.2) zn.a.

Yangjiang background radiation [T14, T16]

33

29.7

n.a. y1 246 340

1.61 (<0, 28.4) e, zn.a.

INTERNAL LOW-LET EXPOSURES
Incidence
Chernobyl-related exposure in Belarus,
Russian Federation and Ukraine [D52]

421

n.a.

0.006 3 nn

n.a.

32.4 (9.78, 84.0) e

n.a.

Chernobyl-related exposure in Ukraine [N6]

98

n.a.

0.004 5n.a.

2.5 (1.1, 5.4) e, oon.a.

Mortality
Extended Techa River Cohort [K49, K50] c

49

18.1

0.30 Sv

865 8126.5 (1.8, 24) e

2.9 (0.8, 4.4) e, pp

Extended Techa River cohort: leukaemia
case-control study [O13] c

60

n.a.

0.38 Sv nn

n.a.

4.6 (1.7, 12.3) en.a.

Semipalatinsk: leukaemia case-control
study [A23] c

22

n.a.

0.89 Sv(median)

n.a.

~ 0.1

n.a.

Thyroid cancer patients [R38]

12

6.3

6 GBq qn.a.

0.39 (n.a, 1.54) e (GBq)–1

8 (104 PY GBq) –1

United States thyrotoxicosis [R3] c, aa

82

n.a.

0.042 Sv

735 255–1.0 rr

n.a.

Observed
cases

Expectedcases

Mean dose

Person-years

Study

Average relative risk

INTERNAL HIGH-LET EXPOSURES
Incidence
Danish and Swedish Thorotrast patients[T30] c

28

1.8

n.a.

25 48015.2 (4.4, 149.6) e, bb

224

Ra ankylosing spondylitis patients [W15]

13

4.2

n.a.

32 8002.4 ccUranium in drinking water – Finland [A25]

35

n.a.

0.06 Bq/L dd

n.a.

0.91 (0.73, 1.13) e

Mortality
Radon-exposed miners [D10]

69

59.5

155 WLM ee

1 085 000n.a.

German Thorotrast patients [V4]

42 c

n.a.

0.83 ff

n.a.

4.9 ggJapanese Thorotrast patients (combineddata) [M14]

10

n.a.

0.68 hh

10 685

12.5 (4.5, 34.7) e

United States Thorotrast patients [T30]

8

1.1

n.a.

8 740

16.8 (0.6, 211.7) e, bb

Portuguese Thorotrast patients [D27] ii

6

0.73

0.80 jj

13 28310.2 (1.24, 471) e, kk

a 90% CI in parentheses derived from published data for the LSS and for theother studies; for latest LSS mortality data [P10] the 90% CIs are derived frommodels (4) and (5) fitted to the data.
b The observed number of cases covers both exposed and unexposed persons.
The excess relative risk was estimated using a linear–exponential
dose–response model, and the associated CI was estimated from the confidence region curves in reference [B9]. The excess absolute risk estimate uses
incidence estimates from the cohort study [B11].
c Excludes cases of chronic lymphoblastic leukaemia.
d Risk estimate based on a linear dose–response model fitted to data for allradiation types [C8].
e 95% CI in parentheses.

f Estimates derived from analysis based on published data, as given in reference[L20].
g Estimates based on method described in the introduction to chapter III ofannex I in the UNSCEAR 2000 Report [U2].
h The observed number of cases covers both exposed and unexposed persons.
Risk estimate based on analysis in references [L9, L20].
i The excess absolute risk for this study is computed on the basis of annual
incidence rate estimates and average follow-up times reported in reference[C9].
j The observed number of cases covers both exposed and unexposed persons.
Risk estimates based on an unmatched analysis of data given in reference [T5].
k Population exposed as children.

UNSCEAR REPORT-PART 4.qxp

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l
m
n
o

p
q
r

s
t
u
v
w
x
y
z
aa
bb
cc

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Excludes cases of chronic lymphoblastic leukaemia. Results are not
restricted according to the date of starting work.
Observed and expected values are for leukaemia excluding chronic lymphoblastic leukaemia.
Values specific to males.
Excess relative risk and excess absolute risk values were calculated fromthe mean dose and the observed and expected cancers (or the relative risk
and confidence interval) reported in the paper.
The values given exclude the 1-year period following the treatment.
Mean cumulative 131I activity.
A re-estimate of the dose to bone marrow in this study indicates a meandose of 0.60 rather than 0.30 Sv. Consequently the excess relative risk
becomes 2.22/Sv [R7].
Based on those with doses above 0.1 Sv.
The values given exclude the period within 2 years of irradiation.
Doses lagged by 2 years.
Tabulation and analysis with a 2-year lag. Risk estimate based on adose–response analysis.
Dose–response trend was approximately zero.
The values given are based on the prospective study population followed
over 1991–1997 [I14].
Mean annual effective dose = 6.4 mSv.
Based on a 2-year latent period.
Some patients from the United Kingdom were included in this analysis [R3].
Risk relative to unexposed controls, adjusted for sex, age and calendar
period [T30].
In the control group, 7 leukaemias were observed, compared with
5.4 expected [W15].

dd Median activity concentration of uranium in well water for the referencegroup [A25].
ee Mean cumulative radon exposure.
ff Dose to bone marrow (Gy) over 10 years based on estimated mean of
20.8 mL injected Thorotrast derived from hospital records, and using
dosimetry from reference [K41].
gg Crude relative risk, based on 7 cases in the control group.
hh Dose to bone marrow (Gy) over 10 years based on estimated mean of
17 mL injected Thorotrast derived from hospital records, and using
dosimetry from reference [K41].
ii Results presented are based on follow-up over the period 5 or more years
after first examination [D27].
jj Dose to bone marrow (Gy) over 10 years based on estimated mean of
20 mL injected Thorotrast derived from hospital records, and using
dosimetry from reference [K42].
kk Based on 1 death in the control group, compared with 1.25 expected [D27].
ll Based on the analysis of reference [Y10].
mm Based on the analysis of reference [S56].
nn Value for controls.
oo Relative risk among those with doses of 10 mSv or more relative to thosewith less than 2 mSv.
pp Value at age 70 years.
qq All expected numbers calculated by means of fitted relative risk model (4),evaluated at zero dose.
rr Non-significant trend with dose (p > 0.5).
ss Based on follow-up of 11 or more years after radiotherapy.
tt Risk estimate based on a dose–response analysis.

Table 45 Coefficients of solid cancer mortality models, fitted to current data for the survivors of the atomic bombingsin Japan [P10]All models are fitted by Poisson maximum-likelihood, using adjustments for dosimetric error as described in appendix B and assuming
35% GSD errors. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model (adjustment for attained age, years since exposure), linear dose response

h0 (a,e, c, s) ⋅ ⎡⎣1 + α ⋅ D ⋅ exp[κ 1 ⋅1s = female + κ 2 ⋅ ln[a − e] + κ 3 ⋅ ln[a]]⎤⎦α=601.02 Sv–1

κ1 =0.603 5

κ2 =0.990 3

κ3 =–2.635Generalized ERR model (adjustment for age at exposure), linear dose response

h0 (a,e, c, s) ⋅ ⎡⎣1 + α ⋅ D ⋅ exp[κ 1 ⋅1s= female + κ 2 ⋅ ln[e]]⎤⎦α=2.302 73 Sv–1

κ1 =0.733 5

κ2 =–0.619 5Generalized ERR model(adjustment for attained age, years since exposure), linear–quadratic dose response

h0 (a,e, c, s) ⋅ ⎡⎣1 + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ 1 ⋅1s = female + κ 2 ⋅ ln[a − e] + κ 3 ⋅ ln[a]]⎤⎦α=408.285 Sv–1

β⁄α =0.292 23 Sv–1

κ1 =

0.663

κ2 =

0.987 1

κ3 =–2.636

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Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D ⋅ exp[κ 1 ⋅ ln[a − e] + κ 2 ⋅ ln[a]]α=1.128 34 × 10–8 Sv–1 a–1

κ1 =0.658 6

κ2 =

2.357

Generalized EAR model, linear–quadratic dose response

h0 (a,e, c, s) + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ1 ⋅ ln[a − e] + κ 2 ⋅ ln[a]]α=7.745 27 × 10–9 Sv–1 a–1β⁄α =0.398 406 Sv–1

κ1 =0.656 5

κ2 =

2.340

Table 46 Coefficients of leukaemia mortality models, fitted to current data for the survivors of the atomic bombings inJapan [P10]All models are fitted by Poisson maximum-likelihood, using adjustments for dosimetric error as described in appendix B and assuming
35% GSD errors. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.

Generalized ERR model, quadratic dose response

h0 (a,e, c, s) ⋅ ⎡⎣1 + β ⋅ D 2 ⋅ exp[κ 1 ⋅ ln[a]]⎤⎦β=

1 012.92 Sv–2

κ1 =–1.555Generalized ERR model, linear–quadratic dose response

h0 (a,e, c, s) ⋅ ⎡⎣1 + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ 1 ⋅ ln[a]]⎤⎦α=864.552 Sv–1β⁄α =1.180 92 Sv–1

κ1 =–1.647

Generalized EAR model, quadratic dose response

h0 (a,e, c, s) + β ⋅ D 2 ⋅ exp[κ 1 ⋅1s = female + κ 2 ⋅ ln[a − e]]β=1.445 75 × 10–3 Sv–2 a–1

κ1 =–0.521 984

κ2 =–0.666 2Generalized EAR model, linear–quadratic dose response

h0 (a,e, c, s) + (α ⋅ D + β ⋅ D 2 ) ⋅ exp[κ 1 ⋅1s = female + κ 2 ⋅ ln[a − e]]α=7.516 50 × 10–4 Sv –1 a–1β⁄α =1.034 55 Sv–1

κ1 =–0.525 26

κ2 =–0.614 1

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Table 47 Coefficients of oesophageal cancer incidence models, fitted to current data for the survivors of the atomic
bombings in Japan [P48] (using DS02 stomach dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, linear dose response

h0 (a,e, c, s) ⋅ [1 + α ⋅ D ]α=

0.527 82 Sv–1

Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D
α=1.452 93 × 10–5 Sv–1 a–1

Table 48 Coefficients of stomach cancer incidence models, fitted to current data for the survivors of the atomic
bombings in Japan [P48] (using DS02 stomach dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, linear dose response

h0 (a,e, c, s) ⋅ [1 + α ⋅ D ⋅ exp[κ 1 ⋅ ln[a]]]α=4.025 03 × 103 Sv–1

κ1 =–2.253Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D ⋅ exp[κ 1 ⋅ ln[a]]α=3.969 25 × 10–7 Sv –1 a–1

κ1 =

1.828

Table 49 Coefficients of colon cancer incidence models, fitted to current data for the survivors of the atomic bombingsin Japan [P48] (using DS02 colon dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, linear dose response

h0 (a,e, c, s) ⋅ [1 + α ⋅ D ⋅ exp[κ 1 ⋅ ln[a]]]α=1.480 80 × 106 Sv –1

κ1 =–3.526

Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D ⋅ exp[κ 1 ⋅ ln[a − e]]α=2.875 27 × 10–9 Sv–1 a–1

κ1 =

3.204

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Table 50 Coefficients of liver cancer incidence models, fitted to current data for the survivors of the atomic bombingsin Japan [P48] (using DS02 liver dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, linear dose response

h0 (a,e, c, s) ⋅ [1 + α ⋅ D ]α=

3.951 06 × 10–1 Sv–1

Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D ⋅ exp[κ 1 ⋅ ln[a]]α=1.037 36 × 10–10 Sv–1 a–1

κ1 =

3.479

Table 51 Coefficients of lung cancer incidence models, fitted to current data for the survivors of the atomic bombingsin Japan [P48] (using DS02 lung dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, linear dose response

h0 (a,e, c, s) ⋅ ⎡⎣1 + α ⋅ D ⋅ exp[κ 1 ⋅1s = female ]⎤⎦α=3.182 24 × 10–1 Sv–1

κ1 =1.480 8Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D ⋅ exp[κ 1 ⋅1s= female + κ 2 ⋅ ln[a]]α=1.00 830 × 10–11 Sv–1 a–1

κ1 =0.400 8

κ2 =

4.211

Table 52 Coefficients of bone cancer incidence models, fitted to current data for the survivors of the atomic bombingsin Japan [P48] (using DS02 skeletal dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, quadratic dose response

h0 (a,e, c, s) ⋅ ⎡⎣1 + β ⋅ D 2 ⋅ exp[κ 1 ⋅ ln[a]]⎤⎦β=6.903 79 × 107 Sv –2

κ1 =–4.472

Generalized EAR model, quadratic dose response

h0 (a,e, c, s) + β ⋅ D 2
β=9.329 40 × 10–6 Sv–2 a–1

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Table 53 Coefficients of non-melanoma skin cancer incidence models, fitted to current data for the survivors of theatomic bombings in Japan [P48] (using DS02 skin dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, quadratic–exponential dose response

h0 (a,e, c, s) ⋅ ⎡⎣1 + β ⋅ D 2 ⋅ exp[γ ⋅ D + κ 1 ⋅ ln[a − e] + κ 2 ⋅ ln[a]]⎤⎦β=2.615 26 × 103 Sv–2γ=

–0.272 Sv–1

κ1 =

3.196

κ2 =

–4.595Generalized EAR model, quadratic–exponential dose response

h0 (a,e, c, s) + β ⋅ D 2 ⋅ exp[γ ⋅ D + κ 1 ⋅ ln[a − e]]β=5.245 49 × 10–9 Sv–2γ=

–0.273 9 Sv–1

κ1 =

2.885

Table 54 Coefficients of female breast cancer incidence models, fitted to current data for the survivors of the atomic
bombings in Japan [P48] (using DS02 breast dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, linear dose response

h0 (a,e, c, s) ⋅ [1 + α ⋅ D ⋅ exp[κ 1 ⋅ ln[a]]]α=1.492 21 × 104 Sv–1

κ1 =–2.304Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D ⋅ exp[κ 1 ⋅ ln[a − e]]α=1.940 38 × 10–5 Sv–1 a–1

κ1 =

1.086

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Table 55 Coefficients of urinary bladder cancer incidence models, fitted to current data for the survivors of the atomic
bombings in Japan [P48] (using DS02 bladder dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, linear dose response

h0 (a,e, c, s) ⋅ [1 + α ⋅ D ]α=

8.988 85 × 10–1 Sv–1

Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D ⋅ exp[κ 1 ⋅ ln[a]]α=6.135 72 × 10–15 Sv–1 a–1

κ1 =

5.748

Table 56 Coefficients of brain and CNS cancer incidence models, fitted to current data for the survivors of the atomic
bombings in Japan [P48] (using DS02 brain dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, linear dose response

h0 (a,e, c, s) ⋅ [1 + α ⋅ D ⋅ exp[κ 1 ⋅ ln[e]]]α=7.431 45 Sv–1

κ1 =–0.989 7Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D
α=4.923 82 × 10–5 Sv–1 a–1

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Table 57 Coefficients of thyroid cancer incidence models, fitted to current data for the survivors of the atomic
bombings in Japan [P48] (using DS02 thyroid dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, linear dose response

h0 (a,e, c, s) ⋅ [1 + α ⋅ D ⋅ exp[κ1 ⋅ ln[e] + κ 2 ⋅ ln[a]]]α=3.80452 × 104 Sv–1

κ1 =–0.4405

κ2 =–2.197

Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D ⋅ exp[κ1 ⋅1s= female + κ 2 ⋅ ln[e]]α=2.628 70 × 10–4 Sv–1 a–1

κ1 =1.362 4

κ2 =–0.388 3

Table 58 Coefficients of all other solid cancer incidence models, fitted to current data for the survivors of the atomic
bombings in Japan [P48] (using DS02 colon dose)
All models are fitted by Poisson maximum-likelihood and adjusted for 35% GSD errors, truncated, using adjustment factors derived
from DS86 [P2]. D = radiation dose (Sv), a = attained age, e = age at exposure, s = sex, c = city.
Generalized ERR model, linear dose response

h0 (a,e, c, s) ⋅ [1 + α ⋅ D ⋅ exp[κ1 ⋅ ln[a − e] + κ 2 ⋅ ln[a]]]α=1.432 20 × 102 Sv–1

κ1 =

1.645

κ2 =

–2.939Generalized EAR model, linear dose response

h0 (a,e, c, s) + α ⋅ D ⋅ exp[κ 1 ⋅ ln[a − e]]α=2.207 51 × 10–7 Sv–1 a–1

κ1 =

2.161

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Table 59 Risk estimates for solid cancer mortality in various current populations, using generalized ERR andgeneralized EAR models (models described in table 45)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom populations) from various models fitted to LSS mortality data [P10],
assuming 35% GSD errors
Model, modifying terms a

Test dose,
Dt (Sv)

Per cent excess
cancer deaths(Sv–1)Per cent radiationinduced cancer deaths(Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)China
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D , sex, age, years SE d
2

EAR, D, age, years SE e

EAR, D + D2, age, years SE f

0.01

5.02

5.70

0.862

15.1

0.1

4.99

5.67

0.859

15.1

1.0

4.75

5.40

0.831

15.4

0.01

7.38

8.31

1.096

13.2

0.1

7.28

8.19

1.086

13.3

1.0

6.44

7.27

0.999

13.7

0.01

3.48

3.95

0.598

15.2

0.1

3.55

4.03

0.612

15.2

1.0

4.26

4.84

0.746

15.4

0.01

5.43

6.12

0.859

14.0

0.1

5.40

6.09

0.856

14.1

1.0

5.12

5.78

0.828

14.3

0.01

3.45

3.89

0.548

14.1

0.1

3.56

4.02

0.566

14.1

1.0

4.57

5.16

0.739

14.3

Japan
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

EAR, D + D2, age, years SE f

0.01

5.62

6.71

0.992

14.8

0.1

5.59

6.67

0.988

14.8

1.0

5.26

6.30

0.950

15.1

0.01

9.06

10.69

1.390

13.0

0.1

8.88

10.49

1.372

13.1

1.0

7.61

9.03

1.236

13.7

0.01

3.90

4.65

0.689

14.8

0.1

3.98

4.75

0.705

14.8

1.0

4.73

5.65

0.854

15.1

0.01

6.49

7.70

1.145

14.9

0.1

6.44

7.65

1.140

14.9

1.0

6.03

7.17

1.094

15.2

0.01

4.12

4.90

0.730

14.9

0.1

4.25

5.05

0.754

14.9

1.0

5.38

6.40

0.976

15.2

Puerto Rico
ERR, D, sex, age, years SE b

0.01

4.37

5.04

0.739

14.7

0.1

4.35

5.01

0.737

14.7

1.0

4.14

4.78

0.713

14.9

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Model, modifying terms a

ERR, D, sex, age AE c

ERR, D + D , sex, age, years SE d
2

EAR, D, age, years SE e

EAR, D + D2, age, years SE f

Test dose,
Dt (Sv)

Per cent excess
cancer deaths(Sv–1)Per cent radiationinduced cancer deaths(Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)

0.01

6.91

7.91

1.004

12.7

0.1

6.80

7.79

0.994

12.8

1.0

5.97

6.85

0.912

13.3

0.01

3.04

3.50

0.515

14.7

0.1

3.11

3.58

0.527

14.7

1.0

3.73

4.30

0.643

15.0

0.01

5.82

6.69

0.971

14.5

0.1

5.78

6.65

0.967

14.5

1.0

5.45

6.28

0.931

14.8

0.01

3.70

4.26

0.619

14.5

0.1

3.82

4.39

0.640

14.6

1.0

4.86

5.60

0.831

14.8

United States
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

EAR, D + D2, age, years SE f

0.01

5.77

6.82

1.031

15.1

0.1

5.73

6.78

1.026

15.1

1.0

5.38

6.38

0.983

15.4

0.01

9.05

10.60

1.417

13.4

0.1

8.87

10.40

1.398

13.4

1.0

7.58

8.92

1.253

14.1

0.01

4.01

4.74

0.719

15.2

0.1

4.10

4.84

0.735

15.2

1.0

4.86

5.75

0.887

15.4

0.01

5.88

6.94

1.010

14.6

0.1

5.84

6.89

1.006

14.6

1.0

5.50

6.50

0.968

14.9

0.01

3.74

4.41

0.644

14.6

0.1

3.86

4.55

0.665

14.6

1.0

4.91

5.80

0.864

14.9

United Kingdom
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

0.01

6.16

7.41

1.019

13.8

0.1

6.12

7.36

1.015

13.8

1.0

5.72

6.89

0.972

14.1

0.01

9.84

11.70

1.406

12.0

0.1

9.63

11.46

1.387

12.1

1.0

8.13

9.73

1.242

12.8

0.01

4.29

5.15

0.711

13.8

0.1

4.38

5.26

0.727

13.8

1.0

5.16

6.21

0.877

14.1

0.01

5.72

6.92

0.991

14.3

0.1

5.69

6.88

0.987

14.4

1.0

5.38

6.51

0.954

14.6

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Model, modifying terms a

225

Test dose,
Dt (Sv)

Per cent excess
cancer deaths(Sv–1)Per cent radiationinduced cancer deaths(Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)

0.01

3.64

4.40

0.632

14.4

0.1

3.76

4.54

0.653

14.4

1.0

4.80

5.81

0.852

14.7

EAR, D + D2, age, years SE fa ERR = generalized excess relative risk, EAR = generalized excess absoluterisk, years SE = years since exposure, age AE = age at exposure.
b ERR = α D [a – e]κ aτ, as per model (14) with quadratic coefficient in dose,sβ, set to 0 (a = attained age, e = age at exposure, s = sex).
c ERR = α D eκ, as per model (15) with quadratic coefficient in dose, β, sets
to 0 (e = age at exposure, s = sex).

d ERR = α [D+βD2] [a – e]κ aτ, as per model (14) (a = attained age,se = age at exposure, s = sex).
e EAR = α D [a – e]κ aτ, as per model (16) with quadratic coefficient in dose,β, set to 0 (a = attained age, e = age at exposure).
f EAR = α [D+βD2] [a – e]κ aτ, as per model (16) (a = attained age,e = age at exposure).

Table 60 Risk estimates for solid cancer mortality by sex in various current populations, assuming a test dose, Dt,of 0.1 Sv, and using generalized ERR and generalized EAR models (models described in table 45)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom populations) from various models fitted to LSS mortality data [P10],
assuming 35% GSD errors
Model, modifying terms a

Sex

Per cent excess
cancer deaths(Sv –1)

Per cent radiationinduced cancer deaths(Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)China
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

EAR, D + D , age, years SE f
2

Males

4.52

5.33

0.747

14.0

Females

5.48

6.03

0.976

16.2

Both

4.99

5.67

0.859

15.1

Males

6.10

7.13

0.874

12.3

Females

8.50

9.30

1.306

14.0

Both

7.28

8.19

1.086

13.3

Males

3.12

3.67

0.515

14.0

Females

4.01

4.41

0.714

16.2

Both

3.55

4.03

0.612

15.2

Males

4.68

5.49

0.752

13.7

Females

6.14

6.71

0.964

14.4

Both

5.40

6.09

0.856

14.1

Males

3.09

3.63

0.498

13.7

Females

4.05

4.43

0.638

14.4

Both

3.56

4.02

0.566

14.1

Japan
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D , sex, age, years SE d
2

Males

5.03

6.31

0.836

13.2

Females

6.13

7.03

1.135

16.2

Both

5.59

6.67

0.988

14.8

Males

7.29

9.07

1.075

11.9

Females

10.42

11.86

1.660

14.0

Both

8.88

10.49

1.372

13.1

Males

3.46

4.35

0.576

13.2

Females

4.48

5.14

0.830

16.2

Both

3.98

4.75

0.705

14.8

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226

UNSCEAR 2006 REPORT: VOLUME I

Model, modifying terms a

EAR, D, age, years SE e

EAR, D + D , age, years SE f2

Sex

Per cent excess
cancer deaths (Sv–1)

Per cent radiationinduced cancer deaths(Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)

Males

5.19

6.53

0.932

14.3

Females

7.65

8.74

1.342

15.3

Both

6.44

7.65

1.140

14.9

Males

3.43

4.31

0.617

14.3

Females

5.05

5.77

0.887

15.4

Both

4.25

5.05

0.754

14.9

Puerto Rico
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

EAR, D + D2, age, years SE f

Males

3.57

4.35

0.566

13.0

Females

5.07

5.63

0.895

15.9

Both

4.35

5.01

0.737

14.7

Males

5.20

6.32

0.723

11.4

Females

8.28

9.14

1.244

13.6

Both

6.80

7.79

0.994

12.8

Males

2.46

3.00

0.390

13.0

Females

3.71

4.11

0.654

15.9

Both

3.11

3.58

0.527

14.7

Males

4.77

5.81

0.825

14.2

Females

6.72

7.44

1.098

14.8

Both

5.78

6.65

0.967

14.5

Males

3.15

3.84

0.546

14.2

Females

4.44

4.91

0.726

14.8

Both

3.82

4.39

0.640

14.6

United States
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

EAR, D + D2, age, years SE f

Males

4.50

5.53

0.746

13.5

Females

6.93

8.00

1.298

16.2

Both

5.73

6.78

1.026

15.1

Males

6.45

7.86

0.946

12.0

Females

11.23

12.86

1.837

14.3

Both

8.87

10.40

1.398

13.4

Males

3.10

3.81

0.514

13.5

Females

5.07

5.85

0.950

16.2

Both

4.10

4.84

0.735

15.2

Males

5.02

6.16

0.875

14.2

Females

6.65

7.61

1.133

14.9

Both

5.84

6.89

1.006

14.6

Males

3.31

4.07

0.579

14.2

Females

4.39

5.02

0.750

14.9

Both

3.86

4.55

0.665

14.6

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Model, modifying terms a

Sex

Per cent excess
cancer deaths (Sv–1)

227

Per cent radiationinduced cancer deaths(Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)United Kingdom
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

EAR, D + D , age, years SE f
2

Males

4.78

6.00

0.729

12.2

Females

7.46

8.72

1.301

14.9

Both

6.12

7.36

1.015

13.8

Males

7.00

8.72

0.939

10.8

Females

12.25

14.20

1.835

12.9

Both

9.63

11.46

1.387

12.1

Males

3.29

4.13

0.503

12.2

Females

5.46

6.38

0.952

14.9

Both

4.38

5.26

0.727

13.8

Males

4.99

6.30

0.887

14.1

Females

6.39

7.45

1.088

14.6

Both

5.69

6.88

0.987

14.4

Males

3.29

4.16

0.587

14.1

Females

4.22

4.92

0.720

14.6

Both

3.76

4.54

0.653

14.4

a ERR = generalized excess relative risk, EAR = generalized excess absoluterisk, years SE = years since exposure, age AE = age at exposure.
b ERR = α D [a – e]κ aτ, as per model (14) with quadratic coefficient in dose,sβ, set to 0 (a = attained age, e = age at exposure, s = sex).
c ERR = α D eκ, as per model (15) with quadratic coefficient in dose, β, sets
to 0 (e = age at exposure, s = sex).

d ERR = α [D+βD2] [a – e]κ aτ, as per model (14) (a = attained age,se = age at exposure, s = sex).
e EAR = α D [a – e]κ aτ, as per model (16) with quadratic coefficient in dose,β, set to 0 (a = attained age, e = age at exposure).
f EAR = α [D+βD2] [a – e]κ aτ, as per model (16) (a = attained age,e = age at exposure).

Table 61 Risk estimates for solid cancer mortality by age-at-exposure group in various current populations, assuming
a test dose, Dt, of 0.1 Sv and using generalized ERR and generalized EAR models (models described in table 45)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current
Chinese, Japanese, Puerto Rican, United States and United Kingdom populations) from various models fitted to LSS mortality data
[P10], assuming 35% GSD errors
Model, modifying factors a

Age at exposure

Per cent excess
cancer deaths (Sv–1)

Per cent radiationinduced cancer
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)China
ERR, D, sex, age, years SE b

0–9

10.72

12.27

2.165

17.6

10–19

8.79

10.03

1.636

16.3

20–29

6.95

7.91

1.197

15.1

30–39

5.18

5.87

0.802

13.7

40–49

3.54

3.99

0.472

11.8

50–59

2.16

2.40

0.240

10.0

60–69

1.07

1.17

0.094

8.0

70+

0.30

0.32

0.020

6.1

All ages

4.99

5.67

0.859

15.1

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UNSCEAR 2006 REPORT: VOLUME I

Model, modifying factors a

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

EAR, D + D , age, years SE f
2

Age at exposure

Per cent excess
cancer deaths (Sv–1)

Per cent radiationinduced cancer
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)

0–9

24.11

27.23

3.831

14.1

10–19

9.07

10.24

1.423

13.9

20–29

6.55

7.39

1.017

13.8

30–39

5.28

5.95

0.792

13.3

40–49

4.36

4.90

0.598

12.2

50–59

3.54

3.95

0.419

10.6

60–69

2.53

2.78

0.237

8.5

70+

1.09

1.17

0.073

6.3

All ages

7.28

8.19

1.086

13.3

0–9

7.63

8.72

1.543

17.7

10–19

6.25

7.13

1.165

16.3

20–29

4.94

5.62

0.853

15.2

30–39

3.69

4.17

0.571

13.7

40–49

2.52

2.84

0.337

11.9

50–59

1.54

1.71

0.171

10.0

60–69

0.76

0.83

0.067

8.0

70+

0.22

0.23

0.014

6.1

All ages

3.55

4.03

0.612

15.2

0–9

9.65

10.98

1.805

16.4

10–19

8.53

9.70

1.530

15.8

20–29

7.30

8.27

1.223

14.8

30–39

5.97

6.73

0.904

13.4

40–49

4.58

5.13

0.600

11.7

50–59

3.21

3.55

0.345

9.7

60–69

1.96

2.14

0.165

7.7

70+

0.90

0.96

0.057

5.9

All ages

5.40

6.09

0.856

14.1

0–9

6.38

7.26

1.196

16.5

10–19

5.64

6.41

1.013

15.8

20–29

4.82

5.46

0.809

14.8

30–39

3.94

4.45

0.598

13.4

40–49

3.02

3.39

0.396

11.7

50–59

2.12

2.34

0.227

9.7

60–69

1.30

1.41

0.109

7.7

70+

0.59

0.63

0.037

5.9

All ages

3.56

4.02

0.57

14.09

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Model, modifying factors a

Age at exposure

Per cent excess
cancer deaths (Sv–1)

229

Per cent radiationinduced cancer
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)Japan
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

EAR, D + D , age, years SE f
2

0–9

11.78

14.18

2.434

17.2

10–19

9.86

11.84

1.898

16.0

20–29

8.03

9.62

1.448

15.1

30–39

6.23

7.43

1.029

13.8

40–49

4.52

5.36

0.663

12.4

50–59

2.99

3.50

0.373

10.7

60–69

1.71

1.98

0.175

8.8

70+

0.57

0.64

0.046

7.1

All ages

5.59

6.67

0.988

14.8

0–9

30.02

35.61

4.942

13.9

10–19

11.37

13.48

1.844

13.7

20–29

8.23

9.75

1.325

13.6

30–39

6.65

7.88

1.048

13.3

40–49

5.60

6.62

0.833

12.6

50–59

4.70

5.53

0.624

11.3

60–69

3.72

4.31

0.406

9.4

70+

1.96

2.21

0.162

7.3

All ages

8.88

10.49

1.372

13.1

0–9

8.39

10.09

1.737

17.2

10–19

7.02

8.42

1.354

16.1

20–29

5.72

6.84

1.034

15.1

30–39

4.44

5.29

0.734

13.9

40–49

3.22

3.82

0.473

12.4

50–59

2.13

2.50

0.266

10.7

60–69

1.23

1.41

0.125

8.9

70+

0.41

0.46

0.033

7.2

All ages

3.98

4.75

0.705

14.8

0–9

11.59

13.91

2.424

17.4

10–19

10.31

12.36

2.074

16.8

20–29

8.93

10.67

1.690

15.8

30–39

7.44

8.85

1.286

14.5

40–49

5.87

6.94

0.893

12.9

50–59

4.31

5.03

0.550

11.0

60–69

2.84

3.27

0.292

8.9

70+

1.32

1.48

0.103

6.9

All ages

6.44

7.65

1.140

14.9

0–9

7.65

9.19

1.605

17.5

10–19

6.81

8.16

1.373

16.8

20–29

5.90

7.05

1.118

15.9

30–39

4.91

5.84

0.850

14.6

40–49

3.87

4.57

0.590

12.9

50–59

2.84

3.31

0.363

11.0

60–69

1.87

2.15

0.192

8.9

70+

0.87

0.97

0.068

6.9

All ages

4.25

5.05

0.754

14.9

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UNSCEAR 2006 REPORT: VOLUME I

Model, modifying factors a

Age at exposure

Per cent excess
cancer deaths (Sv–1)

Per cent radiationinduced cancer
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)Puerto Rico
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D , sex, age, years SE d
2

EAR, D, age, years SE e

EAR, D + D , age, years SE f
2

0–9

8.81

10.18

1.767

17.4

10–19

7.28

8.40

1.339

15.9

20–29

5.90

6.80

1.007

14.8

30–39

4.60

5.30

0.714

13.5

40–49

3.37

3.87

0.460

11.9

50–59

2.23

2.55

0.258

10.1

60–69

1.30

1.48

0.122

8.2

70+

0.47

0.53

0.035

6.5

All ages

4.35

5.01

0.737

14.7

0–9

21.70

24.89

3.388

13.6

10–19

8.19

9.38

1.259

13.4

20–29

5.96

6.83

0.908

13.3

30–39

4.90

5.62

0.726

12.9

40–49

4.21

4.82

0.584

12.1

50–59

3.56

4.07

0.436

10.7

60–69

2.84

3.23

0.284

8.8

70+

1.63

1.83

0.122

6.7

All ages

6.80

7.79

0.994

12.8

0–9

6.29

7.26

1.265

17.4

10–19

5.20

6.00

0.959

16.0

20–29

4.21

4.85

0.721

14.9

30–39

3.29

3.78

0.510

13.5

40–49

2.41

2.76

0.329

11.9

50–59

1.60

1.82

0.184

10.1

60–69

0.93

1.06

0.087

8.2

70+

0.34

0.38

0.025

6.5

All ages

3.11

3.58

0.527

14.7

0–9

10.00

11.55

1.960

17.0

10–19

8.86

10.24

1.671

16.3

20–29

7.67

8.85

1.360

15.4

30–39

6.44

7.42

1.043

14.1

40–49

5.13

5.88

0.730

12.4

50–59

3.75

4.28

0.449

10.5

60–69

2.46

2.79

0.236

8.5

70+

1.14

1.27

0.082

6.4

All ages

5.78

6.65

0.967

14.5

0–9

6.61

7.63

1.298

17.0

10–19

5.85

6.76

1.106

16.4

20–29

5.07

5.84

0.900

15.4

30–39

4.25

4.90

0.689

14.1

40–49

3.38

3.88

0.482

12.4

50–59

2.47

2.82

0.296

10.5

60–69

1.62

1.84

0.156

8.5

70+

0.75

0.84

0.054

6.4

All ages

3.82

4.39

0.640

14.6

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Page 231

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Model, modifying factors a

Age at exposure

Per cent excess
cancer deaths (Sv–1)

231

Per cent radiationinduced cancer
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)United States
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

EAR, D + D , age, years SE f
2

0–9

11.94

14.23

2.480

17.4

10–19

9.92

11.80

1.917

16.2

20–29

8.04

9.53

1.458

15.3

30–39

6.20

7.32

1.035

14.1

40–49

4.43

5.20

0.659

12.7

50–59

2.85

3.30

0.357

10.8

60–69

1.57

1.79

0.158

8.8

70+

0.51

0.58

0.039

6.8

All ages

5.73

6.78

1.026

15.1

0–9

29.20

34.34

4.888

14.2

10–19

11.08

13.01

1.826

14.0

20–29

8.04

9.44

1.315

13.9

30–39

6.53

7.67

1.046

13.6

40–49

5.52

6.47

0.837

12.9

50–59

4.60

5.35

0.616

11.5

60–69

3.52

4.04

0.380

9.4

70+

1.79

2.01

0.141

7.0

All ages

8.87

10.40

1.398

13.4

0–9

8.54

10.17

1.776

17.5

10–19

7.09

8.43

1.373

16.3

20–29

5.75

6.81

1.045

15.3

30–39

4.43

5.23

0.741

14.2

40–49

3.17

3.72

0.472

12.7

50–59

2.04

2.36

0.256

10.8

60–69

1.12

1.28

0.113

8.8

70+

0.37

0.41

0.028

6.9

All ages

4.10

4.84

0.735

15.2

0–9

10.32

12.29

2.095

17.0

10–19

9.16

10.89

1.786

16.4

20–29

7.91

9.38

1.449

15.4

30–39

6.57

7.76

1.096

14.1

40–49

5.16

6.05

0.754

12.5

50–59

3.75

4.35

0.459

10.6

60–69

2.48

2.83

0.242

8.6

70+

1.18

1.31

0.087

6.6

All ages

5.84

6.89

1.006

14.6

0–9

6.82

8.12

1.387

17.1

10–19

6.05

7.19

1.182

16.4

20–29

5.23

6.20

0.959

15.5

30–39

4.34

5.12

0.724

14.1

40–49

3.40

3.99

0.498

12.5

50–59

2.47

2.87

0.303

10.6

60–69

1.63

1.86

0.160

8.6

70+

0.78

0.86

0.057

6.6

All ages

3.86

4.55

0.665

14.6

UNSCEAR REPORT-PART 4.qxp

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Page 232

UNSCEAR 2006 REPORT: VOLUME I

Model, modifying factors a

Age at exposure

Per cent excess
cancer deaths (Sv–1)

Per cent radiationinduced cancer
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)United Kingdom
ERR, D, sex, age, years SE b

ERR, D, sex, age AE c

ERR, D + D2, sex, age, years SE d

EAR, D, age, years SE e

0–9

12.77

15.49

2.510

16.2

10–19

10.64

12.86

1.914

14.9

20–29

8.65

10.44

1.455

13.9

30–39

6.73

8.08

1.037

12.8

40–49

4.87

5.81

0.663

11.4

50–59

3.18

3.75

0.362

9.6

60–69

1.78

2.06

0.158

7.7

70+

0.56

0.63

0.034

5.5

All ages

6.12

7.36

1.015

13.8

0–9

32.06

38.33

4.942

12.9

10–19

12.18

14.54

1.841

12.7

20–29

8.81

10.52

1.320

12.6

30–39

7.14

8.52

1.048

12.3

40–49

6.03

7.18

0.837

11.7

50–59

5.06

5.98

0.618

10.3

60–69

3.96

4.61

0.384

8.3

70+

1.97

2.23

0.127

5.7

All ages

9.63

11.46

1.387

12.1

0–9

9.13

11.07

1.798

16.3

10–19

7.60

9.19

1.371

14.9

20–29

6.19

7.45

1.042

14.0

30–39

4.81

5.77

0.742

12.9

40–49

3.48

4.15

0.475

11.4

50–59

2.27

2.68

0.259

9.7

60–69

1.27

1.48

0.113

7.7

70+

0.40

0.45

0.025

5.5

All ages

4.38

5.26

0.727

13.8

0–9

10.35

12.65

2.132

16.9

10–19

9.17

11.18

1.812

16.2

20–29

7.89

9.60

1.460

15.2

30–39

6.52

7.88

1.092

13.8

40–49

5.07

6.08

0.736

12.1

50–59

3.62

4.29

0.431

10.0

60–69

2.28

2.65

0.207

7.8

70+

0.89

1.01

0.054

5.4

All ages

5.69

6.88

0.987

14.4

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Model, modifying factors a

EAR, D + D2, age, years SE f

233

Age at exposure

Per cent excess
cancer deaths (Sv–1)

Per cent radiationinduced cancer
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)

0–9

6.84

8.36

1.412

16.9

10–19

6.06

7.39

1.199

16.2

20–29

5.21

6.34

0.966

15.2

30–39

4.30

5.20

0.722

13.9

40–49

3.34

4.01

0.486

12.1

50–59

2.38

2.83

0.284

10.1

60–69

1.50

1.75

0.136

7.8

70+

0.59

0.66

0.036

5.4

All ages

3.76

4.54

0.653

14.4

a ERR = generalized excess relative risk, EAR = generalized excess absoluterisk, years SE = years since exposure, age AE = age at exposure.
b ERR = α D [a – e]κ aτ, as per model (14) with quadratic coefficient in dose,sβ, set to 0 (a = attained age, e = age at exposure, s = sex).
c ERR = α D eκ, as per model (15) with quadratic coefficient in dose, β, sets
to 0 (e = age at exposure, s = sex).

d ERR = α [D+βD2] [a – e]κ aτ, as per model (14) (a = attained age,se = age at exposure, s = sex).
e EAR = α D [a – e]κ aτ, as per model (16) with quadratic coefficient in dose,β, set to 0 (a = attained age, e = age at exposure).
f EAR = α [D+βD2] [a – e]κ aτ, as per model (16) (a = attained age,e = age at exposure).

Table 62 Risk estimates for solid cancer mortality in United Kingdom populations, assuming a test dose, Dt, of 0.1 Sv,
using linear generalized ERR models (models described in table 45 and analogues) fitted using DS86 and DS02 dose
estimates, and using follow-up over the periods 1950–1990 and 1950–2000
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current United
Kingdom population) from linear ERR models fitted to LSS mortality data [P10], assuming 35% GSD errors
Periodof fitDoseestimates
used

Model, modifying terms a

Per cent excess
cancer deaths(Sv–1)Per cent radiationinduced cancer
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
cancer death (a)1950–1990

DS86

ERR, D, sex, age, years SE b
ERR, D, sex, age AE c

7.07

8.48

1.128

13.3

11.48

13.66

1.659

12.1

DS02

ERR, D, sex, age, years SE b

6.34

7.60

1.010

13.3

ERR, D, sex, age AE c

10.35

12.31

1.496

12.2

ERR, D, sex, age, years SE b

6.85

8.25

1.137

13.8

ERR, D, sex, age AE c

10.69

12.73

1.539

12.1

ERR, D, sex, age, years SE b

6.12

7.36

1.015

13.8

ERR, D, sex, age AE c

9.63

11.46

1.387

12.1

1950–2000

DS86

DS02

a ERR = generalized excess relative risk, years SE = years since exposure, age
AE = age at exposure.
b ERR = α D [a – e]κ aτ, as per model (14) with quadratic coefficient in dose,sβ, set to 0 (a = attained age, e = age at exposure, s = sex).

c ERR = α D eκ, as per model (15) with quadratic coefficient in dose, β, sets

to 0 (e = age at exposure, s = sex).

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Table 63 Distribution of solid cancer mortality risk estimates for various current populations, using generalized
linear–quadratic–exponential ERR models fitted by Bayesian MCMC (models described in appendix E)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom populations) from various models fitted to LSS mortality data [P10],
assuming 35% GSD errors
Test dose,
Dt (Sv)Mean/centile

Per cent excess cancer
deaths (Sv–1)

Per cent radiation-induced
cancer deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiationinduced cancer death (a)

China
0.01

0.1

1

Mean

1.70

1.94

0.297

15.4

2.5% centile

–2.26

–2.57

–0.39413.8

5% centile

–1.59

–1.80

–0.27914.0

50% centile

1.78

2.03

0.312

15.3

95% centile

4.68

5.32

0.816

17.1

97.5% centile

5.15

5.86

0.892

17.5

Mean

2.25

2.56

0.394

15.4

2.5% centile

–0.91

–1.04

–0.161

13.8

5% centile

–0.41

–0.46

–0.07114.0

50% centile

2.28

2.60

0.400

15.3

95% centile

4.78

5.44

0.833

17.1

97.5% centile

5.22

5.93

0.906

17.5

Mean

4.94

5.63

0.882

15.7

2.5% centile

3.79

4.32

0.690

14.1

5% centile

3.96

4.51

0.719

14.3

50% centile

4.93

5.62

0.879

15.6

95% centile

5.96

6.79

1.054

17.4

97.5% centile

6.16

7.02

1.089

17.8

Japan
0.01

0.1

1

Mean

1.90

2.27

0.339

15.0

2.5% centile

–2.51

–3.00

–0.45013.6

5% centile

–1.76

–2.10

–0.31713.8

50% centile

1.98

2.36

0.356

15.0

95% centile

5.24

6.26

0.927

16.6

97.5% centile

5.77

6.89

1.017

17.0

Mean

2.51

3.00

0.449

15.1

2.5% centile

–1.02

–1.22

–0.182

13.6

5% centile

–0.45

–0.53

–0.08013.8

50% centile

2.53

3.03

0.456

15.0

95% centile

5.34

6.39

0.945

16.6

97.5% centile

5.84

6.99

1.031

17.0

Mean

5.43

6.51

0.997

15.4

2.5% centile

4.06

4.87

0.780

13.9

5% centile

4.27

5.12

0.814

14.1

50% centile

5.42

6.49

0.995

15.3

95% centile

6.68

7.99

1.188

16.9

97.5% centile

6.92

8.28

1.225

17.3

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Test dose,
Dt (Sv)Mean/centile

Per cent excess cancer
deaths (Sv–1)

Per cent radiation-induced
cancer deaths (Sv–1)

235

Years life lost(a Sv–1)

Years life lost/radiationinduced cancer death (a)Puerto Rico
0.01

0.1

1

Mean

1.48

1.70

0.253

15.0

2.5% centile

–1.95

–2.25

–0.33713.3

5% centile

–1.37

–1.58

–0.23713.5

50% centile

1.54

1.78

0.266

14.9

95% centile

4.06

4.68

0.692

16.8

97.5% centile

4.47

5.15

0.757

17.3

Mean

1.95

2.25

0.336

15.0

2.5% centile

–0.79

–0.91

–0.13613.3

5% centile

–0.35

–0.40

–0.06013.5

50% centile

1.98

2.28

0.341

14.9

95% centile

4.14

4.78

0.706

16.8

97.5% centile

4.53

5.23

0.768

17.3

Mean

4.29

4.95

0.753

15.3

2.5% centile

3.25

3.75

0.592

13.6

5% centile

3.40

3.92

0.617

13.8

50% centile

4.27

4.93

0.751

15.2

95% centile

5.23

6.03

0.894

17.1

97.5% centile

5.41

6.24

0.922

17.6

United States
0.01

0.1

1

Mean

1.94

2.30

0.352

15.4

2.5% centile

–2.57

–3.05

–0.46713.9

5% centile

–1.81

–2.14

–0.33014.1

50% centile

2.04

2.41

0.370

15.3

95% centile

5.35

6.33

0.962

16.9

97.5% centile

5.89

6.97

1.050

17.3

Mean

2.57

3.04

0.466

15.4

2.5% centile

–1.04

–1.24

–0.19013.9

5% centile

–0.46

–0.54

–0.08414.1

50% centile

2.60

3.08

0.474

15.3

95% centile

5.45

6.45

0.980

16.9

97.5% centile

5.96

7.05

1.066

17.3

Mean

5.56

6.59

1.033

15.7

2.5% centile

4.22

5.02

0.814

14.3

5% centile

4.43

5.26

0.847

14.5

50% centile

5.54

6.58

1.031

15.6

95% centile

6.75

8.00

1.224

17.3

97.5% centile

6.98

8.27

1.261

17.6

United Kingdom
0.01

Mean

2.07

2.50

0.348

14.0

2.5% centile

–2.74

–3.31

–0.462

12.5

5% centile

–1.92

–2.32

–0.32712.8

50% centile

2.17

2.61

0.366

14.0

95% centile

5.70

6.87

0.952

15.7

97.5% centile

6.29

7.57

1.039

16.1

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Test dose,
Dt (Sv)
0.1

3:39 pm

Mean/centile

Per cent excess cancer
deaths (Sv–1)

Per cent radiation-induced
cancer deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiationinduced cancer death (a)

Mean

2.74

3.30

0.461

14.1

2.5% centile

–1.11

–1.34

–0.18712.6

5% centile

–0.49

–0.59

–0.08312.8

50% centile

2.77

3.34

0.470

14.0

95% centile

5.82

7.01

0.970

15.7

97.5% centile

6.37

7.66

1.055

16.1

Mean

5.90

7.12

1.022

14.4

2.5% centile

4.45

5.38

0.805

13.0

5% centile

4.67

5.65

0.839

13.2

50% centile

5.88

7.10

1.020

14.3

95% centile

7.19

8.67

1.212

16.0

97.5% centile

7.45

8.97

1.248

16.5

Table 64 Distribution of solid cancer mortality risk estimates for various current populations, using generalized
linear–quadratic ERR models fitted by Bayesian MCMC (models described in appendix E)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom populations) from various models fitted to LSS mortality data [P10],
assuming 35% GSD errors
Test dose,
Dt (Sv)Mean/centile

Per cent excess cancer
deaths (Sv–1)

Per cent radiation-induced
cancer deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiationinduced cancer death (a)

China
0.01

0.1

1

Mean

3.60

4.09

0.624

15.3

2.5% centile

1.60

1.82

0.280

13.7

5% centile

1.90

2.15

0.332

14.0

50% centile

3.57

4.06

0.620

15.2

95% centile

5.41

6.16

0.929

16.9

97.5% centile

5.76

6.55

0.986

17.3

Mean

3.70

4.21

0.642

15.3

2.5% centile

1.82

2.07

0.318

13.8

5% centile

2.10

2.38

0.367

14.0

50% centile

3.67

4.17

0.638

15.2

95% centile

5.41

6.14

0.929

16.9

97.5% centile

5.74

6.52

0.982

17.3

Mean

4.59

5.23

0.812

15.6

2.5% centile

3.52

4.00

0.634

14.0

5% centile

3.69

4.20

0.662

14.2

50% centile

4.58

5.22

0.811

15.5

95% centile

5.54

6.30

0.966

17.2

97.5% centile

5.72

6.51

0.996

17.6

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Test dose,
Dt (Sv)Mean/centile

Per cent excess cancer
deaths (Sv–1)

Per cent radiation-induced
cancer deaths (Sv–1)

237

Years life lost(a Sv–1)

Years life lost/radiationinduced cancer death (a)

Japan
0.01

0.1

1

Mean

4.03

4.81

0.715

14.9

2.5% centile

1.78

2.12

0.322

13.5

5% centile

2.09

2.50

0.381

13.7

50% centile

3.98

4.75

0.710

14.9

95% centile

6.12

7.31

1.064

16.5

97.5% centile

6.53

7.80

1.131

16.8

Mean

4.14

4.94

0.736

15.0

2.5% centile

2.01

2.41

0.365

13.5

5% centile

2.31

2.75

0.420

13.7

50% centile

4.09

4.89

0.731

14.9

95% centile

6.10

7.29

1.064

16.5

97.5% centile

6.50

7.76

1.125

16.9

Mean

5.08

6.08

0.924

15.3

2.5% centile

3.80

4.55

0.723

13.8

5% centile

4.00

4.79

0.755

14.0

50% centile

5.06

6.06

0.923

15.2

95% centile

6.23

7.45

1.098

16.8

97.5% centile

6.46

7.73

1.132

17.1

Puerto Rico
0.01

0.1

1

Mean

3.14

3.61

0.534

14.9

2.5% centile

1.39

1.60

0.241

13.2

5% centile

1.64

1.89

0.286

13.5

50% centile

3.10

3.57

0.531

14.8

95% centile

4.73

5.45

0.792

16.6

97.5% centile

5.04

5.81

0.839

17.0

Mean

3.22

3.71

0.550

14.9

2.5% centile

1.58

1.82

0.274

13.3

5% centile

1.81

2.09

0.316

13.5

50% centile

3.19

3.68

0.546

14.8

95% centile

4.72

5.45

0.792

16.6

97.5% centile

5.03

5.79

0.836

17.0

Mean

4.01

4.62

0.696

15.1

2.5% centile

3.04

3.50

0.548

13.5

5% centile

3.19

3.67

0.571

13.8

50% centile

3.99

4.60

0.696

15.0

95% centile

4.87

5.62

0.824

16.8

97.5% centile

5.05

5.82

0.848

17.3

United States
0.01

Mean

4.14

4.89

0.744

15.3

2.5% centile

1.84

2.17

0.337

13.8

5% centile

2.17

2.56

0.398

14.0

50% centile

4.09

4.84

0.739

15.2

95% centile

6.24

7.37

1.102

16.8

97.5% centile

6.64

7.85

1.168

17.1

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Test dose,
Dt (Sv)
0.1

3:39 pm

Mean/centile

Per cent excess cancer
deaths (Sv–1)

Per cent radiation-induced
cancer deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiationinduced cancer death (a)

Mean

4.24

5.02

0.765

15.3

2.5% centile

2.09

2.47

0.382

13.9

5% centile

2.39

2.83

0.440

14.1

50% centile

4.20

4.97

0.760

15.2

95% centile

6.22

7.35

1.101

16.8

97.5% centile

6.60

7.80

1.162

17.1

Mean

5.20

6.16

0.957

15.6

2.5% centile

3.96

4.70

0.756

14.2

5% centile

4.15

4.92

0.787

14.4

50% centile

5.18

6.14

0.957

15.5

95% centile

6.30

7.46

1.131

17.1

97.5% centile

6.52

7.71

1.165

17.4

United Kingdom
0.01

0.1

1

Mean

4.42

5.31

0.736

13.9

2.5% centile

1.96

2.35

0.334

12.5

5% centile

2.30

2.77

0.394

12.7

50% centile

4.36

5.25

0.731

13.9

95% centile

6.67

8.02

1.091

15.5

97.5% centile

7.11

8.54

1.156

15.9

Mean

4.53

5.45

0.757

14.0

2.5% centile

2.22

2.67

0.377

12.5

5% centile

2.54

3.06

0.436

12.7

50% centile

4.48

5.39

0.752

13.9

95% centile

6.65

7.99

1.090

15.5

97.5% centile

7.08

8.51

1.150

15.9

Mean

5.52

6.66

0.948

14.3

2.5% centile

4.18

5.04

0.749

12.9

5% centile

4.38

5.29

0.779

13.1

50% centile

5.50

6.64

0.947

14.2

95% centile

6.72

8.09

1.120

15.8

97.5% centile

6.96

8.38

1.153

16.2

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239

Table 65 Risk estimates for leukaemia mortality in various current populations, using generalized ERR and generalized
EAR models (models described in table 46)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom populations) from various models fitted to LSS mortality data [P10],
assuming 35% GSD errors
Model, modifying factors a

Test dose,
Dt (Sv)

Per cent excess
cancer deaths (Sv–1)

Per cent radiationinduced leukaemia
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
leukaemia death (a)China
ERR, D2, age b

ERR, D + D , age c
2

EAR, D , sex, years SE d
2

EAR, D + D2, sex, years SE e

0.01

0.00

0.00

0.002

36.9

0.1

0.04

0.04

0.016

36.9

1.0

0.42

0.42

0.155

36.9

0.01

0.27

0.27

0.104

38.8

0.1

0.29

0.30

0.114

38.8

1.0

0.57

0.57

0.222

38.8

0.01

0.01

0.01

0.002

30.8

0.1

0.07

0.07

0.022

30.8

1.0

0.70

0.70

0.217

30.8

0.01

0.42

0.42

0.128

30.5

0.1

0.46

0.46

0.140

30.5

1.0

0.84

0.84

0.257

30.5

Japan
ERR, D , age b
2

ERR, D + D2, age c

EAR, D2, sex, years SE d

EAR, D + D2, sex, years SE e

0.01

0.01

0.01

0.001

27.0

0.1

0.05

0.05

0.014

27.0

1.0

0.53

0.53

0.143

27.0

0.01

0.32

0.32

0.092

28.6

0.1

0.36

0.36

0.102

28.6

1.0

0.69

0.69

0.198

28.6

0.01

0.01

0.01

0.002

32.6

0.1

0.07

0.07

0.024

32.6

1.0

0.72

0.72

0.234

32.6

0.01

0.43

0.43

0.139

32.2

0.1

0.47

0.47

0.151

32.2

1.0

0.86

0.86

0.278

32.2

Puerto Rico
ERR, D2, age b

ERR, D + D2, age c

EAR, D2, sex, years SE d

0.01

0.01

0.01

0.001

20.2

0.1

0.06

0.06

0.012

20.2

1.0

0.58

0.58

0.118

20.3

0.01

0.35

0.35

0.075

21.6

0.1

0.38

0.39

0.083

21.6

1.0

0.74

0.75

0.161

21.6

0.01

0.01

0.01

0.002

31.3

0.1

0.07

0.07

0.022

31.3

1.0

0.69

0.69

0.217

31.3

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Model, modifying factors a

EAR, D + D2, sex, years SE e

Test dose,
Dt (Sv)

Per cent excess
cancer deaths (Sv–1)

Per cent radiationinduced leukaemia
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
leukaemia death (a)

0.01

0.41

0.41

0.128

30.9

0.1

0.45

0.45

0.140

30.9

1.0

0.83

0.83

0.257

31.0

United States
ERR, D2, age b

ERR, D + D2, age c

EAR, D2, sex, years SE d

EAR, D + D , sex, years SE e
2

0.01

0.01

0.01

0.001

18.8

0.1

0.08

0.08

0.015

18.8

1.0

0.78

0.79

0.149

18.9

0.01

0.47

0.47

0.093

19.7

0.1

0.52

0.52

0.102

19.7

1.0

1.00

1.01

0.199

19.7

0.01

0.01

0.01

0.002

31.7

0.1

0.07

0.07

0.023

31.7

1.0

0.70

0.71

0.224

31.7

0.01

0.42

0.42

0.133

31.4

0.1

0.46

0.46

0.145

31.4

1.0

0.84

0.85

0.266

31.4

United Kingdom
ERR, D2, age b

ERR, D + D2, age c

EAR, D , sex, years SE d
2

EAR, D + D , sex, years SE e
2

0.01

0.01

0.01

0.001

18.8

0.1

0.06

0.06

0.012

18.8

1.0

0.64

0.64

0.120

18.8

0.01

0.38

0.38

0.075

19.8

0.1

0.42

0.42

0.083

19.8

1.0

0.81

0.82

0.162

19.8

0.01

0.01

0.01

0.002

31.9

0.1

0.07

0.07

0.023

31.9

1.0

0.71

0.71

0.228

32.0

0.01

0.43

0.43

0.135

31.6

0.1

0.46

0.47

0.147

31.6

1.0

0.85

0.86

0.271

31.6

a ERR = generalized excess relative risk, EAR = generalized excess absoluterisk, years SE = years since exposure.
b ERR = β D2 aτ, as per model (17) with linear coefficient in dose, α, set to 0
(a = attained age).
c ERR = α[D+β D2] aτ, as per model (17) (a = attained age).

d EAR = β D2 [a – e]κ, as per model (18) (a = attained age, e = age ats

exposure, s = sex).
e EAR = α [D+β D2] [a – e]κ, as per model (18) with linear coefficient insdose, α, set to 0 (a = attained age, e = age at exposure).

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241

Table 66 Risk estimates for leukaemia mortality by sex for various current populations, assuming a test dose, Dt,of 0.1 Sv, using generalized ERR and generalized EAR models (models described in table 46)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom populations) from various models fitted to LSS mortality data [P10],
assuming 35% GSD errors
Model, modifying factors a

Sex

Per cent excess
leukaemia deaths(Sv–1)Per cent radiationinduced leukaemia
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
leukaemia death (a)China
ERR, D2, age b

ERR, D + D2, age c

EAR, D2, sex, years SE d

EAR, D + D2, sex, years SE e

Males

0.05

0.05

0.017

35.5

Females

0.04

0.04

0.014

38.9

Both

0.04

0.04

0.016

36.9

Males

0.34

0.34

0.126

37.3

Females

0.25

0.25

0.102

40.8

Both

0.29

0.30

0.114

38.8

Males

0.09

0.09

0.026

30.2

Females

0.05

0.05

0.017

31.8

Both

0.07

0.07

0.022

30.8

Males

0.57

0.57

0.170

29.9

Females

0.35

0.35

0.109

31.5

Both

0.46

0.46

0.140

30.5

Japan
ERR, D2, age b

ERR, D + D2, age c

EAR, D2, sex, years SE d

EAR, D + D , sex, years SE e
2

Males

0.06

0.06

0.016

25.4

Females

0.04

0.04

0.013

29.3

Both

0.05

0.05

0.014

27.0

Males

0.42

0.42

0.114

26.9

Females

0.29

0.29

0.090

31.0

Both

0.36

0.36

0.102

28.6

Males

0.09

0.09

0.028

31.6

Females

0.06

0.06

0.019

34.2

Both

0.07

0.07

0.024

32.6

Males

0.58

0.58

0.182

31.2

Females

0.36

0.36

0.121

33.7

Both

0.47

0.47

0.151

32.2

Puerto Rico
ERR, D2, age b

ERR, D + D , age c
2

EAR, D , sex, years SE d
2

EAR, D + D2, sex, years SE e

Males

0.51

0.07

20.2270.0

Females

0.34

0.06

20.261

0.0

Both

0.06

0.06

0.012

20.2

Males

0.51

0.45

21.656

0.0

Females

0.34

0.39

21.415

0.0

Both

0.38

0.39

0.083

21.6

Males

0.51

0.09

30.3200.0

Females

0.34

0.05

32.697

0.0

Both

0.07

0.07

0.022

31.3

Males

0.51

0.56

29.991

0.0

Females

0.34

0.35

32.316

0.0

Both

0.45

0.45

0.140

30.9

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242

UNSCEAR 2006 REPORT: VOLUME I

Model, modifying factors a

Sex

Per cent excess
leukaemia deaths(Sv–1)Per cent radiationinduced leukaemia
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
leukaemia death (a)United States
ERR, D2, age b

ERR, D + D2, age c

EAR, D2, sex, years SE d

EAR, D + D2, sex, years SE e

Males

0.09

0.09

0.017

18.2

Females

0.07

0.07

0.013

19.7

Both

0.08

0.08

0.015

18.8

Males

0.59

0.60

0.114

19.0

Females

0.44

0.44

0.091

20.6

Both

0.52

0.52

0.102

19.7

Males

0.09

0.09

0.027

31.0

Females

0.05

0.05

0.018

32.9

Both

0.07

0.07

0.023

31.7

Males

0.57

0.57

0.176

30.7

Females

0.35

0.35

0.114

32.5

Both

0.46

0.46

0.145

31.4

United Kingdom
ERR, D2, age b

ERR, D + D2, age c

EAR, D2, sex, years SE d

EAR, D + D , sex, years SE e
2

Males

0.08

0.08

0.014

18.4

Females

0.05

0.05

0.010

19.4

Both

0.06

0.06

0.012

18.8

Males

0.50

0.50

0.097

19.3

Females

0.34

0.34

0.069

20.4

Both

0.42

0.42

0.083

19.8

Males

0.09

0.09

0.028

31.4

Females

0.05

0.05

0.018

32.9

Both

0.07

0.07

0.023

31.9

Males

0.58

0.58

0.180

31.0

Females

0.35

0.35

0.115

32.5

Both

0.46

0.47

0.147

31.6

a ERR = generalized excess relative risk, EAR = generalized excess absoluterisk, years SE = years since exposure.
b ERR = β D2 aτ, as per model (17) with linear coefficient in dose, α, set to 0
(a = attained age).
c ERR = α[D+β D2] aτ, as per model (17) (a = attained age).

d EAR = β D2 [a – e]κ, as per model (18) (a = attained age, e = age ats

exposure, s = sex).
e EAR = α [D+β D2] [a – e]κ, as per model (18) with linear coefficient insdose, α, set to 0 (a = attained age, e = age at exposure).

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243

Table 67 Risk estimates for leukaemia mortality by age-at-exposure group in various current populations, assuming
a test dose, Dt, of 0.1 Sv, using generalized ERR and generalized EAR models (models described in table 46)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom populations) from various models fitted to LSS mortality data [P10],
assuming 35% GSD errors

Model, modifying factors a

Age atexposure

Per cent excess
leukaemia deaths(Sv–1)Per cent radiationinduced leukaemia
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
leukaemia death (a)China
ERR, D , age b
2

ERR, D + D2, age c

EAR, D , sex, years SE d
2

EAR, D + D2, sex, years SE e

0–9

0.13

0.13

0.071

53.0

10–19

0.06

0.06

0.024

37.5

20–29

0.04

0.04

0.011

27.4

30–39

0.03

0.03

0.007

21.2

40–49

0.02

0.02

0.004

16.4

50–59

0.02

0.02

0.002

12.4

60–69

0.01

0.01

0.001

9.4

70+

0.01

0.01

0.000

6.7

All ages

0.04

0.04

0.016

36.9

0–9

0.99

0.99

0.542

54.6

10–19

0.43

0.43

0.166

38.4

20–29

0.27

0.27

0.076

27.9

30–39

0.20

0.20

0.043

21.5

40–49

0.15

0.15

0.025

16.5

50–59

0.11

0.11

0.014

12.4

60–69

0.08

0.08

0.008

9.5

70+

0.04

0.04

0.003

6.7

All ages

0.29

0.30

0.114

38.8

0–9

0.10

0.10

0.049

47.1

10–19

0.10

0.10

0.039

40.4

20–29

0.09

0.09

0.030

33.9

30–39

0.08

0.08

0.022

27.5

40–49

0.07

0.07

0.015

21.5

50–59

0.06

0.06

0.009

15.8

60–69

0.04

0.04

0.005

11.0

70+

0.02

0.02

0.002

7.0

All ages

0.07

0.07

0.022

30.8

0–9

0.68

0.68

0.315

46.2

10–19

0.63

0.63

0.251

39.7

20–29

0.57

0.57

0.191

33.3

30–39

0.51

0.51

0.138

27.1

40–49

0.44

0.44

0.093

21.1

50–59

0.36

0.36

0.056

15.6

60–69

0.27

0.27

0.029

10.9

70+

0.15

0.15

0.010

6.9

All ages

0.46

0.46

0.140

30.5

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UNSCEAR 2006 REPORT: VOLUME I

Model, modifying factors a

Age atexposure

Per cent excess
leukaemia deaths(Sv–1)Per cent radiationinduced leukaemia
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
leukaemia death (a)Japan
ERR, D2, age b

ERR, D + D , age c
2

EAR, D2, sex, years SE d

EAR, D + D2, sex, years SE e

0–9

0.12

0.12

0.054

46.1

10–19

0.07

0.07

0.022

30.1

20–29

0.06

0.06

0.014

23.0

30–39

0.05

0.05

0.010

19.2

40–49

0.05

0.05

0.007

16.1

50–59

0.04

0.04

0.005

13.5

60–69

0.03

0.03

0.003

10.7

70+

0.02

0.02

0.001

7.6

All ages

0.05

0.05

0.014

27.0

0–9

0.84

0.85

0.410

48.4

10–19

0.48

0.49

0.151

31.0

20–29

0.39

0.39

0.091

23.5

30–39

0.34

0.34

0.066

19.5

40–49

0.29

0.30

0.048

16.3

50–59

0.25

0.25

0.034

13.6

60–69

0.20

0.20

0.021

10.7

70+

0.10

0.10

0.008

7.7

All ages

0.36

0.36

0.102

28.6

0–9

0.11

0.11

0.053

50.2

10–19

0.10

0.10

0.043

43.5

20–29

0.09

0.09

0.034

36.8

30–39

0.08

0.08

0.025

30.4

40–49

0.07

0.07

0.018

24.2

50–59

0.06

0.06

0.011

18.4

60–69

0.05

0.05

0.006

13.2

70+

0.03

0.03

0.002

8.2

All ages

0.07

0.07

0.024

32.6

0–9

0.70

0.70

0.345

49.2

10–19

0.65

0.65

0.278

42.6

20–29

0.60

0.60

0.216

36.2

30–39

0.54

0.54

0.160

29.8

40–49

0.47

0.47

0.112

23.8

50–59

0.39

0.39

0.071

18.1

60–69

0.31

0.31

0.040

13.0

70+

0.17

0.17

0.014

8.2

All ages

0.47

0.47

0.151

32.2

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Model, modifying factors a

Age atexposure

Per cent excess
leukaemia deaths(Sv–1)

245

Per cent radiationinduced leukaemia
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
leukaemia death (a)Puerto Rico
ERR, D2, age b

ERR, D + D , age c
2

EAR, D2, sex, years SE d

EAR, D + D2, sex, years SE e

0–9

0.10

0.10

0.040

38.0

10–19

0.07

0.07

0.015

22.3

20–29

0.06

0.06

0.011

17.9

30–39

0.05

0.06

0.008

15.0

40–49

0.05

0.05

0.007

12.7

50–59

0.05

0.05

0.005

11.1

60–69

0.04

0.04

0.004

9.1

70+

0.03

0.03

0.002

7.1

All ages

0.06

0.06

0.012

20.2

0–9

0.74

0.74

0.300

40.5

10–19

0.44

0.44

0.101

23.0

20–29

0.38

0.39

0.071

18.3

30–39

0.36

0.36

0.054

15.2

40–49

0.33

0.33

0.043

12.8

50–59

0.31

0.31

0.035

11.1

60–69

0.28

0.28

0.026

9.1

70+

0.20

0.20

0.014

7.1

All ages

0.38

0.39

0.083

21.6

0–9

0.10

0.10

0.047

47.0

10–19

0.09

0.09

0.038

40.6

20–29

0.08

0.09

0.029

34.4

30–39

0.08

0.08

0.022

28.5

40–49

0.07

0.07

0.015

22.8

50–59

0.06

0.06

0.010

17.3

60–69

0.04

0.04

0.006

12.6

70+

0.03

0.03

0.002

7.9

All ages

0.07

0.07

0.022

31.3

0–9

0.66

0.67

0.307

46.1

10–19

0.61

0.61

0.245

39.8

20–29

0.55

0.56

0.188

33.8

30–39

0.50

0.50

0.140

28.0

40–49

0.43

0.44

0.098

22.4

50–59

0.36

0.36

0.062

17.1

60–69

0.28

0.28

0.035

12.4

70+

0.16

0.17

0.013

7.9

All ages

0.45

0.45

0.140

30.9

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UNSCEAR 2006 REPORT: VOLUME I

Model, modifying factors a

Age atexposure

Per cent excess
leukaemia deaths(Sv–1)Per cent radiationinduced leukaemia
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
leukaemia death (a)United States
ERR, D2, age b

ERR, D + D , age c
2

EAR, D2, sex, years SE d

EAR, D + D2, sex, years SE e

0–9

0.12

0.12

0.040

32.3

10–19

0.10

0.10

0.022

22.4

20–29

0.09

0.09

0.016

18.1

30–39

0.08

0.08

0.013

15.8

40–49

0.08

0.08

0.011

13.9

50–59

0.07

0.07

0.009

12.1

60–69

0.06

0.06

0.006

10.0

70+

0.04

0.04

0.003

7.4

All ages

0.08

0.08

0.015

18.8

0–9

0.85

0.85

0.293

34.4

10–19

0.64

0.64

0.148

23.1

20–29

0.56

0.57

0.105

18.4

30–39

0.53

0.53

0.084

16.0

40–49

0.49

0.49

0.069

14.0

50–59

0.45

0.45

0.055

12.1

60–69

0.39

0.39

0.039

10.0

70+

0.23

0.23

0.017

7.4

All ages

0.52

0.52

0.102

19.7

0–9

0.10

0.10

0.050

48.4

10–19

0.10

0.10

0.040

41.7

20–29

0.09

0.09

0.031

35.3

30–39

0.08

0.08

0.023

29.0

40–49

0.07

0.07

0.016

23.0

50–59

0.06

0.06

0.010

17.4

60–69

0.05

0.05

0.006

12.5

70+

0.03

0.03

0.002

7.9

All ages

0.07

0.07

0.023

31.7

0–9

0.68

0.68

0.324

47.4

10–19

0.63

0.63

0.259

40.9

20–29

0.57

0.58

0.200

34.6

30–39

0.51

0.52

0.147

28.5

40–49

0.45

0.45

0.101

22.6

50–59

0.37

0.37

0.064

17.2

60–69

0.29

0.29

0.036

12.4

70+

0.17

0.17

0.013

7.9

All ages

0.46

0.46

0.145

31.4

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Model, modifying factors a

Age atexposure

Per cent excess
leukaemia deaths(Sv–1)

247

Per cent radiationinduced leukaemia
deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiation-induced
leukaemia death (a)United Kingdom
ERR, D2, age b

ERR, D + D2, age c

EAR, D , sex, years SE d
2

EAR, D + D2, sex, years SE e

0–9

0.11

0.11

0.036

34.2

10–19

0.08

0.08

0.017

21.9

20–29

0.07

0.07

0.012

17.2

30–39

0.07

0.07

0.010

15.0

40–49

0.06

0.06

0.008

13.2

50–59

0.06

0.06

0.007

11.4

60–69

0.05

0.05

0.005

9.3

70+

0.03

0.03

0.002

6.3

All ages

0.06

0.06

0.012

18.8

0–9

0.73

0.74

0.270

36.7

10–19

0.52

0.52

0.118

22.7

20–29

0.45

0.46

0.080

17.5

30–39

0.43

0.43

0.065

15.2

40–49

0.40

0.40

0.053

13.3

50–59

0.37

0.37

0.042

11.5

60–69

0.31

0.31

0.029

9.3

70+

0.17

0.17

0.011

6.3

All ages

0.42

0.42

0.083

19.8

0–9

0.10

0.10

0.052

49.2

10–19

0.10

0.10

0.042

42.5

20–29

0.09

0.09

0.032

35.9

30–39

0.08

0.08

0.024

29.4

40–49

0.07

0.07

0.017

23.2

50–59

0.06

0.06

0.010

17.4

60–69

0.05

0.05

0.006

12.1

70+

0.03

0.03

0.002

7.1

All ages

0.07

0.07

0.023

31.9

0–9

0.69

0.70

0.335

48.2

10–19

0.64

0.65

0.269

41.6

20–29

0.59

0.59

0.208

35.2

30–39

0.53

0.53

0.153

28.9

40–49

0.46

0.46

0.105

22.8

50–59

0.38

0.38

0.065

17.1

60–69

0.29

0.29

0.035

12.0

70+

0.16

0.16

0.011

7.0

All ages

0.46

0.47

0.147

31.6

a ERR = generalized excess relative risk, EAR = generalized excess absoluterisk, years SE = years since exposure.
b ERR = β D2 aτ, as per model (17) with linear coefficient in dose, α, set to 0
(a = attained age).
c ERR = α[D+β D2] aτ, as per model (17) (a = attained age).

d EAR = β D2 [a – e]κ, as per model (18) (a = attained age, e = age ats

exposure, s = sex).
e EAR = α [D+β D2] [a – e]κ, as per model (18) with linear coefficient insdose, α, set to 0 (a = attained age, e = age at exposure).

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Table 68 Distribution of leukaemia mortality risk estimates for various current populations, using generalized
linear–quadratic–exponential ERR models fitted by Bayesian MCMC (models described in appendix E)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom populations) from various models fitted to LSS mortality data [P10],
assuming 35% GSD errors
Test dose,
Dt (Sv)Mean/centile

Per cent excess
leukaemia deaths (Sv–1)

Per cent radiation-induced
leukaemia deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiationinduced leukaemia death (a)

China
0.01

0.1

1

Mean

0.01

0.01

0.014

38.7

2.5% centile

–0.39

–0.39

–0.13326.5

5% centile

–0.34

–0.34

–0.11927.8

50% centile

–0.04

–0.04

–0.01438.0

95% centile

0.53

0.53

0.226

52.3

97.5% centile

0.65

0.65

0.312

55.1

Mean

0.14

0.14

0.066

38.5

2.5% centile

–0.20

–0.20

–0.05826.4

5% centile

–0.15

–0.15

–0.04827.7

50% centile

0.09

0.09

0.034

37.5

95% centile

0.60

0.60

0.277

52.8

97.5% centile

0.74

0.74

0.373

55.7

Mean

0.88

0.88

0.360

38.8

2.5% centile

0.49

0.49

0.149

26.5

5% centile

0.53

0.53

0.164

27.8

50% centile

0.80

0.80

0.297

38.0

95% centile

1.48

1.48

0.767

52.7

97.5% centile

1.77

1.77

0.958

55.5

Japan
0.01

0.1

1

Mean

–0.01

–0.01

0.008

29.4

2.5% centile

–0.56

–0.56

–0.12919.6

5% centile

–0.47

–0.47

–0.11320.4

50% centile

–0.04

–0.04

–0.01327.9

95% centile

0.59

0.59

0.188

44.0

97.5% centile

0.72

0.72

0.240

48.1

Mean

0.15

0.15

0.053

29.3

2.5% centile

–0.29

–0.29

–0.06319.5

5% centile

–0.22

–0.22

–0.05020.3

50% centile

0.11

0.11

0.032

27.6

95% centile

0.65

0.65

0.219

44.5

97.5% centile

0.77

0.77

0.281

48.9

Mean

1.03

1.03

0.312

29.6

2.5% centile

0.65

0.65

0.160

19.6

5% centile

0.70

0.70

0.174

20.5

50% centile

1.00

1.00

0.273

28.0

95% centile

1.44

1.44

0.577

44.6

97.5% centile

1.58

1.58

0.708

48.8

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

Test dose,
Dt (Sv)Mean/centile

Per cent excess
leukaemia deaths (Sv–1)

Per cent radiation-induced
leukaemia deaths (Sv–1)

249

Years life lost(a Sv–1)

Years life lost/radiationinduced leukaemia death (a)Puerto Rico
0.01

0.1

1

Mean

–0.01

–0.01

0.006

22.6

2.5% centile

–0.64

–0.64

–0.10914.5

5% centile

–0.53

–0.53

–0.09415.1

50% centile

–0.05

–0.05

–0.01121.0

95% centile

0.63

0.63

0.153

36.0

97.5% centile

0.76

0.76

0.195

40.2

Mean

0.16

0.16

0.043

22.5

2.5% centile

–0.33

–0.34

–0.05314.5

5% centile

–0.25

–0.25

–0.041

15.1

50% centile

0.12

0.12

0.026

20.7

95% centile

0.69

0.69

0.178

36.5

97.5% centile

0.82

0.82

0.227

40.9

Mean

1.10

1.11

0.256

22.8

2.5% centile

0.70

0.70

0.136

14.6

5% centile

0.76

0.76

0.147

15.2

50% centile

1.08

1.09

0.225

21.1

95% centile

1.53

1.54

0.464

36.5

97.5% centile

1.64

1.65

0.569

40.6

United States
0.01

0.1

1

Mean

–0.03

–0.03

0.002

20.5

2.5% centile

–0.89

–0.89

–0.14714.9

5% centile

–0.73

–0.73

–0.12415.3

50% centile

–0.06

–0.06

–0.01319.4

95% centile

0.81

0.82

0.176

29.8

97.5% centile

0.98

0.98

0.212

33.1

Mean

0.20

0.20

0.046

20.5

2.5% centile

–0.47

–0.47

–0.07514.9

5% centile

–0.35

–0.35

–0.05815.3

50% centile

0.16

0.16

0.032

19.2

95% centile

0.89

0.89

0.199

30.1

97.5% centile

1.05

1.06

0.235

33.7

Mean

1.46

1.46

0.301

20.7

2.5% centile

0.92

0.93

0.180

15.0

5% centile

0.99

1.00

0.194

15.4

50% centile

1.43

1.44

0.283

19.5

95% centile

2.01

2.02

0.461

30.3

97.5% centile

2.14

2.15

0.535

33.8

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Test dose,
Dt (Sv)Mean/centile

Per cent excess
leukaemia deaths (Sv–1)

Per cent radiation-induced
leukaemia deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiationinduced leukaemia death (a)United Kingdom
0.01

0.1

1

Mean

–0.02

–0.02

0.003

20.8

2.5% centile

–0.72

–0.72

–0.11614.3

5% centile

–0.59

–0.59

–0.09814.8

50% centile

–0.05

–0.05

–0.01019.4

95% centile

0.66

0.67

0.148

31.8

97.5% centile

0.80

0.81

0.181

35.8

Mean

0.16

0.16

0.040

20.7

2.5% centile

–0.38

–0.38

–0.05814.3

5% centile

–0.28

–0.28

–0.04514.7

50% centile

0.13

0.13

0.026

19.2

95% centile

0.73

0.73

0.168

32.3

97.5% centile

0.87

0.87

0.203

36.7

Mean

1.19

1.20

0.250

21.0

2.5% centile

0.76

0.76

0.144

14.3

5% centile

0.81

0.82

0.155

14.8

50% centile

1.17

1.17

0.229

19.5

95% centile

1.64

1.65

0.410

32.5

97.5% centile

1.75

1.76

0.492

36.8

Table 69 Distribution of leukaemia mortality risk estimates for various current populations, using generalized
linear–quadratic ERR models fitted by Bayesian MCMC (models described in appendix E)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current Chinese,
Japanese, Puerto Rican, United States and United Kingdom populations) from various models fitted to LSS mortality data [P10],
assuming 35% GSD errors
Test dose,
Dt (Sv)Mean/centile

Per cent excess
leukaemia deaths (Sv–1)

Per cent radiation-induced
leukaemia deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiationinduced leukaemia death (a)

China
0.01

0.1

Mean

0.37

0.37

0.167

40.8

2.5% centile

–0.01

–0.01

–0.00427.4

5% centile

0.03

0.03

0.011

28.9

50% centile

0.31

0.31

0.122

40.0

95% centile

0.87

0.87

0.453

55.3

97.5% centile

1.11

1.11

0.612

58.2

Mean

0.41

0.42

0.185

40.7

2.5% centile

0.04

0.04

0.014

27.4

5% centile

0.08

0.08

0.027

28.9

50% centile

0.35

0.35

0.137

40.0

95% centile

0.92

0.92

0.481

55.3

97.5% centile

1.16

1.17

0.649

58.2

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Test dose,
Dt (Sv)
1

251

Per cent excess
leukaemia deaths (Sv–1)

Per cent radiation-induced
leukaemia deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiationinduced leukaemia death (a)

Mean

0.81

0.82

0.354

40.7

2.5% centile

0.43

0.43

0.132

27.4

5% centile

0.47

0.47

0.147

28.9

50% centile

0.72

0.72

0.285

40.0

95% centile

1.47

1.47

0.789

54.9

97.5% centile

1.79

1.79

1.019

57.7

Mean/centile

Japan
0.01

0.1

1

Mean

0.40

0.41

0.137

31.5

2.5% centile

–0.01

–0.01

–0.00420.1

5% centile

0.04

0.04

0.010

21.1

50% centile

0.37

0.37

0.109

29.7

95% centile

0.86

0.86

0.337

48.3

97.5% centile

0.99

0.99

0.440

52.9

Mean

0.45

0.45

0.151

31.5

2.5% centile

0.05

0.05

0.014

20.1

5% centile

0.10

0.10

0.027

21.1

50% centile

0.41

0.41

0.123

29.7

95% centile

0.89

0.89

0.356

48.3

97.5% centile

1.02

1.02

0.469

52.9

Mean

0.90

0.91

0.296

31.5

2.5% centile

0.56

0.57

0.141

20.2

5% centile

0.60

0.60

0.153

21.1

50% centile

0.86

0.86

0.251

29.7

95% centile

1.31

1.31

0.584

47.9

97.5% centile

1.49

1.49

0.740

52.4

Puerto Rico
0.01

0.1

1

Mean

0.43

0.43

0.111

24.5

2.5% centile

–0.01

–0.01

–0.00314.9

5% centile

0.04

0.04

0.009

15.6

50% centile

0.39

0.39

0.090

22.6

95% centile

0.89

0.90

0.272

40.2

97.5% centile

1.02

1.02

0.357

45.0

Mean

0.48

0.48

0.124

24.5

2.5% centile

0.05

0.05

0.011

14.9

5% centile

0.11

0.11

0.022

15.6

50% centile

0.44

0.44

0.101

22.6

95% centile

0.92

0.93

0.288

40.2

97.5% centile

1.05

1.05

0.380

45.0

Mean

0.96

0.96

0.241

24.4

2.5% centile

0.61

0.61

0.119

14.9

5% centile

0.65

0.65

0.129

15.7

50% centile

0.92

0.93

0.205

22.6

95% centile

1.35

1.35

0.470

39.7

97.5% centile

1.49

1.50

0.594

44.3

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Test dose,
Dt (Sv)Mean/centile

Per cent excess
leukaemia deaths (Sv–1)

Per cent radiation-induced
leukaemia deaths (Sv–1)

Years life lost(a Sv–1)

Years life lost/radiationinduced leukaemia death (a)United States
0.01

0.1

1

Mean

0.55

0.55

0.122

21.8

2.5% centile

–0.02

–0.02

–0.00415.2

5% centile

0.05

0.05

0.011

15.7

50% centile

0.51

0.51

0.107

20.4

95% centile

1.13

1.14

0.271

33.1

97.5% centile

1.27

1.28

0.325

37.4

Mean

0.61

0.61

0.136

21.8

2.5% centile

0.07

0.07

0.014

15.2

5% centile

0.14

0.14

0.028

15.7

50% centile

0.58

0.58

0.121

20.4

95% centile

1.17

1.18

0.282

33.1

97.5% centile

1.31

1.32

0.341

37.4

Mean

1.24

1.24

0.273

21.9

2.5% centile

0.79

0.80

0.157

15.2

5% centile

0.85

0.85

0.169

15.8

50% centile

1.21

1.22

0.250

20.4

95% centile

1.72

1.72

0.444

33.0

97.5% centile

1.83

1.84

0.535

37.3

United Kingdom
0.01

0.1

1

Mean

0.45

0.45

0.105

22.4

2.5% centile

–0.02

–0.02

–0.00314.6

5% centile

0.04

0.04

0.009

15.2

50% centile

0.42

0.42

0.089

20.6

95% centile

0.93

0.93

0.238

35.9

97.5% centile

1.05

1.05

0.301

41.0

Mean

0.50

0.50

0.116

22.4

2.5% centile

0.06

0.06

0.012

14.6

5% centile

0.11

0.11

0.023

15.2

50% centile

0.47

0.47

0.100

20.6

95% centile

0.97

0.97

0.251

36.0

97.5% centile

1.08

1.09

0.317

41.0

Mean

1.02

1.02

0.231

22.4

2.5% centile

0.65

0.65

0.126

14.6

5% centile

0.70

0.70

0.135

15.2

50% centile

0.99

1.00

0.205

20.6

95% centile

1.41

1.42

0.406

35.8

97.5% centile

1.52

1.53

0.505

40.7

UNSCEAR REPORT-PART 4.qxp

Table 70 Risk estimates for solid cancer incidence (per cent exposure-induced cancer incidence (REIC)) for various current populations, using generalized ERR andgeneralized EAR models (models described in tables 47–58)
Risk estimates are calculated for a population in equilibrium (underlying cancer incidence and mortality rates, and population structure of current Chinese, Japanese, Puerto Rican, UnitedStates and United Kingdom populations) from various models fitted to LSS incidence data [P48]. Risks are given as per cent per sievert and are assumed to result from acute exposure
Model type

Oesophagus

Stomach

Colon

Liver

Lung

Bone

NMSC

FemalebreastBladderBrain andCNS

Thyroid

All othersolid

Solid total

China
0.93

0.87

0.68

2.63

0.01

0.00

1.42

0.49

0.34

0.40

1.22

9.46

0.1

0.46

0.93

0.87

0.68

2.61

0.13

0.02

1.41

0.49

0.34

0.40

1.21

9.56

1.0

0.44

0.88

0.83

0.64

2.43

1.19

0.17

1.35

0.46

0.30

0.39

1.13

10.21

0.01

0.05

2.25

1.36

0.62

1.73

0.00

0.01

1.38

0.65

0.17

0.82

1.67

10.72

0.1

0.05

2.23

1.34

0.62

1.71

0.00

0.07

1.36

0.64

0.17

0.82

1.65

10.66

1.0

0.05

2.05

1.13

0.56

1.53

0.03

0.46

1.20

0.57

0.16

0.76

1.44

9.94

Japan
ERR

EAR

0.01

0.44

1.71

1.37

1.51

3.22

0.01

0.00

1.51

0.84

0.18

0.39

1.82

13.01

0.1

0.44

1.70

1.37

1.50

3.19

0.13

0.03

1.50

0.84

0.18

0.38

1.81

13.08

1.0

0.41

1.61

1.30

1.42

2.95

1.17

0.23

1.44

0.78

0.16

0.36

1.66

13.49

0.01

0.05

2.60

1.71

0.79

2.36

0.00

0.01

1.64

0.96

0.18

0.88

1.96

13.16

0.1

0.05

2.58

1.67

0.78

2.33

0.00

0.08

1.62

0.94

0.18

0.87

1.93

13.05

1.0

0.05

2.34

1.36

0.70

2.02

0.03

0.55

1.39

0.82

0.17

0.80

1.65

11.88

Puerto Rico
ERR

EAR

0.01

0.38

0.38

1.12

0.23

1.09

0.01

0.00

2.79

0.88

0.23

0.54

2.76

10.40

0.1

0.37

0.38

1.11

0.23

1.08

0.13

0.01

2.77

0.88

0.23

0.54

2.74

10.47

1.0

0.36

0.36

1.07

0.22

1.03

1.19

0.04

2.62

0.83

0.21

0.52

2.51

10.98

0.01

0.05

2.40

1.49

0.70

2.04

0.00

0.01

1.51

0.78

0.17

0.86

1.68

11.69

0.1

0.05

2.38

1.46

0.69

2.01

0.00

0.07

1.49

0.77

0.17

0.86

1.66

11.61

1.0

0.05

2.17

1.21

0.62

1.77

0.03

0.49

1.30

0.67

0.16

0.79

1.43

10.69

United States
ERR

0.01

0.23

0.20

1.74

0.18

4.41

0.02

0.36

6.38

1.84

0.32

1.18

4.08

20.95

0.1

0.23

0.20

1.73

0.18

4.36

0.21

3.49

6.30

1.83

0.31

1.17

4.04

24.06

1.0

0.21

0.19

1.64

0.17

3.94

1.80

16.75

5.59

1.68

0.28

1.11

3.62

37.00

Page 253

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

ERR

10/7/08

Test dose
(Sv)

253

Oesophagus

Stomach

Colon

Liver

Lung

Bone

NMSC

FemalebreastBladderBrain andCNS

Thyroid

All othersolid

Solid total

0.01

0.05

2.49

1.52

0.73

2.02

0.00

0.01

1.41

0.81

0.17

0.86

1.65

11.71

0.1

0.05

2.47

1.49

0.72

2.00

0.00

0.06

1.39

0.79

0.17

0.85

1.63

11.62

1.0

0.05

2.25

1.24

0.65

1.76

0.03

0.41

1.21

0.69

0.16

0.78

1.41

10.64

ERR

0.33

1.33

0.14

3.49

0.02

0.13

4.48

1.42

0.35

0.30

3.17

15.68

0.1

0.53

0.32

1.32

0.14

3.46

0.16

1.26

4.44

1.41

0.35

0.30

3.14

16.83

1.0

0.50

0.30

1.25

0.13

3.15

1.44

7.65

4.01

1.30

0.30

0.28

2.81

23.12

0.01

0.05

2.50

1.53

0.72

1.99

0.00

0.01

1.43

0.76

0.18

0.86

1.74

11.77

0.1

0.05

2.48

1.50

0.71

1.96

0.00

0.07

1.41

0.75

0.18

0.85

1.72

11.70

1.0

0.05

2.27

1.26

0.65

1.75

0.03

0.48

1.24

0.67

0.17

0.78

1.49

10.85

Model type

Sex

Oesophagus

Stomach

Colon

Liver

Lung

Bone

NMSC

FemalebreastBladderBrain andCNS

Thyroid

All othersolid

Solid total

China
ERR

EAR

Males

0.55

1.13

0.84

0.83

1.63

0.19

0.02

0.00

0.69

0.30

0.18

1.14

7.50

Females

0.37

0.71

0.90

0.52

3.62

0.08

0.03

2.88

0.29

0.37

0.64

1.29

11.70

Both

0.46

0.93

0.87

0.68

2.61

0.13

0.02

1.41

0.49

0.34

0.40

1.21

9.56

Males

0.05

2.07

1.21

0.55

1.16

0.00

0.06

0.00

0.54

0.16

0.33

1.54

7.66

Females

0.05

2.40

1.47

0.69

2.28

0.00

0.07

2.78

0.75

0.17

1.33

1.77

13.78

Both

0.05

2.23

1.34

0.62

1.71

0.00

0.07

1.36

0.64

0.17

0.82

1.65

10.66

UNSCEAR 2006 REPORT: VOLUME I

Table 71 Risk estimates for solid cancer incidence (per cent exposure-induced cancer incidence (REIC)) by sex for various current populations, assuming a test dose,Dt, of 0.1 Sv, using generalized ERR and generalized EAR models (models described in tables 47–58)
Risk estimates are calculated for a population in equilibrium (underlying mortality rates and population structure of current Chinese, Japanese, Puerto Rican, United States and United
Kingdom populations) from various models fitted to LSS mortality data [P48]. Risks are given as per cent per sievert and are assumed to result from acute exposure

Page 254

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United Kingdom

UNSCEAR REPORT-PART 4.qxp

EAR

Test dose
(Sv)

254

Model type

Sex

Oesophagus

Stomach

Colon

Liver

Lung

Bone

NMSC

FemalebreastBladderBrain andCNS

Thyroid

All othersolid

Solid total

UNSCEAR REPORT-PART 4.qxp

Model type

Japan
ERR

1.57

2.13

2.30

0.15

0.03

0.00

1.27

0.18

0.18

2.04

12.85

Females

0.18

1.13

1.17

0.89

4.06

0.11

0.03

2.96

0.42

0.18

0.58

1.59

13.29

Both

0.44

1.70

1.37

1.50

3.19

0.13

0.03

1.50

0.84

0.18

0.38

1.81

13.08

Males

0.05

2.27

1.42

0.64

1.42

0.00

0.07

0.00

0.71

0.17

0.34

1.71

8.82

Females

0.06

2.87

1.91

0.92

3.21

0.00

0.10

3.19

1.17

0.19

1.39

2.15

17.15

Both

0.05

2.58

1.67

0.78

2.33

0.00

0.08

1.62

0.94

0.18

0.87

1.93

13.05

Puerto Rico
ERR

0.56

0.49

1.13

0.27

0.71

0.19

0.01

0.00

1.27

0.24

0.21

3.98

9.06

Females

0.20

0.27

1.10

0.19

1.43

0.08

0.00

5.33

0.51

0.22

0.84

1.59

11.76

Both

0.37

0.38

1.11

0.23

1.08

0.13

0.01

2.77

0.88

0.23

0.54

2.74

10.47

Males

0.05

2.14

1.27

0.60

1.31

0.00

0.06

0.00

0.63

0.16

0.33

1.40

7.96

Females

0.05

2.61

1.63

0.78

2.65

0.00

0.08

2.87

0.89

0.18

1.35

1.90

14.99

Both

0.05

2.38

1.46

0.69

2.01

0.00

0.07

1.49

0.77

0.17

0.86

1.66

11.61

United States
ERR

EAR

Males

0.35

0.26

1.82

0.25

2.02

0.24

4.19

0.00

2.75

0.37

0.57

5.31

18.12

Females

0.11

0.15

1.65

0.12

6.65

0.18

2.81

12.42

0.93

0.26

1.76

2.81

29.84

Both

0.23

0.20

1.73

0.18

4.36

0.21

3.49

6.30

1.83

0.31

1.17

4.04

24.06

Males

0.05

2.27

1.33

0.63

1.34

0.00

0.05

0.00

0.65

0.17

0.33

1.41

8.25

Females

0.05

2.66

1.65

0.80

2.64

0.00

0.07

2.74

0.93

0.18

1.35

1.83

14.90

Both

0.05

2.47

1.49

0.72

2.00

0.00

0.06

1.39

0.79

0.17

0.85

1.63

11.62

United Kingdom
ERR

EAR

Males

0.68

0.44

1.44

0.16

2.00

0.19

1.43

0.00

2.08

0.41

0.16

3.87

12.85

Females

0.39

0.21

1.21

0.11

4.93

0.14

1.09

8.88

0.73

0.29

0.44

2.40

20.80

Both

0.53

0.32

1.32

0.14

3.46

0.16

1.26

4.44

1.41

0.35

0.30

3.14

16.83

Males

0.05

2.33

1.38

0.65

1.36

0.00

0.07

0.00

0.65

0.17

0.34

1.57

8.56

Females

0.05

2.64

1.63

0.78

2.57

0.00

0.08

2.83

0.86

0.18

1.36

1.86

14.84

Both

0.05

2.48

1.50

0.71

1.96

0.00

0.07

1.41

0.75

0.18

0.85

1.72

11.70

Page 255

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Males

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ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

0.70

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EAR

Males

255

Reference

Population

Test dose,
Dt (Sv)

Excess cancer mortality(% Sv–1)

Radiation-induced cancer mortality(% Sv–1)

Years life lost(a Sv–1)

Years life lost per radiationinduced cancer death (a)SolidPresent report

Japan

0.01

3.90 a, b, 4.12 c, b

4.65 a, b, 4.90 c, b

0.69 a, b, 0.73 c, b

14.8 a, b, 14.9 c, b

0.1

3.98 a, b, 4.25 c, b

4.75 a, b, 5.05 c, b
3.00 (–0.53, 6.39) d, f

0.71 a, b, 0.75 c, b
0.45 (–0.08, 0.94) d, f

14.8 a, b, 14.9 c, b
15.1 (13.8, 16.6) d, f

4.94 (2.75, 7.29) f, p

0.74 (0.42, 1.06) f, p

15.0 (13.7, 16.5) f, p

2.51 (–0.45, 5.34) d, f
4.14 (2.31, 6.10) f, p

0.85 a, b, 0.98 c, b

15.1 a, b, 15.2 c, b

0.01

4.01 a, b, 3.74 c, b

4.74 a, b, 4.41 c, b

0.72 a, b, 0.64 c, b

15.2 a, b, 14.6 c, b

0.1

4.10 a, b, 3.86 c, b

4.84 a, b, 4.55 c, b
3.04 (–0.54, 6.45) d, f

0.74 a, b, 0.67 c, b
0.47 (–0.08, 0.98) d, f

15.2 a, b, 14.6 c, b
15.4 (14.1, 16.9) d, f

5.02 (2.83, 7.35) f, p

0.76 (0.44, 1.10) f, p

15.3 (14.1, 16.8) f, p

2.57 (–0.46, 5.45) d, f
4.24 (2.39, 6.22) f, p

United Kingdom[L17] aUnited Kingdom

1.0

4.86 a, b, 4.91 c, b

5.75 a, b, 5.80 c, b

0.89 a, b, 0.86 c, b

15.4 a, b, 14.9 c, b

0.01

4.29 a, b, 3.64 c, b

5.15 a, b, 4.40 c, b

0.71 a, b, 0.63 c, b

13.8 a, b, 14.4 c, b

0.1

4.38 a, b, 3.76 c, b
2.74 (–0.49, 5.82) d, f

5.26 a, b, 4.54 c, b
3.30 (–0.59, 7.01) d, f

0.73 a, b, 0.65 c, b
0.46 (–0.08, 0.97) d, f

13.8 a, b, 14.4 c, b
14.1 (12.8, 15.7) d, f

4.53 (2.54, 6.65) f, p

5.45 (3.06, 7.99) f, p

0.76 (0.44, 1.09) f, p

14.0 (12.7, 15.5) f, p

1.0

5.16 a, b, 4.80 c, b

6.21 a, b, 5.81 c, b

0.88 a, b, 0.85 c, b

14.1 a, b, 14.7 c, b

0.001
1.0

10.18 (7.99, 12.65) e
8.67 (7.06, 10.36) e

12.10 (9.46, 15.05) e
10.36 (8.41, 12.42) e

1.53 (1.20, 1.91) e
1.38 (1.11, 1.68) e

12.6 (12.2, 13.0) e
13.3 (12.8, 13.9) e[C35]

United States

0.1

6.95 (5.45, 9.34) f

–

–

–

[C37]

United States

0.1–

7.4 (3.7, 15.0) e, n

–

–

[I11]

United Kingdom

1.0–

8.95 g, 12.07 h

–

–

0.2

–

12.0 i, 8.0 j

1.34 i , 1.09 j

11.2 i , 13.6 j

1.0–

10.9 i, 7.5 j

1.26 i , 1.00 j

11.6 i , 13.3 j

Japan

1.0

7.6 k, l, 4.9 k, m

11.2 l, 7.4 m

1.05 k, l, 0.79 k, m

11.1 k, l, 12.8 k, m

United States

1.0–

12.5 l, a, 9.9 l, c, 9.3 m, a, 6.5 m, c

–

–

–

–

[U4]

[U2]

Japan

United Kingdom

1.0–

14.4 l, a, 12.6 l, c, 10.1 m, a, 7.9 m, c

[L50]

European Union/
United States

1.0

–

9.29

–

–

[L16]

United Kingdom

0.001

–

6.93, 13.79 o

1.04, 1.71 o

12.4, 15.0 oPage 2565.65 a, b, 6.40 c, b

UNSCEAR 2006 REPORT: VOLUME I

4.73 a, b, 5.38 c, b

3:39 pm

United States

1.0

10/7/08

Cancer typeUNSCEAR REPORT-PART 4.qxp

256

Table 72 Comparison of risk estimates for mortality due to solid cancers and to leukaemia derived in this report with those from various other studies

Population

Test dose,
Dt (Sv)

Excess cancer mortality(% Sv–1)

Radiation-induced cancer mortality(% Sv–1)

Years life lost(a Sv–1)

Years life lost per radiationinduced cancer death (a)Leukaemia

Present report

Japan

0.01

0.32 a, b, 0.43 c, b

0.32 a, b, 0.43 c, b

0.09 a, b, 0.14 c, b

28.6 a, b, 32.2 c, b

0.1

0.36 a, b, 0.47 c, b

0.36 a, b, 0.47 c, b
0.15 (–0.22, 0.65) d, f

0.10 a, b, 0.15 c, b
0.05 (–0.05, 0.22) d, f

28.6 a, b, 32.2 c, b
29.3 (20.3, 44.5) d, f

0.45 (0.10, 0.89) f, p

0.15 (0.03, 0.36) f, p

31.5 (21.1, 48.3) f, p

0.15 (–0.22, 0.65) d, f
0.45 (0.10, 0.89) f, p

0.69 a, b, 0.86 c, b

0.20 a, b, 0.28 c, b

28.6 a, b, 32.2 c, b

0.01

0.47 a, b, 0.42 c, b

0.47 a, b, 0.42 c, b

0.09 a, b, 0.13 c, b

19.7 a, b, 31.4 c, b

0.1

0.52 a, b, 0.46 c, b

0.52 a, b, 0.46 c, b
0.20 (–0.35, 0.89) d, f

0.10 a, b, 0.14 c, b
0.05 (–0.06, 0.20) d, f

19.7 a, b, 31.4 c, b
20.5 (15.3, 30.1) d, f

0.61 (0.14, 1.18) f, p

0.14 (0.03, 0.28) f, p

21.8 (15.7, 33.1) f, p

0.20 (–0.35, 0.89) d, f
0.61 (0.14, 1.17) f, p

[L17] aUnited Kingdom

1.00 a, b, 0.84 c, b

1.01 a, b, 0.85 c, b

0.20 a, b, 0.27 c, b

19.7 a, b, 31.4 c, b

0.01

0.38 a, b, 0.43 c, b

0.38 a, b, 0.43 c, b

0.08 a, b, 0.13 c, b

19.8 a, b, 31.6 c, b

0.1

0.42 a, b, 0.46 c, b
0.16 (–0.28, 0.73) d, f

0.42 a, b, 0.47 c, b
0.16 (–0.28, 0.73) d, f

0.08 a, b, 0.15 c, b
0.04 (–0.05, 0.17) d, f

19.8 a, b, 31.6 c, b
20.7 (14.7, 32.3) d, f

0.50 (0.11, 0.97) f, p

0.50 (0.11, 0.97) f, p

0.12 (0.02, 0.25) f, p

22.4 (15.2, 36.0) f, p

1.0

0.81 a, b, 0.85 c, b

0.82 a, b, 0.86 c, b

0.16 a, b, 0.27 c, b

19.8 a, b, 31.6 c, b

0.001
1.0

0.84 (0.02, 2.04) e
1.93 (1.14, 3.37) e

0.84 (0.02, 2.04) e
1.93 (1.14, 3.38) e

0.19 (0.00, 0.53) e
0.44 (0.22, 0.94) e

22.3 (16.4, 32.2) e
22.5 (16.5, 32.7) e[C35]

United States

0.1

0.95 (0.56, 1.96) f

–

–

–

[C37]

United States

0.1

–

0.61

–

–

[I11]

United Kingdom

1.0–0.75 g, 0.83 h

–

–

[U4]

Japan

0.2

–

0.70

0.22

31

1.0

–

1.1

0.34

31

Japan

1.0

1.0 k

0.92

0.3 k

30.6 k

[U2]

[L50]

United States

1.0

–

1.19

–

–

United Kingdom

1.0

–

0.95

–

–

European Union/
United States

1.0–

0.91 (0.03, 2.33) f––

c Model with additive transport of risk, as described in section IV.B.1 of annex I of the UNSCEAR 2000 Report [U2].
d Based on Bayesian MCMC fit (linear–quadratic–exponential fit) (see appendix E for details).
e 95% CI.

257

a Model with multiplicative transport of risk, as described in section IV.B.1 of annex I of the UNSCEAR 2000
Report [U2].
b Model with linear–quadratic dose response, fitted to full dose range in reference [P10].

Page 257

United Kingdom

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0.69 a, b, 0.86 c, b

ANNEX A: EPIDEMIOLOGICAL STUDIES OF RADIATION AND CANCER

United States

1.0

10/7/08

Reference

UNSCEAR REPORT-PART 4.qxp

Cancer type

10/7/08

Test dose (Sv)

Oesophagus

Stomach

Colon

Liver

Lung

Female breast

Bladder

Thyroid

Solid total

Present
report

ERR

0.01

0.44

1.71

1.37

1.51

3.22

1.51

0.84

0.39

13.01

1.0

0.41

1.61

1.30

1.42

2.95

1.44

0.78

0.36

13.49

0.01

0.05

2.60

1.71

0.79

2.36

1.64

0.96

0.88

13.16

1.0

0.05

2.34

1.36

0.70

2.02

1.39

0.82

0.80

11.88

EAR

United States

[U2]

ERR

1.0

0.4

1.9

1.7

1.7

3.7

2.7

0.6

0.8

15.7

Present
report

ERR

0.01

0.23

0.20

1.74

0.18

4.41

6.38

1.84

1.18

20.95

1.0

0.21

0.19

1.64

0.17

3.94

5.59

1.68

1.11

37.00

0.01

0.05

2.49

1.52

0.73

2.02

1.41

0.81

0.86

11.71

1.0

0.05

2.25

1.24

0.65

1.76

1.21

0.69

0.78

10.64

ERR

0.1

–

0.3

2.1

0.2

5.0

2.6

1.6

1.0

18.6

EAR

0.1

–

3.1

1.5

1.2

2.8

2.3

1.1

–

16.9

ERR

0.01

0.54

0.33

1.33

0.14

3.49

4.48

1.42

0.30

15.68

1.0

0.50

0.30

1.25

0.13

3.15

4.01

1.30

0.28

23.12

0.01

0.05

2.50

1.53

0.72

1.99

1.43

0.76

0.86

11.77

1.0

0.05

2.27

1.26

0.65

1.75

1.24

0.67

0.78

10.85

–

–

3.41–5.01 b

2.58–3.98 b–

0.12–0.19 b

12.13–21.98 b

EAR

[C37]

United Kingdom

Present
reportEAR

0.42–0.91 a, b

[L16]

ERR

0.001

[U2]

ERR

1.0

0.5

0.5

1.6

0.2

6.2

6.2

0.2

0.5

19.3

EAR

1.0

0.4

2.1

1.9

1.9

4.3

2.7

0.8

0.7

17.0

a Combined risk for oesophagus and stomach.
b Range of risks for models with: (a) power adjustment to ERR for age and time since exposure; (b) exponential adjustment to ERR for age; (c) exponential adjustment to ERR for age at exposure, and for years since exposure forthose with age at exposure <15; and (d) exponential adjustment to ERR for age at exposure.

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Model typeUNSCEAR 2006 REPORT: VOLUME I

Japan

Publication

3:39 pm

Table 73 Comparison of risk estimates for solid cancer incidence (per cent exposure-induced cancer incidence (REIC)) derived in this report with those from various
other studies
Risks are given as per cent per sievert and are assumed to result from acute exposure
Population

UNSCEAR REPORT-PART 4.qxp

m Model with ERR declining as an exponential function of attained age, as described in section IV.B.1 of annex Iof the UNSCEAR 2000 Report [U2].
n Combined 95% subjective uncertainty interval based on weighted EAR and ERR model, taking account of DDREF.
o Range of risks for models with: (a) power adjustment to ERR for age and time since exposure; (b) exponential
adjustment to ERR for age; (c) exponential adjustment to ERR for age at exposure, and for years since exposurefor those with age at exposure <15; and (d) exponential adjustment to ERR for age at exposure.
p Based on Bayesian MCMC fit (linear–quadratic fit) (see appendix E for details).

258

f 90% CI.
g NIH projection model.
h Multiplicative projection model.
i Constant relative risk.
j Constant relative risk for first 45 years after exposure, risk declining to 0 at attained age 90.
k Males only.
l Model with ERR declining as an exponential function of age at exposure, as described in section IV.B.1 ofannex I of the UNSCEAR 2000 Report [U2].

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Appendix A. Score tests and statistical power
A1. The score test [C43] is a commonly used method for assessing trends of risk with dose. In particular, it has beenused in this way to assess trends of cancer risk with dose in various occupational studies [K27, M12]. This appendix outlines its use for this purpose, and describes how it can be used to assess statistical power. The score is the derivative ofthe log likelihood with respect to the dose trend parameter. In particular, if a relative risk model is assumed in which thecancer risk (whether for incidence or mortality) in cell j of stratum i is given by pij ⋅ [1 + θ ⋅ Dij], then the log (binomial)
likelihood is given by:
S ⎧ Ki⎤ ⎪⎫⎡ Ki⎪
L = C + ∑ ⎨∑ mij ⋅ ln ⎡⎣ M i ⋅ pij ⋅ 1 + θ i ⋅ Dij ⎤⎦ − M i ⋅ ln ⎢ ∑ M i ⋅ pij ⋅ 1 + θ i ⋅ Dij ⎥ ⎬i =1 ⎪⎦ ⎪⎭⎣ j =1⎩ j =1()()

where Mi is the total number of cancer cases or deaths in stratum i, mij is the observed number of cancer cases or deathsKi

in cell j of stratum i (so that∑ m = M ), p is the proportion of the population (e.g. proportion of person-years of
ij

i

ij

j =1

Ki

∑ p = 1 ). (This is the likelihood obtained by conditioning on theobservation) of cell j making up stratum i (so that

ij

j =1

total number, Mi, of cases in each stratum i.)
A2.

If we assume that θi ≡ θ, then:Ki⎧⎫pij ⋅ Dij∑⎪
S ⎪ Kimij ⋅ Dij
dL
⎪
⎪
j =1
= ∑ ⎨∑− M i ⋅ Ki⎬
dθ i =1 ⎪ j =1 1 + θ ⋅ Dij∑ pij ⋅ 1 + θ ⋅ Dij ⎪⎪
⎪⎩
j =1
⎭

(

so that at θ = 0; this reduces to:Ki
S ⎧ KidL⎪⎪⎫= ∑ ⎨∑ mij ⋅ Dij − M i ⋅ ∑ pij ⋅ Dij ⎬dθ θ = 0 i =1 ⎪⎩ j =1⎪⎭
j =1

Therefore:

⎫
⎧ Ki
⎪
⎪ ∑ pij ⋅ 1 + θ ⋅ Dij ⋅ Dij Ki
S
⎡ dL
⎤
⎪
⎪ j =1
=
M
Eθ ⎢
⋅
−
p
⋅
D
∑
∑
i ⎨
ij
ij ⎬
⎥
Ki
j =1
⎣ dθ θ = 0 ⎦ i =1⎪
⎪ ∑ p ⋅ 1+θ ⋅ D
ij
ij⎪⎭⎪⎩ j =1
2
⎧ Ki
⎡ Ki
⎤ ⎫
2
⎪ ∑ pij ⋅ Dij − ⎢ ∑ pij ⋅ Dij ⎥ ⎪S⎪ j =1⎣ j =1⎦ ⎪= θ ⋅ ∑ Mi ⋅ ⎨
⎬
Ki
i =1
⎪
⎪
1 + θ ⋅ ∑ pij ⋅ Dij
⎪
⎪
j =1
⎭
⎩

(

)

(

)

)

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260

UNSCEAR 2006 REPORT: VOLUME I⎡ S Ki
⎤ S
⎡ Ki
⎤
⎡ dL
⎤
=
var
varθ ⎢
m
⋅
D
=
var
∑
θ ⎢ ∑ ∑ ijij ⎥
θ ⎢ ∑ mij ⋅ Dij ⎥⎥⎣ dθ θ = 0 ⎦⎣ i =1 j =1
⎦ i =1⎣ j =1
⎦
2
⎡ Ki
⎧ Ki
⎫ ⎤
2
⎢ ∑ Dij ⋅ pij ⋅ 1 + θ ⋅ Dij⎪ ∑ Dij ⋅ pij ⋅ 1 + θ ⋅ Dij ⎪ ⎥S⎪⎪ ⎥⎢ j =1= ∑ M i ⋅ ⎢ Ki
− ⎨ j =1 Ki
⎬ ⎥
i =1
⎪
⎢ ∑ pij ⋅ 1 + θ ⋅ Dij
pij ⋅ 1 + θ ⋅ Dij ⎪ ⎥
⎪⎩ ∑
⎪⎭ ⎥
⎢⎣ j =1
j =1
⎦

(

)

(

(

)

)

(

)

2
Ki
Ki
Ki
⎡ ⎛ Ki⎞ ⎛⎞ ⎛ Ki⎞ ⎤232⎢ ⎜ ∑ Dij ⋅ pij + θ ⋅ ∑ Dij ⋅ pij ⎟ ⋅ ⎜ 1 + θ ⋅ ∑ pij ⋅ Dij ⎟ − ⎜ ∑ Dij ⋅ pij + θ ⋅ ∑ Dij ⋅ pij ⎟ ⎥S⎢ ⎝ j =1
⎠ ⎝⎠ ⎝ j =1
⎠ ⎥
j =1
j =1
j =1
= ∑ Mi ⋅ ⎢
2
⎥
Ki
⎛
⎞
i =1
⎢
⎥
⎜ 1 + θ ⋅ ∑ pij ⋅ Dij ⎟
⎢
⎥
⎝
⎠
j =1
⎣
⎦

⎡ d2L
⎤
⎤
⎡ dL
=
E
−
⎢
⎥ .)
θ =0
⎥
2
⎢⎣ dθ θ = 0 ⎥⎦⎣ dθ θ = 0 ⎦(It should be noted that this last expression is not the same as varθ = 0 ⎢A3.

dL
Therefore the normalized score, given by Z =dθ θ = 0⎡ dL⎤
Z 0 = Eθ ⎢⎥⎣ dθ θ = 0 ⎦

=

⎡ dL
⎤
varθ ⎢⎥⎣ dθ θ = 0 ⎦⎡ dL ⎤
varθ ⎢⎥⎣ dθ θ = 0 ⎦0.5, has expectation given by:

0.5

2
⎧ Ki
⎡ Ki
⎤ ⎫
2
⎪ ∑ pij ⋅ Dij − ⎢ ∑ pij ⋅ Dij ⎥ ⎪S⎪ j =1⎣ j =1⎦ ⎪θ ⋅ ∑ Mi ⋅ ⎨
⎬
Ki
i =1
⎪
⎪
1 + θ ⋅ ∑ pij ⋅ Dij
⎪
⎪
j =1
⎩
⎭
2
Ki
Ki
Ki
⎡
⎞ ⎛
⎞ ⎛ Ki
⎞ ⎤
⎛ Ki
2
3
2
⎢
⎜ ∑ Dij ⋅ pij + θ ⋅ ∑ Dij ⋅ pij ⎟ ⋅ ⎜ 1 + θ ⋅ ∑ pij ⋅ Dij ⎟ − ⎜ ∑ Dij ⋅ pij + θ ⋅ ∑ Dij ⋅ pij ⎟ ⎥⎢ S⎠ ⎝⎠ ⎝ j =1
⎠ ⎥⎝ j =1
j =1
j =1
j =1
⎥
⎢∑ M i ⋅2
Ki⎛⎞⎥⎢ i =1⎜ 1 + θ ⋅ ∑ pij ⋅ Dij ⎟
⎥
⎢
⎝
⎠
j =1
⎦
⎣

0.5

(A.1)

and has variance 1. It is assumed that the normalized score, Z, is approximately normally distributed, Z ∼ N(Z0,1). Therefore,
if the 100 ⋅ p-centile of the standard normal distribution is Np, so that p = P[N(0,1) ≤ Np), then:P[Z > N1−α ] = P[Z − Z 0 > N1−α − Z 0 ] = P[N(0,1) > N1−α − Z 0 ]If this is to equal p, then:1 − p = P[N (0,1) ≤ N1−α − Z 0 ] = P[N (0,1) ≤ N1− p ]A4.

Therefore it must be that N1–α – Z0 = N1–p, or equivalently that:

Z0 = N1–α – N1–p

(A.2)

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261

Considering a single stratum, with M1 = M, K1 = K, etc., then by (A.1) and (A.2), in order for the cohort to have powerp it must be that:
2
K
K
K
⎛⎛ K
⎞ ⎛
⎞ ⎛ K
⎞ ⎞
2
2
3
2
⎜ ⎜ ∑ D j ⋅ p j + θ ⋅ ∑ D j ⋅ p j ⎟ ⋅ ⎜ 1 + θ ⋅ ∑ p j ⋅ D j ⎟ − ⎜ ∑ D j ⋅ p j + θ ⋅ ∑ D j ⋅ p j ⎟ ⎟ ⋅ N1−α − N1− p⎜⎝ ⎝ j =1⎠ ⎝⎠ ⎝ j =1
⎠ ⎟⎠
j =1
j =1
j =1

(

M=

2
⎛ K
⎡K
⎤ ⎞
2
2
θ ⋅ ⎜ ∑ p j ⋅ D j − ⎢∑ p j ⋅ D j ⎥ ⎟⎜⎝ j =1⎣ j =1
⎦ ⎟⎠

)

2

(A.3)
For small θ and Di this varies approximately as the inverse of the square of the average dose, and as the inverse of thesquare of the expected ERR per unit dose, θ.
A5. Figures I and II in the main text illustrate these formulae with calculations of power for a cohort having the dose
distribution from the latest mortality data set on the survivors of the atomic bombings in Japan [P10], for both bone marrowdose and colon dose. Table A1 gives the dose distribution assumed.

Table A1 Colon and bone marrow person-year-weighted dose distribution in the atomic bombing survivor mortality data,taken from data set used for reference [P10]
Colon dose group (Sv)

Average colon dose (Sv)

Average bone marrow dose (Sv)

Proportion of person-years
follow-up in group0–0.005

0.001 06

0.001 200.446 100.005–0.02

0.011 04

0.012 220.169 46

0.02–0.04

0.030 650.034 52

0.073 60

0.04–0.06

0.051 71

0.058 46

0.049 80

0.06–0.08

0.072 55

0.082 210.031 38

0.08–0.10

0.094 01

0.106 850.023 99

0.10–0.125

0.116 32

0.132 08

0.022 57

0.125–0.15

0.141 460.161 310.017 47

0.15–0.175

0.166 44

0.189 780.017 210.175–0.20

0.190 97

0.218 280.011 65

0.20–0.25

0.228 00

0.260 210.018 09

0.25–0.30

0.278 140.318 080.016 31

0.30–0.50

0.388 960.447 99

0.038 83

0.50–0.75

0.618 250.712 94

0.024 91

0.75–1.00

0.876 56

1.012 810.014 55

1.00–1.25

1.147 071.331 69

0.008 76

1.25–1.50

1.421 71

1.663 34

0.006 06

1.50–1.75

1.689 031.993 72

0.003 39

1.75–2.00

1.959 92

2.303 23

0.002 17

2.00–2.50

2.326 392.734 08

0.003 18

2.50–3.00

2.827 223.163 340.000 52

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Appendix B. Measures of radiation risk, including lifetime risk
B1. Fundamental to the calculation of measures of population risk is the estimation of the instantaneous cancer mortality rate, µc (s,t | a, D) , expressed as cancer deaths per year, that will result for a given cancer type c at age t for persons of sex s following some instantaneously administered radiation dose D given at age a. This is typically evaluated byfitting a model for radiation risk to data corresponding to some exposed cohort. For example, the generalized relative risk
model assumes that the mortality rate for cancer type c at age t, y years after instantaneous exposure to a radiation dose
D administered at age a (so that t = a + y) is given by µc (s,t | a, D) = µc (s,t) ⋅[1 + ERRc (s, a, y, D)] . Similar models
can also be fitted to cancer incidence data. Typically one can multiplicatively separate the radiation dose–response termfrom the temporal modifiers in this expression, as for example µc (s,t | a, D) = µc (s,t) ⋅[1 + Fc (D) ⋅φc (s,a, y)] .
For instance, one might use as the form of dose response the linear–quadratic–exponential expression
Fc (D) = [a ⋅ D + β ⋅ D 2 ] ⋅ exp[γ ⋅ D] (a model suggested by much radiobiological data [U5]) (see also section I.K),
and as the temporal modifier term some empirical exponential function, φc (s, a, y) = exp[κ 0 + κ 1 ⋅ s + κ 2 ⋅ a + κ 3 ⋅ y] .
B2. Once a model for radiation risk has been developed, it is in principle straightforward to use it to estimate the burdenof cancer in some hypothetically exposed population. Fundamental to assessment of risk in such a population, one must
assume “background” or “underlying” mortality rates, µc (s,t) , that this population will experience in the absence ofradiation exposure, both overall and for each cancer type. Moreover, to calculate cancer risks for cancer incidence, cancer
incidence rates must also be specified. These background rates are generally estimated from national morbidity andmortality rates. It is usual to calculate the consequence of an instantaneous exposure to a “test” dose Dt that is assumed
administered at some age a. However, other more general patterns of exposure are possible, and may be derived byobvious generalizations of the calculations below. There are six commonly used measures of population cancer risk,
extensively reviewed elsewhere [B18, L17, T18]. The first measure is excess cancer deaths (ECD) per unit dose:yTyT∫ µ (s,t | a, D ) ⋅ S (s,t | a, D )dt − ∫ µ (s,t) ⋅ S(s,t | a)dtc

ECDc (s,a, Dt ) = a

t

c

t

c

a

Dt

where µc (s,t | a, Dt ) is the instantaneous cancer mortality rate (cancers/year) for cancer type c at age t for persons ofsex s following the assumed dose Dt given at age a. As above, this is evaluated by some model fitted to data.
Sc (s,t | a, Dt ) is the fraction of the population of sex s alive at age a who remain alive at age t (>a), and can be estimated
by

⎡ t
⎤
Sc (s,t | a, Dt ) = exp ⎢ − ∫ µ(s, w | a, Dt )dw ⎥ , where µ(s,t | a, Dt ) = µc (s,t | a, Dt ) + ∑ µl (s,t ) is the all-cause
l≠c
⎣ a
⎦
mortality rate, a summation over the specific cancer type of interest, and all other cancer and non-cancer causes of death.
S(s,t ) = Sc (s,t | a, 0) is the analogous survival probability at 0 radiation dose. If a generalized relative risk model were
to be fitted, in which for cancer type c the mortality rate at age t, y years after exposure to a dose Dt administered at agea (so that t = a + y) is given by µc (s,t | a, Dt ) = µc (s,t ) ⋅[1 + ERRc (s,a, y, Dt )] , then this risk can be written:

ECDc (s,a, Dt ) =
⎧ yT
⎡ t
⎤ ⎫
⎪ ∫ µc (s,t) ⋅[1 + ERRc (s, a,t − a, Dt )] ⋅ S(s,t) ⋅ exp ⎢ − ∫ µc (s, w) ⋅ ERRc (s,a, w − a, Dt )dw ⎥ dt ⎪
⎪a
⎣ a
⎦ ⎪
⎬
⎨ yT
⎪
⎪
⎪
⎪− ∫ µc (s,t) ⋅ S(s,t )dt
⎭
=⎩ a
Dt
Persons are assumed capable of surviving in principle up to the age of yT, at which point they are assumed to die instantaneously (i.e. the population is truncated at that age). The particular yT used does not much matter as long as it isUNSCEAR REPORT-PART 5.qxp

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sufficiently large. Little et al. [L17] used a value of 121 years, as did Bennett et al. [B18]. This measure has been usedin the BEIR V report [C35] and elsewhere [L15, L16, L17]. A very similar measure, excess cancer incidence (ECI) per
unit dose, can also be calculated.
B3.

A population risk measure closely related to ECD is the risk of exposure-induced death (REID) per unit dose:yT∫ [ µ (s,t | a, D ) − µ (s,t )] ⋅ S (s,t | a, D )dtc

REIDc (s, a, Dt ) =

t

c

c

t

a

Dt

As above, when a generalized relative risk model,µc (s,t | a, Dt ) = µc (s,t ) ⋅[1 + ERRc (s,a, y, Dt )] , is assumed, this reduces to:
⎡ t
⎤
µ
(s,t)
⋅
ERR
(s,
a,t
−
a,
D
)
⋅
S(t,
a)
⋅
exp
⎢ − ∫ µc (s, w) ⋅ ERRc (s, a,w − a, Dt )dw ⎥ dt
c
t
∫a c
⎣ a
⎦
REIDc (s, a, Dt ) =DtyTThis risk measure has been employed by many scientific committees [I11, U2, U4] and others [L15, L16, L17], and isarguably the most commonly used such summary risk measure. The ECD measure, which is calculated by taking the difference between the numbers of cancers that would occur in an irradiated population and in an otherwise equivalent unirradiated population, in general gives a somewhat lower value than the REID measure. This is because the former quantity
does not include that fraction (about 20% for the general population in equilibrium) of the people developing a fatal radiation-induced cancer who would have died from some sort of cancer anyway. The analogous quantity calculated for cancer
incidence, risk of exposure-induced cancer incidence (REIC) per unit dose, can also be defined, and has been used bysome [B18].
B4.

The measure of years of life lost (YLL) per unit dose is given by:yT

yT

YLLc (s,a, Dt ) = aa∫ S(s,t | a)dt − ∫ Sc (s,t | a, Dt )dt

As above, when a relative risk model,Dtµc (s,t | a, Dt ) = µc (s,t ) ⋅[1 + ERRc (s,a, y, Dt )] , is assumed, this reduces to:

T
⎡ t
⎤
⎡ t
⎤
dt
−
exp
exp
−
µ(s,
w)dw
⎥
⎢ − ∫ µ(s, w) + µc (s, w) ⋅ RRc (s,a, w − a, Dt )dw ⎥ dt
∫a ⎢⎣ ∫a
∫
⎦
⎣ a
⎦
a
YLLc (s,a, Dt ) =
Dt

yT

y

This measure has been used by many scientific committees [C35, I11, U2, U4] and others [L15, L16, L17]. A related
measure, years of life lost per radiation-induced cancer (YLLRIC), which is given by:

YLLRICc (s, a, Dt ) =YLLc (s,a, Dt )REIDc (s, a, Dt )has also been employed by some [C35, I11, L17, U2].
B5. The non-constancy of all six measures of risk as a function of the test dose Dt should be noted, even when theexcess relative risk ERR(s,a,t,Dt) is linear in Dt; this is a consequence of the non-linearity (in Dt) of the numerators of theabove expressions.

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B6. In calculation of an overall population risk, suitable averages of all of the above measures have to be taken, averaged over the age at exposure distribution in the hypothetical exposed population. Most scientific committees [C35, I11,
U2, U4] and others [B18, L15, L16, L17] use the equilibrium population distribution in the absence of radiation exposure,⎡ a⎤
Sc (s,a) = exp ⎢ − ∫ µ(s,w)dw ⎥⎣ 0⎦
and weight across sexes by the relative birth rates of each sex (in most populations approximately equal). Using the equilibrium distribution has the advantage that the time distribution of the administered pattern of dose does not matter. Assuming
linearity of the excess relative risk ERR(s,a,t,D) in dose D, all risk measures are approximately (asymptotically in the lowdose limit) invariant to arbitrary fractionation of a given test dose, Dt, over time. In principle, other age/sex distributions
could be used to derive aggregate risks, for example the actual population distribution by age and sex at a given time forsome country. However, population risk measures for a population that is not in equilibrium when the radiation dose isgiven will not be (asymptotically in the low-dose limit) invariant to the pattern of test dose distribution.

Appendix C. Modelling of dosimetric error for the data on theatomic bombing survivors
C1. This appendix details the methods used to model dosimetric error, in the data set on the atomic bombing survivor
LSS cohort, for the purpose of fitting the risk models used for calculations of population cancer risk. The methods foradjusting for dosimetric error are reasonably similar to those employed by Pierce et al. [P2, P11, P16], Neriishi et al. [N7],and Little and colleagues [L29, L32, L33, L34, L35, L37]. In general, the true dose D is not known; the only observabledosimetric quantity in any stratum is the nominal (or estimated) (DS02) dose d. Approximately unbiased parameter estimates are obtained by replacing ERR(i,D) (or EAR(i,D)) by E[ERR(i,D) | d] (or by E[EAR(i,D) | d]) in the model fitting,in which this last expression represents the average of the excess relative risk ERR(i,D) (or the excess absolute risk EAR(i,D))
over the stratum with average nominal (DS02) dose d. This approach to measurement error correction is an example of“regression calibration” [C12].
C2. When random errors are assumed to be present in the dose estimates, the true dose D in any stratum is not known;
the only observable dosimetric quantity in any stratum is the nominal (or estimated) dose d. Jablon [J3] investigated theerrors in the Japanese atomic bombing dosimetry and found that these errors were most likely to be distributed lognormally, with a GSD of about 30%. Therefore it is assumed here that the distribution of the nominal dose d conditionalon the true dose D is given by the standard log-normal density function:f (d | D) =⎡ (ln[d] − ln[D])2 ⎤1
exp ⎢ −⎥2 ⋅σ 2σ ⋅ d 2π
⎣
⎦

(C.1)

C3. Pierce et al. [P2, P11, P16] found that a Weibull distribution provided an adequate description of the true dose distribution in the two cities, apart from a low-dose group, which they did not model. Following their example, the probability density of the distribution of the true dose D in each sex (s = male, female) and city (c = Hiroshima, Nagasaki) is
modelled here by the superposition of an extended Weibull density function (similar to that used previously by Little [L32,L49]), with an additional uniform density on the true dose interval [0.0, 0.01] given by:

wsc (D) = ω 1sc ⋅ ⎡⎣ ω 2 sc ⋅ ω 3sc ⋅ Dω 3sc −1 + ω 4 sc ⎤⎦ ⋅ exp ⎡⎣ −ω 2 sc ⋅ Dω 3sc − ω 4 sc ⋅ D ⎤⎦+[1 − ω 1sc ] ⋅100 ⋅1D <0.01(C.2)

C4. In general, the canonical Weibull distribution (with ω4sc = 0) did not adequately fit the current LSS mortality data
[P10], and neither did the density function without the uniform density in the range [0.0, 0.01] (with ω1sc = 1), but thisextended Weibull density function fitted the data very well over the full dose range (including the low-dose group excluded
by Pierce et al. [P2, P11, P16]).

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265

The joint distribution of true dose D and nominal dose d is then given by the density function:

psc (d, D) = f (d | D) ⋅ wsc (D)(C.3)

from which one can numerically integrate to obtain:
d0

Dmax

d0

Dmax

0

0

0

0

Prsc (d ≤ d0 ) = ∫ dq ∫ psc (q, D)dD = ∫ dq ∫ f (q | D) ⋅ wsc (D)dD(C.4)

where Dmax is the maximum assumed true dose, taken to be 6 Sv for the colon and bone marrow. The fitting of the modified Weibull distribution parameters (ω1sc, ω2sc, ω3sc, ω4sc,) for each sex (male, female) and city (Hiroshima, Nagasaki)
separately, is achieved by maximizing the multinomial likelihood of the joint distribution of persons by nominal colon orbone marrow dose (using as dose groups 0.0–0.005, 0.005–0.02, 0.02–0.04, 0.04–0.06, 0.06–0.08, 0.08–0.10, 0.10–0.125,
0.125–0.15, 0.15–0.175, 0.175–0.20, 0.20–0.25, 0.25–0.30, 0.30–0.50, 0.50–0.75, 0.75–1.00, 1.00–1.25, 1.25–1.50,
1.50–1.75, 1.75–2.00, 2.00–2.50, 2.50–3.00, >3.00 Sv). In all fits of the extended Weibull distributions the DS02 colonand bone marrow dose estimates are used, unadjusted for dosimetric error and without the truncation of dose estimates at
4 Sv that have been used in some of the most recent analyses [P10]. However, as noted above, it is implicitly assumed
in the integrations involved in Eq. (C.4) that the true dose (colon, bone marrow) cannot exceed 6 Sv.
C6. It can be shown [C12] that approximately unbiased estimates of the parameters in ERR or EAR models expressedby Eqs (12) and (13) in the main text (particular cases of which are given by expressions (14)–(20)) are obtained by replacing ERR(i,D) or EAR(i,D) in the model fitting by E[ERR(i,D) | d] and E[EAR(i,D) | d], respectively. These last expressions
represent the conditional expectation of the excess relative risk ERR(i,D) or excess absolute risk EAR(i,D) at the true doseD, given the average nominal DS02 dose d; in other words, E[ERR(i,D) | d] is the average of the excess relative risk
ERR(i,D) at the true dose D over the stratum with (person-) averaged nominal dose d, and similarly for the excess absoluterisk. E[ERR(i,D) | d] and E[EAR(i,D) | d] are calculated by numerical integration of the product of the excess relative risk
ERR(i,D) and excess absolute risk EAR(i,D), for example as given by expressions (12)–(20), and the density function, Eq.
(C.3), over the true dose range (0–6 Sv). Numerical integrations are performed using a Rosenbrock-type stiff integration
routine (employing the Shampine parameter set) [P22].

Appendix D. Risk models fitted to the atomic bombing survivor data by classical,
likelihood-based methods
D1. This appendix presents the models used to fit the current LSS cancer mortality [P10] and cancer incidence data
[P48] by classical, likelihood-based methods. The models fitted are of the general form described in section IV of the main
text, namely generalized ERR and generalized EAR models. Generalized ERR models were fitted in which the expectedcancer mortality or incidence rate at age a, for sex s and city c, following exposure at age e to a dose D of radiation is
given by:h0 (a,e, c, s) ⋅[1 + F(D) ⋅φ(a, e, c, s)] = h0 (a, e,c, s) ⋅[1 + ERR(D, a,e, c, s)](D.1)

Likewise, generalized EAR models were fitted in which the expected cancer rate (for mortality or incidence) is given by:

h0 (a,e, c, s) + F(D) ⋅ψ (a, e,c, s) = h0 (a,e, c, s) + EAR(D, a, e, c, s)(D.2)

D2. Poisson disease models were used for all fitting to the LSS data. The models that are used here are fundamentallyfunctions of the (unobserved) “true” organ dose D received by a survivor. In general, the true dose D is not known; theonly observable dosimetric quantity in any stratum i is the nominal (or estimated) (DS02) dose d. As discussed in appendix C, approximately unbiased parameter estimates are obtained by replacing ERR(D,a,e,c,s) (or EAR(D,a,e,c,s)) by
Ei[ERR(D,a,e,c,s)|d] (or by Ei[EAR(D,a,e,c,s)|d]) in the model fitting, in which this last expression represents the averageof the excess relative risk ERR(D,a,e,c,s) (or the excess absolute risk EAR(D,a,e,c,s)) over the stratum i with average nominal (DS02) dose d. Since the adjustment functions φ(a,e,c,s) and ψ(a,e,c,s) do not involve dose, this is equivalent to replacing the dose–response function F(D) by Ei[F(D)|d]. This approach to measurement error correction is an example of“regression calibration” [C12].

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For all the model fitting carried out, two basic forms of dose response F(D) were implemented, namely:

F(D) = (α ⋅ D + β ⋅ D 2 ) ⋅ exp(γ ⋅ D)

(D.3)

and

F(D) = α ⋅ D k(D.4)

D4. For the LSS mortality data, the regression calibration approach was implemented exactly as described in appendix C,
assuming 35% GSD errors. For computational simplicity, in the LSS mortality data Ei[D | d] and Ei[D2 | d] were evaluated, and substituted into F(D). Therefore Eq. (D.3) was replaced by:F(D) = (α ⋅ Ei [D | d] + β ⋅ Ei [D 2 | d]) ⋅ exp(γ ⋅ Ei [D | d])(D.5)

and Eq. (D.4) was replaced by:F(D) = α ⋅ Ei [D | d]k(D.6)

As can be seen, at least for linear–quadratic forms of dose response (γ = 0, k = 1,2), these are equivalent to the exact
regression calibration substitution estimate. Even when departures from pure linear–quadratic forms of dose response areused, these approximations work well. Over the typical range of parameters γ, k fitted, these approximations to Ei[F(D)|d]were found to be accurate to at least 5%, and often better than that, paralleling previous such calculations [L33].
D5. The latest LSS incidence data set did not contain unadjusted doses that would allow us to employ the method of
appendix C. The incidence data file contained measurement-error-adjusted, truncated organ doses, evaluated using themethodology previously employed by Pierce et al. [P2, P11, P16] for the LSS11 mortality data. It should be noted that
this procedure is based on estimation of ratios of Ei[D | d] / d derived for the DS86 dosimetry [P2], assuming 35% GSD
errors. These ratios may possibly not be valid for the updated DS02 dosimetry. The estimated values of Ei[D | d] wereused in this data file and were substituted for D in the various forms of F(D), as above.
D6.

The adjustment factors used in both generalized ERR and EAR models are of the same form, namely:

φ(a, e, c, s) = exp ⎡⎣κ 1 ⋅1s = female + κ 2 ⋅1c = Nagasaki + κ 3 ⋅ ln[a] + κ 4 ⋅ ln[e] + κ 5 ⋅ ln[a − e]⎤⎦= exp ⎡⎣κ 1 ⋅1s = female + κ 2 ⋅1c = Nagasaki ⎤⎦ ⋅ aκ 3 ⋅ eκ 4 ⋅[a − e]κ 5(D.7)

Similar forms of adjustment factors have been employed by many others in analysis of these data [L15, L16, L21, L53,
L90, P46, P47], and fit well. A general motivation for use of this form of adjustment factors as a function of age and age
at exposure is provided by the Armitage–Doll multistage model, as discussed in references [L15, L21]. In particular, thespecial case of this model in which κ3 = –1, so that:φ(a, e, c, s) = exp ⎡⎣κ 1 ⋅1s = female + κ 2 ⋅1c = Nagasaki − ln[a]⎤⎦ = (1 / a) ⋅ exp ⎡⎣κ 1 ⋅1s= female + κ 2 ⋅1c = Nagasaki ⎤⎦(D.8)

has been advocated by Pierce and colleagues [P46, P47]. Other analyses [L5, L16, L53, L90, P4, T1] have employed exponential, rather than power, adjustments to ERR or EAR, of the form:

φ(a, e, c, s) = exp ⎡⎣κ 1 ⋅1s = female + κ 2 ⋅1c= Nagasaki + κ 3 ⋅ a + κ 4 ⋅ e ⎤⎦(D.9)

or composites of the two [P9, P10]. These provide almost the same fit as the power adjustment factors (D.7) to the LSS
data and to data for various other radiation-exposed groups [L16, L53, L90].
D7. In fits to the LSS mortality data, bone marrow dose was used for assessing risks of leukaemia, and colon dose
for risks of all solid cancers. For the incidence data, generally the relevant organ-specific dose was used, except whereindicated otherwise in the tables. In all cases a neutron RBE of 10 was used, as recommended by the ICRP [I11]. Thosesurvivors not in (either) city (>10 km from either hypocentre) were excluded from the LSS incidence data, and survivors

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267

with shielded kerma dose of >4 Gy were excluded from the mortality data [P10]. Tables D1–D4 provide details of themodel fits to the mortality data, and tables D5–D16 to the incidence data.
D8. The form of the background mortality or incidence rate, h0(a,e,c,s), was determined by a forward stepwise process,whereby terms were successively added until no further statistically significant improvement in fit was obtained [M21].
Table D17 details the optimal background model for each mortality and cancer incidence end point considered in tables
D1–D16. Likewise, a forward stepwise process was used to assess the significance of terms modifying the dose response.
A backward stepwise process [M21] was then used to check that the indicated dose-modifying factors were still statistically significant.
D9. The recently published BEIR VII report [C37] employed somewhat unusual adjustment functions to the ERR andEAR for solid cancers, of the form:

φ(a, e, c, s) = exp ⎡⎣κ 1 ⋅1s = female + κ 2 ⋅ ln[a] + κ 3 ⋅ min[e − 30, 0]⎤⎦(D.10)The principal novelty in this is that the adjustment for age at exposure, provided by the κ3 ⋅ min[e – 30,0] term, only varies
under the age of 30. The current study fitted and tested this by use of a slightly more general form of model in which

φ(a, e, c, s) = exp ⎡⎣κ 1 ⋅1s = female + κ 2 ⋅ ln[a] + κ 3 ⋅ min[e − 30, 0] + κ 4 ⋅ max[e − 30, 0]⎤⎦(D.11)

In particular, by constraining κ3 = κ4 in the model fits, it is feasible to test for possible changes in the modifying effectof age at exposure on ERR or EAR at the age of 30. Table D2 details the fit of this model. As can be seen from the table,
this model yields no better fit than the optimal models given in table D1. There is also no evidence for changes in themodifying effect of age at exposure on ERR or EAR at the age of 30.
D10. The BEIR VII report [C37] also employed somewhat unusual adjustment functions to the ERR and EAR for
leukaemia, of the form:⎡κ 1 ⋅1s = female + κ 2 ⋅ ln[a − e] + κ 3 ⋅ min[e − 30, 0]⎤φ(a, e, c, s) = exp ⎢⎥⎣ +κ 4 ⋅ ln[a − e]⋅ min[e − 30, 0]⎦(D.12)

Again, the principal novelty in this is that the adjustments for age at exposure (both as main effect and as interaction withthe effect of time since exposure), provided by the κ3 ⋅ min[e – 30,0] and κ4 ⋅ ln[a – e] ⋅ min[e – 30,0] terms, only varies
under the age of 30. In fits of the generalized EAR model, the constraint κ2 = 0 appears to have been imposed [C37].
Again, this has been fitted and tested by use of a slightly more general form of model in which:⎡κ 1 ⋅1s = female + κ 2 ⋅ ln[a − e] + κ 3 ⋅ min[e − 30, 0]⎤⎢⎥φ(a, e, c, s) = exp ⎢ +κ 4 ⋅ max[e − 30, 0] + κ 5 ⋅ ln[a − e] ⋅ min[e − 30, 0]⎥⎢⎣ +κ 6 ⋅ ln[a − e] ⋅ max[e − 30, 0]⎥⎦(D.13)

Again, by constraining κ3 = κ4 in the model fits, it is feasible to test for possible changes in the modifying effect of age
at exposure on ERR or EAR at the age of 30. Table D4 details the fit of this model. (The constraint κ2 = 0 is not imposedin fits of the model with either interaction term. It is generally unwise to have interaction terms in a model without both
associated main effect terms.) As can be seen from table D4, this model yields no better fit than the optimal leukaemia
models given in table D3. There is also no evidence for changes in the modifying effect of age at exposure on ERR or
EAR at the age of 30, although there is some evidence for interaction between the adjustments for time since exposure,ln[a – e], and either of the min[e – 30,0] or max[e – 30,0] terms.

Modifier a

Dose response

Deviancedf–

13 553.081

31 399Relative risk

αD–

13 423.324

31 398

αD + βD2–

13 420.980

αD

Sex

αD

df

13 492.779

31 39831 397

αD + βD2–

13 472.872

31 397

13 411.768

31 397αDSex

13 484.146

31 397City

13 422.062

31 397αDCity

13 492.710

31 397αDSex, ln[y]13 410.450

31 396αDSex, ln[y]13 407.974

31 396αDSex, ln[e]13 383.743

31 396αDSex, ln[e]13 474.836

31 396αDSex, ln[a]13 393.958

31 396αDSex, ln[a]13 391.765

31 396αDSex, ln[y], ln[e]13 382.864

31 395αDSex, ln[y], ln[e]13 391.749

31 395αDSex, ln[y], ln[a]13 379.354

31 395αDSex, ln[y], ln[a]13 384.375

31 395αDSex, ln[e], ln[a]13 379.611

31 395αDSex, ln[e], ln[a]13 384.792

31 395αDSex, ln[y], ln[e], ln[a]13 378.191

31 394αDSex, ln[y], ln[e], ln[a]13 383.952

31 394αDSex, ln[y], ln[a]13 379.354

31 395αDln[ y], ln[a]13 384.596

31 396βD2Sex, ln[y], ln[a]13 389.278

31 395βD2ln[ y], ln[a]13 390.499

31 396

αD + βD2

Sex, ln[y], ln[a]13 376.676

31 394

αD + βD2ln[y], ln[a]13 380.658

31 395(αD + βD2) exp[γD]Sex, ln[y], ln[a]13 375.654

31 393(αD + βD2) exp[γD]ln[y], ln[a]13 378.904

31 394αDkSex, ln[y], ln[a]13 376.600

31 394αDkln[y], ln[a]13 380.299

31 395αD exp[γD]Sex, ln[y], ln[a]13 377.047

31 394αD exp[γD]ln[y], ln[a]13 381.330

31 395a D = RBE 10 colon dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

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Table D1 Fits of generalized ERR and EAR models to LSS solid cancer mortality data
Data set used for reference [P10]; models assume 35% GSD errors in colon dose; dose errors corrected using methods of appendix C.
The optimal models are shown in boldface

UNSCEAR REPORT-PART 5.qxp

Table D2 Comparison of fits of BEIR VII [C37] models with those of generalized ERR and EAR models to LSS solid cancer mortality data from table D1
Data set used for reference [P10]; models assume 35% GSD errors in colon dose; dose errors corrected using methods of appendix C.
The optimal models are shown in boldface
Modifier a

Dose response

Deviancedf

13 553.081

31 399Relative
risk

αD

–

13 423.324

31 398αDSex, ln[y], ln[a]13 379.354

αDSex, ln[a], min(e – 30, 0)

αD–

13 492.779

31 39831 395αDSex, ln[y], ln[a]13 384.596

31 396

13 378.098

31 395αDSex, ln[a], min(e – 30, 0)

13 384.145

31 396

Sex, ln[a], max(e – 30, 0)

13 388.418

31 395αDSex, ln[a], max(e – 30, 0)

13 389.128

31 396αDSex, ln[a], min(e – 30, 0), max(e – 30, 0)

13 377.914

31 394αDSex, ln[a], min(e – 30, 0), max(e – 30, 0)

13 383.834

31 395αDSex, ln[a], min(e – 30, 0) = max(e – 30, 0) b

13 379.616

31 395αDSex, ln[a], min(e – 30, 0) = max(e – 30, 0) b

13 384.522

31 396

a D = RBE 10 colon dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).
b Coefficient of min(e – 30, 0) is constrained = coefficient of max(e – 30, 0), equivalent to a regression with adjustment for age at exposure e.

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Dose response269

Modifier a

Dose response

Deviancedf–

2 304.986

31 415

Relative risk

αD–

2 166.805

31 414

βD2–

2 159.394

αD + βD2–

αD + βD2df–
2 163.391

31 41431 414

βD2–

2 159.996

31 4142 157.419

31 413αD + βD2–

2 156.286

31 413Sex

2 157.382

31 412

αD + βD2Sex

2 152.052

31 412

αD + βD2City

2 156.728

31 412

αD + βD2

Sex, city

2 147.281

31 411

αD + βD2ln[y]2 145.980

31 412

αD + βD2

Sex, ln[y]2 143.252

31 411

αD + βD2ln[e]2 150.151

31 412

αD + βD2

Sex, ln[e]2 151.746

31 411

αD + βD2ln[a]2 136.589

31 412

αD + βD2

Sex, ln[a]2 147.385

31 411

αD + βD2ln[y], ln[e]2 137.715

31 411

αD + βD2

Sex, ln[y], ln[e]2 143.171

31 410αD + βD2ln[y], ln[a]2 135.696

31 411

αD + βD2

Sex, ln[y], ln[a]2 142.753

31 410αD + βD2ln[e], ln[a]2 136.196

31 411

αD + βD2

Sex, ln[e], ln[a]2 142.412

31 410αD + βD2ln[y], ln[e], ln[a]2 135.673

31 410αD + βD2

Sex, ln[y], ln[e], ln[a]2 142.113

31 409αDln[a]2 145.119

31 413αDSex, ln[y]2 150.796

31 412βD2ln[a]2 139.632

31 413βD2Sex, ln[y]2 146.742

31 412

αD + βD2ln[a]2 136.589

31 412

αD + βD2

Sex, ln[y]2 143.252

31 411(αD + βD2) exp[γD]ln[a]2 133.537

31 411(αD + βD2) exp[γD]Sex, ln[y]2 141.538

31 410αDkln[a]2 134.859

31 412αDkSex, ln[y]2 142.082

31 411

αD exp[γD]ln[a]2 138.449

31 412

αD exp[γD]Sex, ln[y]2 144.620

31 411a D = RBE 10 bone marrow dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

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Table D3 Fits of generalized ERR and EAR models to LSS leukaemia mortality data, assuming 35% GSD errors in red bone marrow dose
Data set used for reference [P10], with dose errors corrected using methods of appendix C. The optimal models are shown in boldface

UNSCEAR REPORT-PART 5.qxp

Table D4 Comparison of fits of BEIR VII [C37] models with those of generalized ERR and EAR models to LSS leukaemia mortality data from table D3
Data set used for reference [P10]; generalized models assume 35% GSD errors in colon dose; dose errors corrected using methods of appendix C.
The optimal models are shown in boldface
Modifier a

Dose response

Deviancedf

2 304.986

31 415

Relative risk

αD + βD2–

2 157.419

31 413αD + βD2ln[a]2 136.589

αD + βD2ln[y], min(e – 30, 0)

αD + βD2–

2 156.286

31 41331 412

αD + βD2

Sex, ln[y]2 143.252

31 411

2 137.205

31 411

αD + βD2

Sex, ln[y], min(e – 30, 0)

2 143.251

31 410ln[y], max(e – 30, 0)

2 144.070

31 411

αD + βD2

Sex, ln[y], max(e – 30, 0)

2 142.444

31 410αD + βD2ln[y], min(e – 30, 0), max(e – 30, 0)

2 137.041

31 410αD + βD2

Sex, ln[y], min(e – 30, 0), max(e – 30, 0)

2 142.015

31 409αD + βD2ln[y], min(e – 30, 0) = max(e – 30, 0) b

2 139.570

31 411

αD + βD2

Sex, ln[y], min(e – 30, 0) = max(e – 30, 0) b

2 143.063

31 410αD + βD2

Sex, ln[y], min(e – 30, 0) = max(e – 30, 0)

2 139.224

31 410αD + βD2

αD + βD2ln[y], min(e – 30, 0), max(e – 30, 0),ln[y] × min(e – 30, 0)

2 134.669

31 409αD + βD2

Sex, ln[y], min(e – 30, 0), max(e – 30, 0),ln[y] × min(e – 30, 0)

2 133.293

31 408

αD + βD2ln[y], min(e – 30, 0), max(e – 30, 0),ln[y] × max(e – 30, 0)

2 133.060

31 409αD + βD2

Sex, ln[y], min(e – 30, 0), max(e – 30, 0),ln[y] × max(e – 30, 0)

2 134.876

31 408

αD + βD2ln[y], min(e – 30, 0), max(e – 30, 0),ln[y] × min(e – 30, 0), ln[y] × max(e – 30, 0)

2 132.813

31 408

αD + βD2

Sex, ln[y], min(e – 30, 0), max(e – 30, 0),ln[y] × min(e – 30, 0), ln[y] × max(e – 30, 0)

2 132.206

31 407

a D = RBE 10 bone marrow dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).
b Coefficient of min(e – 30, 0) is constrained = coefficient of max(e – 30, 0), equivalent to a regression with adjustment for age at exposure e.

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Modifier a

Dose response

Deviancedf–

1 925.653

42 703

Relative risk

αD–

1 919.240

42 702

αD + βD2–

1 917.113

αD

Sex

αD

df

–
1 923.515

42 70242 701

αD + βD2–

1 923.469

42 701

1 919.168

42 701αDSex

1 922.152

42 701City

1 917.732

42 701αDCity

1 921.910

42 701αDln[y]1 919.147

42 701αDln[y]1 923.489

42 701αDln[e]1 918.583

42 701αDln[e]1 920.812

42 701αDln[a]1 918.271

42 701αDln[a]1 922.293

42 701αD

ln[y], ln[e]1 918.147

42 700αD

ln[y], ln[e]1 918.536

42 700αD

ln[y], ln[a]1 918.247

42 700αD

ln[y], ln[a]1 918.429

42 700αD

ln[e], ln[a]1 918.239

42 700αD

ln[e], ln[a]1 917.996

42 700αD

ln[y], ln[e], ln[a]1 918.078

42 699αDln[y], ln[e], ln[a]1 913.974

42 699 bαD–

1 919.240

42 702αD–

1 923.515

42 702βD2–

1 917.256

42 702βD2–

1 923.522

42 702

αD + βD2–

1 917.113

42 701

αD + βD2–

1 923.469

42 701(αD + βD2) exp[γD]–

1 917.086

42 700(αD + βD2) exp[γD]–

1 923.383

42 700αDk–

1 917.176

42 701αDk–

1 923.339

42 701

αD exp[γD]–

1 917.601

42 701

αD exp[γD]–

1 923.501

42 701a D = RBE 10 stomach dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).
b Parameters did not converge.

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Table D5 Fits of generalized ERR and EAR models to LSS oesophageal cancer incidence data
Using DS02 stomach dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface

UNSCEAR REPORT-PART 5.qxp

Table D6 Fits of generalized ERR and EAR models to LSS stomach cancer incidence data
Using DS02 stomach dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface
Modifier a

Dose response

Deviancedf

11 304.964

42 693

Relative risk

αD–

11 265.131

42 692

αD + βD2–

11 263.584

αD

Sex

αD

–

11 262.194

42 69242 691

αD + βD2–

11 259.493

42 691

11 261.969

42 691αDSex

11 260.766

42 691City

11 264.458

42 691αDCity

11 259.838

42 691αDln[y]11 264.971

42 691αDln[y]11 257.131

42 691αDln[e]11 263.019

42 691αDln[e]11 252.494

42 691αDln[a]11 255.425

42 691αDln[a]11 248.148

42 691αD

ln[y], ln[e]11 261.395

42 690αD

ln[y], ln[e]11 247.269

42 690αD

ln[y], ln[a]11 255.275

42 690αD

ln[y], ln[a]11 248.126

42 690αD

ln[e], ln[a]11 255.214

42 690αD

ln[e], ln[a]11 247.804

42 690αD

ln[y], ln[e], ln[a]11 252.828

42 689αDln[y], ln[e], ln[a]11 247.254

42 689αDln[a]11 255.425

42 691αDln[a]11 248.148

42 691βD2ln[a]11 259.144

42 691βD2ln[a]11 250.575

42 691

αD + βD2ln[a]11 254.760

42 690αD + βD2ln[a]11 246.779

42 690(αD + βD2) exp[γD]ln[a]11 253.979

42 689(αD + βD2) exp[γD]ln[a]11 245.876

42 689αDkln[a]11 254.323

42 690αDkln[a]11 246.237

42 690αD exp[γD]ln[a]11 254.819

42 690αD exp[γD]ln[a]11 246.917

42 690a D = RBE 10 stomach dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

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Modifier a

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Modifierr a

Dose response

Deviancedf–

5 301.539

42 696

Relative risk

αD–

5 271.708

42 695

αD + βD2–

5 270.855

αD

Sex

αD

df

–
5 287.660

42 69542 694

αD + βD2–

5 287.224

42 694

5 268.430

42 694αDSex

5 287.584

42 694City

5 269.805

42 694αDCity

5 286.036

42 693αDln[y]5 270.891

42 694αDln[y]5 277.458

42 694αDln[e]5 266.835

42 694αDln[e]5 286.425

42 694αDln[a]5 262.573

42 694αDln[a]5 280.065

42 694αD

ln[y], ln[e]5 263.827

42 693αD

ln[y], ln[e]5 277.171

42 693αD

ln[y], ln[a]5 262.570

42 693αD

ln[y], ln[a]5 276.497

42 693αDln[e], ln[a]5 262.412

42 693αDln[e], ln[a]5 276.193

42 693αD

ln[y], ln[e], ln[a]5 262.255

42 692αD

ln[y], ln[e], ln[a]5 275.759

42 692αDln[a]5 262.573

42 694αDln[y]5 277.458

42 694βD2ln[a]5 265.940

42 694βD2ln[y]5 275.931

42 694

αD + βD2ln[a]5 262.020

42 693αD + βD2ln[y]5 275.434

42 693(αD + βD2) exp[γD]ln[a]5 261.896

42 692(αD + βD2) exp[γD]ln[y]5 275.344

42 692

αDkln[a]5 262.344

42 693αDkln[y]5 275.624

42 693αD exp[γD]ln[a]5 261.896

42 693αD exp[γD]ln[y]5 275.344

42 693a D = RBE 10 colon dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

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Table D7 Fits of generalized ERR and EAR models to LSS colon cancer incidence data
Using DS02 colon dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface

UNSCEAR REPORT-PART 5.qxp

Table D8 Fits of generalized ERR and EAR models to LSS liver cancer incidence data
Using DS02 liver dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface
Modifier a

Dose response

Deviancedf

5 385.654

42 691

Relative risk

αD–

5 370.978

42 690αD + βD2–

5 370.502

αD

Sex

αD

–

5 380.678

42 69042 689αD + βD2–

5 380.323

42 6895 370.934

42 689αDSex

5 380.655

42 689City

5 370.978

42 689αDCity

5 380.657

42 689αDln[y]5 370.302

42 689αDln[y]5 379.040

42 689αDln[e]5 370.676

42 689αDln[e]5 377.081

42 689αDln[a]5 369.410

42 689αDln[a]5 374.957

42 689αDln[y], ln[e]5 369.538

42 688αDln[y], ln[e]5 376.990

42 688αDln[y], ln[a]5 369.357

42 688αDln[y], ln[a]5 374.490

42 688αDln[e], ln[a]5 369.380

42 688αDln[e], ln[a]5 374.843

42 688αDln[y], ln[e], ln[a]5 369.355

42 687αD

ln[y], ln[e], ln[a]5 373.229

42 687αD–

5 370.978

42 690αDln[a]5 374.957

42 689βD2–

5 375.591

42 690βD2ln[a]5 376.808

42 689αD + βD2–

5 370.502

42 689αD + βD2ln[a]5 374.946

42 688(αD + βD2) exp[γD]–

5 370.347

42 688(αD + βD2) exp[γD]ln[a]5 374.943

42 687

αDk–

5 370.886

42 689αDkln[a]5 374.956

42 688

αD exp[γD]–

5 370.529

42 689αD exp[γD]ln[a]5 374.943

42 688a D = RBE 10 liver dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

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Modifier a

Dose response

Deviancedf–

6 243.855

42 697

Relative risk

αD–

6 196.943

42 696

αD + βD2–

6 196.798

αD

Sex

αD

df

6 224.174

42 69642 695

αD + βD2–

6 222.681

42 695

6 181.503

42 695αDSex

6 219.857

42 695City

6 196.531

42 695αDCity

6 223.862

42 695αDSex, ln[y]6 178.245

42 694αDSex, ln[y]6 209.186

42 694αDSex, ln[e]6 181.439

42 694αDSex, ln[e]6 199.128

42 694αDSex, ln[a]6 179.834

42 694αDSex, ln[a]6 180.250

42 694αDSex, ln[y], ln[e]6 177.052

42 693αDSex, ln[y], ln[e]6 185.259

42 693αDSex, ln[y], ln[a]6 177.635

42 693αDSex, ln[y], ln[a]6 180.195

42 693αDSex, ln[e], ln[a]6 179.284

42 693αDSex, ln[e], ln[a]6 180.249

42 693αDSex, ln[y], ln[e], ln[a]6 177.013

42 692αDSex, ln[y], ln[e], ln[a]6 180.036

42 692αDSex

6 181.503

42 695αDSex, ln[a]6 180.250

42 694βD2Sex

6 194.664

42 695βD2Sex, ln[a]6 191.337

42 694

αD + βD2

Sex

6 181.296

42 694

αD + βD2

Sex, ln[a]6 180.227

42 693(αD + βD2) exp[γD]Sex

6 180.487

42 694(αD + βD2) exp[γD]Sex, ln[a]6 179.547

42 693αDkSex

6 181.414

42 694αDkSex, ln[a]6 180.247

42 693αD exp[γD]Sex

6 181.342

42 694αD exp[γD]Sex, ln[a]6 180.232

42 693a D = RBE 10 lung dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

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Table D9 Fits of generalized ERR and EAR models to LSS lung cancer incidence data
Using DS02 lung dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface

UNSCEAR REPORT-PART 5.qxp

Table D10 Fits of generalized ERR and EAR models to LSS bone cancer incidence data
Using DS02 skeletal dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface
Modifierr a

Dose response

Deviancedf

249.461

42 705

Relative risk

αD–

244.791

42 704

αD + βD2–

235.120

βD2

–

βD2

–

242.675

42 70442 703αD + βD2–

238.958

42 703241.03942 704

βD2–

238.937

42 704Sex

237.389

42 703 bβD2Sex

233.61442 704 b

βD2

City

240.989

42 703

βD2City238.930

42 703βD2ln[y]240.840

42 703βD2ln[y]238.853

42 703βD2ln[e]238.83942 703βD2ln[e]237.773

42 703βD2ln[a]236.22242 703βD2ln[a]237.61342 703βD2ln[y], ln[e]237.278

42 702βD2ln[y], ln[e]237.63942 702βD2ln[y], ln[a]236.105

42 702βD2ln[y], ln[a]236.775

42 702βD2ln[e], ln[a]236.126

42 702βD2ln[e], ln[a]237.41242 702βD2ln[y], ln[e], ln[a]236.10342 701βD2ln[y], ln[e], ln[a]234.21742 701αDln[a]239.81042 703αD–242.675

42 704βD2ln[a]236.22242 703

βD2–

238.937

42 704

αD + βD2ln[a]236.02342 702

αD + βD2–

238.726

42 703(αD + βD2) exp[γD]ln[a]236.01042 701(αD + βD2) exp[γD]–238.687

42 702

αDkln[a]235.332

42 702αDk–237.824

42 703αD exp[γD]ln[a]236.299

42 702

αD exp[γD]–238.761

42 703a D = RBE 10 stomach dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).
b Adjustment for sex did not converge.

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Modifier a

Dose response277

Modifier a

Dose response

Deviancedf–

2 234.237

42 700Relative risk

αD–

2 181.366

42 699αD + βD2–2 177.636

αD

Sex

αD

df

2 168.251

42 69942 698

αD + βD2–

2 165.586

42 6982 181.355

42 698αDSex

2 168.195

42 698City

2 177.123

42 698αDCity

2 167.379

42 698αDCity, ln[y]2 175.065

42 697αDln[y]2 147.974

42 698αDCity, ln[e]2 161.213

42 697αDln[e]2 167.670

42 698αDCity, ln[a]2 164.305

42 697αDln[a]2 162.436

42 698αDCity, ln[y], ln[e]2 160.926

42 696αDln[y], ln[e]2 147.854

42 697αDCity, ln[y], ln[a]2 151.978

42 696αDln[y], ln[a]2 147.973

42 697αDCity, ln[e], ln[a]2 159.020

42 696αDln[e], ln[a]2 154.636

42 697αDCity, ln[y], ln[e], ln[a]2 151.122

42 695αDln[y], ln[e], ln[a]2 147.312

42 696αDln[y], ln[a]2 153.145

42 697αDln[y]2 147.974

42 698βD2ln[y], ln[a]2 149.904

42 697βD2ln[y]2 143.777

42 698βD2 exp[γD]ln[y], ln[a]2 144.933

42 696βD2 exp[γD]ln[y]2 138.579

42 697

αD + βD2ln[y], ln[a]2 148.971

42 696

αD + βD2ln[y]2 143.230

42 697(αD + βD2) exp[γD]ln[y], ln[a]2 144.247

42 695(αD + βD2) exp[γD]ln[y]2 138.072

42 696αDkln[y], ln[a]2 147.698

42 696αDkln[y]2 141.524

42 697

αDk exp[γD]ln[y], ln[a]2 138.531

42 695

αDk exp[γD]ln[y]2 133.231

42 696

αD exp[γD]ln[y], ln[a]2 150.861

42 696

αD exp[γD]ln[y]2 145.470

42 697

a D = RBE 10 stomach dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

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Table D11 Fits of generalized ERR and EAR models to LSS non-melanoma skin cancer incidence data
Using DS02 skin dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface

UNSCEAR REPORT-PART 5.qxp

Table D12 Fits of generalized ERR and EAR models to LSS female breast cancer incidence data
Using DS02 breast dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface
Modifier a

Dose response

Deviancedf

4 020.486

22 293

Relative risk

αD–

3 893.514

22 292

αD + βD2–

3 893.512

αD

City

αD

–

3 912.142

22 29222 291

αD + βD2–

3 911.963

22 291

3 893.155

22 291αDCity

3 911.940

22 291ln[y]3 891.953

22 291αDln[y]3 900.082

22 291αDln[e]3 891.686

22 291αDln[e]3 911.871

22 291αDln[a]3 881.872

22 291αDln[a]3 910.297

22 291αD

ln[y], ln[e]3 887.062

22 290αD

ln[y], ln[e]3 899.895

22 290αD

ln[y], ln[a]3 881.870

22 290αD

ln[y], ln[a]3 899.476

22 290αD

ln[e], ln[a]3 881.793

22 290αD

ln[e], ln[a]3 904.857

22 290αD

ln[y], ln[e], ln[a]3 881.543

22 289αDln[y], ln[e], ln[a]3 898.853

22 289αDln[a]3 881.872

22 291αDln[y]3 900.082

22 291βD2ln[a]3 910.535

22 291βD2ln[y]3 925.959

22 291

αD + βD2ln[a]3 881.854

22 290αD + βD2ln[y]3 900.082

22 290(αD + βD2) exp[γD]ln[a]3 881.852

22 290(αD + βD2) exp[γD]ln[y]3 900.081

22 290αDkln[a]3 881.871

22 290αDkln[y]3 900.082

22 290αD exp[γD]ln[a]3 881.852

22 290αD exp[γD]ln[y]3 900.081

22 290a D = RBE 10 stomach dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

Page 279αD

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Background

Modifier a

Dose response279

Modifierr a

Dose response

Deviancedf–

2 564.693

42 703

Relative risk

αD–

2 550.130

42 702

αD + βD2–2 549.083

αD

Sex

αD

df

2 563.278

42 70242 701

αD + βD2–

2 563.008

42 701

2 548.109

42 701αDSex

2 563.114

42 701City

2 549.908

42 701αDCity

2 561.614

42 702αDln[y]2 549.988

42 701αDln[y]2 563.754

42 701αDln[e]2 549.643

42 701αDln[e]2 559.100

42 701αDln[a]2 550.118

42 701αDln[a]2 549.723

42 701αD

ln[y], ln[e]2 549.591

42 700αD

ln[y], ln[e]2 550.912

42 700αD

ln[y], ln[a]2 549.868

42 700αD

ln[y], ln[a]2 549.721

42 700αD

ln[e], ln[a]2 549.424

42 700αD

ln[e], ln[a]2 549.596

42 700αD

ln[y], ln[e], ln[a]2 549.338

42 699αDln[y], ln[e], ln[a]2 549.268

42 699αD–

2 550.130

42 702αDln[a]2 549.723

42 701βD2–

2 556.176

42 702βD2ln[a]2 555.112

42 701

αD + βD2–2 549.083

42 701

αD + βD2ln[a]2 549.309

42 700(αD + βD2) exp[γD]–

2 548.656

42 700(αD + βD2) exp[γD]ln[a]2 549.095

42 699αDk–

2 548.300

42 701αDkln[a]2 546.591

42 700αD exp[γD]–

2 548.656

42 701

αD exp[γD]ln[a]2 549.095

42 700a D = RBE 10 stomach dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

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Table D13 Fits of generalized ERR and EAR models to LSS urinary bladder cancer incidence data
Using DS02 bladder dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface

UNSCEAR REPORT-PART 5.qxp

Table D14 Fits of generalized ERR and EAR models to LSS central nervous system cancer incidence data
Using DS02 brain dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface
Modifier a

Dose response

Deviancedf

1 888.619

42 688Relative risk

αD–

1 881.012

42 687

αD + βD2–

1 880.237

αD

Sex

αD

–

1 877.645

42 68742 686

αD + βD2–

1 877.488

42 686

1 873.972 b

42 686

αDSex

1 875.613

42 686City

1 880.600

42 686αDCity

1 877.585

42 686αDln[y]1 880.237

42 686αDln[y]1 876.777

42 686αDln[e]1 874.928

42 686αDln[e]1 875.032

42 686αDln[a]1 871.623

42 686αDln[a]1 877.331

42 686αDln[y], ln[e]1 871.959

42 685αDln[y], ln[e]1 874.096

42 685αDln[y], ln[a]1 870.675

42 685αDln[y], ln[a]1 873.782

42 685αDln[e], ln[a]1 870.773

42 685αDln[e], ln[a]1 874.513

42 685αDln[y], ln[e], ln[a]1 870.739

42 684αD

ln[y], ln[e], ln[a]1 873.761

42 684αDln[e]1 874.928

42 686αD–

1 877.645

42 687βD2ln[e]1 875.056

42 686

βD2–

1 880.184

42 687

αD + βD2ln[e]1 874.681

42 685

αD + βD2–

1 877.488

42 686(αD + βD2) exp[γD]ln[e]1 873.774

42 684(αD + βD2) exp[γD]–

1 877.472

42 685αDkln[e]1 874.055

42 685

αDk–

1 877.512

42 686

αD exp[γD]ln[e]1 874.769

42 685αD exp[γD]–

1 877.472

42 686

a D = RBE 10 stomach dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).
b Adjustment for sex did not converge.

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Background

Modifier a

Dose response281

Modifier a

Dose response

Deviancedf–

2 972.807

42 700Relative risk

αD–

2 916.527

42 699αD + βD2–

2 914.496

αD

Sex

αD

df

2 910.634

42 69942 698

αD + βD2–2 908.782

42 6982 913.918

42 698αDSex

2 899.530

42 698City

2 916.496

42 698αDCity

2 910.449

42 698αDln[y]2 916.154

42 698αDSex, ln[y]2 899.455

42 697αDln[e]2 898.013

42 698αDSex, ln[e]2 893.558

42 697αDln[a]2 894.746

42 698αDSex, ln[a]2 897.871

42 697αD

ln[y], ln[e]2 892.908

42 697αDSex, ln[y], ln[e]2 893.514

42 696αDln[y], ln[a]2 892.942

42 697αDSex, ln[y], ln[a]2 896.769

42 696αDln[e], ln[a]2 890.965

42 697αDSex, ln[e], ln[a]2 892.970

42 696αDln[y], ln[e], ln[a]2 890.845

42 696αDSex, ln[y], ln[e], ln[a]2 891.211

42 695αDln[e], ln[a]2 890.965

42 697αDSex, ln[e]2 893.558

42 697βD2ln[e], ln[a]2 915.645

42 697βD2Sex, ln[e]2 916.066

42 697

αD + βD2ln[e], ln[a]2 888.188

42 696

αD + βD2

Sex, ln[e]2 891.262

42 696(αD + βD2) exp[γD]ln[e], ln[a]2 887.650

42 695(αD + βD2) exp[γD]Sex, ln[e]2 890.593

42 695αDkln[e], ln[a]2 890.066

42 696

αDkSex, ln[e]2 892.870

42 696

αD exp[γD]ln[e], ln[a]2 888.517

42 696

αD exp[γD]Sex, ln[e]2 891.520

42 696

a D = RBE 10 stomach dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

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Table D15 Fits of generalized ERR and EAR models to LSS thyroid cancer incidence data
Using DS02 thyroid dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface

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Table D16 Fits of generalized ERR and EAR models to LSS incidence data for all other solid cancers
Using DS02 colon dose, adjusted for 35% GSD dose errors, truncated, using adjustment factors derived from DS86 [P2]The optimal models are shown in boldface
Modifier a

Dose response

Deviancedf

11 364.635

42 692

Relative risk

αD–

11 344.768

42 691

αD + βD2–

11 344.767

αD

Sex

αD

City

–

11 347.376

42 69142 690αD + βD2–

11 347.354

42 69011 343.408

42 690αDSex

11 343.055

42 69011 342.650

42 690αDCity

11 340.930

42 690αDCity, Sex

11 337.593

42 689αDln[y]11 342.282

42 690αDCity, ln[y]11 333.001

42 689αDln[e]11 340.399

42 690αDCity, ln[e]11 340.378

42 690αDln[a]11 341.457

42 690αDCity, ln[a]11 337.533

42 689αDln[y], ln[e]11 339.811

42 689αDCity, ln[y], ln[e]11 331.184

42 688αDln[y], ln[a]11 335.856

42 689αDCity, ln[y], ln[a]11 332.288

42 688αDln[e], ln[a]11 339.688

42 689αDCity, ln[e], ln[a]11 336.321

42 688αDln[y], ln[e], ln[a]11 334.509

42 688αDCity, ln[y], ln[e], ln[a]11 330.040

42 687αD

ln[y], ln[a]11 335.856

42 689αDln[y]11 336.354

42 690βD2ln[y], ln[a]11 339.390

42 689βD2ln[y]11 340.330

42 690αD + βD2ln[y], ln[a]11 335.793

42 688

αD + βD2ln[y]11 336.318

42 689(αD + βD2) exp[γD]ln[y], ln[a]11 333.923

42 687(αD + βD2) exp[γD]ln[y]11 334.796

42 688αDkln[y], ln[a]11 335.328

42 688αDkln[y]11 336.195

42 689αD exp[γD]ln[y], ln[a]11 335.811

42 688

αD exp[γD]ln[y]11 336.327

42 689a D = RBE 10 stomach dose (Sv), a = attained age, e = age at exposure, y = years since exposure (= a – e).

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Background modelln[h0 (a,e,c, s)] a

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All solid

κ 0 +κ 1 ⋅ s +κ 2 ⋅ln[a] +κ 3 ⋅ ln[a]2 +κ 4 ⋅ ln[a]3 +κ 5 ⋅ ln[a]4 +κ 6 ⋅ ln[a −e] +κ 7 ⋅ ln[a −e]2 +κ 8 ⋅ e +κ 9 ⋅ e2 +κ 10 ⋅ s ⋅ ln[a] +κ 11 ⋅ s ⋅ ln[a]2+κ 12 ⋅ s ⋅ ln[a]3 +κ 13 ⋅ s ⋅ ln[a −e] +κ 14 ⋅ s ⋅ ln[a −e]2 +κ 15 ⋅ ln[a −e] ⋅ ln[a] +κ 16 ⋅ ln[a −e] ⋅ ln[a]2 +κ 17 ⋅ ln[a −e]⋅ ln[a]3

κ 0 +κ 1 ⋅ s +κ 2 ⋅ c +κ 3 ⋅ ln[a] +κ 4 ⋅ ln[a]2 +κ 5 ⋅ e +κ 6 ⋅ e2

κ 0 +κ 1 ⋅ s +κ 2 ⋅ ln[a] +κ 3 ⋅ ln[a]2 +κ 4 ⋅ e +κ 5 ⋅ e2

Stomachκ 0 +κ 1 ⋅ s +κ 2 ⋅ c +κ 3 ⋅ ln[a] +κ 4 ⋅ ln[a]2 +κ 5 ⋅ ln[a]3 +κ 6 ⋅ ln[a]4 +κ 7 ⋅ ln[a −e] +κ 8 ⋅ s ⋅ ln[a] +κ 9 ⋅ s ⋅ ln[a]2 +κ 10 ⋅ c ⋅ ln[a]+κ11 ⋅ s ⋅ ln[a −e] +κ 12 ⋅ ln[a −e] ⋅ ln[a] +κ 13 ⋅ ln[a −e]⋅ ln[a]2 +κ 14 ⋅ ln[a −e] ⋅ ln[a]3 +κ 15 ⋅ ln[a −e] ⋅ ln[a]4

κ 0 +κ 1 ⋅ s +κ 2 ⋅ ln[a] +κ 3 ⋅ ln[a]2 +κ 4 ⋅ ln[a]3 +κ 5 ⋅ ln[a −e] +κ 6 ⋅ ln[a −e]2 +κ 7 ⋅ s ⋅ ln[a] +κ 8 ⋅ s ⋅ ln[a]2+κ 9 ⋅ s ⋅ ln[a]3 +κ 10 ⋅ ln[a −e] ⋅ ln[a] +κ 11 ⋅ ln[a −e]⋅ ln[a]2 +κ 12 ⋅ ln[a −e] ⋅ ln[a]3Liverκ 0 +κ 1 ⋅ s +κ 2 ⋅ ln[a] +κ 3 ⋅ ln[a]2 +κ 4 ⋅ ln[a −e] +κ 5 ⋅ ln[a −e]2 +κ 6 ⋅ e +κ 7 ⋅ s ⋅ ln[a] +κ 8 ⋅ s ⋅ ln[a]2 +κ 9 ⋅ s ⋅ ln[a −e]+κ10 ⋅ s ⋅ ln[a −e]2 +κ 11 ⋅ ln[a −e]⋅ ln[a] +κ 12 ⋅ ln[a −e] ⋅ ln[a]2 +κ 13 ⋅ ln[a −e]2 ⋅ ln[a] +κ 14 ⋅ ln[a −e]2 ⋅ ln[a]2+κ15 ⋅ e ⋅ ln[a] +κ 16 ⋅ e ⋅ ln[a]2 +κ 17 ⋅ e ⋅ ln[a −e]Lungκ 0 +κ 1 ⋅ s +κ 2 ⋅ ln[a] +κ 3 ⋅ ln[a]2 +κ 4 ⋅ ln[a]3 +κ 5 ⋅ ln[a −e] +κ 6 ⋅ ln[a −e]2 +κ 7 ⋅ e +κ 8 ⋅ s ⋅ ln[a] +κ 9 ⋅ s ⋅ ln[a]2+κ10 ⋅ s ⋅ ln[a −e] +κ 11 ⋅ s ⋅ ln[a −e]2Bone

κ 0 +κ 1 ⋅ s +κ 2 ⋅ c +κ 3 ⋅ ln[a]Non-melanoma skin

κ 0 +κ 1 ⋅ s +κ 2 ⋅ ln[a] +κ 3 ⋅ ln[a]2 +κ 4 ⋅ ln[a]3 +κ 5 ⋅ ln[a −e] +κ 6 ⋅ s ⋅ ln[a] +κ 7 ⋅ s ⋅ ln[a]2 +κ 8 ⋅ s ⋅ ln[a]3Page 284

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LSS solid cancer incidence data

Colon

3:41 pm+κ 18 ⋅ ln[a −e]2 ⋅ ln[a] +κ 19 ⋅ln[a −e]2 ⋅ ln[a]2 +κ 20 ⋅ e ⋅ ln[a] +κ 21 ⋅ e ⋅ ln[a]2 +κ 22 ⋅ e ⋅ ln[a]3
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284

Table D17 Forms of optimal background models assumed in fits of generalized ERR and EAR models to LSS mortality data [P10] and LSS solid cancer incidence
data [P48]Female breast

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Cancer siteln[h0 (a,e,c, s)] a

κ 0 +κ 1 ⋅ c +κ 2 ⋅ ln[min(a, 50)] +κ 3 ⋅ ln[max(a,50)] +κ 4 ⋅ ln[min(a, 50)]2 +κ 5 ⋅ ln[a −e] +κ 6 ⋅ ln[a −e]2 +κ 7 ⋅ e+κ 8 ⋅ ln[a −e] ⋅ ln[min(a, 50)] +κ 9 ⋅ ln[a −e]2 ⋅ ln[min(a, 50)] +κ 10 ⋅ e ⋅ ln[a −e] +κ 11 ⋅ e ⋅ ln[a −e]2

Brain and central
nervous systemκ 0 +κ 1 ⋅ s +κ 2 ⋅ ln[a] +κ 3 ⋅ ln[a]2 +κ 4 ⋅ ln[a]3 +κ 5 ⋅ ln[a]4 +κ 6 ⋅ ln[a −e] +κ 7 ⋅ ln[a −e]2 +κ 8 ⋅ e +κ 9 ⋅ e2 +κ 10 ⋅ s ⋅ ln[a]+κ16 ⋅ ln[a −e]2 ⋅ ln[a] +κ 17 ⋅ ln[a −e]2 ⋅ ln[a]2 +κ 18 ⋅ ln[a −e]2 ⋅ ln[a]3 +κ 19 ⋅ e ⋅ ln[a] +κ 20 ⋅ e ⋅ ln[a]2

κ 0 +κ 1 ⋅ s +κ 2 ⋅ c +κ 3 ⋅ ln[a] +κ 4 ⋅ ln[a −e] +κ 5 ⋅ e +κ 6 ⋅ e 2 +κ 7 ⋅ s ⋅ e +κ 8 ⋅ e ⋅ ln[a]All other solid

κ 0 +κ 1 ⋅ s +κ 2 ⋅ln[a] +κ 3 ⋅ ln[a]2 +κ 4 ⋅ ln[a]3 +κ 5 ⋅ ln[a]4 +κ 6 ⋅ ln[a −e] +κ 7 ⋅ ln[a −e]2 +κ 8 ⋅ e +κ 9 ⋅ e2 +κ 10 ⋅ s ⋅ ln[a] +κ 11 ⋅ s ⋅ ln[a]2+κ 12 ⋅ s ⋅ ln[a]3 +κ 13 ⋅ s ⋅ ln[a −e] +κ 14 ⋅ s ⋅ ln[a −e]2 +κ 15 ⋅ s ⋅ e +κ 16 ⋅ s ⋅ e2
a a = attained age, e = age at exposure, c = city (Hiroshima, Nagasaki), s = sex (male, female).

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10/7/08κ 0 +κ 1 ⋅ s +κ 2 ⋅ ln[a] +κ 3 ⋅ ln[a]2 +κ 4 ⋅ ln[a]3 +κ 5 ⋅ ln[a −e]
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Appendix E. Risk models fitted to the atomic bombing survivor data by
Bayesian Markov Chain Monte Carlo methods, and their use to obtain uncertainty
bounds on population risk
E1. In this appendix we detail the models used to fit the current LSS cancer mortality [P10] by Bayesian Markov chain
Monte Carlo (MCMC) methods. The models fitted are of the general form described in section IV of the main text, generalized ERR models. Generalized ERR models were fitted in which the expected cancer mortality rate at age a, for sex
s and city c, following exposure at age e to a dose D of radiation is given by:

h0 (a,e, c, s) ⋅[1 +F(D) ⋅φ(a, e, c, s)] =h0 (a, e,c, s) ⋅[1 +ERR(D, a,e, c, s)](E.1)

E2. In modelling the latest solid cancer mortality data [P10] the following generalized ERR model was used, in which
the cancer mortality rate for age a, age at exposure e, city c, sex s and “true” colon dose D is given by:⎡ 1 +(α ⋅ D +β ⋅ D 2 ) ⋅ exp[γ ⋅ D] ⋅⎤h0 (a,e, c, s) ⋅ ⎢⎥⎣ exp[κ 1 ⋅ 1s =female +κ 2 ⋅ ln[a −e] +κ 3 ⋅ ln[a]]⎦(E.2)

This is a generalized ERR model that is linear–quadratic–exponential in dose, and that incorporates adjustment to the ERR
for sex s, attained age a and time since exposure a – e. It is very similar to model (14) described in section IV of themain text, differing only in the exponential cell sterilization term exp[γ ⋅ D]. In addition, a variant of this model was fitted
in which the cell sterilization term γ was set to 0, i.e. the model is linear–quadratic in dose.
E3. Likewise, for leukaemia mortality the following generalized ERR model was used, in which the leukaemia mortality rate for age a, age at exposure e, city c, sex s and “true” bone marrow dose D is given by:

h0 (a,e, c, s) ⋅ ⎡⎣ 1 +(α ⋅ D +β ⋅ D 2 ) ⋅ exp[γ ⋅ D] ⋅ exp[κ 3 ⋅ ln[a] +κ 4 ⋅ ln[e]]⎤⎦(E.3)

This is a generalized ERR model that is linear–quadratic–exponential in dose and that incorporates adjustment to the ERR
for attained age a and age at exposure e. It is very similar to model (17) described in section IV, differing only in the
exponential cell sterilization term, exp[γ ⋅ D], and in the additional adjustment for age at exposure, exp[κ4 ⋅ ln[e]]. In addition, a variant of this model was fitted in which the cell sterilization term γ was set to 0, i.e. the model is linear–quadratic in dose. The parametric forms of the background models h0(a,e,c,s) used in models (E.2) and (E.3) are as describedin table D17 in appendix D.
E4. The natural modelling of measurement error in Bayesian MCMC methods is at the individual level. The stratification creates groups of subjects, and so requires transfer of the modelling of measurement error on the individual dose to
the measurement error on the mean dose over the stratum. At an individual level, the “true” dose distribution in each of
the two cities (Hiroshima, Nagasaki) is modelled by an extended Weibull distribution, as described in appendix C. The
probability density of the distribution of true dose D in each sex (s = male, female) and city (c = Hiroshima, Nagasaki)
is modelled by the superposition of an extended Weibull density function (similar to that used previously by Little [L32,L49]), with an additional uniform density on the true dose interval [0.0, 0.01] given by:

w sc (D) = ω1sc ⋅ ⎡⎣ ω 2 sc ⋅ω 3sc ⋅ Dω 3sc −1 + ω4 sc ⎤⎦ ⋅ exp ⎡⎣−ω 2 sc ⋅ Dω 3sc − ω 4 sc ⋅ D ⎤⎦+[1 −ω 1sc ] ⋅100 ⋅1D < 0.01

(E.4)

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E5. As in appendix C, a “classical” measurement error model is employed, since the main component of the measurement error comes from the declaration by the survivor of their location and orientation with respect to the hypocentre at
the time of explosion [R12, R20]. Therefore the distribution of the “nominal” dose d, given the “true” individual dose D,
is assumed here to be log-normal with median D. As in appendix C, and following the example of Pierce and colleagues[P2, P11, P16] and Little and colleagues [B18, L17, L29, L32, L33, L34, L35, L37], the “nominal” dose is assumed here
to be log-normally distributed with 35% GSD errors.
E6. A two-stage method is used for modelling the stratum-specific dosimetric uncertainties, very similar to the method
used by Little and colleagues in references [B18, L17] and described in more detail there. In the first stage, for each stratum i (defined by city, sex and age at exposure group) and dose group j, the distribution of the “true” mean dose D isijcomputed by Monte Carlo integration according to an iterative procedure that we now describe.
(a) Individual “nominal” doses are first sampled in the dose interval, using a trapezoidal distribution adapted to thewidth of the dose interval and parameterized so that the resulting distribution has the mean value specified on thedata file.
(b) Individual “true” doses are then sampled for each of the nij individuals in the stratum, conditional on the sampledindividual “nominal” doses, the current extended Weibull exposure distribution (E.4) and the (fixed) log-normal
error model.
(c) The extended Weibull distribution parameters (E.4) are resampled.
(d) Steps (a–c) are repeated 5,500 times.
(e) By averaging all the nij individual contributions, the mean “true” organ dose for the stratum, Dij , is thereby simulated. The 5000 iterations (discarding the initial “burn-in” 500 iterations) of this whole process yield a sample ofthe stratum mean “true” organ dose D .
ij(f) 500 replicates of steps (a–e) yield a sample of the stratum mean “true” organ dose Dij , from which are computed−12
the sample mean, µij, and normalized variance, σ ij nij , of the mean “true” organdose in the stratum.
(g) This true stratum mean dose distribution is then approximated by a normal or gamma distribution having mean
µij and variance σ ij 2 nij −1 . For groups of 5 subjects or less, the distribution of Dij is skewed, so that a gamma distribution is used, whereas for larger groups the normal distribution is a good approximation to the distributionof D .
ij

Steps (a–g) were performed using a FORTRAN program. This procedure was necessitated by the grouped nature of thedata, in particular by the fact that individual “nominal” doses were not available. In the second stage, the derived distribution of all the D is then used together with the ERR–EAR disease models (E.2) and (E.3) to derive the posterior
ij
distribution of the parameters of these ERR–EAR models. The Bayesian sampling was performed using WinBUGS [S89].
A total of 50,000 samples were taken for leukaemia and solid cancer, after 50,000 samples were discarded in each caseto allow the Markov chains to reach their stationary equilibrium distributions; convergence of the Markov chains wasassessed using the Gelman–Rubin statistic [G28]. Each set of model parameter values from this 50,000 sample was used
to calculate a measure of population cancer risk for the current Chinese, Japanese, Puerto Rican, United States and United
Kingdom populations, using the four risk measures detailed in appendix B and in section I.G of the main text. These samples of 50,000 parameter values are therefore associated with a sample of population cancer risks for these current five
populations. Tables E1 and E2 contain the parameter estimates (with 90% Bayesian CI) for the fitted models (linear–quadratic–exponential and linear–quadratic) for solid cancers and leukaemia.

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Table E1 Means and 90% Bayesian uncertainty intervals on the posterior distribution of solid cancer and leukaemia
generalized linear–quadratic–exponential ERR models to LSS mortality data [P10]All models are fitted by two-step Bayesian MCMC techniques. Uncertainty intervals computed from the last 50,000 samples fromchains, the first 50,000 samples of which had been discarded. D = radiation dose (Sv), a = attained age, e = age at exposure,s = sex.
Solid cancer generalized ERR model (adjustment for sex, attained age and years since exposure), linear–quadratic–exponential dose response

h0 (a,e,c, s) ⋅ ⎡⎣ 1 +(α ⋅ D +β ⋅ D 2 ) ⋅ exp[δ ⋅ D]⋅ exp[κ 1 ⋅1s =female +κ 2 ⋅ ln[(a −e) / 25] +κ 3 ⋅ ln[a / 50]]⎤⎦α=0.164 (–0.170, 0.497) Sv–1

β=0.683 (–0.079, 1.548) Sv–2

δ=

–0.412 (–0.864, 0.403) Sv–1

κ1 =0.575 (0.225, 0.944)

κ2 =1.020 (0.518, 1.579)

κ3 =–2.764 (-3.558, –1.982)

Leukaemia generalized ERR model (adjustment for attained age and age at exposure), linear–quadratic–exponential dose response

h0 (a,e, c, s) ⋅ ⎡⎣ 1 +(α ⋅ D +β ⋅ D 2 ) ⋅ exp[δ ⋅ D] ⋅ exp[κ 1 ⋅ ln[a / 50] +κ 2 ⋅ ln[e / 25]]⎤⎦α=–0.139 (–2.161, 2.350) Sv–1

β=7.368 (0.169, 13.180) Sv–2

δ=

–0.466 (–0.840, 0.014) Sv–1

κ1 =–1.838 (–2.746, –0.977)

κ2 =0.192 (–0.260, 0.681)

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Table E2 Means and 90% Bayesian uncertainty intervals on the posterior distribution of solid cancer and leukaemia
generalized linear–quadratic ERR models to LSS mortality data [P10]All models are fitted by two-step Bayesian MCMC techniques. Uncertainty intervals computed from the last 50,000 samples fromchains, the first 50,000 samples of which had been discarded D = radiation dose (Sv), a = attained age, e = age at exposure,s = sex.
Solid cancer generalized ERR model (adjustment for sex, attained age and years since exposure), linear–quadratic dose response

h0 (a,e, c, s) ⋅ ⎡⎣ 1 +(α ⋅ D +β ⋅ D 2 ) ⋅ exp[κ 1 ⋅1s =female +κ 2 ⋅ ln[(a −e) / 25] +κ 3 ⋅ ln[a / 50]]⎤⎦α=0.347 (0.161, 0.566) Sv–1

β=0.121 (0.004, 0.246) Sv–2

κ1 =0.613 (0.256, 1.005)

κ2 =1.024 (0.531, 1.589)

κ3 =–2.711 (–3.500, –1.944)

Leukaemia generalized ERR model (adjustment for attained age and age at exposure), linear–quadratic dose response

h0 (a,e, c, s) ⋅ ⎡⎣ 1 +(α ⋅ D +β ⋅ D 2 ) ⋅ exp[κ 1 ⋅ ln[a / 50] +κ 2 ⋅ ln[e / 25]]⎤⎦α=1.599 (0.134, 3.313) Sv–1

β=2.125 (0.927, 3.381) Sv–2

κ1 =–1.980 (–2.878, –1.120)

κ2 =0.233 (–0.221, 0.725)

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