United Nations Scientific Committee on the Effects of Atomic Radiation

ATTACHMENT A-23

POWER CALCULATIONS FOR EPIDEMIOLOGICALDETECTION OF HEALTH EFFECTS FROM THE
ACCIDENT AT THE FUKUSHIMA DAIICHI NUCLEAR
POWER STATIONUNSCEAR 2020/2021 Report, Annex B, Levels and effects of radiation exposuredue to the accident at the Fukushima Daiichi Nuclear Power Station: implicationsof information published since the UNSCEAR 2013 Report
Contents
In this attachment, the Committee has assessed whether various possible radiation risks may bediscernible in the more highly exposed or more susceptible segments of the Fukushima
population from epidemiological studies. Statistical power calculations have been performed forthis purpose and judgements reached on the discernibility, above the background rates, of severalof the more radiosensitive cancers and ages at exposure (e.g., thyroid, breast, leukaemia, all solidcancers), both among the general public and workers. A comprehensive description is providedof the methodology adopted and of the important underlying assumptions, many of which weredeliberately chosen so as not to underestimate the likelihood of discernibility.
Notes
The designations 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, orconcerning the delimitation of its frontiers or boundaries.
Information on Uniform Resource Locators (URLs) and links to Internet sites contained in thepresent publication are provided for the convenience of the reader and are correct at the time ofissue. The United Nations takes no responsibility for the continued accuracy of that informationor for the content of any external website.
© United Nations, May 2022. All rights reserved, worldwide.
This publication has not been formally edited.

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

3

Contents

I. PRELIMINARY CONSIDERATIONS ................................................................................. 5
A. Selection of health outcomes for study ............................................................................ 5
B. Statistical approach to assess discernibility of risk .......................................................... 6
C. Statistical power calculations ........................................................................................... 7
D. Statistical measures of health risks in this UNSCEAR report.......................................... 7
E. Selection of age- and sex-specific groups to study .......................................................... 8
F. Selected dose groups ........................................................................................................ 9
II. LIFETIME RISKS TO THE POPULATION FROM THE FUKUSHIMADAIICHI NUCLEAR POWER STATION RADIATION EXPOSURE ............................. 11
A. Calculation of lifetime baseline risk estimates for study groups.................................... 11
B. Calculation of lifetime excess risk and lifetime fractional risk estimates ...................... 12
C. Lifetime leukaemia risk .................................................................................................. 13
D. Lifetime thyroid cancer risk (without a thyroid screening programme) ........................ 17
E. Lifetime female breast cancer risk ................................................................................. 20
F. Lifetime risk of all solid cancer (excluding thyroid cancer and nonmelanoma
skin cancer)..................................................................................................................... 21
III. LEUKAEMIA AND THYROID CANCER RISKS TO AGES 30 OR 40 YEARS ........... 23
A. Leukaemia risk to ages 30 or 40 years after childhood exposure .................................. 23
B. Statistical power to detect thyroid cancer risk (in the absence of a screening
programme) to ages 30 or 40 years after childhood exposure ....................................... 26
IV. IN UTERO EXPOSURE AND CANCER RISK ................................................................. 28
V. THYROID CANCERS DIAGNOSED IN THE FUKUSHIMA HEALTH
MANAGEMENT SURVEY EXAMINATION PROGRAMME ........................................ 29
A. Cumulative baseline risk estimation............................................................................... 29
B. Cumulative excess risk and cumulative fractional risk estimation ................................ 29
VI. STATISTICAL POWER TO DETECT EXCESS RISKS AMONG WORKERS .............. 32
VII.

OVERALL INTERPRETATION AND DISCUSSION ................................................ 34

VIII.

CONCLUSIONS ............................................................................................................ 35

APPENDIX ................................................................................................................................ 37
REFERENCES ........................................................................................................................... 59

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

I.

5

PRELIMINARY CONSIDERATIONS

1.
The basic question addressed by the Committee in this attachment is whether various
possible radiation risks from the Fukushima nuclear accident will likely be discernible in the
more highly exposed or more susceptible segments of the Fukushima Prefecture population oramong the Fukushima Daiichi Nuclear Power Station (FDNPS) emergency workers. Statisticalpower calculations have been performed for this purpose, and judgements reached on thediscernibility, above the background rates, of several of the more radiosensitive cancers and ages
at exposure (e.g., thyroid, breast, leukaemia, all solid cancers, etc.), both among the generalpublic and workers. The rationale, assumptions, data and methodologies for the determinationsof risk are also provided regarding the risk assessment.
2.
A preliminary assessment of statistical power was made in conjunction with the
UNSCEAR 2013 Report [UNSCEAR, 2014], and published subsequently [UNSCEAR, 2016].
The Committee determined that an update was needed because the estimated doses to the public
have been revised, based on the availability of more, and more reliable, data. In addition,
estimated baseline cancer risks have undergone a modest amount of change since the UNSCEAR
2013 Report, and new data have become available to update estimates of radiation risk forvarious cancer endpoints. Several topics to set the stage for the assessment of the ability to detect
possible radiation risks in Fukushima Prefecture are described.
A.

Selection of health outcomes for study

3.
The main health risk of concern from exposure at low radiation doses is the induction ofcancer. In particular, leukaemia, female breast cancer and thyroid cancer are malignancies thatare known to clearly show excesses in incidence following exposure to ionizing radiation,
particularly when exposure occurs at a young age. Statistical power to detect these cancer risks
has therefore been assessed.
4.
In addition, all solid cancers were selected for analysis to reflect the fact that radiation
exposure causes cancer in many organs/tissues of the body and, together with leukaemia,
provides information on overall cancer risk, which is widely used in radiation risk assessment
and radiation protection. Assessing risk for all solid cancers also provides a more statisticallystable risk estimate because larger numbers of cases are available, which can be useful whenassessing small risks at low doses [Walsh et al., 2014]. The rates of “all solid cancers” exclude
nonmelanoma skin cancers because these forms of skin cancer are reported only irregularly andincompletely to cancer registries. Thyroid cancer was analysed separately, but was excluded
from all solid cancers analyses, given that the doses to the thyroid tended to be larger than, andonly partially correlated with, doses to other organs/tissues, because the radionuclides released
from FDNPS included radioisotopes of iodine for which the thyroid gland has enhanced uptake
compared to other organs/tissues. Although female breast cancer also was analysed separately,
it was included in all solid cancer analyses, because the doses to the breast from the radionuclide
releases are similar to those to other organs/tissues, and female breast cancer is considered amajor contributor to “all solid cancers” radiation risk. This attachment refers simply to “all solidcancers” with the implicit understanding that thyroid cancer and nonmelanoma skin cancers are
excluded. Analyses of haematopoietic malignancies other than leukaemia were not conductedbecause little evidence is available supporting an association with radiation exposure at low doses.

6

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

B.

Statistical approach to assess discernibility of risk

5.
In estimating cancer risks, a linear no-threshold dose response was assumed without an
attenuating factor for low doses and low dose rates (dose and dose-rate effectiveness factor(DDREF)). The shape of the dose-response model, as applicable to risks at very low doses andlow dose rates, is uncertain, although epidemiological data developed in the last decade fromcohorts with low doses or doses received in a protracted or fractionated manner, have tended topoint toward risks that are roughly commensurate with a linear no-threshold model [Hauptmann
et al., 2020; NCRP, 2018]. A linear model without incorporation of a DDREF may perhaps beconservative in estimating radiation risks at low doses or low dose rates (i.e., it may
overestimate) [Averbeck, 2009], but it nevertheless can be viewed as a prudent model.
6.
It was assumed that a radiation effect would be discernible if the power of a statistical
test to detect a difference between a disease rate in the exposed population and the corresponding
rate in the unexposed general population (assuming a one-sided significance level of p = 0.05)
is at least 80%, which is a conventional assumption. The statistical power estimation assumed
the following ideal conditions:− The baseline risk was known (approximated by averaging the age- and sex-specific rates
for particular cancers from several essentially unexposed prefectures in Japan to apply tothe corresponding population in Fukushima), and that these comparison cancer registries
had complete cancer ascertainment and accurate diagnoses;− Health effects in the exposed population could be detected without any loss in follow-up,
misdiagnosis or under-ascertainment of cancer diagnosis; and− Statistical power was modelled deterministically, assuming a linear no-threshold modelthat did not account for uncertainty in the risk estimates, except that risk also was assessed
for the 95th percentile upper bounds on the average doses. Given that the ideal criteria
above cannot be fully met, the actual statistical power to detect excess risks is likely tobe somewhat less than calculations assuming those ideal conditions would indicate.
7.
For the analyses of age- and sex-specific populations, higher dose subgroups wereselected for analysis in addition to the total groups, because a higher dose often will tend toachieve greater statistical power than the entire dose range. The mean dose of the selected group
was used to estimate radiation-attributable risk. Because of inevitable uncertainties in the doses,
the statistical power assessments were also made using 95th percentile upper bounds on theestimated mean doses, where the upper bounds were derived from the distributions of doses by
the Committee using Monte Carlo methods (see attachment A-12).
8.
An attempt was made to guard against reliance on one particular data set or resulting
model of risk in order to provide representative coverage of risk estimates and the associatedstatistical power. This conservative strategy was to avoid reliance on a single model that may
have underestimated risk. Therefore, when several sets of risk estimates from major relevant
studies are available, results are presented for each.
9.
The statistical power analyses are limited to residents of Fukushima Prefecture, where
exposures were generally larger than in other prefectures. Because there is very limited andinadequate information available on doses to individuals, the analyses were performed using
mean doses or 95th percentile upper bound on estimated mean doses for municipalities or groupsof evacuees. Dose estimates were developed by the Committee for the 50 non-evacuatedmunicipalities in Fukushima Prefecture and separately for 40 groups of evacuees who followed
one or another evacuation pattern (i.e., start and duration of evacuation, route and final

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

7

destination). (For simplification, this attachment uses just “evacuated municipalities” or“evacuees” to refer to the groups evacuated from evacuation-ordered, -planned and -prepared
areas.) The estimated doses to relevant organs/tissues were employed for the analyses. For threeof the non-evacuated municipalities, a subset of people had been evacuated or relocated, andthose individuals were subtracted from the non-evacuated population in those municipalities.
C.

Statistical power calculations

10.
Statistical power can be defined as “the probability that a test of significance will detect
a deviation from the null hypothesis, should such a deviation exist” [Walmsley and Brown,
2017], or, in simpler terms, power is the probability that a test of significance will detect an effect
if one is present. In these analyses, statistical power is a function of the sample size, thebackground rate of a particular disease, and the magnitude of the effect, which is related to thedose [Gilbert et al., 2020].
11.
The statistical power calculations were implemented using the G*Power program,version 3.1.9.6 [HHU, WEB, 2020], which has been documented by Faul et al. [Faul et al., 2009;
Faul et al., 2007]. The statistical power calculations were performed using the choice to compute
statistical power, given “alpha, sample size and effect size” for Poisson regression. A variety of
scenarios was examined to determine the impact of calculating approximate person-years ofobservation versus simply number of persons. With the small estimated excess relative risks(ERRs) involved in this report, because of the relatively low doses, identical results to at least
the third or fourth decimal place were found using either numbers of persons and cumulative
risks, or person-years and cancer rates per year, so for simplicity, numbers of persons andcumulative risks were used in all analyses. This is similar to what was done in the statisticalpower calculations used for the UNSCEAR 2013 Report [UNSCEAR, 2016]; it was noted thatusing either Poisson or binomial distributions gave similar results.
12.
The calculations used estimates of cumulative baseline risks (CBRs), absent radiation
exposure, and cumulative excess risk (CERs) to calculate cumulative fractional risks (CFRs),
given a dose estimate and employing a linear model, and to estimate notional numbers ofradiation-related cases in the selected population groups. Municipality-average doses estimatedfor the population of Fukushima Prefecture were generally low. Because radiation risks are
uncertain in the low-dose region, the cases “attributable to radiation” calculated here are largely
notional (having been inferred using models) and are used only to assess whether a radiation
impact on health would likely be discernible or not. An attempt was made to make the assessment
somewhat conservative (i.e., using assumptions and values that would tend to increase theestimated statistical power) so that statistical power would not be underestimated. But, as in any
statistical power assessment, the results are valid only if the underlying assumptions and values
used are appropriate.
D.

Statistical measures of health risks in this UNSCEAR report

13.
The indicators of health risk used in this UNSCEAR report are defined as follows(adapted from [UNSCEAR, 2020]):− Lifetime baseline risk (LBR) and CBR: CBR is the cumulative baseline risk of a specificdisease (incidence or mortality) occurring up to a given age in the absence of theparticular exposure under consideration [Walsh et al., 2014]; the LBR is the CBR toage 90. The CBRs and LBRs were calculated as the number of cases in selected

8

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

populations, given their respective sex, size of the population group, and age at the timeof the FDNPS accident;− Lifetime excess risk (LER) and CER: CER specifies the cumulative risk of a specificdisease (incidence or mortality) occurring up to a given age, attributable to a given dose;excess is understood in comparison to a population group not exposed to radiation; theLER is the CER to age 90. The CERs and LERs were calculated, for the selected
populations, using the estimated doses and particular risk models;− Lifetime fractional risk (LFR) and CFR: CFR reflects the relative increase represented
by the CER in relation to the CBR given by the ratio CER/CBR; the LFR is the ratio
LER/LBR to age 90. The CFRs and LFRs reflected the proportional increases compared
to baseline rates that were associated with given doses and risk models in the selected
populations, i.e., the ratio of a respective CER to CBR, or LER to LBR; and− ERR and relative risk (RR): RR is the relative risk at some defined dose, and ERR is theexcess relative risk at that dose (ERR = RR – 1). When only ERRs, and not LFRs, werereported for models in relevant publications, the simplifying assumption was made in this
attachment that the LFRs are approximately equivalent to the ERRs per unit dose times
the relevant estimated dose expressed as a fraction of the unit dose.
E.

Selection of age- and sex-specific groups to study

14.
The 2010 census data [Statistics Bureau of Japan, 2011] were used as an approximation
to the numbers of males and females of various ages in each municipality of Fukushima
Prefecture as of March 2011 (table A-23.1).
15.
For a number of common types of cancer, the best documented and most widespreadsusceptibility factor for cancer is a young age at exposure, as documented in [UNSCEAR, 2013].
The plan therefore was to analyse groups who were young at the time of their initial FDNPS
exposure, with a particular emphasis upon the early childhood group who are likely to be the mostsusceptible to cancer induction by radiation. Specifically, statistical power was calculated basedon the mean doses and numbers of individuals in each municipality or evacuation group who were:− 1-year-old infants, which used aggregated ages of in utero to age 5 years (all assumed
to be age 1 for calculation purposes);− 10-year-old children, aggregated ages 6–19 years (calculated as if age 10 years); and− 20-year-old adults, aggregated ages 20–35 years (calculated as if age 20 years at exposure).
Attention focused particularly on the 1-year-old group, because evidence indicates that young
children are the most susceptible to radiation risks. A similar strategy was used to analyse
statistical power for the UNSCEAR 2013 Report [UNSCEAR, 2016]. Attachments A-13 to A-19
provide dose estimates for exposure at ages 1, 10 and 20 years, respectively, which have beenused for the statistical power analyses.
16.
Children who were in utero at the time of the FDNPS accident are considered as part of
the “age 1 year” group. Fujimori et al. [Fujimori et al., 2014] reported there were nearly
16,000 children in Fukushima Prefecture who were in utero at the time of the accident. With
only a slight inaccuracy, the background cancer rates and radiation-related excess risks for thein utero group are assumed to be comparable to that of the 1-year-old group [Preston et al., 2008],
although a set of statistical power analyses of leukaemia and thyroid cancer in the in utero group
was also performed separately.

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

9

17.
Females tend to have greater radiation-related risks for solid cancers than males, although
the opposite is true for leukaemia. Therefore, analyses were performed separately for females
and males. In addition, statistical power was examined for males and females combined, since
the larger combined numbers of individuals may tend to increase the statistical power. Analyses
were also performed using only the municipalities with cumulative lifetime effective doses>5 mSv, because such analyses sometimes yielded greater statistical power than analyses
including municipalities with all dose levels.
18.
In addition, statistical power was assessed for the detectability of risks to emergency
workers at FDNPS, insofar as the information on worker doses was available.
Table A-23.1. Numbers of Fukushima Prefecture residents in the statistical power analyses, basedon summation of municipality age- and sex-specific data from the 2010 Japan census [Statistics
Bureau of Japan, 2011] or estimated from total numbers in evacuated municipalitiesGroup

Age 1 year

Age 10 years

Age 20 years

Non-evacuated plus evacuated municipalities with mean lifetime effective dose >5 mSv
Male a34 52477 603103 120

Female32 74077 54399 870Total

67 264155 146

202 990

All non-evacuated municipalitiesMale

55 597135 044163 132

Female52 912

128 436

156 628

Total

108 509263 480

319 760

Evacuated municipalities b
Male

3 873

8 088

9 847

Female

3 693

7 708

9 385

Total

7 566

15 79619 232aMunicipalities in which 1-year-olds were estimated to have a mean cumulative lifetime effective dose of >5 mSv.
Estimated as the approximate proportion of total evacuees who were in the given age ranges according to the proportions by age and sex in
Fukushima Prefecture from the 2010 census data [Statistics Bureau of Japan, 2011].

b

F.

Selected dose groups

19.
All the dose values used in this attachment were based on arithmetic rather thanlogarithmic calculations. The assumption is made that the estimated mean dose to residents of agiven age in each municipality is representative of the doses to those residents. Clearly there
would be variation among individual residents in their exposure levels in addition to uncertainties
in average municipality doses, but insufficient information was available to perform any analysis
at the individual level. Therefore, municipality mean doses weighted by numbers of individuals
within each non-evacuated or evacuated municipality were used to derive overall mean doses fordefined age groups. These mean doses and the 95th percentile upper bounds on the mean doses
were used for statistical power analyses.
20.
Mean first-year doses and doses at the 95th percentile upper bound on the mean were
calculated by the Committee for each non-evacuated municipality for effective dose and for theabsorbed dose to the red bone marrow (RBM), breast, thyroid gland and colon. Similarly, mean
and upper bound on mean doses were calculated for groups of evacuees who followed different
evacuation patterns. The 95th percentiles on mean dose estimates were obtained by the

10

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Committee using Monte Carlo sampling of the dose distributions for municipalities andevacuation scenarios.
21.
Yearly increments in effective dose out to 80 years of age (“lifetime dose”) after theaccident were estimated for those initially exposed at ages 1, 10 or 20 years (table A-23.2). Since
fractional increments in dose at age 80 years after the accident were estimated to be very small(0.1% to 0.4% of the initial dose), the dose to 80 years will be considered the “lifetime dose”.
Ratios of the first-year RBM, colon and breast doses to the first-year effective dose were thenapplied to the 80-year effective dose to derive estimates of lifetime doses for those organs/tissues,
based on the ratios found for 1-year-olds. The ratios of organ/tissue doses to the effective dose
ranged from 93% for RBM to 99% for the colon. Lifetime doses to the RBM, colon and breast
after the first year were then estimated in proportion to the lifetime effective dose which had
been modelled (see attachment A-19).
22.
A subset of municipalities with higher average doses was also chosen for statistical power
analysis. For analyses of leukaemia, breast cancer and all solid cancers, the subset was definedas those municipalities with average lifetime cumulative effective doses greater than 5 mSv for
1-year-olds. Nineteen of the 50 non-evacuated municipalities and seven evacuated municipalities
were in this subset.
23.
Doses to the thyroid were notably higher than other doses for some municipalities due tointernal exposures from short-lived radioactive iodine isotopes, because the thyroid gland has ahighly enhanced uptake of iodine compared to other organs. The geographic pattern of thyroid
doses was only partially concordant with that for doses to other organs; for example, lists ofmunicipalities with the five highest average effective doses and five highest thyroid doses had
only one municipality common to both lists.
24.
Because of the limited concordance of thyroid cancer doses to doses for other organs, aseparate higher-dose subset was defined for selected analyses as municipalities with first-year
thyroid doses greater than 5 mGy in the respective age group in both evacuated and nonevacuated municipalities.
Table A-23.2. Estimated lifetime cumulative mean doses (mGy) and 95th percentile upper boundsof mean doses for selected organs/tissues in selected dose groups
Age 1 year a,b

Age 10 years a

Age 20 years a

Evacuated and non-evacuated municipalities, subset with >5 mSv lifetime effective dose c
Red bone marrow
Mean dose

12.5

11.7

9.6

95% upper bound on mean dose

19.9

18.6

14.4

BreastMean dose

13.7

11.4

10.3

95% upper bound on mean dose

21.5

17.5

15.7

Colon
Mean dose

13.9

12.5

9.8

95% upper bound on mean dose

21.8

19.5

15.0

Thyroid gland d
Mean dose

15.9

19.9

17.0

95% upper bound on mean dose

32.0

41.6

33.4

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Age 1 year a,b

Age 10 years a

11

Age 20 years a

Non-evacuated municipalities: all doses
Red bone marrow
Mean dose

8.9

7.7

6.9

95% upper bound on mean dose

14.4

12.2

10.6

BreastMean dose

9.8

8.0

7.7

95% upper bound on mean dose

15.0

12.2

11.8

Colon
Mean dose

9.6

8.5

7.1

95% upper bound on mean dose

15.0

12.9

10.6

Thyroid gland
Mean dose

12.7

11.3

9.4

95% upper bound on mean dose

25.4

22.4

17.6

Evacuated and non-evacuated municipalities: all doses
Thyroid gland
Mean dose

12.5

11.1

9.3

95% upper bound on mean dose

24.8

22.3

17.5

Evacuated municipalities only
Thyroid gland
Mean dose

10.7

8.3

6.4

95% upper bound on mean dose

25.8

20.5

15.7

Evacuee subset with >5 mGy mean dose in first year
Mean dose d

12.0

10.9

11.4

95% upper bound on mean dose

29.4

30.8

30.0

a

Age 1 year includes individuals of ages from in utero to 5 years; age 10 years includes ages 6 to 19 years; age 20 years includes ages 20−35 years.
All mean and upper bound doses were calculated on a municipal population-weighted basis.
b
Based on cumulative mean doses and 95th percentile upper bounds on the means, summed to age 80 years.
c
Municipalities in which the lifetime mean effective dose was >5 mSv for those at age 1 year.
d
For the thyroid gland the higher-dose subsets were the non-evacuated and evacuated municipalities for which the first-year thyroid dose wasgreater than 5 mGy among those of the respective ages.

II.

LIFETIME RISKS TO THE POPULATION FROM THE FUKUSHIMADAIICHI NUCLEAR POWER STATION RADIATION EXPOSURE

A.

Calculation of lifetime baseline risk estimates for study groups

25.
The definition used here of the LBR of the incidence of a selected cancer was the
cumulative incidence of that cancer up to age 90 years [WHO, 2013]. Using representative
Japanese cancer rates [IARC, 2017] from the starting ages, and lifetable methods, the Committee
estimated the LBR of leukaemia, breast cancer, thyroid cancer and all solid cancers as firstprimary malignancies, by adjusting their cumulative rates for rates of prior mortality [Vital
Statistics of Japan, 2019] and prior malignancies, i.e., for cancer-free survival, similar to themethod used in the statistical power assessment in the UNSCEAR 2013 Report [UNSCEAR,
2016]. The lifetables were based on the latest International Agency for Research on Cancer(IARC) publication of age- and sex-specific cancer rates [IARC, 2017], averaged across the rates

12

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

from four long-standing, good quality Japanese prefecture cancer registries (namely, Fukui,
Miyagi, Nagasaki and Yamagata prefectures). More details are given in section I of the appendix.
B.

Calculation of lifetime excess risk and lifetime fractional risk
estimates

26.
For estimation of LERs and LFRs, which were needed for calculating statistical power,
doses were estimated for each applicable organ/tissue and each Fukushima municipality or
evacuated locality, as detailed in attachments A-13 to A-19. Estimated RBM doses were used toassess leukaemia risk, colon doses for the risk of all solid cancer, and breast and thyroid doses
for those respective organs/tissues. Weighted mean doses, with weighting by the number of
persons of selected sex and ages in each municipality, and 95th percentile upper bounds on themeans were then used to calculate the aggregated mean dose and its 95% upper bound.
27.
For the population affected by the FDNPS accident, a LER is an estimate of the excess
number of cancer cases of a selected type, given the size of the exposed population being
analysed and the estimated average dose. The LERs are scaled linearly according to dose, which
is based on the assumption that, for cancer endpoints, the lifetime risks for doses less than a few
hundred mGy or received at a low dose rate are essentially proportional to organ/tissue doses[Walsh et al., 2014].
28.
The LERs and LFRs used in the statistical power attachment [UNSCEAR, 2016] to the
UNSCEAR 2013 Report [UNSCEAR, 2014] were calculated with respect to radiation dose basedon the assumptions that the minimum latency period was 2 years for leukaemia, 3 years forthyroid cancer, and 5 years for breast cancer and all solid cancer, and starting with LFRs in the
WHO Fukushima report [Walsh et al., 2014; WHO, 2013]. These LFRs served as preliminary
CER estimates, which were adjusted for this report by applying ratios of new estimates of ERRs
derived from recent published reports to the formerly used estimates. These are reported as oneof the models used to assess risks in this attachment. The description of the calculation of LFRs
is given for the various risk models applied to each type of malignancy in the relevant sections
below. In developing models, the simplifying assumption was made in this attachment that when
LFRs are not available in relevant publications, ERRs can be substituted for them, which may
be approximately true for low doses. A comparison of the LERs used for the UNSCEAR 2013
Report [UNSCEAR, 2016] with those provided by selected models in the present report is given
in table A-23.3, and the sources of the LERs given in the table are described in the tablefootnotes. More details concerning the calculation of LERs are provided in section II of theappendix.
Table A-23.3. Cumulative lifetime baseline risk (LBR) to age 90 years and lifetime excess risk of theincidence of selected malignancies as absolute per cent increases in risk for an absorbed dose of
100 mGy to relevant organs, as estimated in the UNSCEAR 2013 Report [UNSCEAR, 2016] and in
this assessment
Male
Starting age(years)Baseline risk,
this assessment(LBR (%))

UNSCEAR2013 Report(LER (%))

FemaleThisassessment(LER (%))

Baseline risk,
this assessment(LBR (%))

UNSCEAR2013 Report(LER (%))

This
assessment(LER (%))

All solid cancer (except thyroid cancer)a
1

42.8

2.30

2.37

29.9

3.50

3.86

10

42.8

1.85

1.91

29.9

2.75

3.03

20

42.7

1.40

1.44

29.8

2.10

2.37

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

MaleStarting age(years)Baseline risk,
this assessment(LBR (%))

13

FemaleUNSCEAR2013 Report(LER (%))

This
assessment(LER (%))

Baseline risk,
this assessment(LBR (%))

UNSCEAR2013 Report(LER (%))

This
assessment(LER (%))

Leukaemia b
1

0.63

0.11

0.55

0.47

0.08

0.50

10

0.57

0.07

0.36

0.42

0.05

0.34

20

0.55

0.06

0.06

0.40

0.04

0.04

1

6.09

1.00

1.41

10

6.09

0.66

0.94

6.09

0.42

0.59

Female breast cancer c

20
Thyroid cancer d
1

0.37

0.10

0.16

1.15

0.43

0.60

10

0.37

0.073

0.089

1.15

0.28

0.35

20

0.37

0.039

0.048

1.14

0.15

0.19

a

Based on absorbed colon dose. Calculated by comparing the linear ERR coefficients of Grant et al. [Grant et al., 2017] to those of Preston etal. [Preston et al., 2007], both based on the Japanese Life Span Study (LSS). A comparison of the newer Grant coefficients to the older Preston
coefficients that had been used in the UNSCEAR 2013 Report [UNSCEAR, 2014] gave ratios of 1.10 for females and 1.03 for males.
bBased on absorbed dose to the RBM [Berrington de Gonzaléz et al., 2016; Hsu et al., 2013; UNSCEAR, 2020]. Information concerning
additional models used to assess LFRs and LERs is given in the text (paragraph 32).
c
Based on absorbed breast dose. A ratio of 1.29 was found for the newer LSS risk estimate [Brenner et al., 2018] compared to the former one[Preston et al., 2007] used in the UNSCEAR 2013 Report.
dBased on absorbed thyroid dose. The statistical power assessment for the UNSCEAR 2013 Report [UNSCEAR, 2016] provided the 2013 LER
for age 1, and Walsh et al. [Walsh et al., 2014] provided the LERs for ages 10 and 20. The LERs for the current assessment derive from a recent
UNSCEAR report [UNSCEAR, 2020] and are based on the newest LSS thyroid cancer data [Furukawa et al., 2013]. Information concerning
additional models used to assess LFRs and LERs is given in the text (paragraph 38).

C.

Lifetime leukaemia risk

29.
Leukaemia is often considered a sentinel malignancy after radiation exposure because of
the high relative risk per unit dose compared to most other forms of malignancy, particularly at
young ages at exposure. However, it is an uncommon malignancy compared to many other formsof cancer, which limits the statistical power to detect excess risks after low-dose exposure.
30.
The LBRs for sex and age groups were calculated by lifetable methods similar to thosein the statistical power attachment to the UNSCEAR 2013 Report [UNSCEAR, 2014], but using
updated age-sex specific leukaemia rates from three long-standing prefecture cancer registries inJapan (Fukui, Miyagi and Yamagata prefectures) [IARC, 2017] with adjustment for cancer-freesurvival, as described in section I of the appendix. The LBRs for the present leukaemia
calculations had values similar to those of the UNSCEAR 2013 Report, for instance withrespective values for males of: 0.62% and 0.60% for ages 1–90, 0.57% and 0.58% for ages 10–90,
and 0.53% and 0.57% for ages 20–90 years.
31.
The LER and LFR estimates are based on estimates of the first year absorbed dose to the
RBM in attachment A-15 plus continued lifetime dose proportionate to the lifetime effective
dose (attachment A-19). It should be noted that linear risk estimates were used for leukaemia,
even though the LSS leukaemia data [Hsu et al., 2013] have shown a linear-quadratic curve withupward curvature. At low doses or low dose rates, however, the dose response is consideredapproximately linear [Walsh et al., 2014].

14

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

32.
Four models were applied to estimate the excess risk of leukaemia from radiation
exposures due to the FDNPS accident:(a) First, the LERs and LFRs for sex and age groups in the UNSCEAR 2013 Report werebased on a report by Walsh et al. [Walsh et al., 2014] which provided standardized resultsfrom the WHO Fukushima report [WHO, 2013]. The risk estimates here are based on the
updated rather than the original LBRs;(b) Second, the recent UNSCEAR report [UNSCEAR, 2020] derived CFR estimates for
exposure at ages 1 and 10 years with observation to age 40 years of 3.0 at 100 mGy,
which was based on a study of children in the United Kingdom of Great Britain andNorthern Ireland with radiation exposure from CT examination [Berrington de Gonzaléz
et al., 2016]. The model included both leukaemia proper and myelodysplastic syndromes,of which the latter had a much higher risk coefficient [Berrington de Gonzaléz et al.,2016; Pearce et al., 2012]. The study yielded LFR estimates of 3.0 at 100 mGy for ages
1–40 and 3.1 for ages 10–40 years;(c) Third, the LFR estimates from the LSS study [Hsu et al., 2013; UNSCEAR, 2020] were
2.15 for ages 1–40 and 0.85 for ages 10–40 years;(d) Fourth, a recent analysis of data from nine pooled studies of childhood external radiation
exposures with RBM doses ≤100 mGy was reported [Little et al., 2018]. It yielded an
ERR estimate of 0.84 at 100 mGy for all leukaemia except the chronic lymphocytic type,which was employed as a LFR estimate. This risk estimate from the pooled analysis
included the leukaemia results from the United Kingdom study of computerized
tomography (CT) examinations, but without the myelodysplastic syndrome cases.
33.
Because the UNSCEAR and pooled-study risk estimates were primarily limited to young
ages of observation, from ages 41 to 90 the estimated LFR coefficients from the WHO report oflifetime risk were applied [Walsh et al., 2014; WHO, 2013]. When suitable LFRs for cumulative
lifetime incidence of leukaemia were not available for adult exposures, as in the case of theUnited Kingdom CT study [UNSCEAR, 2020], the ratio of 1.25 was derived from [UNSCEAR,
2020], based on the recent INWORKS worker study ERR for leukaemia mortality [Leuraud etal., 2015] compared to the LSS leukaemia ERR for adults [Kaiser and Walsh, 2013]. The ratio
was applied to the WHO-based risk coefficient to derive a LFR for age 20 at exposure, with theresults shown for age 20 for this assessment model in table A-23.4. None of the models found adifference in the LFRs by sex, so the same LFR model was applied for both sexes. The LFRs at
a 100 mGy RBM dose for these models, as applied to the Fukushima Prefecture population,
are shown in table A-23.4, and these LFRs were applied in performing statistical power
calculations of lifetime leukaemia risk.
34.
Each model, with results shown in table A-23.5, has strengths and disadvantages. The
WHO 2013 Fukushima model, derived from older LSS data, provides a baseline for comparisonof newer models. The LSS model [Hsu et al., 2013; UNSCEAR, 2020] is based on a Japanese
population similar to the Fukushima population and had well-characterized whole-body doses,
but the duration of the single exposure was very brief and many exposures were much higher
than exposures in the Fukushima population. The pooled study of low-dose childhood exposures[Little et al., 2018] was estimated from relatively low total doses (<100 mGy), but based
primarily on one or a few brief exposures, many of which were partial-body exposures. The
UNSCEAR model [UNSCEAR, 2020], based on the study of United Kingdom children with CT
examinations [Berrington de Gonzaléz et al., 2016], had small, fractionated exposures and a goodquality follow-up. However, the LFR included both leukaemia and myelodysplastic syndromes,
and the latter appeared to upwardly bias the risk estimates. In particular, this report showed thehighest risk estimate per 100 mGy of 3.3 (95% CI: 0.4, 11.4) [Berrington de Gonzaléz et al.,

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

15

2016] compared to other models, but the previous report of the United Kingdom CT study with
virtually the same data found that the risk was about 45% smaller if the myelodysplastic
syndrome cases were not included [Pearce et al., 2012]. In addition, a recent Netherlands studyof leukaemia after childhood CT exposures reported ERRs per 100 mGy of 0.21 (95% CI: −0.12,2.40) for leukaemia and 0.04 (95% CI: −0.12, 1.61) for combined leukaemia and myelodysplastic
syndromes [Meulepas et al., 2019], which were appreciably numerically lower than the value
from the United Kingdom CT study used in the UNSCEAR analysis [Berrington de Gonzaléz etal., 2016; UNSCEAR, 2020].
Table A-23.4. Lifetime fractional risks of leukaemia attributable to radiation at a red bone marrow
radiation dose of 100 mGy according to various models
WHO model,
LFR (%)a

This assessment model,
LFR (%)b

LSS model,
LFR (%)c

Pooled study model,
LFR (%)d

Age 1–Male

18.5

87.0

66.3

34.4

Age10–Male

11.5

62.5

24.1

23.9

Age 20–Male a

10.0

12.5

10.6

Age 1–Female

17.5

106

79.4

38.3

Age 10–Female

11.0

79.6

28.0

27.8

Age 20–Female a

9.5

11.9

7.0

Age (year) and sex

a[UNSCEAR, 2016; Walsh et al., 2014; WHO, 2013]. These LFRs were also used for the attained ages of 41–90 years with all models.
[Berrington de Gonzaléz et al., 2016; UNSCEAR, 2020]. Modelling was performed only for ages 1 and 10 years. The basis for the age 20 LFR
is given in paragraph 32.
c[Hsu et al., 2013; UNSCEAR, 2020].
d[Little et al., 2018]. The model was based primarily on childhood and adolescent exposures, so it was not applied for age 20.
b

35.
Table A-23.5 shows the statistical power estimates at 100 mGy using LFRs derived fromthe models mentioned above. LFRs based on the estimated municipality doses are shown formales and females for combined evacuated and non-evacuated municipalities in table A-23.A2
in section II of the appendix. To summarize the results, the estimates of risk based on the meandoses, considered the best estimates of risk, generally did not achieve the 80% statistical power
criterion for detectability of risk. The 95th percentile upper bounds on the mean dose from theMonte Carlo dose calculations sometimes achieved 80% statistical power with the UNSCEAR
model of risk [UNSCEAR, 2020], but not with the other three models. Even though the LFRs
for leukaemia are relatively high compared to almost all solid cancers, there is little indicationof potential detectability of excess risk owing to the low doses and the low background incidenceof leukaemia in the population. For instance, in the subgroup from in utero to five years old at
the time of the FDNPS accident, who are expected to be the most sensitive group for leukaemia,
about 10–50 excess incident cases of leukaemia during their lifetime may be inferred from theestimated dose levels to the red bone marrow, depending on the model used, while the baseline
number (in the absence of radiation exposure from the FDNPS accident) is about 640 cases with
a 95% coverage interval of 590 to 690. Furthermore, this range does not include possible
variations in background rates among geographic localities, changes in rates over calendar time,etc. Even 50 potential excess cases would probably not be discernible among 640 expected cases,
given the estimated range of uncertainty in the baseline risk. In addition, the previous discussion
in paragraph 34 of this attachment suggests that the expected excess leukaemia cases may be
fewer than the highest calculated number of 50 derived from the United Kingdom CT report[Berrington de Gonzaléz et al., 2016].

16

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

36.
In summary, because of the low estimated doses to the children and young adult
populations, excess cases of leukaemia attributable to radiation exposure are generally notdiscernible, although a few findings using the UNSCEAR model [UNSCEAR, 2020] suggest thepossibility of discernible risk (statistical power estimates >0.70 for mean doses) among those ofages 1 or 10 at the time of the accident, as well as when using upper bound dose estimates.
Table A-23.5. Leukaemia, lifetime excess incidence attributable to radiation exposure: statisticalpower for mean and 95th percentile upper bound on mean lifetime doses by sex and age at thetime of the Fukushima Daiichi Nuclear Power Station accident for all residents of evacuated andnon-evacuated municipalities and for those with >5 mSv cumulative lifetime effective dose.
Statistical power is shown for various models of risk
Statistical power using
WHO risk estimates aGroup –
sex and age(years)Meantissuedose e95%ile upperbound onmean dose

Statistical power using
UNSCEAR 2020 risk
estimates b

Statistical power using
LSS risk estimates cStatistical power using
pooled risk estimate dMeantissuedose

Meantissuedose

Meantissuedose

95%ile upperbound onmean dose

95%ile upperbound onmean dose

95%ile upperbound onmean dose

Evacuated and non-evacuated municipalities with cumulative lifetime effective dose > 5 mSv
Male
1

0.10

0.13

0.45

0.77

0.31

0.59

0.15

0.26

10

0.09

0.12

0.44

0.75

0.14

0.22

0.14

0.23

20

0.07

0.09

0.09

0.11

0.08

0.10

0.09

0.12

1

0.08

0.11

0.46

0.78

0.31

0.59

0.14

0.24

10

0.09

0.12

0.66

0.94

0.19

0.32

0.18

0.32

20

0.07

0.08

0.08

0.10

0.07

0.07

0.07

0.08

1

0.11

0.17

0.71

0.96

0.50

0.85

0.21

0.39

10

0.10

0.14

0.72

0.96

0.24

0.43

0.21

0.37

20

0.08

0.11

0.10

0.14

0.08

0.11

0.10

0.14

FemaleBoth sexes

All evacuated and non-evacuated municipalities
Male
1

0.09

0.12

0.39

0.71

0.27

0.53

0.14

0.23

10

0.10

0.13

0.57

0.88

0.13

0.20

0.17

0.30

20

0.07

0.09

0.08

0.10

0.08

0.09

0.09

0.11

1

0.08

0.10

0.41

0.72

0.28

0.53

0.13

0.21

10

0.07

0.09

0.39

0.69

0.12

0.19

0.12

0.19

20

0.07

0.08

0.07

0.09

0.06

0.07

0.07

0.08

1

0.10

0.15

0.63

0.94

0.44

0.79

0.19

0.34

10

0.10

0.15

0.74

0.97

0.18

0.30

0.22

0.39

20

0.08

0.11

0.09

0.13

0.08

0.10

0.09

0.13

FemaleBoth sexes

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Statistical power using
WHO risk estimates aGroup –
sex and age(years)Meantissuedose e95%ile upperbound onmean dose

17

Statistical power using
UNSCEAR 2020 risk
estimates b

Statistical power using
LSS risk estimates cStatistical power using
pooled risk estimate dMeantissuedose

Meantissuedose

95%ile upperbound onmean doseMeantissuedose

95%ile upperbound onmean dose

95%ile upperbound onmean dose

All non-evacuated municipalities only
Male
1

0.09

0.12

0.40

0.72

0.28

0.53

0.14

0.23

10

0.10

0.14

0.57

0.89

0.13

0.20

0.17

0.30

20

0.07

0.09

0.08

0.10

0.08

0.10

0.09

0.11

1

0.08

0.11

0.41

0.73

0.28

0.53

0.13

0.21

10

0.07

0.09

0.40

0.70

0.13

0.19

0.12

0.19

20

0.07

0.08

0.07

0.09

0.06

0.07

0.07

0.08

1

0.10

0.15

0.64

0.94

0.45

0.79

0.19

0.35

10

0.11

0.15

0.75

0.97

0.18

0.43

0.22

0.39

20

0.08

0.10

0.09

0.12

0.08

0.10

0.09

0.13

FemaleBoth sexesa[Walsh et al., 2014; WHO, 2013].
[UNSCEAR, 2020].
c[Hsu et al., 2013].
d[Little et al., 2018].
eBased on cumulative mean doses and 95th percentile upper bounds on the means, summed to age 80 years.
b

D.

Lifetime thyroid cancer risk (without a thyroid screening programme)

37.
A later section on the Fukushima Health Management Survey (FHMS) screening
programme (see paragraphs 63–71 of this attachment) discusses statistical power in the context
of thyroid cancer screening. In the absence of a screening programme, the LBRs were derived
from age- and sex-specific thyroid cancer rates from four long-standing prefecture cancer
registries in Japan (Fukui, Miyagi, Nagasaki and Yamagata prefectures) [IARC, 2017] using
lifetable methods with adjustment for cancer-free survival. The LBRs for thyroid cancer
incidence, absent systematic screening, were low, at about 1.2% for females and 0.4% for males.
The LERs and LFRs for sex and age groups in the UNSCEAR 2013 Report [UNSCEAR, 2014]
were based on a report by Walsh et al. [Walsh et al., 2014], which provided standardized resultsfrom the WHO Fukushima report [WHO, 2013].
38.

For the present report, the LFRs were calculated with four models (see table A-23.6):(a) First, the LFR coefficients from the WHO report were applied [Walsh et al., 2014; WHO,2013], as they had been for the UNSCEAR 2013 Report [UNSCEAR, 2016];(b) Second, the ERR coefficients of the latest report on thyroid cancer risk in the Japan LSS[Furukawa et al., 2013] were used. The study provided ERR coefficients for exposure atage 10 and 20 with follow-up through age 60. The LFRs for ages 1, 10 and 20 years at
100 mGy of exposure were 19.6%, 12.8% and 2.7%, respectively; it was assumed theserisk coefficients were pertinent until age 90;(c) The third estimators were based on approximate averages of linear dose responses for the
Chernobyl thyroid screening studies in Ukraine [Brenner et al., 2011] and Belarus

18

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

[Zablotska et al., 2011] for ages 1 and 10 years at exposure. The LFRs at 100 mGy were
estimated as 58% for age 1 and 18% for age 10 years at exposure;(d) Fourth, the ERR coefficient from a pooled analysis of nine studies of thyroid cancer riskin those exposed as young people to doses up to 100 mGy was applied for ages 1 and 10
at exposure [Lubin et al., 2017]. The ERR coefficient was 86% at 100 mGy for their
combined ages 1 and 10 years at exposure. Since the pooled study data were primarily
for exposures below 20 years of age, the average of the ERRs of thyroid cancer in the
United Kingdom [Haylock et al., 2018] and South Korean [Lee et al., 2019] national
studies of radiation workers was instead shown in the pooled study columns for the
20-year-old groups. The average ERR was approximately 9.2% at 100 mGy.
Since the risk estimates for the Chernobyl and pooled-analysis models were based primarily on
observed risk at younger ages, the LFR estimate from the WHO report was applied for ages 41
to 90. When LFRs were not already available, the LFR estimates were based on ERR coefficients
rather than excess absolute risk (EAR coefficients because ERR coefficients tend to be lesssensitive to screening frequency and often vary less with attained age [UNSCEAR, 2020].
Analyses in the several reports did not indicate that risk coefficients differed significantly by sex,
so identical ones were used for males and females.
39.
The advantages of the LSS risk estimates is that they are based on the Japanese populationand have a long follow-up time. The Chernobyl studies have the advantage of being based on
exposures at relatively low dose rates and include internal exposures, as was the case in
Fukushima. The pooled study has the advantage of having a large number of thyroid cancers andbeing based on low-dose data, while the WHO analysis serves as a comparative baseline for the
newer risk estimates and has the advantage that it directly estimates lifetime risk.
40.
The thyroid gland received larger internal exposures than did other organs or tissues fromshort-lived radioisotopes of iodine in the first few weeks after the FDNPS accident. Doseestimation was implemented by adding together the first-year total absorbed thyroid doses frominternal and external exposure and the continuing doses for the lifetime, as estimated inattachments A-14 and A-18. The average lifetime doses and 95th percentile upper bound lifetime
doses for the thyroid gland are given in table A-23.2.
Table A-23.6. Lifetime fractional risks (LFRs) of thyroid cancer (without systematic population
screening) attributable to radiation at a thyroid radiation dose of 100 mGy according to various models
WHO model,
LFR (%)a

LSS model,
LFR (%)b

Chernobyl model,
LFR (%)c

Pooled study model,
LFR (%)d

Age 1–Male

34.0

19.6

37.7

42.0

Age10–Male

19.5

12.8

19.3

29.7

Age 20–Male

9.20

2.7

Age 1–Female

42.0

19.6

45.2

50.9

Age 10–Female

24.5

12.8

23.2

37.0

Age 20–Female a

9.20

2.7

Age (year)

a

9.20

9.20

[Walsh et al., 2014; WHO, 2013]. These LFRs were also used for the attained ages of 41–90 years with the Chernobyl and pooled-study models.
[Furukawa et al., 2013].
c[Brenner et al., 2011; Zablotska et al., 2011]. Because the model was based primarily on childhood and adolescent exposures, it was not applied
for age 20.
d[Lubin et al., 2017]. Because the model was based primarily on childhood and adolescent exposures, a coefficient representing the average
ERR derived from two large occupational studies of adult exposures [Haylock et al., 2018; Lee et al., 2019] was used for age 20.
b

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

19

41.
Table A-23.7 shows estimated statistical power for the various models of radiation risk
for thyroid cancer. The LFRs for thyroid cancer are generally greater than for other cancer sites,
owing to the extra dose contributed by the first-year internal exposure to radioiodides and therelatively high LFRs per unit dose. The statistical power results suggest that excess thyroid
cancer risk is most likely not detectable, as shown by the results for mean doses. There is also asuggestion that it might be detectable among females of ages in utero to five years if doses were
to correspond to the 95% upper bound estimates. However, for this subgroup about 650 incident
thyroid cancers (with a 95% confidence interval of approximately 600–700 cancers) would beobserved in Fukushima Prefecture over the lifetime in the absence of radiation or systematic
population screening for thyroid cancer, and about 16–50 additional cancers might theoreticallybe attributable to radiation exposure, depending on the risk model used. A statistical power
analysis showed that an excess of 50 cases or less would be undetectable among the much larger
baseline number of thyroid cancers. Moreover, the LBRs of diagnosed thyroid cancer vary
markedly by geographic area; the age-aggregated rates across the four prefectures used toestimate average background rates varied by over 60% for both males and females, and variation
would likely be greater for smaller geographic units such as municipalities. Such variations
would introduce statistical “noise” much larger than the radiation-associated difference that isbeing evaluated, so the ability to discern a meaningful increase in thyroid cancer is thought to beunlikely. (See paragraph 46 of this attachment for further discussion of the detectability of risks.)
Table A-23.7. Thyroid cancer (without systematic screening) lifetime incidence: statistical power
for mean and 95th percentile upper bound on mean lifetime doses by sex and age at the time of
the Fukushima Daiichi Nuclear Power Station accident for all residents of evacuated and nonevacuated municipalities and for those with >5 mGy first-year thyroid doseGroup –
sex andage (years)

Statistical power using
WHO risk estimates a
Meantissuedose e95%ile upperbound onmean dose

Statistical power using
LSS risk estimates b

Statistical power using
Chernobyl risk
estimates cStatistical power using
pooled risk estimate dMeantissuedose

Meantissuedose

Meantissuedose

95%ile upperbound onmean dose

95%ile upperbound onmean dose

95%ile upperbound onmean dose

Municipalities with first year thyroid dose >5 mGy
Male
1

0.16

0.35

0.10

0.18

0.18

0.50

0.20

0.46

10

0.12

0.25

0.09

0.16

0.12

0.25

0.18

0.43

20

0.07

0.11

0.06

0.06

0.07

0.11

1

0.39

0.85

0.16

0.36

0.43

0.89

0.50

0.94

10

0.27

0.68

0.13

0.30

0.25

0.63

0.45

0.93

20

0.10

0.17

0.06

0.08

0.10

0.11

1

0.41

0.88

0.18

0.44

0.49

0.94

0.57

0.97

10

0.38

0.70

0.15

0.36

0.26

0.71

0.52

0.96

20

0.11

0.20

0.06

0.08

0.11

0.14

FemaleBoth sexes

All municipalities
Male
1

0.15

0.33

0.10

0.17

0.17

0.38

0.10

0.43

10

0.12

0.25

0.09

0.16

0.12

0.25

0.19

0.43

20

0.08

0.11

0.06

0.06

0.08

0.11

20

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Group –
sex andage (years)

Statistical power using
WHO risk estimates a

Statistical power using
LSS risk estimates b

Statistical power using
Chernobyl risk
estimates cStatistical power using
pooled risk estimate dMeantissuedose e95%ile upperbound onmean doseMeantissuedose

95%ile upperbound onmean doseMeantissuedose

95%ile upperbound onmean doseMeantissuedose

95%ile upperbound onmean dose

1

0.37

0.82

0.15

0.33

0.40

0.86

0.47

0.92

10

0.28

0.68

0.14

0.30

0.26

0.64

0.48

0.93

20

0.10

0.17

0.06

0.07

0.10

0.11

1

0.39

0.85

0.18

0.41

0.46

0.92

0.54

0.96

10

0.29

0.71

0.16

0.36

0.30

0.71

0.54

0.96

20

0.11

0.20

0.06

0.08

0.11

0.14

FemaleBoth sexes

Non-evacuated municipalities only: all doses
Male
1

0.15

0.32

0.10

0.17

0.17

0.37

0.19

0.43

10

0.12

0.25

0.09

0.16

0.12

0.24

0.18

0.42

20

0.07

0.11

0.06

0.07

0.08

0.11

1

0.35

0.81

0.15

0.33

0.40

0.86

0.46

0.92

10

0.28

0.68

0.14

0.29

0.26

0.62

0.47

0.92

20

0.10

0.17

0.06

0.07

0.10

0.11

1

0.38

0.84

0.17

0.40

0.46

0.91

0.53

0.96

10

0.29

0.69

0.16

0.35

0.29

0.70

0.53

0.95

20

0.11

0.20

0.06

0.08

0.11

0.14

FemaleBoth sexesa[Walsh et al., 2014; WHO, 2013].
[Furukawa et al., 2013].
c[Brenner et al., 2011; Zablotska et al., 2011].
d[Lubin et al., 2017]. For age 20 the statistical power estimates are based on the average of thyroid cancer risk estimates from the United
Kingdom and South Korean national worker studies [Haylock et al., 2018; Lee et al., 2019].
eBased on cumulative mean doses and 95th percentile upper bounds on the means, summed to age 80 years.
b

E.

Lifetime female breast cancer risk

42.
The background incidence of breast cancer is fairly high in females, with LBRs of about
6.1%, as estimated by a lifetable method based on age-specific incidence data from four Japanprefectures, with adjustment for cancer-free survival. The LBRs for the 1, 10 and 20-year-old
age groups were virtually identical because few breast cancers occur before age 25. The lifetime
risk attributable to radiation was based on absorbed breast dose (see table A-23.2). The
UNSCEAR 2013 Report [UNSCEAR, 2014] and WHO report [Walsh et al., 2014; WHO, 2013]
had used a risk coefficient based on the Preston et al. [Preston et al., 2007] analysis of breast
cancer risk in the LSS. It is used here with updated lifetime LBR incidences. A recent report[Brenner et al., 2018] updated the LSS risk estimate and found an average 29% increase in breast
cancer risk per 100 mGy compared to the earlier study [Preston et al., 2007], and the factor 1.29
was used to generate the LFRs for the Brenner et al. model. The statistical power for lifetime
breast cancer risk is shown in table A-23.8 for both models. Based on the statistical power results,

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

21

it is not expected that an excess of breast cancer would be detectable, even at the 95th percentile
upper bound on the mean dose after early childhood exposure.
Table A-23.8. Female breast cancer lifetime incidence: statistical power for mean and upper bounddoses by age at the time of the Fukushima Daiichi Nuclear Power Station accident for residents of
all non-evacuated and evacuated municipalities, and for those with >5 mSv cumulative lifetime
effective dose
LSS risk model aGroup –
age at first
exposure (years)

Mean cumulative
organ dose c
LFR (%)

Statistical power

UNSCEAR 2013 risk model b

95%ile upper boundon mean dose

Mean cumulative
organ dose

95%ile upper boundon mean dose

Statistical power

Statistical power

Statistical power

Municipalities with lifetime effective dose >5 mSv
1

3.2

0.40

0.70

0.29

0.52

10

1.8

0.33

0.58

0.24

0.42

20

1.0

0.20

0.34

0.15

0.24

All municipalities
1

2.1

0.33

0.60

0.24

0.43

10

1.2

0.29

0.50

0.21

0.36

20

0.7

0.18

0.29

0.14

0.21

a

[Brenner et al., 2018].
[UNSCEAR, 2014; Walsh et al., 2014; WHO, 2013].
c
Based on cumulative mean doses and 95th percentile upper bounds on the means, summed to age 80 years.
b

F.

Lifetime risk of all solid cancer (excluding thyroid cancer andnonmelanoma skin cancer)

43.
The LBRs for all solid cancer, excluding thyroid cancer and nonmelanoma skin cancer,
were based on the age- and sex-specific rates of the aforementioned four prefecture cancer
registries. Nonmelanoma skin cancer is conventionally excluded from lifetable calculations
because it is reported inconsistently to cancer registries and it usually confers little healthdetriment. Thyroid cancer was excluded because the geographic pattern and magnitude of doses
to the thyroid from the FDNPS accident differed substantially from those to other organs andtissues. The LBRs are based on lifetable calculations of cumulative rates from theaforementioned four unexposed prefectures, with adjustment for cancer-free survival([UNSCEAR, 2014] as shown in the appendix, table A-23.A1). The LFR for all solid cancer in
the Committee’s previous statistical power assessment [UNSCEAR, 2016] was based on an
incidence analysis of the Japanese LSS study [Preston et al., 2007], which has recently been
updated with further follow-up of the LSS cohort [Grant et al., 2017]. The sex-specific ratios of
the newer linear risk coefficients [Grant et al., 2017] to the previous ones were applied forevaluating all solid cancer risk. The ratios were 1.10 for females and 1.03 for males. The
estimated mean doses to the colon, as shown in table A-23.2, were applied in estimating the LFRs.
44.
The LBRs indicated that without radiation exposure about 29% of females and 42% ofmales would develop a solid cancer during their lifetime. These LBRs were virtually identical
for the 1, 10 and 20-year-old age groups, because nearly all solid cancers occur after 25 years ofage. The LFRs (LERs as a percentage of the LBRs) and the estimated statistical power to detect
those LFRs are shown in table A-23.9.

22

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Table A-23.9. All solid cancer (except thyroid cancer and nonmelanoma skin cancer) lifetime
incidence: statistical power for mean and upper bound doses by sex and age at the time of the
Fukushima Daiichi Nuclear Power Station accident for residents of all non-evacuated municipalities,
for non-evacuated municipalities plus evacuated municipalities, and for the subset of municipalities
with >5 mSv cumulative lifetime effective dose [Grant et al., 2017]
Non-evacuated plus evacuatedmunicipalities

Non-evacuated municipalities
Group –
sex and age(years)Mean colon dose a
LFR (%)

Statistical power

95%ile upper boundon mean dose

Mean colon dose

95%ile upper boundon mean dose

Statistical power

Statistical power

Statistical power

Municipalities with lifetime effective dose >5 mSv
Male
1

0.8

0.24

0.43

0.24

0.43

10

0.5

0.26

0.46

0.26

0.47

20

0.3

0.17

0.27

0.17

0.28

1

1.8

0.55

0.86

0.54

0.87

10

1.2

0.59

0.88

0.80

0.99

20

0.7

0.36

0.62

0.37

0.64

1

1.2

0.58

0.89

0.57

0.89

10

0.8

0.62

0.91

0.80

0.99

20

0.5

0.38

0.65

0.39

0.67

FemaleBoth sexes

All municipalities
Male
1

0.5

0.20

0.36

0.20

0.35

10

0.4

0.23

0.40

0.23

0.41

20

0.2

0.16

0.24

0.15

0.25

1

1.2

0.46

0.78

0.46

0.77

10

0.9

0.52

0.82

0.51

0.84

20

0.5

0.32

0.55

0.32

0.56

1

0.8

0.49

0.81

0.48

0.80

10

0.6

0.55

0.85

0.54

0.87

20

0.3

0.34

0.59

0.34

0.59

FemaleBoth sexesaBased on cumulative mean colon doses and 95th percentile upper bounds on the means, summed to age 80 years.

45.
For the best estimate of municipality doses (mean doses), the statistical power results
indicated that a radiation-related risk of all solid cancer (excluding thyroid cancer andnonmelanoma skin cancer) was generally unlikely to be discernible, as shown in the columns formean doses in table A-23.9. A potential exception to this occurred for females initially exposed
at age 10, with a related value for both sexes: statistical power achieved the 80% criterion for themean dose, indicating that one might potentially see a radiation-associated excess in this
subpopulation (but see caveats in the next paragraph). Furthermore, if the mean doses were

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

23

actually at the 95% upper bounds (upper bound columns), the estimates indicated it may bepossible to detect risks for females and both sexes combined.
46.
There are several reservations to the finding of high statistical power for risk estimatesof all solid cancer:(a) First, because there is a high baseline risk and large numbers of individuals, even a verysmall relative risk – in this case relative risks of 1.005 to 1.018 – can show substantial
statistical power. But these potentially “statistically significant” expected relative risks
of under 1.02 would not be very meaningful, and genuine risks versus subtle confounding
effects would be difficult to discern;(b) Second, a key assumption in the present calculation of statistical power is that both the
LBRs and the LERs are known precisely and accurately (without bias), so that there isno inherent uncertainty in those estimates. That is undoubtedly not the case. For instance,
total cancer baseline incidence rates differ appreciably in different Japanese prefectures[UNSCEAR, 2014], with differences of 5–15% that far exceed the magnitude of theradiation-associated risk. Baseline incidence rates (LBRs) in smaller geographic units,
such as municipalities, would be expected to have even larger variability due to thestatistical instability associated with smaller numbers, plus the influence ofsociodemographic confounding factors that produce variability in LBRs;(c) Third, the LERs also have inherent uncertainty – for example, uncertainties in thecoefficients and the dose-response model used. If the variability in rates and theuncertainties in the excess risk coefficient and modelling parameters could be factored
into the estimation, the statistical power values probably would be materially smaller andexcess risk would not be expected to be discernible because any signal of radiation risk
would be masked by the additional background “noise”;(d) Fourth, while the upper 95% confidence bound on the mean is a possible value, it is notnearly as likely as some value nearer the mean [Poole, 1987]. (This can be visualized bythinking of a Gaussian, “bell shaped”, curve. A narrow vertical slice near the 95th percentile
encompasses much less area under the curve than a vertical slice of equal width near themean, where the size of the area relates approximately to the likelihood that it contains thetrue value.) Because of the background variability in rates and other reasons listed above, it
is expected that an excess risk of all solid cancer is not very likely to be discernible.

III. LEUKAEMIA AND THYROID CANCER RISKS TO AGES 30 OR
40 YEARS
47.
Exposure of children shows the highest radiation relative risks for leukaemia and thyroid
cancer [UNSCEAR, 2013], and these cancers have the highest ERR expression of those risks inthe first decades after exposure [Furukawa et al., 2013; Hsu et al., 2013]. Therefore, analyses
were conducted of whether cumulative excess risks of leukaemia or thyroid cancer are likely to be
seen up to 30 or 40 years of age for those at 1 or 10 years of age at the time of the FDNPS accident.
A.

Leukaemia risk to ages 30 or 40 years after childhood exposure

48.
The CBR estimates to ages 30 or 40 years for those at age 1 or 10 years at exposure in
Fukushima Prefecture were calculated with lifetable methods, using rates of leukaemia by sex
and 5-year age intervals derived from four prefecture cancer registries in Japan (Fukui, Miyagi,
Nagasaki and Yamagata Prefectures)and adjusted for malignancy-free survival.

24

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

49.
The CER and CFR values for leukaemia up to age 30 or 40 years after exposure at ages
1 or 10 years were estimated based on three models:(a) First, the recent UNSCEAR report [UNSCEAR, 2020] provided values to derive CFR
estimates for exposure to 100 mGy at age 1 year and observation to ages 30 or 40 as 2.9
and 3.0, respectively, for their preferred model, which was based on a study of leukaemia
and myelodysplastic syndromes among children in the United Kingdom with radiation
exposure from CT examinations [Berrington de Gonzaléz et al., 2016]. For age 10 at
exposure, the CFR estimates to ages 30 or 40 were 3.0 and 3.1, respectively, at 100 mGy;(b) The second model consisted of LFR estimates at 100 mGy from the LSS study [Hsu etal., 2013; UNSCEAR, 2020], which were 2.75 for ages 1–30, 2.15 for ages 1–40, 1.15
for ages 10–30 and 0.85 for ages 10–40 years at 100 mGy;(c) Third, a recent analysis of data from nine pooled studies of childhood external radiation
exposures with RBM doses ≤100 mGy [Little et al., 2018] yielded an ERR estimate of
0.84 at 100 mGy, which was employed as a LFR estimate.
50.
Calculations were based on dose to the RBM. The estimated RBM mean doses and 95%
upper bounds were calculated for the appropriate age intervals by adding to the first-year RBM
dose the subsequent cumulative effective doses, adjusted for the ratio of RBM dose to effective
dose, from the second year to age 28 or 30 years to allow for a two-year latency period between
dose and associated leukaemia risk.
51.
CFR values for estimating leukaemia risk up to ages 30 or 40 for the various risk models
are given in section II of the appendix. The statistical power results are shown in table A-23.10 foreach of the risk models. The model based on CT examinations in the United Kingdom indicateddiscernible risk, especially in the subsets of those exposed at 1 or 10 years of age with >5 mSv
mean lifetime dose. The LSS also had a suggestion of discernible risk (statistical power ≥0.70)
for age 1 at exposure. However, there was generally little indication of detectable risk at themean doses for the models based on the LSS cohort or pooled studies, though there were results
suggesting discernible risk at the 95th percentile upper bound dose.
52.
The greatest statistical power was for those initially exposed at age 1 (that is, in utero to
five years) and observed up to 40 years of age. Including both sexes for this scenario, anddepending on the risk model applied, about 10–40 cases may be attributable to radiation exposurecompared to about 160 LBR baseline cases in the absence of radiation exposure from the FDNPS
accident, with an estimated range of uncertainty in the baseline estimate of about 50 cases. Of
note, the discussion of suggested upward bias (see paragraph 34 of this attachment) in the United
Kingdom CT study [Berrington de Gonzaléz et al., 2016] that produced the highest risk estimates[UNSCEAR, 2020] among the models for lifetime leukaemia risk is also directly applicable tothe 30 or 40 year risk estimates here. It suggests the calculated excess of 40 cases may also be
upwardly biased.

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

25

Table A-23.10. Leukaemia: statistical power to detect excess malignancies up to age 30 or 40 years
after childhood radiation exposure in both evacuated and non-evacuated municipalities, using
estimated doses, linear excess relative risk models of estimated cumulative excess leukaemia risk
from the three models [Hsu et al., 2013; Little et al., 2018; UNSCEAR, 2020] and cumulated age/sexspecific baseline risk of leukaemia from four Japan prefectures [UNSCEAR, 2020]

Group – sex (M, F,
or B for both)and age (years)

Statistical power using
UNSCEAR 2020 risk estimates a
Mean tissue
dose d95%ile upperbound onmean dose

Statistical power using LSS
risk estimates b

Statistical power using pooledanalysis risk estimate c

Mean tissue
dose

Mean tissue
dose

95%ile upperbound onmean dose

95%ile upperbound onmean dose

Municipalities with cumulative lifetime effective dose >5 mSv
M, ages 1–30e

0.55

0.86

0.52

0.83

0.14

0.22

F, ages 1–30

0.48

0.79

0.45

0.75

0.13

0.20

B, ages 1–30

0.77

0.98

0.73

0.97

0.19

0.32

M, ages 1–40

0.70

0.95

0.48

0.80

0.16

0.27

F, ages 1–40

0.63

0.92

0.43

0.74

0.15

0.25

B, ages 1–40

0.90

0.99

0.70

0.96

0.23

0.41

M, ages 10–30

0.57

0.87

0.18

0.31

0.14

0.21

F, ages 10–30

0.49

0.79

0.16

0.27

0.12

0.19

B, ages 10–30

0.79

0.98

0.26

0.46

0.18

0.31

M, ages 10 –40

0.81

0.98

0.18

0.31

0.18

0.31

F, ages 10–40

0.75

0.97

0.17

0.28

0.17

0.28

B, ages 10–40

0.96

0.99

0.26

0.47

0.26

0.47

All municipalitiesaM, ages 1–30

0.51

0.82

0.47

0.79

0.13

0.20

F, ages 1–30

0.44

0.75

0.41

0.72

0.12

0.18

B, ages 1–30

0.72

0.97

0.68

0.95

0.17

0.29

M, ages 1–40

0.65

0.94

0.44

0.76

0.15

0.25

F, ages 1–40

0.59

0.90

0.39

0.69

0.14

0.23

B, ages 1–40

0.87

0.99

0.65

0.94

0.21

0.37

M, ages 10–30

0.49

0.80

0.16

0.26

0.12

0.18

F, ages 10–30

0.42

0.71

0.14

0.23

0.11

0.16

B, ages 10–30

0.70

0.95

0.22

0.38

0.16

0.26

M, ages 10–40

0.72

0.96

0.16

0.26

0.16

0.26

F, ages 10–40

0.66

0.93

0.15

0.24

0.15

0.24

B, ages 10–40

0.92

0.99

0.22

0.40

0.22

0.39

[UNSCEAR, 2020].
[Hsu et al., 2013; UNSCEAR, 2020].
c[Little et al., 2018].
dUsing RBM cumulative mean doses or 95th percentile upper bound on the means summed to ages 28 (for risk to age 30) or 38 (for risk to age 40)
e
Age at initial exposure through age of final observation. Risk was calculated assuming a two-year lag of the doses to account for the minimum
latency period.
b

26

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

B.

Statistical power to detect thyroid cancer risk (in the absence of ascreening programme) to ages 30 or 40 years after childhood exposure

53.
The CBR estimates to ages 30 or 40 years for those in Fukushima Prefecture at age 1 or
10 years at exposure were calculated using sex- and age-specific rates of thyroid cancer from thecancer registries of four essentially unexposed prefectures, parallel to the approach used forleukaemia.
54.

Three models were used in estimating CFRs for thyroid cancer up to ages 30 or 40 years:(a) First, thyroid cancer CER estimates of radiation risk after exposure at ages 1 or 10 years
were adapted from a recent UNSCEAR report [UNSCEAR, 2020], based on LFRestimates of radiation risk at ages 30 or 40 from the Japanese atomic bombing survivors
LSS [Furukawa et al., 2013; Jacob et al., 2014];(b) Second, CFRs were also calculated based on studies of thyroid cancer after exposure toradioactive iodine isotopes from the Chernobyl accident. Approximate averages of the
ERR reports from the Ukraine [Brenner et al., 2011] and Belarus [Zablotska et al., 2011]were used for the CFRs, namely, ERR estimates of 0.58 and 0.18 at 100 mGy for ages 1
and 10 years at exposure, respectively. Estimates based on EAR vary highly because of
screening effects, stable iodine intake levels, different baseline rates etc., so only ERR
modelling was used in deriving the CFR estimates;(c) Third, a pooled study of thyroid cancer in nine cohorts with childhood external radiation
absorbed thyroid doses under 200 mGy was recently reported by Lubin et al. [Lubin etal., 2017]. An ERR estimate of 0.86 per 100 mGy for the subset of the Lubin et al. study
with absorbed radiation doses up to 100 mGy was used as the CFR estimate.

55.
The modelled ERRs did not differ in a consistent manner by sex or age at exposure[UNSCEAR, 2020]. More details of methods and coefficients for calculating the CERs and CFRs
are provided in section IV of the appendix. The evacuees tended to have relatively high thyroid
doses but are only a small fraction of the prefecture population, so the results of the statisticalpower analyses are shown in table A-23.11 for the combined non-evacuated and evacuatedmunicipalities.
56.
The complementary strengths of the models of thyroid cancer risk can be briefly stated.
The UNSCEAR [UNSCEAR, 2020] risk coefficients from the LSS study have the advantage ofbeing based on the Japanese population, with well-characterized doses and substantial numbersof tumours. The average intake of stable iodine in the diet and other lifestyle characteristics are
therefore likely to be similar to the population affected by the FDNPS accident. The CFRs werealso explicitly calculated for the relevant ages [UNSCEAR, 2020]. The Chernobyl thyroid cancer
risk coefficients are based primarily on protracted 131I exposures over weeks to months, as are
the Fukushima data, rather than one brief exposure as for the LSS. The thyroid doses werereasonably well characterized, being based primarily on measurements of radioiodine in the
thyroid shortly after the accident in the Ukraine [Likhtarov et al., 2014] or other detailed models
in Belarus. In the Chernobyl studies the thyroid cancer detection was more systematic than in
the LSS because it was based on screening programmes. The pooled study of Lubin et al. [Lubin
et al., 2017] has the advantage of being based on a large number of thyroid cancers, because it
incorporated data from nine different studies with a thyroid dose range of 100 mGy and below,
but some studies may have had surveillance bias that increased the risk estimates and most of
the studies included brief exposures.

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

27

57.
Table A-23.11 indicates that the magnitude of excess risks was fairly similar for thethree models of radiation-related thyroid cancer, as shown by the similar evidence regarding
statistical power. For all the models, however, statistical power was inadequate to consider theexcess risk of thyroid cancer at young ages (in the absence of a systematic screening
programme) to be detectable. This owes a large part to the fact that thyroid cancer rarely
presents clinically at young ages.
Table A-23.11. Thyroid cancer (without systematic screening): statistical power to detect excess
cancer by age 30 or 40 years after childhood radiation exposure in the combined evacuated andnon-evacuated municipalities, using models of thyroid cancer risk based on the Japanese Life Span
Study [UNSCEAR, 2020], the Chernobyl studies in the Ukraine [Brenner et al., 2011] and Belarus[Zablotska et al., 2011], or a study of nine pooled data sets [Lubin et al., 2017]

Group – sex (M,F, or B for both)and age (years)

Japanese LSS risk
estimates
CFR (%)aMean
tissuedose b

95%ile upperbound onmean dose

Chernobyl risk estimates

Pooled study risk estimate

Mean
tissuedose

Meantissuedose

95%ile upperbound onmean dose

95%ile upperbound onmean dose

Municipalities with first year absorbed thyroid dose >5 mGy
M, ages 1–30c

13.9

0.10

0.19

0.08

0.12

0.10

0.17

F, ages 1–30

13.9

0.17

0.40

0.12

0.23

0.16

0.36

B, ages 1–30

13.9

0.20

0.48

0.13

0.26

0.19

0.43

M, ages 1–40

10.7

0.12

0.23

0.10

0.19

0.14

0.29

F, ages 1–40

10.7

0.24

0.57

0.20

0.46

0.31

0.71

B, ages 1–40

10.7

0.28

0.65

0.22

0.53

0.36

0.79

M, ages 10–40

6.3

0.09

0.15

0.07

0.09

0.17

0.40

F, ages 10–40

6.3

0.14

0.33

0.09

0.16

0.40

0.88

B, ages 10–40

6.3

0.16

0.38

0.10

0.18

0.47

0.93

All municipalitiesaM, ages 1–30

10.9

0.10

0.18

0.08

0.12

0.10

0.16

F, ages 1–30

10.9

0.17

0.38

0.11

0.21

0.16

0.34

B, ages 1–30

10.9

0.20

0.45

0.13

0.25

0.18

0.40

M, ages 1–40

8.4

0.12

0.22

0.10

0.18

0.14

0.28

F, ages 1–40

8.4

0.22

0.54

0.19

0.46

0.29

0.68

B, ages 1–40

8.4

0.26

0.62

0.21

0.50

0.34

0.76

M, ages 10–40

3.5

0.09

0.15

0.07

0.09

0.18

0.42

F, ages 10–40

3.5

0.15

0.33

0.09

0.16

0.43

0.90

B, ages 10–40

3.5

0.16

0.38

0.10

0.18

0.50

0.95

CFR values are given for the Japanese LSS risk estimates.
Using thyroid cumulative mean doses or 95th percentile upper bound on the means summed to ages 27 (for risk to age 30) or 37 (for risk to age 40).
Ages shown are age at exposure to final age at observation. Excess risk was calculated assuming a three-year minimum latency period. Data
for ages 10–30 are not reported because the numbers of cases were small during the 20-year period.

b
c

28

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

IV. IN UTERO EXPOSURE AND CANCER RISK58.
The embryonic and fetal stages of development are believed to be sensitive to cancer
induction by radiation exposure [Wakeford, 2013; Wakeford and Little, 2003]. This is seen
most clearly for childhood leukaemia, though in utero exposure may also raise risk forchildhood solid tumours as well. Wakeford [Wakeford, 2013] estimated the ERR of childhood
leukaemia from in utero exposure as 5.1 (95% CI: 2.8, 7.6) per 100 mGy based on a numberof studies of medical radiation exposure. This risk estimate is statistically compatible with thatderived for young children in the LSS exposed during the atomic bombings of Japan. Studies
of Japanese atomic bombing survivors exposed in utero have found an increased risk of cancer
in adulthood, and this increased risk is at about the same level as the risk experienced bysurvivors exposed in childhood [Preston et al., 2008].
59.
For the analyses of statistical power among those exposed in utero in non-evacuatedmunicipalities, the in utero mean doses were 0.2 mGy (95% upper bound on the mean of
0.7 mGy) for RBM, and 4.3 mGy (95% upper bound on mean of 12.5 mGy) for the thyroid gland.
The RBM doses were averaged over all 40 weeks of pregnancy, while the thyroid doses wereaveraged over weeks 10–40, the period for which there is thyroid functional activity. About
16,000 children received in utero FDNPS exposures in Fukushima Prefecture [Fujimori et al., 2014].
60.
To estimate statistical power for childhood leukaemia the baseline rates from four
unexposed prefectures were averaged and cumulated for ages 0–19 years and the risk coefficientby Wakeford [Wakeford, 2013] was applied. The results using either the mean cumulative RBM
dose or the 95% upper bound on the mean dose all yielded statistical power <10% for leukaemia
in children. When the 95% upper bound on the risk coefficient of Wakeford was applied to theupper bound on the mean dose, the statistical power was still <15%.
61.
Statistical power results for thyroid cancer for the in utero group were obtained for up to
age 30 years and to age 40 years, using the CFRs of the various models (see paragraph 38 of this
attachment) for age 1 year for both the cumulative mean dose and 95% upper bound on the mean.
In no case did the statistical power exceed 10%.
62.
Statistical power analyses of the in utero cohort were also conducted for breast cancer
and all solid cancers up to ages 30 and 40 years. The detection of excess incidence of female
breast cancer was modelled from the recent LSS report [Brenner et al., 2018], using ERR
coefficients appropriate for exposure at age 0 and attained ages of 30 or 40. For both the female
in utero subgroup with effective doses over 5 mSv and for the entire female in utero group, thestatistical power was less than 10% for either attained age 30 or 40, even at the 95% upper boundof mean dose. For the incidence of all solid cancers, risk coefficients were derived from therecent LSS paper on Japanese atomic bombing survivors [Grant et al., 2017], using ERR
coefficients appropriate for exposure at age 0 years and attained ages of 30 or 40 years from theirmodel. The results for in utero males, females and both sexes combined showed statistical power
under 10% for both the estimated mean dose and the 95% upper bound on the mean. The low
statistical power for the in utero group results from the combination of relatively low doses, asmall sample size (under 16,000 in the analysis), and low frequencies of cancer below ages 30 or 40.

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

29

V. THYROID CANCERS DIAGNOSED IN THE FUKUSHIMA HEALTH
MANAGEMENT SURVEY EXAMINATION PROGRAMME63.
The purpose of these analyses is to estimate whether excess thyroid cancer is likely to bediscernible in the FHMS cohort of those with ultrasound examinations who were exposed atabout age 1 year or age 10 years, and screened and observed out to the ages of 30 or 40 years.
Given that the rates of thyroid cancer detected by the FHMS ultrasound thyroid screening
programme are much higher than those observed in the general Japanese population ofcomparable age, very few of whom have had ultrasound screening, a strategy had to be developed
to estimate expected thyroid cancer rates to ages 30 or 40 years for the FHMS. In doing this, theassumption was made that the thyroid cancer incidences observed in the FHMS screenings
represent the baseline rates for the analysis of statistical power. This is potentially a conservative
assumption, because it would result in greater statistical power for a given assumed ERR than iflower baseline rates were used, due to factoring out an assumed radiation component from theexisting rates. Based on other studies, the minimum latency period for radiation induction of
thyroid cancer is about three to four years.
A.

Cumulative baseline risk estimation

64.
Because the FHMS thyroid ultrasound examinations have found uniquely highfrequencies of suspected/confirmed thyroid cancer, an ad hoc methodology was employed toestimate rates from existing FHMS data and to project them into the future up to age 30 or
40 years. The steps in this process are described in section V of the appendix.
65.
The approach to estimate the CBR of cancer to age 30 or 40 years was to use the annual
rates of thyroid cancers derived from the second and third rounds of screening for various age
groups as indicated in reference [FHMS, 2016; FHMS, 2017; FHMS, 2020] out to age 20 years,
after which the rates were projected forward to age 30 or 40 years by assuming that the annual
rates with continued screening would increase in proportion to the corresponding age-relatedbaseline rates of thyroid cancer (without screening or radiation exposure) in the Japanese
population.
66.
A number of assumptions had to be made to model risk in the FHMS. Approximations
had to be made of the number of participants who were initially exposed in the age range fromin utero to age 5 years, and the number whose first-year thyroid doses were >5 mGy, since
relevant data were not available. The subsequent second and third screening examinations wereconsidered to represent incident cancers, which are the primary basis of the calculations.
Specifically, the numbers of FHMS examinees and thyroid cancers by age and sex were obtained
from the second and third examinations up to age 20 years, and age-specific annual rates of“suspected or confirmed” thyroid cancers detected by fine needle aspiration biopsy in the FHMS
examination program were estimated from those data.
B.

Cumulative excess risk and cumulative fractional risk estimation

67.
Thyroid doses have been estimated for a subset of FHMS participants but are generally
not available. Therefore, the mean doses, including both external and internal radioiodine doses,
and upper bounds on the means for municipalities and evacuated locations for the 1-year-old
group were used to estimate thyroid doses and to estimate the proportion who had received over
5 mGy. A minimum latency period of 3 years was assumed in calculating radiation risks.

30

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

68.
The recent UNSCEAR report [UNSCEAR, 2020] has estimated CBR and CFR risks forthyroid cancer after exposure beginning at age 1 or 10 years, basing the CBRs on a Ukrainian
population. However, because the background rates in FHMS with ultrasound screening differ
from those in the Ukraine, the CBR values from the UNSCEAR report could not be used directly
for this assessment. Therefore, only the CFRs (the ratios of the CERs to the corresponding CBRs)
in that report, which were based on the most recent LSS thyroid cancer ERR estimates [Furukawa
et al., 2013], were used; the ratios were applied to the baseline cumulative thyroid cancer rates
derived as described above. Because the risk coefficients did not vary significantly by sex, they
were applied to both sexes to estimate CER risks. The LSS-based CFRs for a dose of 100 mGy
and up to age 30 were 0.94 for exposure at age 1 and 0.42 at age 10 years; the CFRs for up to
age 40 were 0.70 and 0.34, respectively, for a 100 mGy dose.
69.
Approximate averages from the Chernobyl studies [Brenner et al., 2011; Zablotska et al.,2011] of the ERR coefficients for those of ages 0 to about 4 years, and those of older childhood ages,
were applied to the age 1 year and age 10 years data, respectively. These averaged ERR coefficients,
applied as CFRs, were 0.58 at 100 mGy at age 1 year and 0.18 at age 10 years. An estimate was also
employed from the study by Lubin et al. [Lubin et al., 2017], which pooled low-dose data from ninedifferent studies of thyroid cancer after external irradiation before about age 20. The ERR of 0.86 at
100 mGy, based on the dose-response slope at doses up to 100 mGy, was used to calculate the CER
and CFR. The ERR did not differ consistently by sex or age at exposure in that study. Further detailsof modelling the CBR, CER and CFR estimates are provided in section V of the appendix.
70.
For those who were of ages 18 years or older at the time of screening, participation rates
for the second and third rounds of FHMS thyroid screening were only 25.7% and 16.4%,
respectively. Therefore, the conservative assumption was made that after 20 years of age, 30%of the FHMS cohort would have baseline thyroid cancer rates corresponding to receiving
screening at the FHMS-designated ages up to ages 30 or 40 years, while the remaining 70%
would have baseline thyroid cancer rates corresponding to the rates elsewhere in Japan, where
there is almost no population ultrasound screening of young adults. With the estimates from theexisting data to age 20 years and the assumptions about future incident thyroid cancers, the CBRs
up to age 30 for those exposed at age 1 were about 0.32% for females and 0.27% for males; therespective values to age 40 were about 0.53% for females and 0.48% for males. Results of thestatistical power analysis are shown in table A-23.12. The results indicate that, at theCommittee’s best estimates of doses (mean dose), a risk of excess thyroid cancer is not expected
to be detectable using any of the models of thyroid cancer risk, although there is one weak
exception (statistical power >0.70) for combined sexes at ages 10–40. The few 95th percentile
upper bound statistical power results that suggest possible detectability should be treatedcircumspectly because of geographic variability in thyroid cancer rates (see paragraph 41), otherunaccounted for factors that increase uncertainties (see paragraph 46), and because several
conservative assumptions were made.

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

31

Table A-23.12. Thyroid cancer, Fukushima Health Management Survey: statistical power to detect
excess cancer by age 30 or 40 years after childhood radiation exposure, using models of thyroid
cancer risk based on the Japanese atomic bombing survivor Life Span Study [UNSCEAR, 2020], theChernobyl studies in the Ukraine [Brenner et al., 2011] and Belarus [Zablotska et al., 2011], or studyof nine pooled data sets [Lubin et al., 2017]
Group – sex (M,F, or B for both)and age (years)

Japanese LSS risk estimates
Mean tissue
dose aChernobyl risk estimates

95%ile upperbound onmean dose

Mean tissue
dose

95%ile upperbound onmean dose

Pooled study risk estimate
Mean tissue
dose

95%ile upperbound onmean dose

Municipalities with first year absorbed thyroid dose >5 mGy
M, ages 1–30b

0.20

0.46

0.13

0.25

0.18

0.41

F, ages 1–30

0.21

0.50

0.13

0.28

0.19

0.45

B, ages 1–30

0.31

0.73

0.18

0.42

0.28

0.67

M, ages 10–30

0.17

0.41

0.09

0.16

0.38

0.87

F, ages 10–30

0.19

0.47

0.09

0.17

0.44

0.92

B, ages 10–30

0.26

0.68

0.11

0.24

0.64

0.99

M, ages 1– 40

0.21

0.48

0.13

0.26

0.19

0.43

F, ages 1–40

0.22

0.52

0.14

0.29

0.20

0.47

B, ages 1–40

0.33

0.75

0.19

0.44

0.30

0.69

M, ages 10–40

0.18

0.43

0.09

0.16

0.41

0.89

F, ages 10–40

0.20

0.50

0.10

0.18

0.47

0.94

B, ages 10–40

0.28

0.71

0.12

0.25

0.68

0.99

All municipalities
M, ages 1–30

0.19

0.44

0.12

0.24

0.17

0.39

F, ages 1–30

0.20

0.47

0.13

0.26

0.19

0.42

B, ages 1–30

0.30

0.70

0.17

0.40

0.27

0.64

M, ages 10–30

0.17

0.42

0.09

0.16

0.41

0.88

F, ages 10–30

0.20

0.48

0.10

0.17

0.47

0.93

B, ages 10–30

0.28

0.69

0.12

0.24

0.69

0.99

M, ages 1–40

0.20

0.45

0.13

0.25

0.18

0.41

F, ages 1–40

0.21

0.49

0.13

0.27

0.19

0.44

B, ages 1–40

0.32

0.72

0.18

0.41

0.28

0.66

M, ages 10–40

0.19

0.44

0.09

0.16

0.45

0.91

F, ages 10–40

0.21

0.51

0.10

0.18

0.51

0.95

B, ages 10–40

0.30

0.72

0.12

0.25

0.73

0.99

a

Using thyroid cumulative mean doses or 95th percentile upper bound on the means summed to ages 27 (for risk to age 30) or 37 (for risk to age 40).
bAges shown are age at exposure to final age at observation.

71.
Table A-23.13 addresses the impacts of the FHMS screening programme upon estimatesof potential thyroid cancer attributable to radiation. It compares the cumulative background(CBRs) and excess (CERs) thyroid cancer with and without the FHMS thyroid screening
programme for up to ages 30 or 40. The table indicates that the estimated thyroid cancer CBRs
up to ages 30 or 40 were roughly 2–9 times greater for the FHMS than if there were no systematic
ultrasound screening, and the numbers of excess suspected or confirmed thyroid cancers would
increase proportionately.

32

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Table A-23.13. Thyroid cancer without systematic screening or with Fukushima Health Management
Survey screening: estimates of cumulative baseline risks and cumulative excess risks, given the
cumulative fractional risks for the Japanese Life Span Study [Furukawa et al., 2013; UNSCEAR, 2020]
Group

Japanese LSS risk estimates

CBR a

CER a

CFR (%)

Females, all doses
N/S, ages 1–30b

8.0

0.82

10.3

FHMS, ages 1–30b

32.1

3.3

10.3

N/S, ages 1–40

21.9

1.8

8.4

FHMS, ages 1–40

53.0

4.4

8.4

N/S, ages 10–30

7.9

0.37

4.6

FHMS, ages 10–30

33.4

1.5

4.6

N/S, ages 10–40

21.9

0.90

4.1

FHMS, ages 10–40

54.3

2.2

4.1

Males, all doses
N/S, ages 1–30a

2.2

0.22

10.3

FHMS, ages 1–30

27.1

2.8

10.3

N/S, ages 1–40

5.2

0.43

8.4

FHMS, ages 1–40

48.0

4.0

8.4

N/S, ages 10–30

2.2

0.10

4.6

FHMS, ages 10–30

26.5

1.2

4.6

N/S, ages 10–40

5.2

0.21

4.1

FHMS, ages 10–40

47.4

1.9

4.1

a

The calculation of CBR is per 10,000 males or females. The cumulative excess risks shown are per 10,000 individuals, given mean cumulative
absorbed thyroid doses of about 11 mGy for age 30 and 12 mGy for age 40 (with a dose lag of three years to account for the minimum latency period).
b“N/S” stands for “not screened” CBRs and corresponding CFRs (see paragraphs 53–55 of this attachment). “FHMS” indicates CBRs modelledfrom the FHMS data and extended to ages 30 or 40 years, as described in the text (paragraphs 63–70 of this attachment). Ages shown are age at
exposure to final age at observation.

VI. STATISTICAL POWER TO DETECT EXCESS RISKSAMONG WORKERS72.
The discernibility of an excess of all solid cancer (except thyroid cancer andnonmelanoma skin cancer) was assessed among the identified 21,776 male emergency workers
after the FDNPS accident [Kitamura et al., 2018]. The estimated average effective dose was low(about 12.5 mSv in the first year after the accident), but statistical power calculations were also
performed using a hypothetical 30 mSv upper bound on the average effective dose. To calculate
an LBR, the assumption was made that all the workers were 20 years old at the time of exposure,which would yield a larger LBR and LER than exposure at older ages. The calculation of LBRs
followed the description in section I of the appendix.
73.
As in the general population, the LER was estimated by applying the adjusted LFRcoefficient for 20-year-old males as shown in table A-23.9. The resulting statistical power
estimates for both the mean dose and hypothetical upper bound dose shown in table A-23.14
indicate no discernible risk of solid cancer. A dose-response statistical power analysis conductedby a Japanese oversight committee confirmed the findings reported here: the expected 60-year
incidence of all solid cancer among the potential worker cohort of about 19,000 achieved less

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

33

than 40% statistical power with a dose-response analysis based on the LSS dose-response
coefficient [JNIOSH, 2020].
74.
Shimura et al. [Shimura et al., 2015] identified a subset of 174 FDNPS emergency
workers who had received an effective dose of greater than 100 mSv. Similar statistical power
calculations were performed for this subset, using the stated mean effective dose of
approximately 140 mSv. Because the assessment of the contribution of internal doses waslimited, a hypothetical upper bound effective dose was taken to be 250 mSv, as shown intable A-23.14. The statistical power to detect an excess risk in this subset of higher dose workers
is very low, owing primarily to the small number of such workers.
75.
The risk of excess thyroid cancer among adult worker populations is somewhat uncertain.
A variety of studies, including ones with 167,000, 94,000 and 90,000 radiation workers have notfound a statistically significant excess risk of thyroid cancer incidence [Haylock et al., 2018;
Hunter et al., 2013; Kitahara et al., 2018; Lee et al., 2019]. On the other hand, a case-control
study of Chernobyl clean-up workers with an estimated mean thyroid dose of 62 mGy reported
a significant excess risk, though possible surveillance bias could not be ruled out [Kesminiene
et al., 2012].
76.
A subset of 1,757 FDNPS emergency workers was identified who had estimated thyroid
doses of >100 mGy. The mean dose in this subset was estimated to be about 370 mGy, with an
upper bound on mean dose of about 1 Gy. Using the thyroid cancer LBR and LER estimates for
those 20 years old at exposure, the statistical power for detecting an excess risk in this subset
was assessed. The results of the statistical power analysis, shown in table A-23.14, indicate thatan excess risk would not be expected to be discernible for either the subset of workers withestimated thyroid doses of >100 mGy or for all FDNPS emergency workers, although thediscernibility of risks with long-term thyroid screenings could not be estimated.
Table A-23.14. Statistical power to detect excess cancer risks among Fukushima Daiichi Nuclear
Power Station emergency workers
All emergency workers (N = 21 135)
Assuming all organs/tissues received a uniform first-year dose to give amean effective dose of 12.5 mSv; also, assuming a 30 mSv upper bounddose (no doses from subsequent years were added)

Statistical power atmean dose

Statistical power atassumed upperbound dose

All solid cancers

0.09

0.17

Leukaemia

0.10

0.21

Thyroid cancer a,b

0.06

0.06

Workers with >100 mSv (N = 174), mean ~140 mSv, and an assumed 250 mSv upper bound dose(no doses from subsequent years were added)
All solid cancer

0.09

0.13

Leukaemia

0.09

0.13

Thyroid cancer a,b

0.06

0.07

0.18

0.53

Workers with thyroid doses >100 mGy (N = 1 757),
estimated mean thyroid dose 370 mGy, assumed upper bound dose 1 Gy aabUnder the assumption of no special thyroid screening.
Assuming only a uniform whole-body dose.

34

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

VII. OVERALL INTERPRETATION AND DISCUSSION77.
The goal of the statistical power calculations was to determine if radiation risks forseveral of the most radiosensitive cancers and ages at exposure would likely be discernible above
the background rates of those diseases, given the number of relevant individuals and average
doses in Fukushima Prefecture. Potential risks to FDNPS emergency workers were also examined.
78.
Statistical power analyses were conducted using municipality-average doses and 95%
upper bounds on the average doses. Risks were projected using a linear no-threshold model. The
approach intentionally built some conservatism into the risk estimation, so as not tounderestimate risk. The results generally do not point toward an expectation of discernible risks,
given the estimated doses to the population, although there appeared to be some possibility ofdetecting an excess leukaemia risk in the group who were of ages in utero to five years at thetime of the FDNPS accident. Parallel calculations at the 95% upper bound of doses, even thoughsuch doses are less likely, have also supported the notion that risks would not be discernible forthe most part. As pointed out, especially in paragraph 46 of this attachment, there are reasons toview the several suggestions of potentially detectable elevated risks at the upper bound doses asprobably unrealistic in view of the intrinsic variability in cancer rates among localities.
79.
Several conservative assumptions were made in estimating statistical power (i.e.,
assumptions that would tend to overestimate statistical power). The often considerable
uncertainties in the lifetime baseline risks (for example, for thyroid cancer) were not accounted
for, and such uncertainties would tend to diminish statistical power. Modelling of the Japaneseatomic bombing survivor LSS indicates that for a variety of cancer endpoints, the ERR estimateof risk decreases with increasing attained age [Preston et al., 2007]. The modelling of lifetime
excess risk here, however, assumed the ERR was constant at all attained ages, which would tend
to overestimate risk at the older ages when the models were based primarily on data for younger
ages. It was assumed that malignancies in the exposed population could be detected without any
under-ascertainment or misdiagnosis. For the analysis of the risk of excess thyroid cancer in the
FHMS cohort, a continuing participation rate higher than that actually observed was used toproject future risk, and a constant rather than declining rate of thyroid cancer detection was
projected although the observed detection rate was declining with successive screenings. These,
and other somewhat conservative assumptions, were incorporated to help guard against
underestimating the statistical power to detect risks.
80.
On the other hand, several limitations of the results of the statistical power analyses
should be kept in mind. Ad hoc methods were used to update and adjust the excess rates (LERs,
CERs) without conducting a full reassessment with the sophisticated methods that are sometimes
employed but require more detailed modelling and more information to implement (e.g., [Little
et al., 2010; UNSCEAR, 2008]). However, most of the adjustments and risk coefficients wererather small, so it is not believed that validity was seriously compromised. Perhaps the greatest
limitation is that insufficient information was available to model statistical power for doseresponse analyses based on individuals. The only statistical power analysis available based on
an individual dose distribution was of FDNPS emergency workers; it confirmed the present
findings of low statistical power to detect radiation risk for all solid cancer among the workers[JNIOSH, 2020]. The calculation of statistical power assumed that both LBRs and LERs were
known precisely and accurately (without bias), and realistic uncertainties in those estimates could
not be taken into account. Furthermore, the power analyses did not incorporate uncertainties in
the forms of the models or of the risk-coefficients employed, though they did evaluate dose
uncertainties. When calculated LFRs were not available in the original sources, the statisticalpower analyses were based on simple ERR models rather than EAR models, because EAR

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

35

coefficients were not always available and EAR models are difficult to generalize acrosspopulations, ages and variations in the prevalence of disease screening.
81.
The statistical power analyses have implications for potential epidemiological studies ofFukushima risks from ionizing radiation after the FNDPS accident. Epidemiological study
designs are likely to include only a fraction of the population of a given age range affected by
the FDNPS accident. This implies that the statistical power would probably be less than for the
prefecture-wide analyses reported here. Although a dose-response analysis for individuals would
likely increase the statistical power if the doses were well estimated, for Fukushima Prefecture
residents there are substantial uncertainties in reconstructing individual doses that would restrict
the statistical power.

VIII. CONCLUSIONS
82.
Analyses were conducted of lifetime risks for particularly radiosensitive malignancies:
leukaemia, thyroid cancer and female breast cancer, as well as all solid cancers (excluding
thyroid cancer and nonmelanoma skin cancer). For the best estimates of organ/tissue doses,
radiation-related risks for these malignancies are not expected to be discernible. For risks at
young ages – up to ages 30 or 40 years – the risk of thyroid cancer (without a systematic thyroid
screening programme) was not likely to be detectable based on the best estimates (means) oforgan/tissue doses, although there was mixed evidence regarding possible discernibility ofleukaemia risk after exposure in early childhood. Projections of the FHMS ultrasound thyroid
screening programme up to ages 30 or 40 years did not indicate discernible risks using any ofthree different risk models. Taking dose uncertainties into account, there were a few indications
that at the 95% upper bound on the average doses a few risks might be detectable in the future,
but those are thought to be unlikely in that, among other things, the variation in backgroundcancer rates will be greater than has been assumed in this assessment. All in all, there was no
indication that large excesses of cancer would be expected, and no clear indication that theestimated numerically small excesses would likely be discernible.

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

37

APPENDIXI.

CALCULATION OF LIFETIME BASELINE RISKS

A1.
The statistical power attachment [UNSCEAR, 2016] to the UNSCEAR 2013 Report[UNSCEAR, 2014] contained information from formal calculations of LBRs for most of thecancer endpoints being considered here by age at initial exposure and sex [Walsh et al., 2014;
WHO, 2013]. Given that the baseline cancer rates used in the UNSCEAR 2013 Report are now
somewhat outdated, this assessment updated the previous LBR estimates using newer baseline
cancer incidence rates [IARC, 2017].
A2.
Specifically, in the 2013 statistical power calculations [UNSCEAR, 2016], the LBRs werebased on the average of cancer incidence rates for 2000–2004 in four prefectures (Fukui, Miyagi,
Nagasaki and Yamagata) that had reasonably good, long-term prefecture-wide cancer registries[IARC, 2013; Katanoda et al., 2012]. The most recent update of IARC cancer incidence rates for
2008–2012 [IARC, 2017] in the four Japanese prefectures was used for the present calculations.
The age- and sex-specific rates for leukaemia, thyroid cancer, breast cancer (females only) and allsolid cancer (less nonmelanoma skin cancer and thyroid cancer) were obtained from the IARC
tabulations for the prefectures. However, in the case of leukaemia, Nagasaki Prefecture was
excluded in calculating the average rates, because it appeared that at older ages the rates were
substantially elevated above those of the other prefectures, perhaps because of the radiation
exposure by the atomic bombings of Nagasaki, or because of the high prevalence in Nagasaki ofadult T-cell leukaemia which is related to HTLV-1 viral exposure [Tajima et al., 1990]. The agesex specific leukaemia rates were applied to the numbers of persons then at risk of an incident firstprimary cancer which were derived after accounting for prior cumulative rates of mortality [Vital
Statistics of Japan, 2019] and cancer diagnosis (designated as “cancer-free survival”).
A3.
These LBRs, shown in table A-23.A1, were then applied to Fukushima Prefecture
subgroups by determining the numbers of individuals in the various subgroups defined by sex,
age and municipality from the 2010 Japan census data [Statistics Bureau of Japan, 2011].
Additional individuals who were in utero at the time of the FDNPS accident were added to thecensus numbers and included in the calculations. Table A-23.A1 compares the cumulative
lifetime baseline risks from the UNSCEAR 2013 Report and the present report.
Table A-23.A1. Cumulative lifetime baseline risk (to age 90 years) estimates of selected malignancies,
for the UNSCEAR 2013 Report [UNSCEAR, 2014] and this UNSCEAR report
Male
Starting age(years)UNSCEAR 2013 Report(LBR (%))

FemaleThis assessment(LBR (%))

UNSCEAR 2013 Report(LBR (%))

This assessment(LBR (%))

All solid cancer (except thyroid cancer and nonmelanoma skin cancer)a
1

40.6

42.8

29.0

29.9

10

40.7

42.8

29.1

29.9

20

40.7

42.7

29.1

29.8

Leukaemia
1

0.60

0.63

0.43

0.47

10

0.58

0.57

0.41

0.42

20

0.57

0.55

0.40

0.40

38

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

MaleStarting age(years)UNSCEAR 2013 Report(LBR (%))

FemaleThis assessment(LBR (%))

UNSCEAR 2013 Report(LBR (%))

This assessment(LBR (%))

1

5.5

6.1

10

5.5

6.1

20

5.6

6.1

Breast cancer

Thyroid cancer a
1

0.21

0.37

0.77

1.15

10

0.21

0.37

0.77

1.15

20

0.21

0.37

0.76

1.14

a

Thyroid cancer is treated separately in what follows because doses to the thyroid gland in some geographic areas were substantially greaterthan doses to other organs. The 2013 LBR thyroid cancer estimates were based on Walsh et al. [Walsh et al., 2014].

II.

CALCULATION OF LIFETIME EXCESS RISKS AND LIFETIMEFRACTIONAL RISKS

A4.
It would theoretically be inappropriate to apply the final cumulative dose to all personyears at risk, as that would imply that the dose received at some late age could affect risk at an
earlier time. However, in this study most of the dose was received at early ages; for example, forthose exposed at age 1 year, over 70% of the dose was received by 10 years after initial exposure,
95% by 40 years and about 99% by 60 years. For thyroid cancer, a large percentage of the lifetime
thyroid dose was received in the first year. Using the calculated lifetime dose (to age 80 years)
therefore has virtually no impact on the risk assessment.
A5.
Estimated LERs and LFRs for the radiation-related incidence of all solid cancer, breast
cancer, leukaemia and thyroid cancer by sex for those of ages 1, 10 or 20 years at the time of the
FDNPS accident were developed based on recent risk estimates. For breast cancer and all solidcancer (except nonmelanoma skin cancer and thyroid cancer), the calculation started with therespective age/sex-specific LERs from the WHO Fukushima report [WHO, 2013] which theytermed “lifetime attributable risks” (LARs). These were provided in dose-standardized form by
Walsh et al. [Walsh et al., 2014]. The applicable estimated doses were to the colon for all solidcancer and the female breast, RBM (leukaemia) and thyroid gland. An LFR is simply the ratioof the LER to its corresponding LBR. For all solid cancer the LERs and LFRs were updated byusing the ratio of the newest ERR coefficients for all solid cancer from Grant et al. [Grant et al.,2017] to the ERRs previously used from Preston et al. [Preston et al., 2007]. The ratio of thosecoefficients was 1.10 for females and 1.03 for males. For female breast cancer the ratio of theage-specific ERR coefficients derived from the recent report of Brenner et al. [Brenner et al.,2018] to the coefficients of the earlier Preston et al. report [Preston et al., 2007] were applied to
update the LERs and LFRs. The estimated ratio was 1.29.
A6.
The application of the LFRs to the study subgroups defined by sex and age (ages 1, 10,
20 years) entailed several steps:(a) Calculation of organ/tissue doses for municipalities by age groups. For each municipality
in Fukushima Prefecture, the numbers of individuals for each sex/age subgroup were
determined from the 2010 Japan census data [Statistics Bureau of Japan, 2011], or they
were derived from [Fujimori et al., 2014] for those exposed in utero. The person-weighted
mean lifetime doses for prefecture residents for the colon (for all solid cancer), RBM (forleukaemia), breast and thyroid gland were estimated by the Committee. Similarly, theANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

39

95th percentile upper bound estimates of the mean doses were calculated by theCommittee by Monte Carlo sampling. Lifetime doses were calculated separately forevacuated and non-evacuated municipalities. Because cumulative doses out to age 80
were estimated by the Committee only for effective dose and absorbed dose to thethyroid, ratios of first-year organ/tissue dose to first-year effective dose were used toestimate lifetime doses for the other organs/tissues of interest, e.g., mean lifetime RBM
dose for an age group in a given municipality (and similarly for the 95th percentile upperbound). For example, for leukaemia this was carried out by summing the year-one RBM
dose, and the second year to age 80 cumulative dose times the year-one ratio of the RBM
dose to effective dose. This was performed for ages 1, 10 and 20 years for eachmunicipality, where typical yearly effective doses to age 80, given a particular year-one
dose, were as estimated by the Committee (RBMAv-Y1 / Eff-doseAv-Y1) = average ratio of
RBM dose to effective dose in year one, based on average doses across municipalities;(b) Derivation of the mean and 95th percentile upper bound of the mean doses for the subset
of municipalities that had estimated lifetime effective doses of >5 mSv at age 1. The
estimated mean lifetime doses (estimated cumulative dose over 80 years) and estimated95th percentile upper bound on the mean lifetime doses were derived in a similar way. If
a fraction of individuals was evacuated from a municipality (which notably occurred inthree municipalities), only the non-evacuated subset was used. The applicable numbersof individuals for the subgroups defined by sex and age at exposure were estimated fromthe census data [Statistics Bureau of Japan, 2011];(c) Another subset included the evacuees, who experienced different patterns of evacuation(40 scenarios in all), and the person-weighted mean doses for relevant organs/tissues
were similarly obtained for that subset. The numbers of individuals in the subgroups
defined by sex and age at exposure were again estimated. The first-year doses, which
took into account both evacuation and destination sites and calendar times, wereconverted to approximate cumulative lifetime doses for individual cancer sites, using thesimplifying assumption that their destination site was their residence for the remaining
lifetime;(d) The LFRs and LERs per 1 mGy were then multiplied by the estimated lifetime mean dose
for the subsets of people in various age, sex and dose groups. Strictly speaking, the LFRs
and LERs are not precisely linearly related to dose, but at doses below a few hundredmGy a linear dose transform is a good approximation [Walsh et al., 2014]. The LFR (ratioof the LER to the respective LBR) can be used to calculate statistical power;(e) For the subgroups selected, the estimated LFRs and LERs were also calculated using the95th percentile upper bounds on the respective mean dose estimates. The 95th percentile
upper bounds of the first-year mean doses for the relevant organs were derived for eachmunicipality in Fukushima Prefecture by Monte Carlo sampling as part of the dosimetry
evaluation (see attachments A-13 to A-19).
A7.
Table A-23.A2 provides a selection of the LFRs that were used to calculate hypothetical
lifetime risks of leukaemia attributable to radiation according to various models. A descriptionof the models is given in paragraph 32 of this attachment. Similarly, table A-23.A3 provides a
selection of the LFRs that were used to calculate estimated lifetime risks of thyroid cancer(without population screening) according to the designated models, as described in paragraph 38of this attachment.

40

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Table A-23.A2. Leukaemia, lifetime fractional risk (LFR) attributable to radiation exposure: LFRs
for mean and 95th percentile upper coverage bound on mean lifetime doses by sex and age at
the time of the Fukushima Daiichi Nuclear Power Station accident for all residents of evacuatedand non-evacuated municipalities and for those with >5 mSv cumulative lifetime effective dose.
LFRs are shown for various models of risk
LFRs (%) using WHO
risk estimates aGroup – sex
and age(years)Meantissuedose e95%ile upperbound onmean dose

LFRs (%) using
UNSCEAR 2020 risk
estimates b

LFRs (%) using LSS risk
estimates c

LFRs (%) using pooledrisk estimate dMeantissuedose

Meantissuedose

Meantissuedose

95%ile upperbound onmean dose

95%ile upperbound onmean dose

95%ile upperbound onmean dose

Municipalities with cumulative effective dose >5 mSv
Male
1

2.31

3.74

10.9

17.6

8.29

13.4

4.30

6.96

10

1.32

2.09

7.16

11.4

2.76

4.37

2.74

4.34

20

0.96

1.47

1.20

1.84

1.02

1.56

1

2.19

3.54

13.2

21.4

9.92

16.0

4.79

7.75

10

1.26

2.00

9.13

14.5

3.21

5.09

3.18

5.05

20

0.91

1.40

1.14

1.75

0.67

1.03

FemaleAll municipalities
Male
1

1.64

2.66

7.70

12.5

5.87

9.55

3.05

4.96

10

0.89

1.41

4.82

7.65

1.85

2.94

1.84

2.92

20

0.69

1.06

0.86

1.32

0.73

1.12

1

1.55

2.52

9.38

15.3

7.02

11.4

3.39

5.52

10

0.85

1.35

6.14

9.75

2.16

3.43

2.14

3.40

20

0.65

1.00

0.82

1.26

0.48

0.74

Femalea[Walsh et al., 2014; WHO, 2013].
[UNSCEAR, 2020].
c[Hsu et al., 2013].
d[Little et al., 2018].
eBased on cumulative mean doses and 95th percentile upper bounds on the means, summed to age 80 years.
b

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

41

Table A-23.A3. Thyroid cancer (without systematic screening), lifetime fractional risk (LFR)
attributable to radiation exposure: LFRs for mean and 95th percentile upper coverage bound on
the mean lifetime doses by sex and age at the time of the Fukushima Daiichi Nuclear Power
Station accident for all residents of evacuated and non-evacuated municipalities and for thosewith thyroid doses >5 mGy in the first year. LFRs are shown for various models of risk
LFRs (%) using WHO
risk estimates aGroup – sex andage (years)

Meantissuedose e95%ileupperbound onmean dose

LFRs (%) using LSS
risk estimates b
Meantissuedose

95%ileupperbound onmean dose

LFRs (%) using
Chernobyl risk
estimates c
Meantissuedose

95%ileupperbound onmean dose

LFRs (%) using
pooled risk estimate dMeantissuedose

95%ileupperbound onmean dose

Municipalities with first year absorbed thyroid dose >5 mGy
Male
1

5.40

10.9

3.11

6.26

5.99

12.0

6.68

13.4

10

3.88

8.12

2.55

5.33

3.83

8.02

5.91

12.4

20

1.56

3.07

0.46

0.90

1.56

3.07

1

6.67

13.42

3.11

6.26

7.19

14.5

8.09

16.3

10

4.87

10.2

2.55

5.33

9.65

6.16

7.35

15.4

20

1.56

3.07

0.46

0.90

1.56

1.82

FemaleAll municipalities
Male
1

4.24

8.45

2.44

4.87

4.70

9.37

5.24

10.4

10

2.16

4.34

1.42

2.85

2.14

4.29

3.30

6.62

20

0.85

1.61

0.25

0.47

0.85

1.61

1

5.24

10.4

2.44

4.87

5.64

11.2

6.35

12.7

10

2.72

5.45

1.42

2.85

2.57

5.76

4.10

8.23

20

085

1.61

0.25

0.47

0.85

1.01

Femalea[Walsh et al., 2014; WHO, 2013].
[Furukawa et al., 2013].
[Brenner et al., 2011; Zablotska et al., 2011].
d[Lubin et al., 2017].
eBased on cumulative mean doses and 95th percentile upper bounds on the means, summed to age 80 years.
bcIII. LEUKAEMIA RISK: CALCULATION OF CUMULATIVEFRACTIONAL RISK FROM AGES 1 OR 10 YEARS TO AGES 30
AND 40 YEARSA8.
The CFRs of the models employed for estimating leukaemia risks from ages 1 or 10 years
to 30 and 40 years of age are given in table A-23.A4. The values are scaled to the estimatedcumulative doses for all municipalities in Fukushima Prefecture up to 28 or 38 years of age,respectively, to allow for a two-year dose latency period. The same coefficients per 100 mGy
were applied for both sexes, as no statistically significant differentials by sex were reported.
Cumulative baseline rates are also reported for males and females.

42

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Table A-23.A4. Leukaemia, cumulative fractional risk (CFR) attributable to radiation exposure: CFRs
for mean and 95th percentile upper coverage bound on the mean cumulative doses by age at thetime of the Fukushima Daiichi Nuclear Power Station accident to ages 30 or 40 years, for all
residents of evacuated and non-evacuated municipalities and for municipalities with >5 mSv
cumulative effective doses. LFRs are shown for various models of risk

Age initial
exposure to
final age of
observation

Cumulative
baseline risk:
male; female(%)

CFRs using LSS risk
estimates b

CFRs using UNSCEAR
2020 risk estimates a
At meantissuedose(%) d

At 95%ile
upper boundon mean dose(%)

At meantissuedose(%)

At 95%ile
upper boundon meandose (%)

CFRs using pooledanalysis risk estimates c
At meantissuedose(%)

At 95%ile
upper boundon mean dose(%)

Municipalities with cumulative effective dose >5 mSv
1–30

0.113; 0.096

32.2

52.0

30.5

49.3

9.3

15.1

1–40

0.146; 0.129

34.9

56.4

25.0

40.5

9.8

15.8

10–30

0.062; 0.052

28.5

45.1

10.9

17.3

8.0

12.6

10–40

0.095; 0.086

32.1

50.8

8.8

13.9

8.7

13.8

All municipalities
1–30

0.113; 0.096

23.3

37.9

22.1

36.0

6.7

11.0

1–40

0.146; 0.129

24.1

39.2

17.3

28.1

6.7

11.0

10–30

0.062; 0.052

24.1

39.2

9.2

15.0

6.7

11.0

10–40

0.095; 0.086

24.9

40.5

6.8

11.1

6.7

11.0

a

[UNSCEAR, 2020].
[Hsu et al., 2013; UNSCEAR, 2020].
c[Little et al., 2018].
dUsing RBM cumulative mean doses or 95th percentile upper bound on the means summed to ages 28 (for risk to age 30) or 38 (for risk to age 40).
b

IV. THYROID CANCER RISK: CALCULATION OF CUMULATIVEEXCESS RISK AND CUMULATIVE FRACTIONAL RISK TO AGES
30 OR 40 YEARS
A9.
Three approaches were used in estimating CERs for thyroid cancer (without systematic
thyroid screening) up to ages 30 or 40 years. First, thyroid cancer CER estimates of radiation
risk after exposure at ages 1 or 10 years were [UNSCEAR, 2020] based on ERR estimates fromthe Japanese atomic bombing survivor LSS [Furukawa et al., 2013; Jacob et al., 2014] whichwere adopted as CFR estimates. The CFR estimates at a thyroid dose of 100 mGy were 0.196,
0.128 and 0.027 at ages 1, 10 and 20 years, respectively.
A10. Second, CFRs were calculated based on two studies of exposure to radioactive iodine
isotopes from the Chernobyl accident in the Ukraine [Brenner et al., 2011] and Belarus[Zablotska et al., 2011]. Both studies had individual estimates of dose from 131I based mainly on
thyroid measurements made within a few weeks after the accident, with additional modelling ofrelated characteristics [Likhtarov et al., 2014] (but with fewer individual thyroid measurementsof radioactive iodine exposure in the Belarus study). In neither study did the ERR estimates differ
statistically significantly by sex: female ERRs tended to be larger in the Ukraine study, but male
ERRs were nominally larger in the Belarusian study. For those exposed at ages 0–3 or 0–4 years,
the ERRs were 0.74 (95% CI: not estimable) and 0.40 (95% CI: 0.10, 1.5) per 100 mGy for theANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

43

Ukrainian and Belarusian studies, respectively. At ages 4 or 5 to 11 years, the ERRs were0.16 (95% CI: 0, 0.85) and 0.20 (95% CI: 0.041, 0.62) per 100 mGy, respectively. For calculating
CFR estimates, approximate averages of the ERRs were used as notional coefficients: CFR of
0.58 per 100 mGy for exposure at age 1 year and 0.18 for age 10 years.
A11. Third, CFRs were also calculated based on a recent pooled analysis of nine studies of
low-dose childhood external radiation exposure [Lubin et al., 2017]. The ERR coefficientderived from individuals who received thyroid doses up to 100 mGy was used as the CFR: 0.86
at 100 mGy. Since Lubin et al. [Lubin et al., 2017] did not find statistically significant differencesin risk coefficients by age at exposure, the same coefficient was applied for those exposed at
1 and 10 years of age.

V. FUKUSHIMA HEALTH MANAGEMENT SURVEY THYROIDCANCER RISK: CALCULATIONS BASED ON THE FUKUSHIMAHEALTH MANAGEMENT SURVEY ULTRASOUND THYROID
EXAMINATION PROGRAMME
A12. Data were evaluated for the first three rounds of FHMS ultrasound thyroid examinations(hereafter, “screenings”) to apply risk coefficients to estimate the likelihood that excess thyroid
cancer would be detectable in the FHMS study.
A13. The rates of ascertained thyroid cancers has declined with successive rounds of ultrasoundscreening, even though usual sporadic thyroid cancer rates increase with age. The decreaseprobably reflects a “harvesting effect” when there are multiple rounds of screening within a few
years: the first “prevalence” screening detects both indolent pre-existing cancers and early incident
cancers, and the early detection leads to lower numbers of detected cases at later screenings [Jacobet al., 2014]. Thus, the overall rates of thyroid cancer per year on average for the intervals (fromthe FDNPS accident to the first screening, then between successive screenings) were estimated tobe 2.0, 1.2 and 0.6 per 10,000 persons per year for the first three rounds of screening, respectively.
The second and third screenings were considered to represent incident cancers (i.e., newly
occurring cancers), which are the primary basis of the calculations.
A14. The analysis was based on the number of screened people at the second round ofscreening because:− The first round of screening primarily reflected the prevalence of detectable thyroid cancers
before radiation effects would be expressed (the first round of screening began about six
months after the FDNPS accident and continued until about four years after the accident),
and many of these thyroid cancers would have been present at the time of the accident;− It was thought unlikely that the number of future screened people would increase beyond
the second round number of participants. This was borne out by the decreasing numbers
screened in rounds 1, 2 and 3: 300,476, 270,516 and 217,904, respectively. Beginningwith round 2, an unknown number of individuals who were exposed in utero (out of a
total of about 16,000 in Fukushima Prefecture [Fujimori et al., 2014]) were also screened
and were included in the age 1 year numbers used in the calculations. Analyses also added
into the baseline rate the age-appropriate rate of thyroid cancer detected at the firstscreening, which increased the statistical power slightly. Since the first screening
occurred within roughly 0.5–4 years after the accident, the first screening thyroid cancer
rate was only added in for the 10-year-old cohort, because, in the first screening, nothyroid cancers were diagnosed among those younger than 6 years of age at the accident,
so the rate was zero for those considered to be exposed at one year of age.

44

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

A15. First, the numbers of screened people in the subcohorts had to be established. As
elaborated below, the number of screened people and cancers ascertained, by age and sex, wasbased on numbers in the second and third rounds of screening.
A16. Certain approximations had to be made to model risk in the FHMS. Because the numbersof screened males and females by age at initial exposure were not available, approximate
fractions of the screened FHMS cohort by age and sex were made from the results of the firstround screening, which was conducted soon after the accident. The fraction of total screened
people who were tallied as ages 0–5 years at the time of the first round of screening wasconsidered to be the fraction in the age 1 year group, and the remaining proportion, those of ages
6–18 years at first round screening, was considered the fraction in the 10-year-old group. These
fractions were applied to the second round of screening to define the age 1 year and age 10 years
groups. The proportion of those with first-year doses >5 mGy also was approximated from thecorresponding proportion of 1-year-old infants in Fukushima Prefecture who were inmunicipalities with first-year thyroid doses >5 mGy; this proportion was applied todefine >5 mGy groups in the FHMS.
A17. The modelling of thyroid cancer in the FHMS approximated the observed yearly rates ofthyroid cancer based on male and female observed cancers by year of age and categorized into
the FHMS-reported age-category denominators. Estimates of average intervals of time from the
FDNPS accident to first screening, first to second screening, and second to third screening werethen applied to derive estimates of annual rates of thyroid cancer. The estimated intervals were
1.95, 2.10 and 2.15 years, respectively.
1.

Calculation of cumulative baseline risks

A18. Because the FHMS thyroid ultrasound examinations have found high frequencies ofsuspected/confirmed thyroid cancer, there is no other existing reference with comparable
baseline thyroid cancer rates. Therefore an ad hoc methodology was employed to estimate CBRsof thyroid cancer from existing FHMS data, and then to project CBRs into the future up to ages
30 or 40 years. The approach was to use the actual rates of thyroid cancer found in the rounds ofscreening for various age groups up to 20 years of age. As described above, the numbers of
thyroid cancers by screening age and sex were obtained in rounds 2 and 3 of the FHMS screening,and sex and age-specific annual rates of “suspected or confirmed” thyroid cancers detected byfine-needle aspiration biopsy were estimated up to age 20 years. The rates for various age groups
were derived from [FHMS, 2016; FHMS, 2017; FHMS, 2020].
A19. After age 20 years the rates were projected forward to age 30 or 40 years by assuming
the annual thyroid cancer rates with screening would increase in proportion to the corresponding
age-related baseline rates of thyroid cancer (without both radiation and systematic screening) in
the Japanese population. The rates were calculated to increase by about 5.6% per year of age formales and 5.7% for females, based on four tumour registries in unexposed prefectures. However,
because the proportion participating in the FHMS screening after about age 18 years fell off
sharply in the second and third rounds of screening to 25.7% and 16.4%, respectively, themodelling was made more realistic by taking the participation rate into account. A conservative
assumption (that is, overestimating the theoretical number of detected cancers) was made that30% continued to be examined from age 20 to ages 30 or 40 years, while the remaining 70%
would have only baseline thyroid cancer rates for ages 20 to 30, or 20 to 40, corresponding to
the rates elsewhere in Japan where there is almost no population ultrasound screening of young
adults. This projection is also likely to be conservative, because it ignores the “harvesting” effectof the early detection of thyroid cancers.

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

A20.

45

The steps to estimate the CBRs are briefly described:(a) The sex- and age-specific person-weighted average annual rates of thyroid cancer were
calculated for the second and third rounds of screening (“incident cancers”) based on thenumbers of thyroid cancers found in various age categories, as given in FHMS reports.
Average intervals between screenings were estimated to convert the risks to averageannual rates;(b) Beginning with the sex- and age-specific numbers of screened people and thyroid cancers
observed in the second and third rounds of FHMS screening, annual sex- and age-specificcancer rates were estimated from the screening data to derive the cumulative thyroid
cancer risk up to age 20 years. To implement this, lifetable calculations were performed,
adjusting for cancer-free survival, i.e., decrementing the population at risk yearly to
account for the estimated proportions with prior mortality (based on all-Japan rates) or
prior thyroid cancers (from the FHMS data);(c) After 20 years of age, the annual percentage increase in thyroid cancer rates wascalculated (using the averages of rates from four essentially unexposed prefectures –(Fukui, Miyagi, Nagasaki and Yamagata) [IARC, 2017] based on the ratio of the rates atage 35–39 years to the rates at age 20–24 years, separately for males and females. The
annual percentage increases were then applied to estimate thyroid cancer rates for ages
21–30 years or 21–40 years. The increments were about 5.6% per year for males and5.7% for females. Annual proportional increments were applied to the age 20 cumulative
screening rates of thyroid cancer for 30% of the cohort, the proportion assumed tocontinue with screening, and the baseline thyroid cancer rates without screening wereapplied to the remaining 70%.
A21. Table A-23.A5 shows basic data for the second and third rounds of screening, from which
the FHMS CBRs were generated. Information on the first-round screening is presented also, eventhough first screening results were used only to increment the baseline rate of thyroid cancer atage 10 years (using the rate for ages 11–15 years).
Table A-23.A5. Number of participants, number of cytologically diagnosed suspicious/malignant(S/M) thyroid tumours and annual rates of tumours by sex and age group at screening, for the
first through third screenings
Age at screening (years)a
First screening

0–5

6–10

11–15

16–22

Total

No. males

45 06547 20143 351

16 068151 685

No. S/M thyroid cancers b

0

0

11

28

39

Yearly rate c,d

0

0

1.3

9.0

1.3

No. females

42 73244 80442 76918 486148 791

No. S/M thyroid cancers

0

1

19

57

77

Yearly rate

0

0.11

2.3

15.8

2.7

Second screening

2–7

8–12

13–17

18–23

Total

No. males

31 76443 68345 52615 414

136 387

No. S/M thyroid cancers

0

5

9

18

32

Yearly rate

0

0.55

0.94

5.6

1.1

No. females

30 07241 69944 01718 322

134 110

No. S/M thyroid cancers

0

3

22

14

39

Yearly rate

0.34

2.4

3.6

1.4

1.4

46

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Age at screening (years)a
Third screening

4–9

10–14

15–19

20–24

Total

No. males

32 24241 66231 821

4 325

110 050

No. S/M thyroid cancers

0

4

7

2

12

Yearly rate

0

0.45

1.0

2.2

0.50

No. females

30 45640 00431 7605 627

107 847

No. S/M thyroid cancers

0

5

12

1

18

Yearly rate

0

0.58

1.8

0.83

0.77

a

Ages at the time of screening.
S/M = suspicious or malignant thyroid cancer by cytological determination.
cRates given per 10,000 person-years. Numbers of S/M cancers and participants by age and sex were obtained from [FHMS, 2016; FHMS, 2017;
FHMS, 2020].
dYearly rates assumed mean intervals of 1.95 years from the accident to the first screening, 2.1 years from the first to the second screening, and2.15 years from the second to the third screening.
b2.

Calculation of cumulative excess risks and cumulative fractional risks

A22. As described in paragraphs 23–24 of this attachment, first-year thyroid doses for nonevacuated and evacuated municipalities were estimated by the Committee, and the temporal
course of continuing thyroid doses was also estimated. The cumulative dose estimates out to ages
27 or 37 years (to allow for a three-year latency period to ages 30 or 40 years, respectively) wereapplied to the risk coefficients for CER estimation.
A23. The recent UNSCEAR report [UNSCEAR, 2020] has estimated CBR and CER risks forthyroid cancer after exposure beginning at age 1 or 10 years, basing the CBRs on the Ukrainian
population. However, because the background rates in the FHMS with ultrasound screening
differ from those in the Ukraine, the UNSCEAR report could not be used directly for thisassessment. Therefore, the CFRs, the ratios of the CERs to the corresponding CBRs, in the
UNSCEAR report were used by applying the CFRs to the baseline thyroid cancer rates (CBRs)
derived in the FHMS. For those exposed at age 1 year, the CFRs after a thyroid dose of 100 mGy
were about 0.79 and 0.68 for follow-up to ages 30 and 40 years, respectively, in the UNSCEAR
report [UNSCEAR, 2020]. The corresponding ratios for those exposed at age 10 years wereabout 0.41 and 0.35 at 100 mGy, respectively. The risk coefficients derived in the report did notvary significantly by sex in the UNSCEAR report analysis, so the same CFRs were applied toboth sexes in their estimation of risks.
A24. CFRs were also calculated based on two studies of exposure to 131I and otherradioisotopes of iodine from the Chernobyl accident in the Ukraine [Brenner et al., 2011] andBelarus [Zablotska et al., 2011], as described in section IV of this appendix. The notional averagerisk coefficients from those studies for ages 1 and 10 years were applied to the FHMS baselinerisks. The statistical power results are shown in the main text, table A-23.12. In addition, CFRs
based on the pooled analysis of nine studies of external irradiation and thyroid cancer risk atdoses of 100 mGy or less by Lubin et al. [Lubin et al., 2017] were applied to the calculated
CBRs, with statistical power results shown in table A-23.12.

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

47

VI. ARITHMETIC MEAN OR AVERAGE DOSES AND UPPERBOUNDS (95TH PERCENTILES) ON THE MEANS FORMUNICIPALITIES IN FUKUSHIMA PREFECTURE AND FOR
EVACUATED GROUPS
A25. This section comprises a number of tables that contain the Committee’s estimates of thearithmetic mean doses in the first year and over a lifetime, to those residing in each of the nonevacuated municipalities of Fukushima Prefecture and for those residents evacuated from one orother locations of Fukushima Prefecture. In addition, the tables provide the estimates of upperbounds (95th percentile) on the mean doses.
A26. For the non-evacuated municipalities in Fukushima Prefecture the mean first-year doses
and upper bounds are tabulated for effective dose and absorbed dose in the thyroid, breast, colonand RBM for three ages at exposure: adults (table A-23.A6), 10-year-old children (table A-23.A7)
and 1-year-old infants (table A-23.A8). In addition, the means and upper bounds on the absorbed
dose to thyroid and to the RBM for a fetus in the first year after the accident are tabulated(table A-23.A9). Table A-23.A10 shows the means and upper coverage bounds on the effective
dose over a lifetime for adults, 10-year-old children and 1-year-old infants for Fukushima
Prefecture (excluding evacuated areas).
A27. For the evacuated locations, the arithmetic mean and upper bound doses in the first year
are tabulated for effective dose and absorbed dose in the thyroid for three ages at exposure: adults(table A-23.A11), 10-year-old children (table A-23.A12) and 1-year-old infants (table A-23.A13).
Table A-23.A6. Mean and upper bounda on the effective dose, absorbed dose to thyroid, absorbed
dose to breast, absorbed dose to colon and absorbed dose to red bone marrow for adults in thefirst year after the accident for municipalities in Fukushima Prefecture (excluding evacuated areas)
Effective dose(mSv)

Thyroid dose(mGy)

Breast dose(mGy)

Colon dose(mGy)

RBM dose(mGy)

Municipality
Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Aizubange Machi

0.69

1.1

1.2

2.5

0.75

1.2

0.69

1.1

0.68

1.1

Aizumisato Machi

0.30

0.53

0.82

2.0

0.30

0.50

0.29

0.49

0.28

0.46

Aizuwakamatsu Shi

0.55

0.94

1.1

2.5

0.59

0.94

0.55

0.89

0.53

0.85

Asakawa Machi

0.47

0.76

1.0

2.1

0.49

0.76

0.47

0.72

0.45

0.69

Bandai Machi

0.49

0.81

1.3

2.8

0.51

0.79

0.48

0.75

0.46

0.72

Date Shi

2.6

4.4

6.2

17

2.8

4.4

2.5

4.0

2.5

3.9

Fukushima Shi

3.8

6.2

7.2

15

4.1

6.2

3.7

5.7

3.7

5.6

Furudono Machi

0.43

0.72

1.2

2.5

0.44

0.69

0.43

0.67

0.40

0.63

Hanawa Machi

0.41

0.69

1.2

2.5

0.42

0.64

0.40

0.62

0.38

0.59

Hinoemata Mura

0.079

0.17

0.46

1.3

0.07

0.11

0.07

0.13

0.07

0.12

Hirata Mura

0.40

0.67

1.0

2.1

0.42

0.65

0.40

0.62

0.39

0.60

Inawashiro Machi

0.49

0.88

1.9

5.3

0.48

0.77

0.45

0.73

0.43

0.71

Ishikawa Machi

0.24

0.42

0.73

1.7

0.24

0.38

0.24

0.38

0.22

0.36

Iwaki Shi

0.84

1.5

2.6

7.5

0.87

1.4

0.84

1.3

0.79

1.2

Izumizaki Mura

0.95

1.5

1.5

2.9

1.0

1.6

0.96

1.5

0.94

1.4

Kagamiishi Machi

0.97

1.6

1.5

3.1

1.1

1.7

0.98

1.5

0.95

1.5

Kaneyama Machi

0.084

0.17

0.47

1.2

0.07

0.12

0.08

0.14

0.07

0.12

48

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Effective dose(mSv)

Thyroid dose(mGy)

Breast dose(mGy)

Colon dose(mGy)

RBM dose(mGy)

Municipality

a

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Kawamata Machi

1.6

2.7

3.5

9.3

1.7

2.7

1.6

2.5

1.6

2.4

Kitakata Shi

0.41

0.69

0.93

2.0

0.44

0.67

0.41

0.64

0.40

0.62

Kitashiobara Mura

0.89

1.4

1.9

3.9

0.95

1.5

0.88

1.4

0.86

1.3

Koori Machi

3.5

5.6

8.6

20

3.7

5.6

3.4

5.1

3.3

5.0

Koriyama Shi

2.6

4.1

3.5

6.3

2.9

4.4

2.6

4.0

2.6

3.9

Kunimi Machi

1.6

2.6

4.5

11

1.7

2.5

1.5

2.3

1.5

2.2

Miharu Machi

1.4

2.2

2.0

3.7

1.5

2.3

1.4

2.2

1.4

2.1

Minamiaizu Machi

0.12

0.25

0.52

1.4

0.12

0.20

0.12

0.22

0.11

0.20

Minamisoma Shi

2.3

4.0

11

37

2.1

3.2

2.0

3.3

1.9

2.9

Mishima Machi

0.26

0.46

0.67

1.5

0.27

0.43

0.26

0.42

0.25

0.40

Motomiya Shi

2.1

3.4

3.3

6.4

2.3

3.5

2.1

3.2

2.1

3.1

Nakajima Mura

0.48

0.80

1.0

2.1

0.52

0.80

0.49

0.76

0.47

0.73

Nihonmatsu Shi

3.1

5.1

5.6

12

3.4

5.2

3.1

4.7

3.0

4.6

Nishiaizu Machi

0.14

0.27

0.57

1.4

0.13

0.22

0.13

0.23

0.12

0.21

Nishigo Mura

1.5

2.5

2.2

4.1

1.7

2.6

1.6

2.4

1.5

2.4

Ono Machi

0.42

0.74

1.1

2.8

0.45

0.70

0.43

0.69

0.41

0.64

Otama Mura

2.6

4.4

4.6

9.4

2.9

4.6

2.7

4.3

2.7

4.2

Samegawa Mura

0.44

0.74

1.1

2.5

0.45

0.70

0.43

0.68

0.41

0.64

Shimogo Machi

0.23

0.40

0.64

1.5

0.24

0.37

0.23

0.37

0.21

0.34

Shinchi Machi

1.1

2.0

4.3

14

1.1

1.7

1.1

1.7

1.0

1.6

Shirakawa Shi

1.3

2.1

2.0

3.7

1.4

2.2

1.3

2.0

1.3

2.0

Showa Mura

0.25

0.46

0.66

1.6

0.26

0.42

0.25

0.42

0.24

0.39

Soma Shi

1.2

2.2

5.4

19

1.1

1.7

1.1

1.8

1.0

1.6

Sukagawa Shi

1.2

2.0

1.9

3.5

1.4

2.1

1.3

1.9

1.2

1.9

Tadami Machi

0.16

0.29

0.55

1.3

0.16

0.25

0.16

0.26

0.15

0.24

Tamakawa Mura

0.31

0.54

0.81

1.9

0.32

0.50

0.31

0.49

0.29

0.46

Tamura Shi

0.65

1.1

1.4

3.5

0.70

1.1

0.66

1.1

0.64

1.0

Tanagura Machi

0.79

1.3

1.7

3.6

0.84

1.3

0.79

1.2

0.76

1.2

Ten-ei Mura

1.8

3.1

2.6

4.9

2.1

3.3

1.9

3.0

1.9

2.9

Yabuki Machi

0.63

1.0

1.1

2.3

0.69

1.0

0.64

0.98

0.62

0.95

Yamatsuri Machi

0.32

0.55

0.79

1.8

0.33

0.53

0.31

0.51

0.30

0.48

Yanaizu Machi

0.26

0.45

0.71

1.6

0.26

0.41

0.25

0.41

0.24

0.39

Yugawa Mura

0.67

1.1

1.2

2.4

0.73

1.1

0.68

1.0

0.66

1.0

Upper bound is the 95th percentile of the mean dose.

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

49

Table A-23.A7. Mean and upper bound on the effective dose, absorbed dose to thyroid, absorbed
dose to breast, absorbed dose to colon and absorbed dose to red bone marrow for 10-year-old
children in the first year after the accident for municipalities in Fukushima Prefecture (excluding
evacuated areas)
Effective dose(mSv)

Thyroid dose(mGy)

Breast dose(mGy)

Colon dose(mGy)

RBM dose(mGy)

Municipality
Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Aizubange Machi

0.84

1.4

2.0

4.3

0.84

1.3

0.86

1.3

0.80

1.2

Aizumisato Machi

0.36

0.66

1.5

3.8

0.34

0.54

0.35

0.57

0.32

0.53

Aizuwakamatsu Shi

0.66

1.1

1.8

4.4

0.66

1.0

0.68

1.1

0.64

1.0

Asakawa Machi

0.56

0.93

1.7

3.9

0.55

0.83

0.57

0.87

0.53

0.80

Bandai Machi

0.60

1.0

2.1

4.8

0.57

0.87

0.59

0.90

0.55

0.84

Date Shi

3.1

5.3

9.2

27

3.1

4.9

3.2

5.0

3.0

4.7

Fukushima Shi

4.5

7.4

10

23

4.6

7.0

4.7

7.1

4.4

6.7

Furudono Machi

0.53

0.90

2.0

4.6

0.49

0.75

0.52

0.80

0.47

0.73

Hanawa Machi

0.50

0.85

2.0

4.6

0.46

0.70

0.49

0.75

0.44

0.68

Hinoemata Mura

0.10

0.22

1.0

2.7

0.07

0.11

0.08

0.13

0.07

0.11

Hirata Mura

0.49

0.83

1.7

3.9

0.47

0.71

0.49

0.75

0.45

0.69

Inawashiro Machi

0.61

1.1

3.2

9.0

0.53

0.84

0.55

0.88

0.51

0.82

Ishikawa Machi

0.30

0.52

1.4

3.3

0.27

0.41

0.28

0.44

0.26

0.40

Iwaki Shi

1.0

1.8

4.2

12

0.97

1.5

1.1

1.6

0.94

1.4

Izumizaki Mura

1.1

1.9

2.2

4.8

1.2

1.8

1.2

1.8

1.1

1.7

Kagamiishi Machi

1.2

1.9

2.3

5.1

1.2

1.8

1.2

1.9

1.1

1.8

Kaneyama Machi

0.11

0.23

1.0

2.7

0.07

0.12

0.08

0.14

0.07

0.12

Kawamata Machi

2.0

3.3

5.2

14

2.0

3.0

2.0

3.1

1.9

2.9

Kitakata Shi

0.50

0.84

1.6

3.6

0.49

0.74

0.50

0.77

0.47

0.71

Kitashiobara Mura

1.1

1.7

2.9

6.2

1.1

1.6

1.1

1.7

1.0

1.6

Koori Machi

4.2

6.8

13

31

4.2

6.2

4.3

6.4

4.0

6.0

Koriyama Shi

3.1

4.9

4.7

8.9

3.2

4.9

3.3

5.0

3.1

4.7

Kunimi Machi

1.9

3.2

6.9

17

1.9

2.8

1.9

2.9

1.8

2.7

Miharu Machi

1.7

2.7

2.9

5.7

1.7

2.6

1.8

2.7

1.7

2.5

Minamiaizu Machi

0.16

0.32

1.1

3.0

0.12

0.20

0.13

0.23

0.12

0.20

Minamisoma Shi

2.8

5.3

17

60

2.3

3.5

2.6

4.0

2.2

3.4

Mishima Machi

0.32

0.56

1.3

3.0

0.30

0.46

0.32

0.49

0.29

0.45

Motomiya Shi

2.5

4.0

4.6

9.5

2.6

3.9

2.6

4.0

2.5

3.7

Nakajima Mura

0.59

0.97

1.7

3.7

0.58

0.88

0.60

0.92

0.55

0.85

Nihonmatsu Shi

3.7

6.0

7.9

18

3.8

5.8

3.9

5.9

3.7

5.5

Nishiaizu Machi

0.17

0.34

1.1

3.0

0.14

0.23

0.15

0.25

0.14

0.22

Nishigo Mura

1.8

3.0

3.1

6.2

1.9

2.9

2.0

3.0

1.8

2.8

Ono Machi

0.51

0.91

1.9

5.0

0.50

0.77

0.52

0.82

0.48

0.74

Otama Mura

3.2

5.3

6.5

14

3.3

5.2

3.4

5.3

3.2

5.0

Samegawa Mura

0.53

0.91

1.9

4.3

0.50

0.77

0.53

0.82

0.49

0.75

50

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Effective dose(mSv)

Thyroid dose(mGy)

Breast dose(mGy)

Colon dose(mGy)

RBM dose(mGy)

Municipality
Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Shimogo Machi

0.28

0.51

1.2

2.9

0.26

0.40

0.27

0.43

0.25

0.39

Shinchi Machi

1.4

2.6

6.9

23

1.2

1.8

1.3

2.1

1.2

1.8

Shirakawa Shi

1.6

2.5

2.8

5.8

1.6

2.4

1.6

2.5

1.5

2.3

Showa Mura

0.30

0.57

1.2

3.3

0.28

0.45

0.30

0.48

0.27

0.44

Soma Shi

1.4

2.9

8.7

32

1.2

1.9

1.4

2.2

1.2

1.8

Sukagawa Shi

1.5

2.4

2.7

5.4

1.5

2.3

1.6

2.4

1.5

2.2

Tadami Machi

0.20

0.37

1.1

2.8

0.17

0.27

0.18

0.29

0.17

0.26

Tamakawa Mura

0.38

0.67

1.5

3.6

0.35

0.54

0.37

0.58

0.34

0.53

Tamura Shi

0.78

1.4

2.3

5.8

0.78

1.2

0.81

1.3

0.75

1.2

Tanagura Machi

0.95

1.6

2.7

6.0

0.94

1.4

0.98

1.5

0.90

1.4

Ten-ei Mura

2.2

3.7

3.6

7.3

2.3

3.7

2.4

3.7

2.2

3.5

Yabuki Machi

0.76

1.2

1.8

4.0

0.77

1.2

0.79

1.2

0.74

1.1

Yamatsuri Machi

0.39

0.68

1.4

3.4

0.36

0.57

0.38

0.60

0.35

0.56

Yanaizu Machi

0.31

0.56

1.3

3.2

0.29

0.45

0.30

0.48

0.28

0.44

Yugawa Mura

0.81

1.3

1.9

4.2

0.82

1.2

0.84

1.3

0.79

1.2

Table A-23.A8. Mean and upper bound on the effective dose, absorbed dose to thyroid, absorbed
dose to breast, absorbed dose to colon and absorbed dose to red bone marrow for 1-year-old
infants in the first year after the accident for municipalities in Fukushima Prefecture (excluding
evacuated areas)
Effective dose(mSv)

Thyroid dose(mGy)

Breast dose(mGy)

Colon dose(mGy)

RBM dose(mGy)

Municipality
Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Aizubange Machi

0.98

1.6

2.3

5.1

0.98

1.5

0.99

1.5

0.91

1.4

Aizumisato Machi

0.42

0.76

1.8

4.5

0.39

0.63

0.40

0.66

0.36

0.58

Aizuwakamatsu Shi

0.78

1.3

2.1

5.2

0.77

1.2

0.79

1.2

0.72

1.1

Asakawa Machi

0.66

1.1

2.1

4.7

0.64

0.97

0.66

1.0

0.59

0.90

Bandai Machi

0.70

1.2

2.5

5.7

0.67

1.0

0.68

1.1

0.62

0.95

Date Shi

3.7

6.2

11

32

3.6

5.7

3.7

5.8

3.4

5.3

Fukushima Shi

5.3

8.7

12

27

5.4

8.3

5.4

8.3

5.0

7.6

Furudono Machi

0.62

1.0

2.4

5.5

0.57

0.88

0.61

0.93

0.53

0.81

Hanawa Machi

0.58

0.99

2.4

5.5

0.54

0.82

0.57

0.87

0.50

0.76

Hinoemata Mura

0.12

0.24

1.2

3.2

0.072

0.12

0.087

0.15

0.068

0.11

Hirata Mura

0.57

0.97

2.1

4.6

0.55

0.83

0.57

0.87

0.51

0.77

Inawashiro Machi

0.70

1.3

3.7

10

0.62

0.98

0.63

1.0

0.57

0.91

Ishikawa Machi

0.34

0.60

1.6

4.0

0.31

0.47

0.33

0.51

0.28

0.44

Iwaki Shi

1.2

2.1

5.1

15

1.1

1.7

1.2

1.9

1.1

1.6

Izumizaki Mura

1.3

2.2

2.7

5.7

1.4

2.1

1.4

2.1

1.3

1.9

Kagamiishi Machi

1.4

2.2

2.7

6.0

1.4

2.2

1.4

2.2

1.3

2.0

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Effective dose(mSv)

Thyroid dose(mGy)

Breast dose(mGy)

Colon dose
(mGy)

51

RBM dose(mGy)

Municipality
Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Mean

Upper
bound

Kaneyama Machi

0.12

0.25

1.2

3.2

0.079

0.13

0.093

0.16

0.074

0.12

Kawamata Machi

2.3

3.8

6.1

16

2.3

3.5

2.3

3.6

2.1

3.2

Kitakata Shi

0.59

0.98

1.9

4.3

0.57

0.86

0.58

0.89

0.53

0.80

Kitashiobara Mura

1.3

2.0

3.3

7.1

1.3

1.9

1.3

1.9

1.2

1.8

Koori Machi

5.0

8.0

15

37

4.9

7.4

4.9

7.4

4.5

6.8

Koriyama Shi

3.6

5.7

5.5

10

3.8

5.8

3.8

5.8

3.5

5.3

Kunimi Machi

2.3

3.7

8.2

20

2.2

3.3

2.2

3.3

2.0

3.0

Miharu Machi

2.0

3.1

3.4

6.7

2.0

3.1

2.1

3.1

1.9

2.8

Minamiaizu Machi

0.18

0.37

1.3

3.6

0.14

0.23

0.15

0.26

0.13

0.21

Minamisoma Shi

3.3

6.2

21

72

2.7

4.1

3.0

4.7

2.5

3.8

Mishima Machi

0.38

0.65

1.5

3.6

0.35

0.53

0.36

0.56

0.32

0.50

Motomiya Shi

2.9

4.7

5.4

11

3.0

4.6

3.0

4.6

2.8

4.3

Nakajima Mura

0.69

1.1

2.0

4.4

0.67

1.0

0.69

1.1

0.62

0.95

Nihonmatsu Shi

4.4

7.1

9.2

21

4.5

6.8

4.5

6.9

4.1

6.3

Nishiaizu Machi

0.20

0.38

1.4

3.6

0.16

0.25

0.17

0.29

0.15

0.24

Nishigo Mura

2.2

3.5

3.6

7.2

2.3

3.5

2.3

3.5

2.1

3.2

Ono Machi

0.60

1.1

2.3

6.1

0.58

0.89

0.61

0.95

0.53

0.83

Otama Mura

3.7

6.2

7.6

16

3.9

6.2

3.9

6.2

3.6

5.7

Samegawa Mura

0.62

1.1

2.3

5.2

0.59

0.90

0.61

0.94

0.54

0.83

Shimogo Machi

0.33

0.58

1.4

3.5

0.30

0.46

0.32

0.50

0.28

0.44

Shinchi Machi

1.6

3.0

8.3

28

1.4

2.1

1.6

2.4

1.3

2.0

Shirakawa Shi

1.8

3.0

3.4

6.9

1.9

2.9

1.9

2.9

1.7

2.6

Showa Mura

0.35

0.66

1.5

4.0

0.33

0.52

0.34

0.56

0.30

0.48

Soma Shi

1.7

3.3

10

39

1.4

2.2

1.6

2.5

1.3

2.0

Sukagawa Shi

1.7

2.8

3.2

6.4

1.8

2.7

1.8

2.8

1.7

2.5

Tadami Machi

0.23

0.43

1.3

3.4

0.19

0.30

0.21

0.33

0.18

0.28

Tamakawa Mura

0.44

0.77

1.7

4.3

0.41

0.63

0.43

0.67

0.38

0.58

Tamura Shi

0.92

1.6

2.8

6.7

0.91

1.4

0.94

1.5

0.85

1.3

Tanagura Machi

1.1

1.9

3.2

7.1

1.1

1.7

1.1

1.7

1.0

1.5

Ten-ei Mura

2.6

4.3

4.2

8.5

2.7

4.3

2.8

4.3

2.5

4.0

Yabuki Machi

0.89

1.4

2.1

4.7

0.90

1.4

0.91

1.4

0.83

1.3

Yamatsuri Machi

0.45

0.79

1.7

4.0

0.42

0.66

0.44

0.70

0.39

0.62

Yanaizu Machi

0.37

0.64

1.6

3.9

0.34

0.52

0.35

0.55

0.31

0.48

Yugawa Mura

0.94

1.6

2.3

5.0

0.96

1.5

0.97

1.5

0.89

1.3

52

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Table A-23.A9. Mean and upper bound on the absorbed dose to thyroid and absorbed dose to redbone marrow for a fetus in the first year after the accident for municipalities in Fukushima Prefecture(excluding evacuated areas)
Absorbed dose to thyroid of fetus (mGy)

Absorbed dose to RBM of fetus (mGy)

Municipality
Mean

Upper bound

Mean

Upper bound

Aizubange Machi

0.89

1.7

0.41

0.67

Aizumisato Machi

0.65

1.4

0.18

0.28

Aizuwakamatsu Shi

0.84

1.6

0.36

0.58

Asakawa Machi

0.78

1.5

0.28

0.46

Bandai Machi

0.97

1.9

0.31

0.50

Date Shi

4.3

10

1.4

2.3

Fukushima Shi

4.8

10

2.1

3.4

Furudono Machi

0.89

1.8

0.25

0.40

Hanawa Machi

0.88

1.8

0.23

0.38

Hinoemata Mura

0.41

1.1

0.047

0.070

Hirata Mura

0.77

1.5

0.24

0.39

Inawashiro Machi

1.4

3.4

0.26

0.42

Ishikawa Machi

0.59

1.3

0.14

0.23

Iwaki Shi

2.0

4.7

0.56

0.91

Izumizaki Mura

1.0

1.8

0.56

0.91

Kagamiishi Machi

1.1

1.9

0.56

0.92

Kaneyama Machi

0.42

1.1

0.051

0.076

Kawamata Machi

0.72

1.4

0.25

0.41

Kitakata Shi

1.3

2.6

0.52

0.85

Kitashiobara Mura

5.9

14

1.9

3.1

Koori Machi

2.2

3.7

1.5

2.4

Koriyama Shi

3.2

7.8

0.87

1.4

Kunimi Machi

1.3

2.3

0.81

1.3

Miharu Machi

0.45

1.1

0.074

0.11

Minamiaizu Machi

7.8

23

1.0

1.7

Minamisoma Shi

0.54

1.2

0.16

0.25

Mishima Machi

2.1

3.9

1.2

1.9

Motomiya Shi

0.74

1.5

0.30

0.48

Nakajima Mura

3.7

7.6

1.7

2.9

Nihonmatsu Shi

0.48

1.2

0.083

0.13

Nishiaizu Machi

1.4

2.4

0.89

1.5

Nishigo Mura

0.85

1.8

0.24

0.39

Ono Machi

3.03

6.0

1.47

2.4

Otama Mura

0.85

1.7

0.26

0.42

Samegawa Mura

0.57

1.2

0.19

0.30

Shimogo Machi

3.1

8.4

0.58

0.94

Shinchi Machi

1.3

2.2

0.75

1.2

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Absorbed dose to thyroid of fetus (mGy)53

Absorbed dose to RBM of fetus (mGy)

Municipality
Mean

Upper bound

Mean

Upper boundShirakawa Shi

0.54

1.2

0.15

0.23

Showa Mura

3.9

11

0.55

0.90

Soma Shi

1.3

2.1

0.72

1.2

Sukagawa Shi

0.48

1.1

0.11

0.17

Tadami Machi

0.64

1.3

0.18

0.29

Tamakawa Mura

1.2

2.4

0.44

0.72

Tamura Shi

1.7

2.8

1.1

1.7

Tanagura Machi

0.82

1.5

0.39

0.63

Ten-ei Mura

0.68

1.4

0.25

0.40

Yabuki Machi

0.57

1.2

0.15

0.24

Yamatsuri Machi

0.88

1.6

0.40

0.64

Yanaizu Machi

0.89

1.7

0.41

0.67

Yugawa Mura

0.65

1.4

0.18

0.28

Table A-23.A10. Mean and upper bound on the effective dose over a lifetime for adults, 10-year-old
children and 1-year-old infants for Fukushima Prefecture (excluding evacuated areas)

Municipality

Effective dose over a
lifetime to adults (mSv)

Effective dose over a lifetime to
10-year-old children (mSv)

Effective dose over a lifetime
to 1-year-old infants (mSv)

Mean

Upper bound

Mean

Upper bound

Mean

Upper bound

Aizubange Machi

2.8

4.6

3.2

5.1

3.6

5.8

Aizumisato Machi

1.1

1.9

1.2

2.1

1.4

2.4

Aizuwakamatsu Shi

2.0

3.2

2.2

3.6

2.5

4.1

Asakawa Machi

1.8

2.8

2.0

3.1

2.2

3.5

Bandai Machi

1.7

2.8

1.9

3.1

2.2

3.5

Date Shi

11

18

12

20

14

23

Fukushima Shi

16

26

18

29

21

33

Furudono Machi

1.6

2.6

1.8

2.9

2.1

3.3

Hanawa Machi

1.5

2.4

1.7

2.7

1.9

3.1

Hinoemata Mura

0.24

0.42

0.27

0.48

0.30

0.53

Hirata Mura

1.5

2.4

1.7

2.7

1.9

3.0

Inawashiro Machi

1.9

3.1

2.1

3.5

2.4

3.9

Ishikawa Machi

0.90

1.5

1.0

1.6

1.1

1.8

Iwaki Shi

2.4

3.9

2.7

4.4

3.1

5.0

Izumizaki Mura

4.0

6.3

4.4

7.0

5.0

8.0

Kagamiishi Machi

4.1

6.6

4.5

7.4

5.1

8.4

Kaneyama Machi

0.27

0.47

0.30

0.53

0.34

0.58

Kawamata Machi

6.7

11

7.4

12

8.5

13

Kitakata Shi

1.6

2.5

1.8

2.8

2.0

3.2

Kitashiobara Mura

3.6

5.7

4.0

6.4

4.5

7.2

Koori Machi

15

23

17

26

19

29

Koriyama Shi

11

18

13

20

14

23

54

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Effective dose over a
lifetime to adults (mSv)

Municipality

Effective dose over a lifetime to
10-year-old children (mSv)

Effective dose over a lifetime
to 1-year-old infants (mSv)

Mean

Upper bound

Mean

Upper bound

Mean

Upper boundKunimi Machi

6.5

10

7.2

11

8.2

13

Miharu Machi

6.0

9.4

6.6

10

7.6

12

Minamiaizu Machi

0.43

0.76

0.48

0.86

0.54

0.95

Minamisoma Shi

8.3

13

9.4

15

11

17

Mishima Machi

1.0

1.6

1.1

1.8

1.3

2.1

Motomiya Shi

9.1

14

10.1

16

11

18

Nakajima Mura

1.9

3.0

2.1

3.3

2.4

3.8

Nihonmatsu Shi

14

22

15

24

17

27

Nishiaizu Machi

0.49

0.84

0.55

0.94

0.62

1.1

Nishigo Mura

6.8

11

7.5

12

8.5

14

Ono Machi

1.7

2.7

1.9

3.0

2.1

3.4

Otama Mura

12

19

13

22

15

25

Samegawa Mura

1.6

2.6

1.8

2.9

2.1

3.3

Shimogo Machi

0.45

0.75

0.52

0.87

0.59

0.99

Shinchi Machi

4.2

6.8

4.7

7.6

5.4

8.7

Shirakawa Shi

5.4

8.5

6.0

9.5

6.8

11

Showa Mura

1.0

1.6

1.1

1.8

1.2

2.1

Soma Shi

4.3

7.0

4.9

8.0

5.6

9.0

Sukagawa Shi

5.3

8.3

5.9

9.2

6.7

11

Tadami Machi

0.48

0.79

0.54

0.90

0.60

1.0

Tamakawa Mura

1.2

1.9

1.3

2.1

1.5

2.4

Tamura Shi

2.3

3.9

2.8

4.6

3.2

5.4

Tanagura Machi

3.3

5.2

3.6

5.8

4.1

6.5

Ten-ei Mura

8.3

14

9.1

15

10

17

Yabuki Machi

2.5

3.9

2.8

4.4

3.1

5.0

Yamatsuri Machi

0.69

1.2

0.8

1.3

0.91

1.5

Yanaizu Machi

1.0

1.6

1.1

1.8

1.2

2.0

Yugawa Mura

2.8

4.4

3.1

4.9

3.5

5.5

Table A-23.A11. Mean and upper bound on the effective dose in the first year to adults evacuatedfrom localities in Fukushima Prefecture, including doses received before and during the evacuation
and at the destination
Scenario destination
Location

Effective dose (mSv)

Scenario
Prefecture

Location

Thyroid dose (mGy)

Mean

Upper
bound

Mean

Upper
bound

Futaba01(FT1)

Saitama Ken

Saitama Shi

0.36

1.0

1.7

3.8

Futaba02(FT2)

Ibaraki Ken

Kasama Shi

0.79

2.1

8.3

28

Futaba03(FT3)

Ibaraki Ken

Yuki Shi

0.59

1.3

4.9

15

Futaba04(FT4)

Fukushima Ken

Koriyama Shi

2.5

4.3

7.1

19

Futaba05(FT5)

Tochigi Ken

Sano Shi

0.61

1.0

1.6

2.4

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Scenario destination
Location

Effective dose (mSv)

Scenario
Prefecture

Location

55

Thyroid dose (mGy)

Mean

Upper
bound

Mean

Upper
bound

Kawauchi06(TM1)

Niigata Ken

Niigata Shi

0.046

0.072

1.1

1.7

Tomioka07(TM2)

Chiba Ken

Chiba Shi

0.50

1.0

2.3

5.0

Tomioka

08(TM3)Chiba Ken

Chiba Shi

0.39

0.65

2.0

3.9

Tomioka09(TM4)

Fukushima Ken

Iwaki Shi

0.78

1.8

5.5

17

Naraha10(NR1)

Tochigi Ken

Nasushiobara Shi

0.79

1.3

2.1

3.6

Naraha11(NR2)

Chiba Ken

Chiba Shi

0.47

1.0

3.0

8.0

Naraha12(NR3)

Fukushima Ken

Iwaki Shi

0.78

1.8

5.4

17

Naraha13(NR4)

Tochigi Ken

Mooka Shi

0.60

1.0

1.6

2.3

Naraha14(NR5)

Fukushima Ken

Iwaki Shi

0.66

1.4

3.7

11

Okuma

15(OK1)

Fukushima Ken

Aizuwakamatsu Shi

0.65

1.5

3.4

9.0

Okuma

16(OK2)

Fukushima Ken

Tamura Shi

0.81

1.7

3.3

8.2

Futaba17(OK3)

Tokyo Ken

Shinjuku Ku

0.33

0.87

2.4

6.6

Tamura18(OK4)

Fukushima Ken

Tamura Shi

1.2

3.1

2.6

5.5

Odaka

19(OK5)

Tochigi Ken

Nasushiobara Shi

0.95

1.7

3.6

9.1

Namie

20(NM1)

Tokyo Ken

Shinjuku Ku

0.25

0.56

2.5

7.1

Namie

21(NM2)

Fukushima Ken

Soma Shi

1.1

2.3

7.0

22

Namie

22(NM3)

Fukushima Ken

Koriyama Shi

2.3

3.8

3.3

5.4

Tsushima

23(NM4)

Fukushima Ken

Nihonmatsu Shi

3.1

5.6

8.0

21

Namie

24(NM5)

Yamagata Ken

Yonezawa Shi

0.38

1.2

5.9

20

Iitate25(IT1)

Fukushima Ken

Koriyama Shi

2.6

4.5

9.2

25

Iitate26(IT2)

Fukushima Ken

Kitashiobara Mura

0.41

0.64

2.0

3.0

Iitate27(IT3)

Saitama Ken

Saitama Shi

0.37

1.0

3.5

8.6

Iitate28(IT4)

Fukushima Ken

Iitate Mura

5.5

9.0

9.1

16

Odaka

29(OD1)

Tokyo Ken

Shinjuku Ku

1.2

3.9

15.2

54

Odaka

30(OD2)

Yamagata Ken

Tsuruoka Shi

0.10

0.16

0.8

1.3

Haramachi31(OD3)Kanagawa Ken

Yokohama Shi

0.22

0.57

1.4

2.3

Odaka

32(OD4)

Tokyo Ken

Shinjuku Ku

0.73

2.3

11.6

40

Odaka

33(OD5)

Saitama Ken

Saitama Shi

0.52

1.6

6.2

20

Haramachi34(HK1)

Kanagawa Ken

Yokohama Shi

0.32

0.91

2.9

7.7

Iitate35(HK2)

Yamagata Ken

Yamagata Shi

0.21

0.44

1.9

3.2

Kashima36(HK3)Kanagawa Ken

Yokohama Shi

1.1

2.4

5.9

18

Haramachi

37(HK4)

Fukushima Ken

Soma Shi

1.2

2.7

8.1

25

Hirono Town

38 (NIRS 10)

Fukushima Ken

Ono Shi

0.76

2.0

2.2

5.1

Katsurao
Village

39 (NIRS 12)

Fukushima Ken

Fukushima Shi

2.9

4.7

3.6

5.6

Katsurao
Village Office

40 (NIRS 14)

Fukushima Ken

Fukushima Shi

3.9

7.3

8.0

21

56

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Table A-23.A12. Mean and upper bound on the effective dose in the first year to 10-year-old
children evacuated from localities in Fukushima Prefecture, including doses received before andduring the evacuation and at the destination
Scenario destination
Location

Effective dose (mSv)

Scenario
Prefecture

Location

Thyroid dose (mGy)

Mean

Upper
bound

Mean

Upper
bound

Futaba01(FT1)

Saitama Ken

Saitama Shi

0.44

1.2

3.0

6.3

Futaba02(FT2)

Ibaraki Ken

Kasama Shi

0.99

2.5

12

38

Futaba03(FT3)

Ibaraki Ken

Yuki Shi

0.79

1.8

8.3

25

Futaba04(FT4)

Fukushima Ken

Koriyama Shi

3.006

5.3

11

29

Futaba05(FT5)

Tochigi Ken

Sano Shi

0.75

1.2

2.8

4.2

Kawauchi06(TM1)

Niigata Ken

Niigata Shi

0.10

0.16

2.4

3.8

Tomioka07(TM2)

Chiba Ken

Chiba Shi

0.65

1.3

4.2

8.5

Tomioka

08(TM3)Chiba Ken

Chiba Shi

0.51

0.84

3.7

6.8

Tomioka09(TM4)

Fukushima Ken

Iwaki Shi

1.0

2.3

9.4

28

Naraha10(NR1)

Tochigi Ken

Nasushiobara Shi

0.96

1.6

3.5

6.0

Naraha11(NR2)

Chiba Ken

Chiba Shi

0.62

1.3

5.2

13

Naraha12(NR3)

Fukushima Ken

Iwaki Shi

1.0

2.4

9.0

27

Naraha13(NR4)

Tochigi Ken

Mooka Shi

0.74

1.2

2.8

4.1

Naraha14(NR5)

Fukushima Ken

Iwaki Shi

0.84

1.8

6.4

17

Okuma

15(OK1)

Fukushima Ken

Aizuwakamatsu Shi

0.80

1.8

5.3

13

Okuma

16(OK2)

Fukushima Ken

Tamura Shi

1.0

2.1

5.4

13

Futaba17(OK3)

Tokyo Ken

Shinjuku Ku

0.44

1.1

4.3

11

Tamura18(OK4)

Fukushima Ken

Tamura Shi

1.4

3.5

4.1

8.0

Odaka

19(OK5)

Tochigi Ken

Nasushiobara Shi

1.2

2.2

6.2

15

Namie

20(NM1)

Tokyo Ken

Shinjuku Ku

0.36

0.82

4.6

12

Namie

21(NM2)

Fukushima Ken

Soma Shi

1.4

3.1

12

37

Namie

22(NM3)

Fukushima Ken

Koriyama Shi

2.7

4.4

4.8

7.6

Tsushima

23(NM4)

Fukushima Ken

Nihonmatsu Shi

3.7

6.6

11

29

Namie

24(NM5)

Yamagata Ken

Yonezawa Shi

0.58

1.8

10

33

Iitate25(IT1)

Fukushima Ken

Koriyama Shi

3.1

5.3

13

32

Iitate26(IT2)

Fukushima Ken

Kitashiobara Mura

0.54

0.82

3.9

5.9

Iitate27(IT3)

Saitama Ken

Saitama Shi

0.51

1.3

6.3

15

Iitate28(IT4)

Fukushima Ken

Iitate Mura

6.5

11

13

24

Odaka

29(OD1)

Tokyo Ken

Shinjuku Ku

1.5

5.0

22

75

Odaka

30(OD2)

Yamagata Ken

Tsuruoka Shi

0.14

0.20

1.6

2.6

Haramachi31(OD3)Kanagawa Ken

Yokohama Shi

0.30

0.70

2.8

4.4

Odaka

32(OD4)

Tokyo Ken

Shinjuku Ku

0.96

3.0

17

55

Odaka

33(OD5)

Saitama Ken

Saitama Shi

0.74

2.2

11

34

Haramachi34(HK1)

Kanagawa Ken

Yokohama Shi

0.44

1.2

5.3

13

Iitate35(HK2)

Yamagata Ken

Yamagata Shi

0.32

0.59

3.9

6.2

Kashima36(HK3)Kanagawa Ken

Yokohama Shi

1.4

3.1

9.9

29

Haramachi

37(HK4)

Fukushima Ken

Soma Shi

1.6

3.6

14

42

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Scenario destination
Location

Effective dose (mSv)

Scenario
Prefecture

Location

57

Thyroid dose (mGy)

Mean

Upper
bound

Mean

Upper
bound

Hirono Town

38 (NIRS 10) Fukushima Ken

Ono Shi

0.92

2.3

3.7

7.8

Katsurao Village

39 (NIRS 12) Fukushima Ken

Fukushima Shi

3.5

5.6

4.8

7.2

Katsurao Village
Office40 (NIRS 14) Fukushima Ken

Fukushima Shi

4.6

8.7

12

31

Table A-23.A13. Mean and upper bound on the effective dose in the first year to 1-year-old
infants evacuated from localities in Fukushima Prefecture, including doses received before andduring the evacuation and at the destination
Scenario destination
Location

Effective dose (mSv)

Scenario
Prefecture

Location

Thyroid dose (mGy)

Mean

Upper
bound

Mean

Upper
bound

Futaba01(FT1)

Saitama Ken

Saitama Shi

0.54

1.4

3.9

7.9

Futaba02(FT2)

Ibaraki Ken

Kasama Shi

1.2

3.1

16

50

Futaba03(FT3)

Ibaraki Ken

Yuki Shi

0.95

2.2

11

33

Futaba04(FT4)

Fukushima Ken

Koriyama Shi

3.6

6.5

16

44

Futaba05(FT5)

Tochigi Ken

Sano Shi

0.91

1.4

3.8

6.1

Kawauchi06(TM1)

Niigata Ken

Niigata Shi

0.15

0.24

3.6

5.9

Tomioka07(TM2)

Chiba Ken

Chiba Shi

0.78

1.6

5.7

11

Tomioka

08(TM3)Chiba Ken

Chiba Shi

0.62

1.0

5.1

9.2

Tomioka09(TM4)

Fukushima Ken

Iwaki Shi

1.2

2.7

12

34

Naraha10(NR1)

Tochigi Ken

Nasushiobara Shi

1.2

1.9

4.6

7.9

Naraha11(NR2)

Chiba Ken

Chiba Shi

0.74

1.5

6.6

16

Naraha12(NR3)

Fukushima Ken

Iwaki Shi

1.2

2.7

11

34

Naraha13(NR4)

Tochigi Ken

Mooka Shi

0.89

1.4

3.8

5.8

Naraha14(NR5)

Fukushima Ken

Iwaki Shi

1.0

2.1

8.1

21

Okuma

15(OK1)

Fukushima Ken

Aizuwakamatsu Shi

0.98

2.1

7.0

17

Okuma

16(OK2)

Fukushima Ken

Tamura Shi

1.2

2.6

7.7

19

Futaba17(OK3)

Tokyo Ken

Shinjuku Ku

0.53

1.3

5.6

14

Tamura18(OK4)

Fukushima Ken

Tamura Shi

1.7

4.1

5.4

10

Odaka

19(OK5)

Tochigi Ken

Nasushiobara Shi

1.5

2.6

8.6

21

Namie

20(NM1)

Tokyo Ken

Shinjuku Ku

0.44

1.0

5.9

15

Namie

21(NM2)

Fukushima Ken

Soma Shi

1.7

3.6

15

45

Namie

22(NM3)

Fukushima Ken

Koriyama Shi

3.2

5.3

6.3

10

Tsushima

23(NM4)

Fukushima Ken

Nihonmatsu Shi

4.4

7.7

13

34

Namie

24(NM5)

Yamagata Ken

Yonezawa Shi

0.69

2.1

13

41

Iitate25(IT1)

Fukushima Ken

Koriyama Shi

3.6

6.3

16

39

Iitate26(IT2)

Fukushima Ken

Kitashiobara Mura

0.69

1.0

5.7

9.3

Iitate27(IT3)

Saitama Ken

Saitama Shi

0.64

1.5

8.5

18

Iitate28(IT4)

Fukushima Ken

Iitate Mura

7.8

13

16

29

Odaka

29(OD1)

Tokyo Ken

Shinjuku Ku

1.9

6.3

30

103

Odaka

30(OD2)

Yamagata Ken

Tsuruoka Shi

0.17

0.25

2.2

3.5

58

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Scenario destination
Location

Effective dose (mSv)

Scenario

Thyroid dose (mGy)

Prefecture

Location

Mean

Upper
bound

Mean

Upper
bound

Haramachi31(OD3)Kanagawa Ken

Yokohama Shi

0.39

0.85

4.0

6.6

Odaka

32(OD4)

Tokyo Ken

Shinjuku Ku

1.2

3.8

23

77

Odaka

33(OD5)

Saitama Ken

Saitama Shi

0.93

2.8

15

45

Haramachi34(HK1)

Kanagawa Ken

Yokohama Shi

0.55

1.5

7.2

17

Iitate35(HK2)

Yamagata Ken

Yamagata Shi

0.42

0.74

5.7

9.7

Kashima36(HK3)Kanagawa Ken

Yokohama Shi

1.6

3.6

12

35

Haramachi

37(HK4)

Fukushima Ken

Soma Shi

2.0

4.5

19

58

Hirono Town

38 (NIRS 10)

Fukushima Ken

Ono Shi

1.1

2.8

4.7

9.8

Katsurao
Village

39 (NIRS 12)

Fukushima Ken

Fukushima Shi

4.1

6.6

5.5

8.3

Katsurao
Village Office

40 (NIRS 14)

Fukushima Ken

Fukushima Shi

5.4

10

14

37

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

59

REFERENCES
Averbeck, D. Does scientific evidence support a change from the LNT model for low-doseradiation risk extrapolation? Health Phys 97(5): 493-504 (2009).
Berrington de Gonzaléz, A., J.A. Salotti, K. McHugh et al. Relationship between paediatric CT
scans and subsequent risk of leukaemia and brain tumours: assessment of the impact ofunderlyng conditions. Br J Cancer 114(4): 388-394 (2016).
Brenner, A.V., D.L. Preston, R. Sakata et al. Incidence of breast cancer in the Life Span Studyof atomic bomb survivors: 1958-2005. Radiat Res 190(4): 433-444 (2018).
Brenner, A.V, M.D. Tronko, M. Hatch et al. I-131 dose response for incident thyroid cancers in
Ukraine related to the Chornobyl accident. Environ Health Perspect 119(7): 933-939 (2011).
Faul, F., E. Erdfelder, A. Buchner et al. Statistical power analyses using G*Power 3.1: Tests forcorrelation and regression analyses. Behav Res Methods 41(4): 1149-1160 (2009).
Faul, F., E. Erdfelder, A-G. Lang et al. G*Power 3: A flexible statistical power analysis program
for the social, behavioral, and biomedical sciences. Behav Res Methods 39(2): 175-191(2007).
FHMS Thyroid ultrasound examination (preliminary baseline screening). Supplemental reportof the FY 2015 survey. Proceedings of the 23rd Prefectural Oversight Committee
Meeting for Fukushima Health Management Survey, Fukushima Medical University.
[Internet]
Availablefrom(http://fmu-global.jp/download/thyroid-ultrasoundexamination-supplemental-report-of-the-fy-2015-survey/?wpdmdl=1632) on 6 June 2016.
FHMS. Report of second-round thyroid ultrasound examinations (first full-scale thyroid
screening program). Proceedings of the 28th Prefectural Oversight Committee Meetingfor Fukushima Health Management Survey, Fukushima Medical University. [Internet]
Available from (http://fmu-global.jp/download/thyroid-ultrasound-examinations-firstfull-scale-thyroid-screening-program/?wpdmdl=3608) on 23 October 2017.
FHMS. Summary results of thyroid screening (1st to 4th rounds). Proceedings of the 39th
Prefectural Oversight Committee Meeting for Fukushima Health Management Survey,
Fukushima Medical University. Available from (http://www.pref.fukushima.lg.jp/
uploaded/attachment/401334.pdf) on 31 August 2020. (Japanese).
Fujimori, K., H. Kyozuka, S. Yasuda et al. Pregnancy and birth survey after the Great East Japan
Earthquake and Fukushima Daiichi Nuclear Power Plant accident in Fukushima
prefecture. Fukushima J Med Sci 60(1): 75-81 (2014).
Furukawa, K., D. Preston, S. Funamoto et al. Long-term trend of thyroid cancer risk among
Japanese atomic-bomb survivors: 60 years after exposure. Int J Cancer 132(5): 12221226 (2013).
Gilbert, E.S., M.P. Little, D.L. Preston et al. Issues in interpreting epidemiologic studies of
populations exposed to low-dose, high-energy photon radiation. J Natl Cancer Inst
Monogr 2020(56): 176-187 (2020).
Grant, E.J., A. Brenner, H. Sugiyama et al. Solid cancer incidence among the life span study ofatomic bomb survivors: 1958-2009. Radiat Res 187(5): 513-537 (2017).
Hauptmann, M., R.D. Daniels, E. Cardis et al. Epidemiological studies of low-dose ionizing
radiation and cancer: summary bias assessment and meta-analysis. J Natl Cancer Inst
Monogr 2020(56): 188-200 (2020).

60

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

Haylock, R.G.E., M. Gillies, N. Hunter et al. Cancer mortality and incidence following external
occupational radiation exposure: an update of the 3rd analysis of the UK national registry
for radiation workers. Br J Cancer 119(5): 631-637 (2018).
HHU. G*Power: Statistical power analyses for Mac and Windows. [Internet] Available from(http://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-undarbeitspsychologie/gpower.html ) on 22 April 2020.
Hsu, W.L., D.L. Preston, M. Soda et al. The incidence of leukemia, lymphoma and multiple
myeloma among atomic bomb survivors: 1950-2001. Radiat Res 179(3): 361-382 (2013).
Hunter, N., I.S. Kuznetsova, E.V. Labutina et al. Solid cancer incidence other than lung, liverand bone in Mayak workers: 1948-2004. Br J Cancer 109(7): 1989-1996 (2013).
IARC. Cancer incidence in five continents. Vol. X: IARC Scientific Publication No. 164.
International Agency for Research on Cancer, Lyon, 2013.
IARC. Cancer incidence in five continents. Vol. XI: IARC Scientific Publication No. 166.
International Agency for Research on Cancer, Lyon, 2017.
Jacob, P., T.I. Bogdanova, E. Buglova et al. Thyroid cancer risk in areas of Ukraine and Belarus
affected by the Chernobyl accident. Radiat Res 165(1): 1-8 (2006).
Jacob, P., J.C. Kaiser and A. Ulanovsky. Ultrasonography survey and thyroid cancer in the
Fukushima Prefecture. Radiat Environ Biophys 53(2): 391-401 (2014).
JNIOSH. Epidemiological study on the health effects in radiation workers. The first period(FY2014 to FY2018), Third Party Committee Report. National Institute of Occupational
Safety and Health, Japan, 2020.
Kaiser, J.C. and L. Walsh. Independent analysis of the radiation risk for leukaemia in children
and adults with mortality data (1950–2003) of Japanese A-bomb survivors. Radiat
Environ Biophys 52(1): 17-27 (2013).
Katanoda, K., W. Ajiki, T. Matsuda et al. Trend analysis of cancer incidence in Japan using datafrom selected population-based cancer registries. Cancer Sci 103(2): 360-368 (2012).
Kesminiene, A., A-S. Evrard, V.K. Ivanov et al. Risk of thyroid cancer among Chernobyl
liquidators. Radiat Res 178(5): 425-436 (2012).
Kitahara, C.M., D.L. Preston, G. Neta et al. Occupational radiation exposure and thyroid cancer
incidence in a cohort of U.S. radiologic technologists, 1983-2013. Int J Cancer 143(9):2145-2149 (2018).
Kitamura, H., T. Okubo, K. Kodama et al. Epidemiological study of health effects in Fukushima
nuclear emergency workers–study design and progress report. Radiat Prot Dosim 182(1):40-48 (2018).
Lee, W.J., D.L. Preston, E.S. Cha et al. Thyroid cancer risks among medical radiation workers
in South Korea, 1996–2015. Environ Health 18(1): 19 (2019).
Leuraud, K., D.B. Richardson, E. Cardis et al. Ionising radiation and risk of death fromleukaemia and lymphoma in radiation-monitored workers (INWORKS): an international
cohort study. Lancet Haematol 2(7): e276-e281 (2015).
Likhtarov, I., L. Kovgan, S. Masiuk et al. Thyroid cancer study among Ukrainian children
exposed to radiation after the Chornobyl accident: improved estimates of the thyroid
doses to the cohort members. Health Phys 106(3): 370-396 (2014).

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

61

Little, M.P., R. Wakeford, D. Borrego et al. Leukaemia and myeloid malignancy among people
exposed to low doses (<100 mSv) of ionising radiation during childhood: a pooled
analysis of nine historical cohort studies. Lancet Haematol 5(8): e346-e358 (2018).
Little, M.P., R. Wakeford, J.H. Lubin et al. The statistical power of epidemiological studies
analyzing the relationship between exposure to ionizing radiation and cancer, with specialreference to childhood leukemia and natural background radiation. Radiat Res 174(3):387-402 (2010).
Lubin, J.H., M.J. Adams, R. Shore et al. Thyroid cancer following childhood low-dose radiation
exposure: a pooled analysis of nine cohorts. J Clin Endocrinol Metab 102(7): 2575-2583(2017).
Meulepas, J.M., C.M. Ronckers, A. Smets et al. Radiation exposure from pediatric CT scans andsubsequent cancer risk in the Netherlands. J Natl Cancer Inst 111(3): 256-263 (2019).
NCRP. NCRP Commentary No. 27: Implications of recent epidemiologic studies for the linear
nonthreshold model and radiation protection. National Council on Radiation Protection
and Measurements, Bethesda, MD, 2018
Pearce, M.S., J.A. Salotti, M.P. Little et al. Radiation exposure from CT scans in childhood andsubsequent risk of leukaemia and brain tumours: a retrospective cohort study. Lancet
380(9840): 499-505 (2012).
Poole, C. Beyond the confidence interval. Am J Public Health 77(2): 195-199 (1987).
Preston, D.L., H. Cullings, A. Suyama et al. Solid cancer incidence in atomic bomb survivors
exposed in utero or as young children. J Natl Cancer Inst 100(6): 428-436 (2008).
Preston, D.L., E. Ron, S. Tokuoka et al. Solid cancer incidence in atomic bomb survivors: 19581998. Radiat Res 168(1): 1-64 (2007).
Shimura, T., I. Yamaguchi, H. Terada et al. Radiation occupational health interventions offered
to radiation workers in response to the complex catastrophic disaster at the Fukushima
Daiichi Nuclear Power Plant. J Radiat Res 56(3): 413-421 (2015).
Statistics Bureau of Japan. 2010 Population Census: Basic complete tabulation on populationand households. Table 3-1: Population (total and Japanese population), by age (single
years) and sex, percentage by age, average age and median age - Japan, all Shi, all Gun,
Prefectures and 20 major cities. [Internet] Available from (https://www.e-stat.go.jp/en/
stat-search/files?page=1&layout=datalist&toukei=00200521&tstat=000001039448&
cycle=0&year=20100&tclass1=000001045009&tclass2=000001046265) on 28 April 2020.
(2011).
Tajima, K. and the T- and B-cell Malignancy Study Group. The 4th nation-wide study of adult
T-cell leukemia/lymphoma (ATL) in Japan: estimates of risk of ATL and its geographicaland clinical features. Int J Cancer 45(2): 237-243 (1990).
UNSCEAR. Effects of Ionizing Radiation. Volume I: Report to the General Assembly, Scientific
Annexes A and B. UNSCEAR 2006 Report. United Nations Scientific Committee on the
Effects of Atomic Radiation. United Nations sales publication E.08.IX.06. United
Nations, New York, 2008.
UNSCEAR. Sources, Effects and Risks of Ionizing Radiation. Volume II: Scientific Annex B.
UNSCEAR 2013 Report. United Nations Scientific Committee on the Effects of Atomic
Radiation. United Nations sales publication E.14.IX.2. United Nations, New York, 2013.

62

ANNEX B, ATTACHMENT A-23: POWER CALCULATIONS FOR EPIDEMIOLOGICAL DETECTION OF HEALTH EFFECTS …

UNSCEAR. Sources, Effects and Risks of Ionizing Radiation. Volume I: Report to the General
Assembly and Scientific Annex A. UNSCEAR 2013 Report. United Nations ScientificCommittee on the Effects of Atomic Radiation. United Nations sales publication
E.14.IX.1. United Nations, New York, 2014.
UNSCEAR. Power calculations for epidemiological studies that underpin the commentary on
health implications in the 2013 Fukushima Report (Attachment 1). United Nations
Scientific Committee on the Effects of Atomic Radiation. [Internet] Available from(https://www.unscear.org/unscear/en/publications/Fukushima_WP2015.html). 2016
UNSCEAR. Sources, Effects and Risks of Ionizing Radiation. Report to the General Assemblyand Scientific Annexes A and B. UNSCEAR 2019 Report. United Nations ScientificCommittee on the Effects of Atomic Radiation. United Nations sales publications
E.20.IX.5. United Nations, New York, 2020.
Vital Statistics of Japan. Volume 1, General mortality Table 5.15 Trends in deaths and death
rates (per 100 000 population) by sex, age and causes of death: Japan. [Internet] Available
fromhttps://www.e-stat.go.jp/en/stat-search/files?page=1&layout=dataset&toukei=
00450011&stat_infid=000022220040 (2019).
Wakeford, R. The risk of childhood leukaemia following exposure to ionising radiation—areview. J Radiol Prot 33(1): 1-25 (2013).
Wakeford, R. and M.P. Little. Risk coefficients for childhood cancer after intrauterine
irradiation: a review. Int J Radiat Biol 79(5): 293-309 (2003).
Walmsley, A.L.E. and M.C. Brown. What is power? Statistics Teacher, American Statistical
Association (ASA). [Internet] Available from (https://www.statisticsteacher.org/2017/09/15/what-is-power/) on 15 September 2017.
Walsh, L., W. Zhang, R.E. Shore et al. A framework for estimating radiation-related cancer risks
in Japan from the 2011 Fukushima nuclear accident. Radiat Res 182(5): 556-572 (2014).
WHO. Health risk assessment from the nuclear accident after the 2011 Great East Japan
earthquake and tsunami, based on a preliminary dose estimation. World HealthOrganization, Geneva, 2013.
Zablotska, L.B., E. Ron, A.V. Rozhko et al. Thyroid cancer risk in Belarus among children andadolescents exposed to radioiodine after the Chornobyl accident. Br J Cancer 104(1):181-187 (2011).