SOURCES, EFFECTS AND RISKSOF IONIZING RADIATION
United Nations Scientific Committee on the
Effects of Atomic Radiation
UNSCEAR 2019
Report to the General Assembly,
with Scientific Annexes

UNITED NATIONSNew York, 2020

NOTE
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Corrigendum to Sales No. E.20.IX.5

16 September 2022

Sources, Effects and Risks of Ionizing Radiation: United
Nations Scientific Committee on the Effects of Atomic
Radiation 2019 Report to the General Assembly, with
Scientific Annexes
Corrigendum
1.

Annex A (Evaluation of selected health effects and inference of risk due toradiation exposure), page 132, paragraph 408, line 8
For msex=1, −1 for males, females, respectively
read msex = –1, +1 for males, females, respectively

V.22-10459 (E)*2210459*

ANNEX A
EVALUATION OF SELECTED HEALTH EFFECTS AND
INFERENCE OF RISK DUE TO RADIATION EXPOSURE

CONTENTSI.

INTRODUCTION....................................................................................................................................................... 23

II.

LEUKAEMIA INCIDENCE AFTER REPEATED LOW-DOSE EXPOSURE DURING CHILDHOOD ........ 29
A. Motivation ........................................................................................................................................................ 29
B.

Recapitulation of previous UNSCEAR publications .......................................................................... 30

C.

Review of recent epidemiological literature ...................................................................................... 30

D. Definition of scenario................................................................................................................................... 35
E.

Results ................................................................................................................................................................38

F.

Discussion of scenario calculations ........................................................................................................ 40

G. Conclusions ..................................................................................................................................................... 51
III. LEUKAEMIA MORTALITY AFTER REPEATED LOW-DOSE EXPOSURE DURING ADULTHOOD ..... 52
A. Motivation ........................................................................................................................................................ 52
B.

Recapitulation of previous UNSCEAR publications .......................................................................... 52

C.

Review of recent epidemiological literature ...................................................................................... 53

D. Definition of scenario................................................................................................................................... 57
E.

Results ................................................................................................................................................................60

F.

Discussion of scenario calculations ........................................................................................................ 62

G. Conclusions ..................................................................................................................................................... 69

21

22 UNSCEAR 2019 REPORT

IV. SOLID CANCER MORTALITY AFTER ACUTE AND PROTRACTED EXPOSURE..................................... 70
A. Motivation ........................................................................................................................................................ 70
B.

Recapitulation of previous UNSCEAR publications .......................................................................... 71

C.

Review of recent epidemiological literature ...................................................................................... 72

D. Definition of scenario................................................................................................................................ 107
E.

Results ............................................................................................................................................................. 111

F.

Discussion of scenario calculations ..................................................................................................... 114

G. Conclusions .................................................................................................................................................. 126
V. THYROID CANCER RISK AFTER EXPOSURE DURING CHILDHOOD .................................................... 128
A. Motivation ..................................................................................................................................................... 128
B.

Recapitulation of previous UNSCEAR publications ....................................................................... 128

C.

Review of recent epidemiological literature ................................................................................... 129

D. Definition of scenario................................................................................................................................ 131
E.

Results ............................................................................................................................................................. 134

F.

Discussion of scenario calculations ..................................................................................................... 136

G. Conclusions .................................................................................................................................................. 145
VI. RISK OF CIRCULATORY DISEASES AFTER RADIATION EXPOSURE .................................................... 146
A. Motivation ..................................................................................................................................................... 146
B.

Recapitulation of previous UNSCEAR publications ....................................................................... 146

C.

Review of recent epidemiological literature ................................................................................... 147

D. Definition of scenario................................................................................................................................ 153
E.

Results ............................................................................................................................................................. 155

F.

Discussion of scenario calculations ..................................................................................................... 158

G. Conclusions .................................................................................................................................................. 163
VII. RESEARCH NEEDS................................................................................................................................................ 163
VIII. GENERAL CONCLUSIONS ................................................................................................................................. 165
A. Health effects ............................................................................................................................................... 165
B.

Risk assessments ......................................................................................................................................... 166

ACKNOWLEDGEMENTS ............................................................................................................................................. 171
Members of the Expert Group........................................................................................................................ 171
APPENDIX A. JUDGEMENT OF CREDIBLE INTERVALS .................................................................................... 173
REFERENCES .................................................................................................................................................................. 177

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 23

I. INTRODUCTION
1.
The United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) in its
UNSCEAR 2012 Report, annex B summarized the current methodologies to estimate uncertainties inrisk estimates for radiation-induced cancer [U8]. A key outcome was realizing the need to go beyond
purely statistical uncertainties and to consider, as far as possible, other sources of uncertainty,* 1 e.g.
those due to dose estimates, or choice of model* for analysing epidemiological data.
2.
The Committee agreed at its sixty-second session (1–5 June 2015) to conduct initial feasibility
work on a proposal to perform evaluations of selected health effects and inferences of risk fromexposure to ionizing radiation. The risk evaluations for the present report were carried out according tothree criteria:(a) Importance for decisions involving the safe use of ionizing radiation or addressing public
controversy;(b) Availability of a sufficient amount of information to allow a meaningful assessment of
uncertainties;(c) Existence of one or more recent epidemiological studies related to the topic of the risk
evaluation.
3.
Risk evaluations that meet these criteria fulfil an important part of the mandate of the Committee,
which is to assess and report levels and effects of exposure to ionizing radiation. Evaluations of key healthrisks from low-dose and low-dose-rate exposures, such as those caused by the accident at the Fukushima
Daiichi nuclear power station, are of particular interest. The present state of knowledge of these healthrisks after such exposures is limited. Great care has to be taken in extrapolating health effects from acute-,moderate- or high-dose exposures to health risks from low-dose and low-dose-rate exposures.
4.
The aim of the present annex was not to perform an exhaustive review of the literature about risks
at low doses and dose rates, nor to perform a meta-analysis* or a combined analysis of results available
for specific health risks at low doses, but rather to identify the most pertinent information to assess risks
for key health risks in specific situations of exposure to ionizing radiation.
5.
This annex provides evaluations of those health effects that meet the three criteria mentioned
above, with a view to quantifying the risks of those effects at lower doses and dose rates than was
possible before. The quantitative risk evaluations of these selected health effects are based on specificsituations involving low to moderate doses for which meaningful assessments of the uncertainties in therisk estimates can be made. Low and moderate doses are defined as about 10 to about 100 mGy and as
about 100 mGy to about 1 Gy, respectively [U8]. The Committee expects that the results will improve
the application of risk models to other exposure situations.

1

Technical terms are explained in the glossary and are marked with an asterisk (*) the first time that they appear.

24 UNSCEAR 2019 REPORT

6.
The global approach used can be broken down into five main steps with some interrelation
between steps 1 and 2:−

Step 1: Definition of specific scenarios and the motivation behind their choice;−

Step 2: Literature search for information to enable the calculation of cumulative excess risk;−

Step 3: Calculation of cumulative excess risk for each scenario and comparison with the risk
derived from the Life Span Study (LSS) applied to the scenario (“risk transfer”);−

Step 4: Discussion of further uncertainties;−

Step 5: Identification of the preferred risk inference, which is the one that best fits thecharacteristics of the considered scenario, based on an expert judgement on the magnitude of
all the uncertainties associated with it.

1. Step 1: Definition of specific scenarios and the motivation behind theirchoice7.
Five specific scenarios have been elaborated, corresponding to realistic situations of exposure toradiation, for which enough information is currently available to allow quantitative assessments ofradiation risk to be made:(a) Risk of leukaemia after low-dose exposure during childhood with a total absorbed dose* to thered bone marrow (RBM) of 20 mGy. 2 Recent results from relevant epidemiological studies havebeen reviewed. A scenario has been elaborated to reflect possible exposure to repeated computed
tomography (CT) scans during childhood;(b) Risk of leukaemia after a moderate dose (200 mGy to the RBM) during adulthood with special
emphasis on possible differences between acute* and protracted exposure* and on transfer of risk
estimates to other populations. Recent results from relevant epidemiological studies have beenreviewed. A scenario has been elaborated to reflect possible prolonged occupational exposure of a
population of workers in the nuclear industry;(c) Risk of solid cancer after a dose at the borderline of low to moderate doses (100 mGy) to thewhole-body during adulthood with special emphasis on possible differences between acute andprotracted exposure and on transfer of risk estimates to other populations. Recent results fromrelevant epidemiological studies have been reviewed. A scenario has been elaborated to reflectpossible prolonged occupational exposure of a population of workers in the nuclear industry;(d) Risk of thyroid cancer after exposure to a moderate dose (500 mGy) to the thyroid duringchildhood and adolescence. New studies of the impact of ultrasonography screenings and dose
uncertainties in the dose response were considered. Other focuses are on the dependence on timesince exposure by considering studies of thyroid cancer after exposure due to radioiodine releasedby the Chernobyl accident and among the Japanese atomic bombing survivors, and on the transfer
of risk estimates among different populations;(e) Risk of circulatory disease after acute whole-body exposure (1.5 Gy) was estimated. New
studies of the atomic bombing survivors were considered and risks of different disease types
2

If not stated otherwise absorbed doses refer to low-LET radiation.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 25(cerebrovascular diseases and heart diseases) were considered. A focus was on the form of thedose–response relationships.* To mitigate various concerns arising in assessments of the risk ofcirculatory disease, the risks estimated from the LSS at a relatively high dose (1.5 Gy low-linear
energy transfer (LET)* radiation) were applied to the entire population in Japan, rather thantransferring those risks to another population with different characteristics.

2. Step 2: Literature search for information to enable the calculation of
cumulative excess risk
8.
The aim of the present annex was not to perform an exhaustive review of the literature, but just to
identify the main epidemiological studies able to provide the needed information and risk models forthe elaboration of the scenarios and the calculation of cumulative excess risk.
9.
For each of the five scenarios, a comprehensive literature review was performed in PubMed to
identify articles providing information on the quantification of risk for the selected health effect, and forthe specific situation of exposure considered. Those publications were reviewed that appeared after themost recent UNSCEAR publication on the specific topic (leukaemia, solid cancer, thyroid cancer,
circulatory diseases). Any articles published after June 2017 were only considered if they were crucial
for the conclusions of this annex. The sections dealing with the review of the literature are limited tothose studies presenting original epidemiological results with quantitative dose–risk relationships
pertinent to the selected health effects and the specific exposure situation. As a minimum requirement,
only those publications that mentioned a dose–risk coefficient (relative risk, excess relative risk* orexcess absolute risk* per unit dose) were included.
10. The reviews of epidemiological studies were conducted according to the UNSCEAR 2017 Report,annex A [U10]. Further, as there has been a special emphasis on low-dose-rate studies in annex B of the
UNSCEAR 2017 Report [U10], only key publications on this topic were included in this annex.
Biological aspects were not addressed in this annex as the Committee is preparing a separate annex on“Biological mechanisms relevant for the inference of cancer risks from low-dose radiation”.

3. Step 3: Calculation of cumulative excess risk for each scenario andcomparison with the risk derived from the Life Span Study applied to thescenario (“risk transfer”)
11. For each of the five scenarios, calculation of cumulative excess risk was performed using risk
models derived from a study based on the same population as considered in the scenario. A parallel
calculation of cumulative excess risk was performed using risk models derived from the LSS, so that acomparison of the results could be made.
12. The details of the scenario (reference rates, age at exposure, age at the end of follow-up, sex, etc.)
were defined based on the study providing the risk model. Expressions like “exposure at age 1” meanthat the exposure took place in the year before the first birthday. Similarly, “up to age 30” means up to
the 30th birthday.
13.

The indicators of health risk used in this annex are defined as follows:−

Cumulative baseline risk (CBR), which is the cumulative baseline risk* of a specific disease(incidence or mortality) occurring up to a given age in the absence of the particular exposure
under consideration [W6];26 UNSCEAR 2019 REPORT−

Cumulative excess risk (CER), which specifies the cumulative risk of a specific disease(incidence or mortality) occurring up to a given age, attributable to a given dose; excess isunderstood in comparison to a population group not exposed to radiation;−

Cumulative fractional risk (CFR), which reflects the relative increase in the cumulative excess
risk (CER) in relation to the cumulative baseline risk (CBR) given by the ratio CER/CBR;−

Survival fraction, which is the fraction of a population that is still alive at a given time point(here age).

14. To calculate the CER, the methodology of risk of exposure-induced death/risk of exposureinduced cases (REID/REIC) was used. The REID/REIC gives an estimate of the probability that anindividual will die from (or be diagnosed with) a specific disease associated with the exposure [T7].
The Committee has usually used it as a measure of lifetime risk. However, in this annex, the
REID/REIC has also been used as an indicator of cumulative risk up to a given age at the end of followup (varying from 30 to 90 years according to the scenario).
15. Both additive transfer of risk estimates (based on the transfer of the excess absolute risk (EAR))
and multiplicative transfer (based on the transfer of the excess relative risk (ERR)) were used to
calculate the CER for each scenario. The additive transfer assumes that the excess risk of a cancer isgiven directly by the EAR model, thus the baseline rates* of cancer for the population are not includedin the calculation. A multiplicative transfer of risk assumes that the excess risk of a cancer due toexposure to radiation is proportional to the baseline rate of that cancer in the population and is obtainedby multiplying the baseline rate by the ERR model for the dose of interest. Differences between risks
inferred by the additive and multiplicative transfers provide an indication of the magnitude of
uncertainty in the transfer of radiation effects in an epidemiological study to risk estimates in a
population of interest.
16. Estimated CERs are associated with 95% confidence intervals* (95% CI). Confidence intervals
were calculated using Monte Carlo uncertainty propagation.* In this annex, the term “credibleintervals”* was developed to account for the impact of sources of uncertainties that required
quantification by use of professional judgement. The term “credible interval” is frequently thought tobe used in the context of a Bayesian approach only. However, this is not the case, because the definition
is much broader. The credible interval refers to an interval defined from the distribution of the degree ofbelief of the value of the quantity of interest within which a certain probability is assigned (e.g. 95%)
representing the assessor’s degree of belief that the true value of a quantity of interest falls within theinterval. Credible intervals were calculated as described in appendix A. Latency period* (lag time, theminimum time between exposure and occurrence of an excess risk in an exposed population) wasmodelled using different methods according to the scenario.

4.

Step 4: Discussion of further uncertainties

17. Seven major sources of uncertainties were considered systematically for each of the scenarios, as
determined in annex A of the UNSCEAR 2017 Report [U10]:(a) Selected populations: A clear definition of the criteria used to select the study population(inclusion and exclusion criteria) is needed. Also detailed description of the source population
characteristics should be available: in particular, age, sex, start of exposure, duration of follow-up,
percentage lost to follow-up. Data to assess the possibility of a selection bias* should be available;

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 27(b) Exposure assessment: The determination of radiation dose for epidemiological studies isnotoriously difficult to do. The uncertainty depends on the quality of the methods used to estimate
individual exposures, and on assumptions needed to estimate the doses. The nature, type andmagnitude of measurement errors can play a role on the potential impact of these uncertainties on
the estimated dose–risk relationship. Other points that may need to be considered, when discussing
uncertainties in radiation exposure, include the possibility of other sources of radiation exposure,the quality of the radiation, consideration of possible internal exposures, and the distribution ofdose within the body;(c) Health outcome assessment: Problems may arise because of diagnostic errors, so that wrong
diseases are reported to registries or are mentioned in death certificates. Another potentially serious
problem is related to modifications of the International Statistical Classification of Diseases andRelated Health Problems (ICD) over the course of time;(d) Study design: Each epidemiological design has inherent limitations. There are always crucial
questions to be answered: Was there any potential design specific bias* in the considered studies?
Was the follow-up reasonably complete? Was follow-up related to exposure or outcome? Were thecomparison groups appropriate?
(e) Confounding factors:* Confounding occurs when a third factor is associated both with theexposure under study and the health outcome of interest and is not on the causal pathway between
exposure and effect. It can lead to bias in the results pertaining to the exposure–disease association.
Among others, smoking, alcohol or chemicals (like benzene) are potential confounders for theeffect of radiation exposure on cancer risk. Again, important questions have to be addressed: Were
all important confounders assessed in the study? Were other carcinogens considered that can beassociated with radiation exposure? Since all factors influencing cancer risk are not known,
unknown confounding factors need to be borne in mind, although the potential effect of thesecannot be quantified;(f) Statistical methods and model uncertainties: Many uncertainties can be linked to the risk
models, including the shape of the dose–risk relationship, the quantification of modifying effects(such as sex, age, time since exposure and duration of radiation exposure for chronic exposures*)and the determination of the latency period between exposure and effect. Usually, several options
for risk models exist, and it may be difficult to decide which provides the best description of thesituation. Using the wrong model may result in serious misjudgements. One way out of thisproblem is to use the technique of multi-model inference* that has been proposed to account formodel uncertainty.* Another main element is the transfer of risk between populations. Two
populations can differ substantially with respect to specific characteristics that are relevant for the
expression of cancer risk and are difficult to account for, for example, ethnicity, social status,
industrialization, and background cancer spectrum. Therefore, the determination of the transfer ofrisk, additive (using an EAR model) or multiplicative (using an ERR model) can have an important
impact on the estimated cumulative risk;(g) Other sources of uncertainty: Incidence or mortality baseline rates of specific diseases are
essential for the estimation of the respective cumulative excess risks. Baseline rates, especiallybaseline rates of cancer have been collected for many modern populations and they are usuallybased on large numbers of cases, leading to good statistics. Uncertainties in the cumulative risk
related to baseline rates most often occur when such rates are not available for the target populationof interest. For example, baseline rates may not be available for subgroups of a modern population,or for populations exposed many decades ago. Also, current baseline rates for some diseases maychange markedly in the future because of, for example, changes in environmental factors or

28 UNSCEAR 2019 REPORT

lifestyle, or improvements in diagnostic and/or therapeutic measures. In particular, uncertainties
can arise from a more thorough diagnosis of health effects in the exposed population (suchdiagnoses are frequently triggered by a specific event, like a nuclear reactor accident). This socalled “screening effect” may be even more serious when more sensitive diagnostic techniques areused than in the past (for example, for the detection of thyroid cancer).
18.

The following sources of uncertainty were not considered:(a) Individual susceptibility: The data used for the quantification of uncertainties in this annex
were obtained from epidemiological studies of populations. Individual susceptibility is covered by
the risk coefficients obtained from these studies. As the uncertainties mentioned in this annex refer
to populations and not to individual persons, individual susceptibility is not explicitly addressed
here. This aspect, however, might play a role, when the selected populations differ in the number ofsusceptible individuals;(b) Dose and dose-rate effectiveness factor (DDREF): The DDREF is a concept that is used inradiation protection. The Committee concluded in its UNSCEAR 2017 Report, annex B [U10] that“on the basis of the possible risk estimates derived from studies of low-dose and low-dose-rateradiation exposures and the new scientific developments in understanding of the mechanisms ofradiation-induced cancer, the Committee intends to continue not to use the concept of a single
reduction factor for its future radiation risk estimates at low doses and low dose rates”. Thus,
DDREF is not applied in this annex.

5.

Step 5: Identification of the preferred risk inference

19. In this annex, for all five scenarios, the preferred risk inference has been defined as the one thatbest fits the characteristics of the considered scenario, based on an expert judgement of the magnitudeof the associated uncertainties. Nevertheless, other routes of determination could have been considered.
For example, one could have looked for the estimate which answers best the expectation of the targeted
population. To illustrate this, it is likely that a worker exposed to radiation throughout his or her
professional career would consider as “more informative” (“more preferable”) to know their lifetime
risk of developing cancer, a risk estimate up to the attained age* of 90, whereas the criteria in thisannex lead to preference of a risk estimate up to the age of 60, because this is the age until which solid
epidemiological observations are available. The aim was neither to identify the most reliable risk
estimate for a specific person (a kind of “individualized approach”), nor to determine the risk estimate
most suitable to very different situations of exposure (a kind of “overarching approach” such as thatused in radiation protection). The aim was to evaluate the ability of the recent scientific literature toprovide elements to quantify risks for key health effects in specific situations of exposure to ionizing
radiation. The preferred risk inferences identified in this annex may therefore not be the best ones toinfer risk in another context.
20. For each scenario, the preferred risk inference was identified, and the motivation for this choice
was explained. The most important criterion was a minimum of assumptions needed to transfer theeffect per unit dose observed in an epidemiological study to the risk per unit dose in the scenario.
The widths of the confidence intervals in the different risk projections were also considered.
21. An attempt was made to quantify the potential impact of the different sources of uncertainty on
the estimated cumulative excess risk. The impact of the different sources of uncertainty, besides theANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 29

statistical uncertainty, 3 was classified into four categories according to the variation they are expected
to induce on the reported confidence interval of the CER: (a) very small—less than a factor of 1.1 (or avariation of 10%); (b) small—between a factor of 1.1 to 1.5 (or a variation between 10 and 50%);(c) moderate—between a factor of 1.5 to 2 (or a variation between 50 and 100%); and (d) large—
greater than a factor of 2 (or a variation of more than 100%). As the statistical uncertainties range up toa factor of 5 or substantially more, the nomenclature (very small, small, moderate and large) is deemed
to be appropriate. From this evaluation of the different sources of uncertainty on the preferred risk
inference of the CER, credible intervals (also called “credibility intervals”) were assessed, based on theprocedure described in appendix A. These credible intervals are intended to reflect both the statisticaluncertainty and the potential impact of the additional sources of uncertainty considered to be multiplicative.

II. LEUKAEMIA INCIDENCE AFTER REPEATED LOW-DOSE
EXPOSURE DURING CHILDHOOD

A. Motivation
22. The risk of leukaemia related to low levels of radiation exposure during childhood is of particularinterest, because there is substantial information of increased risk from epidemiological studies.
Specific attention is given to the exposure of children and young adults (below 20 years of age),
because current knowledge indicates that, for the same dose, the increase in risk is higher than that after
exposure during adulthood.
23. The primary epidemiological basis for estimating leukaemia risk from exposure to ionizing
radiation is the LSS cohort of survivors of the atomic bombings in Japan. Within a few years of thebombings, there was evidence of an excess incidence of leukaemia among the survivors. For the samedose, the relative increase in incidence appeared to be larger than that observed for most of the solidcancers, and the minimum latency period between exposure and occurrence of disease appeared to be
shorter. A strong decrease in the slope of the dose–response relationship with increasing age at
exposure and with increasing attained age was also observed.
24. This evidence is mostly derived from acute exposure* situations with moderate-to-high doses.
However, the risks associated with repeated low-dose exposures are more relevant to patients withmore than one CT scan. CT scan imaging provides substantial medical benefits for the diagnosis ofmany diseases. It is widely used in health care, but effective doses* from CT scans may be at least 5–20
times higher than those from routine conventional radiology. Consequently, CT scans contribute a large
portion of overall exposure currently received during medical diagnostic procedures [U3]. The
magnitude of this exposure has raised concerns about its potential adverse effects, particularly for therisk of leukaemia after exposure during childhood.

3

Random or statistical uncertainties arise from random fluctuations in an outcome of a measurement or a study. In the context of
this annex, the statistical uncertainty is expressed by the uncertainty distributions of the calculated cumulative baseline and excess
risks, and especially by their confidence intervals.

30 UNSCEAR 2019 REPORT

25. Myelodysplastic syndromes (MDS) are rare bone marrow diseases in childhood and often
progress to acute myeloid leukaemia (AML). In the past, MDS was sometimes referred to as preleukaemia or smoldering leukaemia. Clinical classification of MDS was only defined in the 1980s, and
it is likely that some cases identified as AML in the early years would have been classified as MDS
today. Additional information on MDS is provided in section II.F.1(c). Therefore, in the present
section, the literature providing information about radiation and the risk of leukaemia or leukaemia+MDS 4 in the recent years was considered.

B. Recapitulation of previous UNSCEAR publications
26. Annex A of the UNSCEAR 2006 Report [U3] included a comprehensive review of studies
providing results on the risk of leukaemia after exposure to ionizing radiation. A model derived from the
LSS was available to estimate both the ERR and EAR of leukaemia per unit dose. No specific analysis
was proposed for exposure during childhood, but this model integrated the modifying effect of age at
exposure: for exposure below 20 years of age, the ERR per unit dose was 8.3 (90% CI: 4.9, 13.7) Sv−1,
approximately 2 to 3 times higher than the coefficient estimated after exposure during adulthood. The
EAR per 10,000 person–years per unit dose was 2.8 (90% CI: 2.0, 3.7) Sv−1, similar to the coefficientestimated after exposure during adulthood [P8, U3].
27. The most recent relevant UNSCEAR report for leukaemia risks associated with external exposure
during childhood is the UNSCEAR 2013 Report, annex B [U6]. This annex presented a review of theresults from studies of populations or exposure situations including persons exposed to natural
background radiation, the atomic bombing survivors, persons exposed to deposited radionuclides (fromthe Chernobyl accident and weapon tests), persons living near nuclear facilities, persons undergoing
diagnostic radiology and persons undergoing radiotherapy.
28. The Committee concluded in the UNSCEAR 2013 Report, annex B [U6] that there is little doubtthat leukaemia (other than chronic lymphocytic leukaemia, CLL) is induced by radiation, with aminimum latency period of approximately two years. The risk estimates at low doses were based onmathematical models, because the observational studies had not shown a statistically significant
increase in leukaemia incidence at doses to the RBM of less than about 400 mGy [U6]. A linear–quadratic dose–response model appeared to provide a better fit to the data than a linear non-thresholdmodel, with allowance for dependencies on sex, age at exposure and time since exposure. Overall, therisk of leukaemia associated with an exposure during childhood appeared to be three to fivefold greaterthan that with the same exposure during adulthood [U6].

C. Review of recent epidemiological literature
29. A comprehensive literature review was performed to identify articles providing information of thequantification of the risk of leukaemia or leukaemia+MDS associated with radiation exposure,published since the UNSCEAR 2013 Report, annex B [U6]. Selection was based on the presence of thefollowing keywords in the title or abstract: leukaemia and (radiation or radiologic) and cohort and
4

The expression “leukaemia+MDS” describes the sum of leukaemia and MDS cases.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 31

publication date from 2012 onwards. Using these criteria, case–control studies providing estimates ofleukaemia risk associated with radiation exposure were also identified. This research did not separate
articles according to age at radiation exposure, so the same literature review was used for both
sections II and III. A total of 181 references were obtained, and three additional articles were added
after a complementary search. Populations or exposure situations considered included the atomic
bombing survivors, persons undergoing diagnostic radiology, persons undergoing radiotherapy, persons
exposed to natural background radiation, and persons living near nuclear facilities.
30. Not all these publications provided original results allowing the estimation of a dose–risk
relationship after exposure during childhood or provision of information in the low-dose range. For
instance, two cohorts of children exposed to radiation following the release of radionuclides from the
Chernobyl nuclear power plant accident observed elevated standardized incidence ratios for leukaemia(1.7 and 1.9, respectively, in Belarus and Ukraine), but did not provide estimates of the dose–risk
relationship [H2, O4]. Several other studies considered the relationship between exposure to natural
background radiation and leukaemia risk but were ecological studies and therefore not based on
individual data [S14]. Another example is about studies of the incidence of leukaemia among children
living near nuclear facilities. Many studies were published in recent years [C8, J2, L5, M11, N10], butthese studies are not able to provide an estimate of the dose–risk relationship.
31. The following review is limited to studies presenting original epidemiological results, based on
individual data, with a quantitative dose–risk relationship for populations exposed during childhood tomoderate- or low-level external radiation. A total of 13 articles were selected.

1.

Studies of the Japanese atomic bombing survivors

32. The most comprehensive publication detailing results on leukaemia incidence among the Japaneseatomic bombing survivors was published in 2013 by Hsu et al. [H4]. This article presents analyses of
the increased incidence of leukaemia, lymphoma and multiple myeloma from radiation, updated withadditional data obtained during the 14 years since the last comprehensive report on these malignancies.
These analyses were based on incidence data in the tumour and leukaemia registry for 113,011 cohort
members with 3.6 million person–years of follow-up from the late 1950s to the end of 2001. A total of
312 cases of leukaemia other than CLL or adult T-cell leukaemia (ATL) (neither of which appear to beradiation-related) was observed, among them 183 received less than 100 mGy.
33. The article provided analyses of the excess risk for all types of leukaemia, other than CLL or
ATL, and also provided analyses of specific leukaemia subtypes (acute lymphoblastic leukaemia(ALL), CLL, AML, chronic myeloid leukaemia (CML) and ATL). For all types of leukaemia other than
CLL or ATL, models considering a linear–quadratic relationship between dose to the RBM andincidence were fitted, with a modifying effect of sex, age at exposure, and time since exposure. Muchof the evidence for this non-linearity arose from data on AML. The increased incidence of leukaemia
generally declined with attained age or time since exposure. Nevertheless, the radiation-associatedexcess incidence of AML had persisted throughout the follow-up period (55 years after the bombings).
MDS was not specifically considered in the analyses by Hsu et al. [H4]. Indeed, MDS cases werediagnosed in the early years, and were probably categorized as AML. But as misdiagnosis of MDS as
AML was likely to be independent of dose, it should not affect the estimate of the estimated dose–risk
relationship [H4]. More details on the risk models are given in section II.D.3.

32 UNSCEAR 2019 REPORT

2.

Studies of children exposed due to medical procedures

34. Five epidemiological studies of cancer risks after CT examinations during childhood were
published between 2012 and 2015, in the United Kingdom [P1], Australia [M1], China (Taiwan) [H5],France [J6, J7] and Germany [K26]. For leukaemia, the results of these studies are consistent and show anincreased incidence in children who had been exposed to several CT scans (table 1). A second analysis of
the UK study was published in 2016 [B4]. For leukaemia+MDS, the results of these studies are consistentand show an increased incidence in children who had been exposed to several CT scans (table 1).
Table 1. Excess relative risk estimates obtained in childhood CT scan studies
ERR: Excess relative risk; MDS: Myelodysplastic syndromes; CI: Confidence interval
Pathology definition

Number of cases

ERR per 100 mGy

95% CI

SourceAll leukaemia+MDS

74

3.6 a

(0.5, 12.0)[P1] bAll leukaemia+MDS

246

3.9 a

(1.4, 7.0)

[M1]

All leukaemia+MDS

19

5.7

a

(−7.9, 19.3)[J6]

All leukaemia

12

0.9

c

(−1.9, 3.7)

[K26]

All leukaemia+MDS

72

3.3 a

(0.4, 11.4)[B4] baLinear ERR model, with 2-year lag time.

b

Successive analyses of the same UK CT-scan data.

35. Approximation of the ERR from the published hazard ratio* per mGy=1.009 (95% CI: 0.98,
1.04). However, methodological limits due to retrospective assessment of radiation exposure fromCT scans and lack of statistical power* need to be taken into consideration. For CT scans, the most
discussed limitations are denominated as “reverse causation*” (when a CT scan is ordered due tosymptoms of a cancer which has not yet been detected, but ends up being detected sometime later)and “confounding by indication” (when a CT scan is ordered due to a condition which itself
increases cancer risk) [B9, B13, W5]. Especially, there was some discussion about the potential
impact of cancer predisposing factors on the estimated dose–risk relationship [C7, J5, J7, M14]. In
addition, due to the identification of cases with previous history of malignancy, a potential for
selection bias is also possible.
36. Due to its design characteristics, size and duration of follow-up, the most important study
published in recent years is of children in the United Kingdom who were subjected to CT scans (the UK
childhood CT-scan cohort) [B4, P1]. This retrospective cohort study* included 178,364 patients without
previous cancer diagnoses who were first examined with CT between 1985 and 2002, when they wereyounger than 22 years of age. Absorbed doses to the RBM, according to age, sex, examination type andyear of scan, were estimated on the basis of typical machine settings for CT scans in the United
Kingdom at the time. The total number of person–years over the period 1985–2008 was 1.7 million.
The mean cumulative dose to the RBM was about 12 mGy. Cancer incidence data were obtained bylinkage with the NHS Central Registry (NHSCR). The first analysis included 74 cases of leukaemia+MDS. The estimated linear ERR per unit absorbed dose for leukaemia+MDS was 0.036 (95% CI:
0.005, 0.118) mGy−1. The estimated ERR for leukaemia excluding MDS (65 cases) was 0.019 (95% CI:–0.012, 0.079) mGy−1, whereas the estimated ERR per unit dose for MDS only (9 cases) was 6.098(95% CI: >0, 145.4) mGy−1 [P1].

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 33

37. A second analysis was published in 2016 after consideration of additional sources of medical
information. This verification led to the exclusion of 2 cases and 104 non-cases with previously
unreported cancer [B4]. Based on 72 cases of leukaemia+MDS, the updated analysis showed an
estimated linear ERR per unit absorbed dose for leukaemia+MDS of 0.033 (95% CI: 0.004, 0.114)
mGy−1 [B4]. No results were provided for leukaemia and MDS separately. For comparison, thecorresponding estimated linear ERR per unit absorbed dose derived from the cohort of atomic bombing
survivors was 0.045 (95% CI: 0.016, 0.188) mGy−1 (excluding MDS) [P1]. A dose to the RBM of
greater than 30 mGy was associated with a significantly increased incidence of leukaemia+MDS
compared to a dose of less than 5 mGy (relative risk, RR=2.63; 95% CI: 1.09, 6.24) [B4].
38. The roles of radiotherapy and chemotherapy in the occurrence of subsequent leukaemia afterchildhood cancer were investigated in a recent case–control study in France. The study involved
35 cases and 140 controls, selected from the cohort of French Childhood Cancer Survivors Study(FCCSS). The doses to the RBM were estimated individually, as well as the doses of chemotherapy
drugs. A significant positive trend in the incidence of secondary leukaemia with radiation dose wasobserved, after accounting for heterogeneity in the dose to the RBM, but was no longer significant after
adjustment for chemotherapy [A2]. The application of data from cancer survivors to the general public
requires some caution as there may be a role of cancer predisposing factors in the survivors.

3.

Other studies

39. A population-based case–control study investigated whether acute leukaemia is increased amongchildren who were less than six years of age at the time of the Chernobyl accident, living incontaminated regions of Belarus, the Russian Federation and Ukraine up to the year 2000. Two controls
were matched to each case on sex, birth year and residence. The median dose to the RBM was less than
10 mGy. For all three countries combined, the incidence of leukaemia increased significantly withincreasing radiation dose; the estimated ERR per unit dose was 32.4 (95% CI: 8.78, 84.0) Gy−1. This
was largely accounted for by the significant dose–response in Ukraine, in which the estimatedregression coefficient was roughly five times greater than the estimate for Belarus. The dose–response
was not statistically significant in either Belarus or the Russian Federation. The authors considered thatthe dose–response observed in Ukraine might be overestimated. They concluded that their study
provided no convincing evidence of an increased risk of childhood leukaemia as a result of exposure toradiation from the Chernobyl accident [I3]. Another case-control study* was later conducted in themost radioactively contaminated territories of Ukraine, including 246 leukaemia cases and
492 randomly selected controls. Ninety-two per cent of the persons had cumulative doses of less than
10 mGy. The association between dose of radiation exposure and leukaemia risk was statisticallysignificant among those with doses ranging from 10 to 314 mGy. The estimated ERR per unit dose was
20.9 (95% CI: 5.6, 43.2) Gy−1 [N8].
40. Several studies of the incidence of childhood leukaemia related to exposure to natural backgroundradiation were published in Europe in recent years. In the United Kingdom, a population-based recordbased case–control study comparing 9,058 cases of childhood leukaemia and 11,912 controls showed
an association with gamma radiation (ERR per unit dose=0.12 (95% CI: 0.03, 0.22) mSv−1), but notwith radon concentrations in the areas of residence at birth [K11]. The expanded Swiss cohort study(530 cases) found a statistically significant association between the estimated cumulative exposure to
gamma radiation and leukaemia [S15]. In Finland, a nationwide register-based case–control study
observed a positive association between exposure to natural background radiation and incidence ofchildhood leukaemia, but this was not significant (1,093 cases) [N5]. In France, a study combined ageographical approach (9,056 cases of acute leukaemia) and a case–control approach (2,763 cases of34 UNSCEAR 2019 REPORT

acute leukaemia and 30,000 controls). The incidence of acute leukaemia was not associated with thelevels of exposure to radon or gamma radiation in municipalities or with cumulative dose to the RBM[D5]. These studies were limited mainly by the quality of the exposure reconstruction. Especially,
calculation of RBM dose due to radon is particularly uncertain. Efforts are ongoing to improve
exposure and dose assessment. Also, some studies were limited by a low statistical power and lack ofcontrol of other potential risk factors. Up to now, the results from these studies do not provide
conclusive results nor do they allow derivation of a dose–risk relationship.

4.

Synthesis of studies

41. In recent years, new results have been published from studies of children exposed for medical
reasons (CT scans or therapy) or to environmental exposures (natural background or post-accidental
settings). Results from these studies complemented those obtained from the cohort of atomic bombing
survivors. These results are largely consistent with the existence of a dose–risk relationship for the riskof leukaemia or leukaemia+MDS after exposure during childhood, with a higher risk coefficient thanestimated from studies considering exposure during adulthood. This is consistent with previous
knowledge [U6]. Results from studies of natural background exposure are less clear, but this could be
related to limitation in the reconstruction of individual exposure.
42. As leukaemia is a relatively rare disease [K1], most studies have been based on small numbers ofcases, often only several tens. Thus, such studies may have limited statistical power, especially todetermine the impact of modifiers of the dose–risk relationship, such as age at exposure or time since
exposure, or to quantify a dose–risk relationship for specific leukaemia subtypes. Collaborative
research projects aiming to perform pooled analyses of individual data have been launched to cope withthis limitation, such as the EPI-CT European project (Epidemiological study to quantify risks for
paediatric CT and to optimize doses) [B10, W7].
43. Based on a comprehensive review of the incidence of childhood leukaemia following exposure toionizing radiation, Wakeford proposed an estimation of the ERR per unit dose for childhood leukaemia
of about 50 Sv−1 [W1]. The author considered that this ERR estimate was broadly applicable tocircumstances of low-dose exposure.
44. More recently, Little et al. [L17] published the results of a pooled analysis* of the association
between leukaemia incidence and low-dose radiation exposure in childhood. The pooled analysis
included data from nine eligible cohorts from Canada (the Canadian tuberculosis fluoroscopy cohort),
France (the French haemangioma cohort), Japan (the LSS), Sweden (the Gothenburg haemangioma
cohort and the Stockholm haemangioma cohort), the United Kingdom (the UK CT-scan cohort), andthe United States (the Massachusetts tuberculosis fluoroscopy cohort, the Rochester thymus
enlargement cohort and the US scoliosis cohort). The pooled database included 262,573 people who
had been exposed to less than 100 mSv before 21 years of age and enrolled before 2005. The meanfollow-up was 19.6 years and the mean cumulative dose to the RBM was 19.6 mSv. Overall, thenumber of leukaemia cases (excluding CLL) was 221, including 79 AML, 8 MDS, 36 CML and
40 ALL. The relative rate at 100 mSv was reported as 2.56 (95% CI: 1.09, 5.06) for AML, 3.09 (1.41,
5.92) for AML+MDS and 5.66 (1.35, 19.71) for ALL. However, the relative rate of CML did notappear to increase 0.36 (95% CI: 0.00, 2.36). There were few indications of between-cohort
heterogeneity or departure from linearity. For AML and MDS combined and for ALL, the dose
responses remained significant when analyses were restricted to doses <50 mSv. Excess absolute rates*
at 100 mSv were in the range of 0.1–0.4 cases or deaths per 10,000 person–years.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 35

D. Definition of scenario
45. The scenario selected was of repeated exposure to CT scans during childhood, for which pertinent
results on the risk of leukaemia+MDS had been published in recent years. As risk models and data on
baseline rates of incidence were available, assessment of the risk of leukaemia incidence wasconsidered, in contrast to the adulthood analyses, which concentrated on mortality (see section III).

1.

Exposure scenario

46. The scenario was designed to reflect the characteristics of the UK childhood CT-scan cohort [B4,P1]. This cohort considered the incidence of leukaemia+MDS. In this cohort, age at CT exposure
ranged from 0 to 21 years. Estimated doses to the RBM per scan varied between 2 and 9 mGy for ahead scan, between 3 and 4 mGy for a chest scan and between 2 and 4 mGy for an abdomen scan. Most
children underwent only one scan, but some received several. The mean cumulative dose to the RBM
was about 12 mGy. Follow-up for leukaemia began two years after the first CT. Age at the end offollow-up ranged from 6 to 45 years. The mean duration of follow-up was 10 years.
47.

The defined scenario has the following characteristics:(a) UK population;(b) Population composed of 50% males and 50% females;(c) Exposure to four CT scans during the same year, either at age 1 or 10;(d) Mean dose to the RBM of 5 mGy per CT scan, leading to a cumulative dose of 20 mGy
received in a single year;(e) Alive two years after the first CT scan (i.e. at ages 3 and 12, respectively);(f) Follow-up of leukaemia+MDS incidence to age 30 (approximately the mean attained age at the endof the follow-up) or age 40 (approximately the maximum attained age at the end of the follow-up).

2.

Reference data

48. For the calculations of the survival function and baseline risk of leukaemia, the following data
sources were used:(a) Survival function: age- and sex-specific death rates per 1,000 population registered in Englandand Wales in 2010 [O3];(b) Baseline risk of leukaemia: age- and sex-specific incidence rates per 100,000 population in the
United Kingdom for years 2011–2013 [C2]. Leukaemia was defined according to the ICD-10 codes
as C91–C95 [W10]. As the estimated incidence of paediatric MDS in Europe is very low (1 to
4 cases per million per year) compared to leukaemia incidence, MDS incidence has not been
considered in the baseline rates used for the scenario.
49. Leukaemia baseline incidence rates decrease between birth and age 20, and then increase with age,
maximum rates being reached after 85 years of age. Figure I presents the evolution of leukaemia
incidence baseline rates in the United Kingdom over the age range considered in the scenario.

36 UNSCEAR 2019 REPORT

Figure I. Baseline rates of leukaemia incidence in the United Kingdom in the period of 2011−2013
per category of attained age
PY: person–years

3.

Risk models

50. Risk models considered leukaemia or leukaemia+MDS. No model for specific leukaemia subtype
has been considered. MDS cases were not specifically diagnosed before the 1980s and not systematically
before 2000. It is likely that some cases identified as AML in the early years would have been classified
as MDS, if they had been diagnosed with modern criteria [H4]. Therefore, it has been considered thatthe best way to ensure consistency in risk comparison was to use models of leukaemia risk when basedon olden exposure (as the models derived from the LSS [H4]) and a model of leukaemia+MDS risk
when based on recent exposures (as the model derived from the UK CT-scan study [B4]).
51. Risk models derived from the cohort of Japanese atomic bombing survivors were thosepublished by Hsu et al. [H4] for the risk of leukaemia other than CLL or ATL. Both ERR and EAR
models were considered.
52. The ERR model (denoted thereafter as the LSS ERR model) was linear–quadratic in dose with alog–linear effect modification* depending on attained age and time since exposure (TSE), but not on sex:
ERR = (β1 d + β2 d2) exp (α ln(age/70) + γ ln(TSE/40))
where d is the absorbed dose to the RBM, and β1=0.79 (95% CI: 0.03, 1.93) Gy−1; β2=0.95 (95% CI:
0.34, 1.80) Gy−2; α=−1.09 (95% CI: −2.01, −0.27); and γ=−0.81 (95% CI: −1.31, −0.28).
53. The EAR model (denoted thereafter as the LSS EAR model) was linear–quadratic in dose with
log–linear effect modification depending on attained age and age at exposure (AE), with different dosecoefficients for males and females:
EAR = (β1 d + β2 d2) exp (α1 (AE–30)/10 + α2 ∙ln(age/70) + τ ∙sex)

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 37

where d is the absorbed dose to the RBM; sex=0 for males and 1 for females, and β1=1.06 (Gy 10,000PY)−1; β2=1.09 (Gy2 10,000 PY)−1; α1=0.41; α2=−1.45; and τ=−0.42.
54. The risk assessment was also conducted using the ERR model derived from the UK childhood
CT-scan cohort [B4] for the incidence of leukaemia+MDS. The model was a linear ERR model. Modelparameters are indicated below:
ERR = β dwhere d is the cumulative dose to the RBM, and β=33 (90% CI: 4, 114) Gy−1.
55. The risk model behaviour is presented in figure II, for the age characteristics considered in the
scenario. Based on the LSS ERR model, the decrease of the ERR with age appears steeper after
exposure at age 1 than after exposure at age 10, whereas no variation with age is considered in the UK
childhood CT-scan model.
Figure II. Excess relative risk of leukaemia+MDS at 20 mGy as a function of attained age, for age at
exposure of 1 and 10 years obtained from LSS [H4] and UK childhood CT-scan [B4]4.

Risk-transfer methods

56. For the transfer of risk from the atomic bombing survivors population to the scenario, both
multiplicative (based on the LSS ERR model) and additive (based on the LSS EAR model) risk
transfers were used.
57. For the application of all models (derived from the cohort of atomic bombing survivors or fromthe UK childhood CT-scan cohort) to the scenario, a minimum lag time was considered between
exposure and effect. This lag time was modelled as a sigmoid function, varying between 0 and 2 years
and centred on 1.5 years, similarly as applied in the Interactive RadioEpidemiological Program (IREP)
package [K14].

38 UNSCEAR 2019 REPORT

E. Results
58. The estimated cumulative risk of leukaemia up to ages 30 and 40, associated with the childhood
CT-scan scenario at ages 1 and 10 are presented in tables 2 and 3, respectively. The survival fraction
was 99% at age 30 and 98% at age 40, which indicates that most of the population was presumed to bealive at the end of follow-up. The CBR was about 9 to 12 per 10,000 persons from age 1 to 30, or 40,
and about 4 to 7 per 10,000 persons from age 10 to 30, or 40, respectively. This difference can be
explained by the peak in the baseline rates of ALL between age 2 and 7.
59. The estimated CFR varied between 0.16 and 0.60, according to the models used. This means thatthe estimated CERs represent an important proportion of the CBR (about one sixth to two thirds), even
if the cumulative dose to the RBM considered in the scenario was small (20 mSv). This large
percentage was due to the high excess relative risk of leukaemia among children (figure II).
60. Results indicate a CER between 1.8 and 7.0 after exposure at age 1 and between 0.8 and 4.4 after
exposure at age 10; the CER associated with an exposure at age 1 was 1.5 to 5 times higher than thatassociated with an exposure at age 10. The CER estimated from the LSS ERR model was higher thanthat estimated from the LSS EAR model, however, the associated confidence interval was
systematically larger with the ERR model.
61. After exposure at age 1 and follow-up to age 30, the agreement in the estimated CER between the
LSS ERR model and the CT-scan ERR model was very good: CER=5.0 (95% CI: −0.6, 30) and5.3 (95% CI: 0.6, 18.3) per 10,000 persons, respectively. For follow-up to age 40, the difference in theestimated CER was less than 40%.
62. After exposure at age 10, the estimated CER was 3 to 4 times higher with the CT-scan ERR
model. The agreement between the LSS ERR model and the CT-scan ERR model was better with afollow-up to age 30 than to age 40. This may reflect a greater difficulty of the CT-scan ERR model totake into account the variation of risk with age, as this model does not integrate any modifying effect ofattained age and age at exposure (figure II).
63. Comparison of the confidence intervals associated with the CER estimates between the LSS ERR
model and the CT-scan ERR model shows that the CT-scan ERR model leads to a narrower confidence
interval in the scenario with exposure at age 1, but a comparable confidence interval in the scenario
with exposure at age 10 years.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 39

Table 2. Cumulative risk of leukaemia incidence for a scenario of a child receiving 20 mGy to the RBM
from CT scans at 1 year of age
CBR: Cumulative baseline risk; CER: Cumulative excess risk, estimated using the REIC methodology;
CFR: Cumulative fractional ratio; CI: Confidence interval; LSS: Life Span Study
Cumulative baseline risk a
Risk model

All-cause mortality
per 10 000 persons

Leukaemia incidence
per 10 000 persons

Cumulative leukaemia risk associatedwith the exposure scenario b
CER per 10 000
persons (95% CI)

CFR(CER/CBR)EXPOSURE AT 1 YEAR OF AGE, FOLLOW-UP TO 30 YEARS OF AGE b c
LSS incidence models [H4]
ERR transfer
EAR transfer

76

9.1

5.0 (−0.6, 30)

0.55

1.8 (0.1, 4.4)

0.19

5.3 (0.6, 18.3)

0.58

CT-scan incidence model d [B4]
ERR transfer

76

9.1

EXPOSURE AT 1 YEAR OF AGE, FOLLOW-UP TO 40 YEARS OF AGE b c
LSS incidence models [H4]
ERR transfer
EAR transfer

164

12

5.1 (−0.8, 30)

0.43

1.9 (0.0, 4.6)

0.16

7.0 (0.9, 24)

0.60

CT-scan incidence model d [B4]
ERR transfer

164

12

a

United Kingdom population, unweighted average of males and females—without exposure—alive at exposure age.

b

Cumulative dose to RBM of 20 mGy due to four CT scans received in the same year.

c

Up to 30 (40) years of age means up to the 30th (40th) birthday.

dIncluding MDS.

40 UNSCEAR 2019 REPORT

Table 3. Cumulative risk of leukaemia for a scenario of a child receiving a dose of 20 mGy to the RBM
from CT scans at 10 years of age
CBR: Cumulative baseline risk; CER: Cumulative excess risk, estimated using the REIC methodology;
CFR: Cumulative fractional ratio; CI: Confidence interval; LSS: Life Span Study
Cumulative baseline risk a
Risk model

All-cause mortality
per 10 000 persons

Leukaemia risk per
10 000 persons

Cumulative leukaemia risk associatedwith the exposure scenario b
CER per 10 000
persons (95% CI)

CFR(CER/CBR)EXPOSURE AT 10 YEARS OF AGE, FOLLOW-UP TO 30 YEARS OF AGE b c
LSS incidence models [H4]
ERR transfer

66

EAR transfer

4.4

1.0 (−0.2, 3.5)0.23

0.8 (0.0, 1.6)

0.18

2.7 (0.3, 9.3)

0.60

CT-scan incidence model d [B4]
ERR transfer

66

4.4

EXPOSURE AT 10 YEARS OF AGE, FOLLOW-UP TO 40 YEARS OF AGE b c
LSS incidence models [H4]
ERR transfer
EAR transfer

154

7.1

1.2 (−0.1, 3.9)

0.17

1.0 (0.0, 1.9)

0.14

4.4 (0.5, 15)

0.62

CT-scan incidence model d [B4]
ERR transfer

154

7.1

a

United Kingdom population, unweighted average of males and females—without exposure—alive at exposure age.

b

Cumulative dose to RBM of 20 mGy due to four CT scans received in the same year.

c

Up to 30 (40) years of age means up to the 30th (40th) birthday.

dIncluding MDS.

F. Discussion of scenario calculations
1.

Sources of uncertainties(a) Selected populations
64. The scenario was designed to reflect the characteristics of the United Kingdom childhood CTscan cohort [B4, P1]. The pattern of exposure (4 scans in 1 year, at age 1 or 10) was driven bydescriptive data from the UK childhood CT-scan cohort. The level of cumulative dose (20 mGy) was arounded value, intended to be realistic, even if a little bit higher than the mean cumulative dose to theRBM (12 mGy). To be coherent with the age at the end of follow-up of the UK childhood CT-scancohort, the cumulative leukaemia risks up to attained ages 30 and 40 were calculated.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 41

65. The LSS cohort included a large portion of the atomic bombing survivors who were within
2.5 km of the hypocentres at the time of the bombings, an age- and sex-matched sample of people who
were between 2.5 and 10 km from the hypocentres, and a sample of about 27,000 persons who wereregistered as residents of either Hiroshima or Nagasaki in 1950, but were not in the cities at the time of
the bombings. The cohort members were recruited on the basis of the 1950 Japanese National Census
and of additional information collected by the Atomic Bomb Casualty Commission (ABCC). The risk
model for leukaemia was based on the 113,011 cohort members in the LSS for whom dose estimates
were available. Almost 60% of the cohort members were females and 41% were less than 20 years ofage at the time of the bombings [H4].
66. The UK childhood CT-scan study included patients without previous malignant disease who werefirst examined with CT between 1985 and 2002 when they were younger than 22 years of age. Patientswere scanned at hospitals within 81 National Health Service (NHS) regional services in Great Britain(England, Wales and Scotland). The cohort was assembled from historical data from computerized
information systems from the participating hospitals and, for a small number of patients in fivehospitals, from paper or film records. Cancer incidence, mortality and loss-to-follow-up data wereobtained by linkage with the NHSCR from 1 January 1985 to 31 December 2008. In order to reduce thepossibility of inclusion of patients who had CT scans because a cancer was suspected, patients who had
an exit date of less than two years after the first scan were excluded from the analysis of the incidenceof leukaemia+MDS. Patients who could not be traced by the NHSCR were also excluded, as well as
those who had missing information or inaccurate information on the date of CT scan [P1].
67. In the UK CT-scan cohort, the collection of additional clinical information from radiology
information system databases and underlying causes of death reports led to the exclusion of two cases(out of a total of 74 patients with leukaemia+MDS) and 104 non-cases with previously unreported
cancer [B4]. These 106 patients should not have been included in the initial cohort, as they did notrespect the selection criteria. Furthermore, children with previous cancer are susceptible to have more
CT scans and higher cumulated doses, and their inclusion may therefore bias the estimated risk. Indeed,
exclusion of these patients led to a reduction in the estimated ERR of about 8% compared to the initial
estimate, from 0.036 (95% CI: 0.005, 0.118) mGy−1 [P1] to 0.033 (95% CI: 0.004, 0.114) mGy−1 [B4].
Furthermore, the collection of additional clinical information has been possible only for about 40% of
the cohort, so it cannot be ruled out that other cases should have been excluded due to previous
unreported cancer. Nevertheless, it is unlikely that the global impact on the confidence interval could be
more than 50%, and the uncertainty associated to this factor is considered to be small.
68. It should be noted that the model derived from the UK CT-scan study was based on a population
with a range of age at exposure and attained age much narrower than the LSS. One option would havebeen to use a model derived from an LSS subset limited to children at the time of the bombings. Indeed,
about a third of the leukaemia cases were diagnosed in survivors who were exposed as children.
Nevertheless, it is not clear if such a model would have been able to quantify the modifying effect ofage at exposure and attained age.

(b) Exposure assessment
69. The exposure parameters and doses considered in the present scenario are fixed and considered
without uncertainty. Nevertheless, uncertainties and measurement errors exist in the cohorts used toderive the scenario and the risk models.

42 UNSCEAR 2019 REPORT

70. In the LSS, the following points have to be considered while discussing uncertainty in the
exposure assessment:(a) The radiation dose received by each survivor was estimated based on location, orientation andother information at the time of the bombings. Some uncertainties remain about, for example, thesizes and locations of explosions, radiation emitted and shielding. While continuing efforts havebeen made to improve the dosimetry [C9, C10], much of the information provided for thedosimetry system was collected through interviews 5–10 years after the exposure;(b) Dose estimates were adjusted for possible biases based on a statistical method of regression
calibration* with a plausible error model which assumes a 35% multiplicative error [P3]. Pierce etal. [P3] found that the excess cancer-risk estimates in the LSS could increase by 6–17% ifallowance was made for dose estimation errors, while the standard errors of the parameter
estimates remained essentially unchanged. This result reflects the fact that the additional variationin the data due to dose errors was small;(c) The current approach to dose uncertainty in the LSS deals with the classical errors* that arise
primarily from errors in each survivor’s reported location and shielding. But another type of error,
Berkson errors,* can arise from the data used to characterize the explosion (height, power, location,
radiation emitted) [P4, P5]. This approach made adjustments on both gamma and neutron doses.
More recently, to account for the uncertainty involved in the assumed true-dose distribution, a new
approach involving simulation-extrapolation under a mixture of classical and Berkson errors has
been considered and applied to the cancer-incidence analysis in the LSS [M9], where the risk
estimates appeared to be fairly comparable to those by the conventional adjustment;(d) There is additional uncertainty in the LSS dose estimates due to sources other than errors inthe survivors’ reported exposure data, including some shared errors* due to parameters in the dose
calculations such as the heights above ground, yields and locations of the hypocentres of the
explosions, but uncertainty from these sources is thought to be small compared to that frominaccuracies in the input data for survivors [K8];(e) There are also concerns about additional uncertainties potentially linked to the consideration ofinternal contamination, or exposure to the “black rain” that contained radioactive materials and fell
after the atomic bombings. However, up to now, there has been no clear evidence for long-term
deleterious health effects from such exposures [S1] and, thus, they are unlikely to have a large
impact on the current risk evaluations.
71. In the childhood CT-scan cohorts, the following points have to be considered in the discussion of
uncertainty in the exposure assessment:(a) The reconstruction of doses to organs associated with past CT scans requires taking account ofdifferent types of CT scanners, different CT-scan technologies, and details of the CT examinations
and protocols over the last decades. Most studies, including the UK CT-scan study, used rather
crude data, including date of the scan, body region scanned, sex and date of birth [B4, P1]. In earlychildhood, age is only a crude proxy for the relevant anatomical characteristics that influence doseas the distribution of height and weight for the same age can be broad;(b) The variability* of examinations, types of scan and protocols makes it difficult to reconstructdoses, and the level of detail available for dose reconstruction differs in different periods. Dosesdue to CT-scan examinations decreased in the recent years, while the doses from the United
Kingdom cohort reflect doses before 2000 and did not take into account variability of doses linked
to protocols and CT machines. Missing information on examinations or on the characteristics of theANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 43

children is also a source of uncertainty. Due to the method of dose reconstruction, measurement
errors should be mainly of the Berkson type, but they are shared among members of a group (forexample, for patients at a specific hospital). Such uncertainties and measurement errors could be
important and affect the reliability of risk estimates. Up to now, models derived from CT-scanstudies did not consider dose uncertainties. Improvement in the strategy of dose reconstruction andconsideration of uncertainties are intended to be conducted in the frame of the EPI-CT European
research project, an epidemiological study to quantify risks for paediatric CT [T5]. Due to thereconstruction scheme for individual doses, it is likely that measurement errors will be mostly of
the Berkson type. Nevertheless, as uncertainties are not all truly Berkson type, the potential impact on
the estimated dose–risk relationship and on the confidence intervals will depend on the degree and themagnitude of errors;(c) Missing information about CT-scan characteristics and possible repeated scans may lead tomissing doses and therefore, to underestimation of the cumulative doses;(d) The absence of consideration of any doses linked to other medical examinations (e.g. CT
nuclear medicine procedures, catheterization) also constitutes a source of uncertainty.

(c) Health outcome assessment
72. Leukaemia is a group of cancers belonging to a broader group known as cancers of thehaematopoietic and lymphoid tissues. There are four main types of leukaemia: ALL, AML, CLL and
CML, as well as several less common types, such as ATL. Classification of leukaemia evolved overtime, with the definition of the disease becoming more and more precise. This evolution may lead todiscrepancies in the composition of the “leukaemia category” considered by different data sources(registries for baseline rates) or epidemiological studies. Also, the frequency of specific subtypes varies
between countries. For example, Japan is known to have lower rates of CLL but higher rates of ATL
than many other industrialized countries. Little evidence exists of an association between ionizing
radiation exposure and CLL or ATL risk. The analysis by Hsu et al. showed that the dose–risk
relationship may vary according to leukaemia subtypes. The shape of the dose response appeared todepend on subtype, but also modification by age at exposure appeared to be greater for some subtypes
than others, and the age-dependence for baseline rates also differed by subtype [H4]. If the proportionof childhood leukaemia subtypes is different in the United Kingdom than in Japan, then this may be asource of bias when transferring the risk from the LSS population to a United Kingdom population. In
the future, consideration of separate leukaemia subtypes for such comparison of estimated cumulative
risks is warranted.
73. In the scenarios, United Kingdom age- and sex-specific incidence rates for all types of leukaemia(defined according to the ICD-10 codes C91–C95) were used [W10]. The model derived from thecohort of Japanese atomic bombing survivors considered the risk of leukaemia other than CLL or ATL[H4]. Nevertheless, this difference should not cause problems in the assessment of risk. Indeed, CLL
occurs in the population only at old ages; the CLL rate before age 40 is almost zero and appears mainly
at older ages. Furthermore, ATL, which is infection-based and can be endemic in some areas of Japan(especially near Nagasaki), is very rare in the United Kingdom. As the maximum age at the end offollow-up in the scenario is only 40, using reference rates excluding CLL would have little impact on
the estimated risk.
74. Both MDS and leukaemia are due to abnormalities in the bone marrow. The classification of
AML and MDS includes clinical data (previous history, age) and biological characteristics

44 UNSCEAR 2019 REPORT(morphology, cytochemistry, immunophenotype, cytogenetic and molecular biology). MDS is a rare
condition in childhood and often progresses to AML. MDS in children may be consecutive toradiotherapy treatment for a previous cancer, so care is required to ensure that patients with a previous
cancer are excluded from any analysis. The estimated incidence of paediatric MDS in Europe varies
from 1 to 4 cases per million per year and is equal in males and females, whereas the leukaemia
incidence among children under 15 years of age is about 40 to 50 cases per million per year. Thus,
whether children with MDS are included does not have an important impact on estimated baseline rates.
75. Risk assessment was conducted using the ERR model derived from the UK childhood CT-scancohort [B4], which considered incidence of both leukaemia and MDS. The article did not present
results for leukaemia only. Nevertheless, even if MDS cases represented only a small fraction of thetotal number of cases (9 MDS cases for 65 leukaemia cases), the impact on the estimated dose–risk
relationship might be important. In the previous analysis in the UK childhood CT-scan cohort, theestimated ERR for leukaemia excluding MDS was reduced to 0.019 (95% CI: −0.012, 0.079) mGy−1,
whereas the estimated ERR per unit dose for MDS only was 6.098 (95% CI: >0, 145.4) mGy−1, but
associated with a very large confidence interval due to the small number of cases [P1].
76. One study investigated the incidence of MDS among the atomic bombing survivors and theassociated dose–response relationship [I7]. The results demonstrated a significant linear dose–response
relationship over 40 to 60 years after radiation exposure. The estimated ERR per unit dose was
4.3 (95% CI: 1.6, 9.5; p<0.001) Gy−1. The incidence of MDS was significantly greater for those
exposed when young. MDS was not specifically considered in the analyses by Hsu et al. [H4], but theauthors discussed the potential impact of MDS on the estimated risk of leukaemia, and especially of
AML. They stated that it was likely that some cases identified as AML in the early years would havebeen classified as MDS, if they had been diagnosed with modern criteria. As misdiagnosis of MDS as
AML was likely to be independent of dose, they concluded that it would not affect the estimate of ERR
for the risk of AML, although it would tend to increase the EAR estimate [H4].
77. Classification of MDS evolved in the recent years. In the past, cases were not identified as MDS
and were often misdiagnosed as AML. In addition, MDS is a rare pathology, regrouping heterogeneous
diseases. For these different reasons, the assessment of MDS risk associated with radiation exposure isvery uncertain today. Nevertheless, the estimated ERR per unit dose estimated specifically for MDS in
the UK childhood CT-scan cohort, based on 9 cases [P1], appears to be particularly high. This result
makes it questionable to include MDS in the assessment of leukaemia risk. Indeed, the relative impactof the inclusion or not of MDS in the estimated ERR per unit dose is close to 50% (estimated ERR per
unit dose decreased from 0.036 to 0.019 mGy−1). Ongoing studies should provide information to better
determine the risk of MDS associated to radiation exposure, and its potential impact of the dose–risk
relationship estimated for leukaemia+MDS.

(d) Study design
78. Two to three years after the atomic bombings of Hiroshima and Nagasaki, a number of physiciansin Hiroshima and Nagasaki noted a markedly increased incidence rate of leukaemia in children living
near the hypocentres [H4]. Nevertheless, before 1950 some cases may have been missed because ofdeath from infectious disease before leukaemia diagnosis. Therefore, in the early 1950s, the Atomic
Bomb Casualty Commission researchers together with haematologists in Hiroshima and Nagasakilaunched the Leukaemia-Registry to ascertain all potential cases of leukaemia and other haematologicalmalignancies in the two areas, including cases that occurred in the late 1940s. The Leukaemia Registry
remained active until the late 1980s, when it was replaced by the city and prefecture population-based

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 45

Tumour Registries. Study of the incidence of leukaemia, lymphoma and multiple myeloma among thesurvivors of the atomic bombings began on 1 October 1950. The end of follow-up was the earliest dateof diagnosis of the first primary malignancy (of any type), the date of death, the date of loss to followup or 31 December 2001. There is no nationwide cancer registration system in Japan. So, people who
moved from the Hiroshima/Nagasaki regions may have been lost to follow-up due to migration out of thecatchment areas of the local registry. The followed-up person–years are consolidated from the contact
records in the Adult Health Study (AHS). For the last publication on leukaemia risk with follow-up to2001, the percentage of cohort members lost to follow-up was estimated to be less than 1% [H4].
79. In the UK CT-scan study, cancer incidence, mortality and loss-to-follow-up data were obtained bylinkage with the NHSCR from 1 January 1985 to 31 December 2008. A total of 33,372 patients (about
30% of the initial dataset) were excluded because they could not be traced by the NHSCR because theirnames or dates of birth in the Radiology Information System (RIS) databases were incomplete.
According to the authors, the availability of information on persons is considered to be independent of
the dose received and incidence of leukaemia, so this relatively high percentage of persons excluded
from the study should not bias the estimated risk. Altogether, 178,604 persons were included in theanalyses of the incidence of leukaemia [P1].

(e) Confounding factors
80. The established risk factors for childhood leukaemia are Down syndrome, sex (it occurs more
frequently with boys than with girls), chemotherapeutic drugs and exposure to ionizing radiation.
Exposure to 50 Hz electric and magnetic fields (ELF-EMF) has also been suggested as a risk factor forchildhood leukaemia [I1], but the evidence remains controversial [A3]. Many other factors have beensuggested, such as genetic and infectious risk factors, maternal reproductive history, birthcharacteristics, exposure to hydrocarbons and pesticides, alcohol use, cigarette smoking and illicit druguse, but, to date, most of them have been found to be weakly and inconsistently associated withchildhood leukaemia [B1, W8].
81. A major limitation of CT-scan studies is the potential bias by reverse causation [B9, U6, W5].
Reverse causation implies that it is the early symptoms of undetected cancer, that are the indication for
the CT scans, rather than the CT scans per se that are causing the apparent excess incidence of cancer.
In the literature, two circumstances of such a bias are differentiated:(a) The previously unreported cancer of interest has only been reported after the first CT scan. In
most CT-scan studies, the possibility for such a bias has been considered through the application ofdifferent latency periods between the CT scan and occurrence of the cancer (from 1 year to morethan 10 years), but most of the published studies were limited by their duration of follow-up.
Whereas such a bias appears possible for brain tumours [B9], it is much more unlikely forleukaemia, as most of the leukaemia cases occurring during childhood or young age are acute, withno known early symptoms occurring more than one year before diagnosis;(b) A type of cancer different from the cancer of interest in the study had not been reported. This
may introduce a systematic bias, because cancer therapy may cause leukaemia and may have been
the clinical indication for several CT scans [B4].
82. Several CT studies are under way, such as the EPI-CT study in Europe, which hopefully shouldbe able to establish the reasons for the CT examinations in subgroups of patients, and then to better
assess the potential effects of reverse causation [W5, W7].

46 UNSCEAR 2019 REPORT

83. Confounding by indication is another potential source of bias of CT-scan studies [B13]. Indeed,some conditions, such as infections or neurological conditions, may require CT examinations for the
purpose of diagnosis but also involve increased susceptibility to leukaemia and the prevalence ofleukaemia predisposing factors among children undergoing CT-scan examinations (for example, Down
syndrome or Crohn’s disease) may modify the estimated dose–risk relationship. In the UK CT-scanstudy the collection of additional clinical information on leukaemia related conditions from radiology
information system databases, underlying causes of death and pathology reports, led to a change in theestimated ERR per unit dose from 0.036 (95% CI: 0.005, 0.118) mGy−1 to 0.037 (95% CI: 0.005, 0.126)
mGy−1 [B4] compared to the previous estimate [P1]. In the French CT-scan cohort, the ERR related to
CT exposure differed in persons with or without predisposing factors. The authors concluded thatpredisposing factors were acting as modifying factors of the dose–risk relationship rather than as
confounding factors [C7, J5, M14]. Nevertheless, as the risk estimates in patients without predisposing
factors were close to the unadjusted ERR in the overall cohort, the impact of predisposing factors
appeared limited. Another recent study based on simulation in the Netherlands concluded that associations
between radiation exposure from paediatric CT scans and leukaemia reported in previous studies areunlikely to be substantially confounded by unmeasured cancer susceptibility syndromes [M6]. A new
analysis in the Netherlands concluded that indication bias is likely to be negligible or small among adults[M7]. Nonetheless, the absence of information on the reasons for CT scans in most published studies
remains a problem, and collection of such data when possible is recommended for future studies.

(f) Statistical methods and model uncertainties
84. Many uncertainties can be linked to the risk models, including the shape of the dose–risk
relationship, the quantification of modifying effects related to age and time since exposure, the latency
period between exposure and risk, and the nature of the risk transfer (EAR or ERR).
85. In the scenarios considered, the statistical uncertainty associated with risk coefficients wasconsidered through confidence intervals, and, where available, both EAR and ERR models wereconsidered. Uncertainty remains in the modelling of effect modification depending on age at exposure,attained age or time since exposure, of the dose–response relationship or of the latency. Especially, therisk model derived from the UK CT-scan study does not consider a modifying effect of age at exposure[B4], whereas the LSS model demonstrated a strong decrease of the estimated ERR per unit dose withincreasing age at exposure. Between exposure at ages 1 and 10, the reduction in the ERR is higher thana factor two [H4]. The mean age at exposure of the cases in the UK CT-scan study being about
10 years, it is probable that the ERR estimated for age at exposure of 1 year is expected to be higher
than the age-independent value used in these calculations.
86. The risk models derived from the LSS are based on a large population (113,011 cohort members)
and a long duration of follow-up (from 1950 to 2001). This large database allowed a detailed analysisof the excess risk for all types of leukaemia other than CLL or ATL [H4]. The models were linear–quadratic in dose with effect modification depending on attained age and time since exposure. BothERR and EAR models were derived. Uncertainty in the risk estimates at low doses was thought tooriginate from various sources, including variation of estimated baseline rates, uncertainty in the dose
estimates, residual confounding and interaction, potential confounding due to uncontrolled risk factors,
and exposure to other sources of radiation [O6]. In addition, due to the late beginning of follow-up(1950, which was five years after the bombings), a greater uncertainty is associated with the risk
estimated in the first years after exposure. As the estimated risk coefficients are very high at very young
ages at exposure, this point may impact especially the estimated risk in the scenario with exposure at
1 year of age.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 47

87. To assess the impact of model selection on the estimates of the ERR, Walsh and Kaiser [W4]refitted nine previously published leukaemia-risk models to the LSS mortality data from theperiod 1950–2000. For a comprehensive characterization of the model uncertainties, Akaike
Information Criterion* (AIC)-weighted risk averages were calculated using the multi-model inferencetechnique. The authors considered several exposure scenarios, including childhood exposure at ages 2,
7, 12 and short times since exposure of 5 and 10 years. For young age at exposure and short time since
exposure, the number of leukaemia cases was very low for attained age up to 25. Less than 30 cases
have been recorded in the dose range 0–4 Gy. Hence, risk estimation at doses below 100 mGy for theabove-mentioned scenarios was mainly based on extrapolation of the model results. The 95% CI of the
ERR estimates using the multi-model inference technique were very large (upper bound up to fourtimes larger than the combined estimate), and always included the value zero (absence of increasing
risk per unit dose) [W4]. The central ERR estimates based on multi-model inference agreed with that
estimated in the UK childhood CT-scan study [B4].
88. The uncertainties associated with risk estimates based on the UK childhood CT-scan cohortmodel have so far not been fully quantified. The confidence intervals associated with the risk
coefficients only reflect a portion of the complete uncertainty. The main limit of the CT-scans studies isthe absence of estimates for age-modifying effects, due to the current limited power. A second limit isthat, up to now, no study has provided estimates of the EAR; only ERR estimates are available.

(g) Other sources of uncertainty
89. The calculation of risk is based on the assumption that the risk of leukaemia over a given periodof follow-up can be predicted [W6]. One major underlying assumption is that the selected reference
rates are stable over the whole period of risk assessment, which here is up to a duration of 39 years(scenario of exposure at age 1 with a follow-up to age 40).
90. Statistics available for recent periods do not show a strong variation of leukaemia rates at young
ages over time [I2]. Nevertheless, the evolution of mortality rates and of leukaemia incidence in the
future is very difficult to predict, but the evolution of diagnostic practices and of treatment during thelast decades clearly indicate that the assumption of stability over time is uncertain. This uncertainty
applies to the estimation of the CBR and CER.
91. For the estimation of the baseline risk for leukaemia, age- and sex-specific incidence rates per
100,000 population in the United Kingdom for the period 2011–2013 were used. These rates wereassumed to present no uncertainty in the considered scenarios. Furthermore, the survival function wasconsidered known and not affected by uncertainties.

2.

Preferred risk inference(a) Selection of the preferred risk inference
92. The preferred risk inference for the selected scenario of repeated exposure to CT scans in the
United Kingdom is the one derived from the UK childhood CT-scan cohort for exposure at the ageof 1 year with a dose to the RBM of 20 mGy and follow-up to 30 years [B4]. The estimated CER for
leukaemia+MDS is 5.3 per 10,000 persons (95% CI: 0.6, 18.3) with a CFR of 0.58.

48 UNSCEAR 2019 REPORT

93. This estimate was derived from a study which had been used as the basis to determine thecharacteristics of the scenario. Thus, no further assumption was required. Therefore, this estimate ispreferred to the one derived from the LSS study for which assumptions were needed regarding thetransfer of risk estimates obtained from another population, the extrapolation from high doses, and thetransposition from a different dose-rate pattern. Nevertheless, several sources of uncertainties can havea non-negligible impact on this estimate.
94. The estimation of risk up to 30 years of age is preferred as the more informative scenario
compared to that up to 40 years of age, as 30 years corresponds approximately to the mean age at theend of follow-up of the UK childhood CT-scan study. The model based on the UK study does not take
into account the decrease of the relative rate with age attained (figure II).

(b) Discussion of the impact of sources of uncertainty
95. The main sources of uncertainties associated with this risk estimate are summarized in table 4.
The subsequent paragraphs give the reasons for the grading of the uncertainties (very small, small,moderate or large).
96. Selected populations: The impact of population selection is considered to be small, especiallybecause the scenario was determined to mimic the characteristics of the UK childhood CT-scan study,
from which the risk model is derived. Exclusion of cases with previous cancer is an imperative selection
criterion. Exclusion of 2 cases due to the collection of additional clinical information in the UK childhood
CT-scan study demonstrated the importance of this criterion. Nevertheless, the impact of erroneous
inclusions appears to be quantitatively small as far as it remains limited to a small number of cases.
97. Exposure assessment: The reconstruction of organ doses associated with past CT scans in the UK
study was rather crude as it did not consider specific data or the body size of the patients. Due to thereconstruction of individual doses, it is likely that measurement errors will be mostly of the Berkson
type, but not all of them truly Berkson. The potential impact on the estimated dose–risk relationship andon the confidence intervals will also depend on degree and magnitude of shared errors. The impact of
uncertainty linked to exposure reconstruction has not yet been estimated in published CT-scan studies.
98. Health outcome assessment: CLL is very rare before the age of 40, and therefore exclusion of
CLL is considered to be of very small impact. The rate of MDS in the United Kingdom population
before age 40 is very low compared to leukaemia rates, and would be of negligible impact of theestimated (absolute) risk. In the LSS, analyses suggested a relation between radiation exposure and the
ERR of MDS of the same order of magnitude as for leukaemia [I7]. Conversely, in the UK childhood
CT-scan study, the ERR estimated for MDS appears to be very high (and very uncertain, based on only
nine cases) and much higher than that estimated for leukaemia alone [P1]. Inclusion of MDS cases in
addition to leukaemia cases appears to change the estimated ERR per unit dose from 3.6 to 1.9 Gy−1, so
a reduction by about a factor of 1.9. Thus, the potential impact of outcome assessment in the case ofleukaemia is considered to be moderate.
99. Study design: About 30% of the initial dataset of the UK CT-scan study was excluded because of
missing data for linkage with the NHSCR. Nevertheless, the probability of missing data is independent of
the dose, and the potential impact of study design on the estimated risk is therefore considered as small.
100. Confounding factors: Two major sources of bias have been discussed in the context of CT-scanstudies; reverse causation and confounding by indication [B13, W5]. Whereas a bias due to reverse
causation appears possible for brain tumours, it is much more unlikely for leukaemia, as mostleukaemia cases occurring during childhood or young age are acute, with no known early symptoms

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 49

occurring more than one year before diagnosis. Indeed, the persistence of the relationship after
excluding the data for the first one or two years after CT-scan seems coherent with this. The possibilityof a bias due to confounding by indication cannot be ruled out, but several recent results suggested thatpredisposing factors are unlikely to substantially confound the estimated dose–risk relationship [J5,M6]. The impact of these two confounding factors is therefore considered to be small.
101. Statistical methods and model uncertainties: The model derived from the UK CT-scan study is a
simple linear risk model. At the present time, the model does not allow consideration of a potential
modifying effect of age at exposure and time since exposure on the estimated risk. The potential impactof this limitation is considered small for attained age as the scenario is designed to mimic thecharacteristics of the study population (which will not be true if one considers an older age at the end offollow-up). For age at exposure, the model derived from the UK CT-scan study does not allow
considering a decrease of the ERR per unit dose with increasing age at exposure, which may lead to anunderestimation of the risk for very young ages at exposure. The impact of this limitation for the
scenario of exposure at age 1 is considered to be potentially large (higher than a factor two).
102. Other sources of uncertainty: The scenario relies on the hypothesis that the baseline risk ofleukaemia is stable over time, up to the end of follow-up. Based on the recent statistics for the United
Kingdom, which do not show a large variation of leukaemia baseline rates since 2000, the impact of
this assumption is considered to be small.
Table 4. Characterization of the main sources of uncertainty associated with the preferred risk
inference of leukaemia following repeated exposure to CT scans during childhood
SourceCharacterization of source

Judgedimpact a

Selected
populations

Scenario based on the UK childhood CT-scan study

Very small

Erroneous inclusion of a limited number of cases with previous cancer

Small

Exposure
assessment

Crude estimation of dose to the RBM based on protocols, lack of individual
information

Small

Existence of missing data

Small

Inclusion of CLL—very rare before age 30

Very small

Consideration of MDS in baseline rates—low rate compared to leukaemia

Very small

Impact of MDS cases on the estimated ERR

Moderate

Study design

Linkage with the UK NHSCRVery small

Confounding factors

Higher rate of predisposing factors among children with CT-scan

Small

Reverse causation

Small

Statistical methods
and model
uncertainty

Latency

Small

Absence of assessment of the modifying effect of age at exposure and timesince exposure

Large

Other sources of
uncertainty

Hypothesis of stability of the baseline risk up to the end of follow-up

Small

Health outcome
assessmenta

The impact of the different sources of uncertainty is classified into four categories according to the variation that they are
expected to induce on the reported CER: very small—less than a factor of 1.1; small—between a factor of 1.1 to 1.5; moderate—
between a factor of 1.5 to 2; and large—greater than a factor of 2.

50 UNSCEAR 2019 REPORT(c) Concluding remarks on the preferred risk inference
103. The preferred risk inference has been derived from a study in which the characteristics of the
population, the exposure situation and the period of follow-up to the study are very similar to thescenario. The ERR associated with a dose to the RBM of 20 mGy (the basic quantity in the risk
estimation) of about 0.7 is supported by a number of childhood CT-scan studies (table 1), a recent
pooled analysis of low-dose studies [L17] and a review of childhood leukaemia studies in various
exposure conditions [W1].
104. Three systematic errors* in the preferred risk inference for leukaemia+MDS after CT scans
during childhood scenario may be moderate or even large. Two (the inclusion of MDS in the risk model
classified as moderate, the inclusion of patients with previous cancer classified as small) may lead to an
overestimation of the CER, whereas the third one (mean age at exposure in the study cohort of [B4]considerably higher than one, classified as large) may lead to an underestimation of the risk.
105. The Committee judged the credible interval for the preferred risk inference of the CER based on
the consideration of the sources of uncertainties in table 4 which are additional to the statisticaluncertainty in table 2. The sources of uncertainties listed above are considered to essentiallycompensate each other in the preferred estimate but question the significance of the result. Monte Carlocalculations were performed to estimate the impact of small stochastic uncertainties (appendix A).
Leukaemia incidence up to age 30 after CT scans at age 1 with a total dose to the RBM of 20 mGy is
estimated to be five cases among 10,000 persons with a 95% credible interval from about 0 to 20 cases.
106. Very few estimations of leukaemia CER over a limited period are available in the literature. An
assessment of the health risk from the nuclear accident after the Great East Japan earthquake andtsunami in 2011 was performed by an expert group of the World Health Organization (WHO), based on
hypothetical lifetime dose to the RBM. The cumulative risk of leukaemia attributable to estimatedRBM doses due to the accident was calculated for the residents of the different villages of the
Fukushima Prefecture, considering either a follow-up of 15 years after the accident or a follow-up to
the attained age of 89 years old [W9]. Table 5 presents the results of the WHO health risk assessment
and those of the preferred risk inference in the present report for the same dose level (20 mGy). Basedon the results of the LSS (figure II) it is expected that all major contributions to the CER accumulate
within the first 29 years after exposure. Thus, the results for CER in the preferred risk inference may becompared to the WHO estimate for a follow-up to 89 years of age. The estimated CERs appear to be of
the same order of magnitude: the preferred risk inference of the present report appears to be slightly
higher (factor of two) than the one in the WHO report. The good agreement is obtained in spite oflarger differences in the scenarios and calculations. The WHO assessment is based on baseline rates inJapan, whereas the present report used baseline rates in the United Kingdom. WHO used a mortality
model to calculate incidence [W9], whereas the present report used the Hsu et al. [H4] incidence model,
which was not available at the time of the WHO report. Also, WHO [W9] used the mean of the ERR
and EAR models for risk transfer, whereas the preferred risk inference in the present report is based ona study of a population similar to the one in the scenario and thus avoiding large uncertainties in thetransfer of observed effects from the study population to another population.
107. For age at exposure of 10, again the present estimation of CER based on Berrington de González
et al. [B4] (table 3) is larger than the WHO result by a factor of two. According to the discussion ofsystematic errors, the present estimation of CER is expected to be slightly too large. Thus, there is avery good agreement between the present analysis and the WHO report [W9] for leukaemia risk after
exposure at age of 10 years.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 51

Table 5. Comparison of estimates of cumulative leukaemia incidence (per 10,000 persons) after
exposure at age of 1 with a dose to the RBM of 20 mGy
CBR: Cumulative baseline risk; CER: Cumulative excess risk; EAR: Excess absolute risk; ERR: Excess relative risk

SourceWHO [W9] a

Present report

WHO [W9] a

15 years after the accident

29 years

Up to 89 years of age

3.0

9.1

52

Preferred model

1.6

5.3 b

2.6

ERR transfer from LSS

2.7

5.0

c

3.8

EAR transfer from LSS

0.54

1.8 c

1.3

Follow-up time
CBR
CER

a

Risk estimate from the work of the WHO expert group on the assessment of the health risk from the nuclear accident after theGreat East Japan earthquake and tsunami in 2011 based on hypothetical RBM doses [W9]. ERR and EAR risk models derived
from the LSS mortality data ([W9], annex J). Preferred model is an unweighted average of ERR and EAR transfer. Baseline
incidence rates derived from the recent Japanese population, sex averaged absolute risk scaled from about 26 mGy (mean RBM
dose in region 1 in the WHO report) to 20 mGy.
b

Risk estimate from the present report. ERR risk model derived from the UK CT-scan incidence study [B4]. Baseline rates
derived from the current United Kingdom population.

c

Risk estimate from the present report. ERR risk model derived from the LSS incidence study [H4]. Baseline rates derived fromthe current United Kingdom population.

G. Conclusions
108. Rather consistent results were obtained for leukaemia+MDS up to age 30 after CT scans at age 1
with a total dose to the RBM of 20 mGy. Among 10,000 persons with an assumed cumulative baseline
incidence of 9 cases, a cumulative excess incidence of about 5 cases of leukaemia+MDS was estimatedwith a 95% credible interval from about 0 to about 20 cases. Larger uncertainties exist for longer times
after exposure.
109. The agreement in the estimated CER between the LSS ERR model and the CT-scan ERR model
after exposure at age 1 and follow-up to age 30 is very good (less than a 10% difference). The
confidence interval associated with the CER appears to be larger when using the LSS ERR model thanwhen using the UK CT-scan ERR model.
110. For exposure during early childhood and follow-up until young adulthood, CT-scan studies
provide a pertinent source of information to assess the risk associated with medical diagnostic
exposure. The use of risk models derived from CT-scan studies requires no extrapolation or transfer,whereas using risk models derived from the atomic bombing survivors rely on uncertain assumptions(because of a different situation of exposure, higher doses, different dose rate and ages at exposure). At
the present time, the calculation of the CER associated with radiation exposure based on risk models
derived from either the LSS or from a CT-scan study, appears to be coherent when scenarios focus
precisely on the characteristics of the CT-scan study population (low dose, exposure at young age, shortduration of follow-up). This strengthens the confidence in the method used to assess radiation risks
based on a transfer of the estimates derived from the LSS to other populations and exposure conditions.
Nevertheless, sources of uncertainty in the childhood CT-scan studies are not negligible and can have amoderate to large impact on the preferred risk inference.

52 UNSCEAR 2019 REPORT111. Extrapolation of the results of the CT-scan study to exposures received at different ages of
exposure or longer follow-up embracing a larger part of adulthood is questionable, because they do notdeal with age dependencies. Under these conditions, it seems that the use of transfer risk models
derived from the LSS remains a method of choice for risk estimations. In the future, methods should be
extended to combine information from several studies in the development of risk models better suitedto transfer risks between different populations or extend predictions beyond the observed age range.
112. Some of the limitations of the CT-scan studies may be reduced in the future. Extension of theduration of follow-up would allow a better assessment of the modifying effect of age. Additional
information on any predisposing factors and the medical reasons for scans will help to clarify potential
biases. Analyses based on larger numbers of cases should allow specific analyses of leukaemia
subtypes, and better distinction of the risk associated to leukaemia and to MDS. Also, the developmentof studies in different countries will provide better knowledge about the coherence of the estimated
ERR and EAR risks between countries. Other studies are ongoing in Spain [B11] and in the
Netherlands [M5]. International projects such as EPI-CT (Epidemiological study to quantify risks for
paediatric CT and to optimize doses), with a large statistical power (includes nine national European
cohorts with a total of about 1 million children), and with a focus on dosimetric reconstruction andminimization of bias, are expected to provide more precise results [B10, O2, T5, V1, W7].

III. LEUKAEMIA MORTALITY AFTER REPEATED LOW-DOSE
EXPOSURE DURING ADULTHOOD

A. Motivation
113. Leukaemia related to repeated radiation exposure during adulthood is of particular interest,
because there is a substantial amount of information on an increased incidence of the disease fromepidemiology. There is however much uncertainty about the risks of leukaemia after repeated or
protracted low-dose exposure. Since the beginning of the nuclear industry in the mid-1940s,
occupationally-exposed workers were usually monitored individually with dosimeters. Some of theseworkers were exposed to low-dose protracted or intermittent radiation exposure over their wholeoccupational life, and some of these workers have accumulated moderate or even high cumulative
doses. In some countries, large populations of such workers have been included in epidemiologicalcohorts and followed up for up to 60 years.

B. Recapitulation of previous UNSCEAR publications
114. The UNSCEAR 2006 Report, annex A [U3] included a comprehensive review of studies
providing results on the incidence of leukaemia after exposure during adulthood. The most informative
studies regarding the demonstration of a dose–risk relationship was considered to be those of the cohortof the atomic bombing survivors, workers involved in the nuclear industry, radiologists and othermedical X-ray workers, ankylosing spondylitis patients and other medically exposed groups, and theANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 53

Techa River residents. The report concluded that the available information confirmed the evidence of acurvilinear dose–response relationship. Little indication existed of an association between the incidenceof leukaemia and uranium or plutonium exposure. Based on the LSS [P8], the ERR per unit dose
estimated for exposure at 20–40 years of age was 3.6 (90% CI: 2.0, 6.0) Sv−1, and the EAR per 10,000
person–years per unit dose was 2.7 (90% CI: 1.7, 3.9) Sv−1 [U3].
115. The UNSCEAR 2012 Report, annex B [U8] was aimed at reviewing the sources of uncertainty
and methodology of risk assessment. Leukaemia risk was not considered in the selected risk
evaluations, but leukaemia was considered in the general review of sources of uncertainties.
116. The UNSCEAR 2013 Report, annex A [U7] on the health effects of the Fukushima accident
similarly reviewed the available results for leukaemia incidence after exposure during adulthood,especially in a post-accident context. The estimated risks for the workers at the Fukushima Daiichi
nuclear power station by the Committee and those from WHO [W9] were in good agreement. The
workers at the Fukushima Daiichi nuclear power station who incurred the highest external exposure
received doses to the RBM of at most 200 mGy. Owing to the small number of workers involved, theconclusion of the UNSCEAR 2013 Report was that no discernible effect of the radiation exposure on
leukaemia incidence was expected [U7].

C. Review of recent epidemiological literature
117. A literature review was performed to identify articles providing information on the quantificationof the risk of leukaemia associated with radiation exposure. The same research procedure as describedin section II.C was applied.
118. Articles providing results for leukaemia risk after exposure during adulthood were considered.
However, not all these publications provided original results allowing estimation of a dose–risk
relationship after exposure during adulthood in the low to moderate dose range, or were able to derive
an estimate of the dose–risk relationship. For instance, an analysis of cancer mortality in a cohort of
43,763 radiologists in the United States, published by Berrington de González et al. [B3] estimated a
relative risk were through a comparison with a cohort of 64,990 psychiatrists. An increased death rate
was observed among the radiologists for AML and MDS (n=41; RR=1.62; 95% CI: 1.05, 2.50). These
rates were dominated by those persons who had graduated before 1940 (RR=4.68; 95% CI: 0.91,
24.18). The authors concluded that the excess risk of AML and MDS mortality in radiologists who had
graduated before 1940 was likely to have been due to occupational radiation exposure. Nevertheless,due to a lack of information on individual radiation doses, no dose–risk relationship could be derived.
Another study by Rajaraman et al. [R2] based on a prospective cohort of 90,957 radiological
technologists in the United States showed no elevated incidence of leukaemia among workers who
performed fluoroscopically guided interventional procedures, however no dose–risk relationship could
be derived as no individual data on radiation doses were available.
119. The following review was limited to studies presenting original epidemiological results, based on
individual data, with a quantitative dose–risk relationship for populations exposed to moderate- or lowlevel ionizing radiation during adulthood. Fourteen articles were selected, related to the atomic
bombing survivors, to workers exposed due to their occupation (nuclear workers, miners and Chernobyl
liquidators), and to environmental exposures (residents of the Techa River).

54 UNSCEAR 2019 REPORT

1.

Life Span Studies of the Japanese atomic bombing survivors

120. A comprehensive publication on cancer mortality among the atomic bombing survivors was
published in 2012 [O5]. The study included 86,611 persons, followed up over the period 1950–2003.
Dose estimates were based on the DS02 dosimetry system. There were 318 registered deaths fromleukaemia. Models considering a linear–quadratic relationship between dose to the RBM and risk ofleukaemia provided a better fit than linear models. The sex-averaged ERR of leukaemia was 3.1 (95%
CI: 1.8, 4.3) at 1 Gy and 0.15 (95% CI: −0.01, 0.31) at 0.1 Gy with the linear–quadratic model, with amodifying effect of attained age. No specific results were provided for adults at the time of exposure.
More details on the risk models are given in section III.D.3.
121. Several articles were recently published proposing methodological improvements in thequantification of the dose–response relationship for leukaemia. In particular, Walsh and Kaiser [W4]applied a model-averaging procedure to nine published leukaemia-risk models, developed from theepidemiological data on mortality from the studies of the Japanese atomic bombing survivors. The
model-averaged ERR at 1 Sv was not found to be statistically significant for attained ages of 7 and 12,
but was statistically significant for attained ages of 17, 22 and 55. This approach was further developedby extending the number of considered models to 40, and developing a rigorous selection approach[K3]. Compared to the previous analysis, the ERR for leukaemia mortality from the multi-model
inference was similar for doses between 0.5 and 2.5 Sv, but at lower doses, the ERR estimates weremarkedly reduced. The multi-model inference produced risk estimates with a positive 2.5% percentile,
only above doses of some 300 mGy. A detailed analysis of leukaemia incidence among the Japaneseatomic bombing survivors was also published in 2013 [H4]. The main results of this study are presented
in section II.C.1.

2.

Studies of workers exposed to radiation

122. The most important study published in recent years on repeated exposure over the working livesof occupationally-exposed workers is the International Nuclear Workers Study (INWORKS) [R5, R6].
The cohort included 308,297 workers monitored for exposure and employed for at least one year by the
French Atomic Energy Commission (CEA), AREVA Nuclear Cycle (formerly COGEMA), andElectricité de France (EDF) (the French cohort); workers employed by the British Atomic Weapons
Establishment, British Nuclear Fuels Limited (BNFL), the UK Atomic Energy Authority, British
Energy Generation, the UK Ministry of Defence, and other organizations providing data to the UK
National Registry for Radiation Workers (NRRW) (the United Kingdom cohort); and workers
employed by the US Department of Energy’s Hanford Site, Savannah River Site, Oak Ridge National
Laboratory, Idaho National Laboratory, and the Portsmouth Naval Shipyard (the United States cohort).
Analyses of leukaemia risk were published previously for each of the three national cohorts separately:using a linear model, the estimated ERRs per unit dose for leukaemia excluding CLL were 3.96 Gy−1
(n=60; 90% CI: <0, 16.82) [M4], 1.71 Gy−1 (n=198; 90% CI: 0.06, 4.29) [M12], and 1.7 Gy−1 (n=369;
95% CI: −0.22, 4.7) [S5], respectively, for the French, United Kingdom and United States cohorts. The
INWORKS used recorded photon doses only, excluding neutron doses and doses from internal emitters.
The absorbed dose to the RBM was calculated for each worker based on the results of their individual
monitoring for external exposure. Cumulative doses were lagged by 2 years. The exposure period
ranged from 1945 to 2005. The total follow-up over the period 1945–2005 was 8.2 million person–
years. The mean duration of follow-up was 27 years. The mean cumulative dose to the RBM was
16 mGy (maximum 1.2 Gy), accrued at very low rates (mean 1 mGy per year). The total number ofleukaemia deaths (excluding CLL) was 531. For the whole cohort, the estimated linear ERRs ofleukaemia mortality per unit absorbed dose was 1.95 (90% CI: 0.50, 3.73) Gy−1 and 2.96 (90% CI:

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 55

1.17, 5.21) Gy−1 after excluding CLL. The strongest association was observed for CML, with an ERR
per unit dose of 10.45 (90% CI: 4.48, 19.65) Gy−1 [L7].
123. A sensitivity analysis involved fitting the same model to restricted ranges of cumulative dose.
This analysis allows excluding person–years with the highest cumulative doses, to assess their impact on
the estimated dose–risk relationship. In the dose range 0–300 mGy, the estimated linear ERR of leukaemia
mortality per unit dose was 2.52 (90% CI: 0.70, 4.73) Gy−1 and 3.31 (90% CI: 1.13, 6.00) Gy−1 after
excluding CLL [L7]. When restricted ranges within the dose range 0–300 mGy (e.g. 0–250, 0–200 …)
were used, the estimated ERR per unit dose was no longer significantly different from zero. The
confidence intervals obtained for the subcohort corresponding to the restricted dose range 0–300 mGy
were only slightly wider than those obtained for the whole cohort, because the number of cases
excluded was very small. In the dose range 0–300 mGy, the estimated linear ERR of CML mortality
per unit dose was 8.68 (90% CI: 2.44, 18.4) Gy−1 [L7].
124. Complementary analyses of the INWORKS dataset were performed using a nested case–control
approach in order to investigate further the impact of time since exposure and age at exposure on theestimated ERR per unit dose. A significant positive dose–response association was confirmed using afixed lag model for non-CLL leukaemia mortality; ERR was 2.80 (90% CI: 0.96, 5.10) Gy−1. The fitted
lag was estimated to be about 13–17 years, but the difference was not significant. A non-significant
time-since-exposure effect was suggested for non-CLL leukaemia mortality. A higher ERR per unit
dose was confirmed for CML mortality [D1].
125. More recently, a comprehensive analysis of the French cohort of nuclear workers was published,on a subset similar to the one included in the INWORKS. The cohort included 59,004 workers followed
up to 2004. Estimates of dose–mortality associations were obtained using linear ERR models. For non-CLL
leukaemia, the estimated ERR per unit dose was 3.52 (90% CI: <0, 16.0) Gy−1, but was not statisticallysignificant, due to the small number of cases (57 deaths) [L8].
126. An analysis of leukaemia incidence has been performed in the cohort of Mayak workers (theRussian Federation) [K28]. The cohort included 22,373 workers employed at the Mayak Production
Association main facilities between 1948 and 1982 and followed up to the end of 2004. The cohort
included 25% females. A total of 535,877 person–years was accumulated in the study cohort.
Information on tobacco-smoking was obtained from medical records for 89% of the workers in thecohort. The doses to the RBM were estimated using the Mayak Workers Dosimetry System 2008. The
mean cumulative external gamma-dose to the RBM was 0.39 Gy (0.41 Gy for males and 0.33 Gy forfemales) while 90% of the workers received doses below 1.15 Gy. The analysis included 77 cases ofleukaemia, comprising 24 cases with AML, 21 cases with CLL and 13 cases with CML. The estimatedlinear ERR for leukaemia incidence per unit dose to the RBM from external gamma exposure was
1.89 (90% CI: 0.89, 3.74) Gy−1 and 3.46 (90% CI: 1.57, 7.65) Gy−1 after excluding CLL. After
adjustment for internal exposure, a significant effect modifier of time since exposure and age at
exposure was observed.
127. An analysis of leukaemia risk has been performed on the cohort of Eldorado uranium miners andprocessors, based on both incidence and mortality data [Z4]. The cohort included 92% of males. The
mortality analysis included 17,660 workers first employed in 1932–1980 and followed up over 1950–1999
and 16,770 workers followed up over 1969–1999 for the incidence analysis. The mean cumulative
gamma doses weighted by person–years and lagged by 5 years for the Eldorado cohort among males
was 52.2 mSv (from 0 to 3,420.0 mSv) in the mortality and 41.5 mSv (from 0 to 2,921.0 mSv) in theincidence analysis. The number of leukaemia deaths was 34 in the mortality analysis and of leukaemia
cases 53 in the incidence analysis. No statistically significant association between gamma doses andleukaemia mortality or incidence was found. Nevertheless, interpretation of the results was limited by

56 UNSCEAR 2019 REPORT

the small numbers of cases and the fact that the main exposure was due to radon. Within the Eldorado
cohort, Port Hope workers were predominantly exposed to external gamma radiation. The mean
cumulative person–year weighted mean cumulative radon exposure was 15.9 working level month(WLM)* and the mean cumulative whole-body gamma dose was 134.4 mSv. ERRs for radon exposureand gamma doses were calculated separately. There was no significant association between leukaemia
mortality and gamma dose, radon exposure, or a combination of both [Z3].
128. The risk of death from leukaemia in relation to occupational exposure to prolonged low-level
external and internal radiation was analysed by Kreuzer et al. [K25] in the cohort of the former
Wismut uranium miners. The cohort included 58,972 miners with mortality follow-up from 1946 to2013. The dose to the RBM from low-LET (mainly external gamma radiation) and high-LET (mainly
due to radon gas and radon decay products) radiation was estimated. The mean cumulative low-LET
and high-LET doses to the RBM among exposed miners were 48 and 9 mGy, respectively. A linear
model was used for analyses, as the linear-quadratic model provided no statistically significantly
better fit. No significant dose–risk relationships were observed for non-CLL leukaemia (n=120) in
relation to both low-LET (ERR per unit dose of 2.18 (95% CI: −0.41, 6.37) Gy−1) and high-LET
radiation (ERR per unit dose of 16.65 (95% CI: −1.13, 46.75) Gy−1). A significant positive dose–risk
relationship was found for CML (n=31) with low-LET radiation (ERR per unit dose of 7.20 (95% CI:
0.48, 24.54) Gy−1) and for all myeloid leukaemia (n=99) with high-LET radiation (ERR per unit doseof 26.02 (95% CI: 2.55, 68.99) Gy−1). No association with death from CLL (n=70) with either type ofradiation was observed [K25].
129. A nested case–control study of incidence was conducted on a cohort of 110,645 Ukrainian
clean-up workers of the 1986 Chernobyl nuclear power plant accident [Z2]. Absorbed doses to the
RBM were estimated individually by the Realistic Analytical Dose Reconstruction with Uncertainty
Estimation (RADRUE) method. A significant linear dose–response was observed for all types ofleukaemia (137 cases) with a linear non-threshold (LNT) model resulting in an ERR per unit dose of
1.26 (95% CI: 0.03, 3.58) Gy−1. Non-significant positive dose–responses were noted for both CLL andnon-CLL (ERR per unit dose of 0.76 (95% CI: <−0.38, 3.84) Gy−1 and 1.87 (95% CI: −0.02,
6.54) Gy−1, based on 79 and 58 cases, respectively). The authors concluded that the estimated dose–risk
relationship was statistically consistent with the estimates for the Japanese atomic bombing survivors.
However, the exposure estimates were based on proxy interviews, which imply a high uncertainty andpotential for bias [U7].

3.

Other studies

130. Leukaemia incidence has been analysed for the Techa River cohort, including persons residing inriverside villages between 1950 and 1961, when the releases from the plutonium-production complex of
the Mayak Production Association contaminated the river. The cohort included 28,233 persons. Doses
to the RBM due to internal and external exposure were estimated individually using the Techa River
Dosimetry System (TRDS)-2009. Over the period 1953–2007, 72 leukaemia cases (excluding CLL)
were observed. The estimated ERR per unit dose was 1.2 (95% CI: 0.4, 2.5) Gy−1 for all types ofleukaemia and 2.2 (95% CI: 0.8, 5.4) Gy−1 for non-CLL leukaemia. The data were consistent with alinear dose–response relationship with no evidence of modification. There was no evidence of a dose–
response for CLL [K18].

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 57

4.

Synthesis of studies

131. In recent years, new results were published from the studies of adults exposed to externalradiation due to their occupation (nuclear workers, miners and Chernobyl liquidators) or theirenvironment (residents of the Techa River). The results from these studies complemented thoseobtained from the cohort of atomic bombing survivors.
132. These results are mostly consistent with a dose–risk relationship for leukaemia risk after
exposures during adulthood, with a lower risk coefficient than that estimated in studies considering
exposure during childhood. This is coherent with previous knowledge [U6]. Furthermore, these studies
improve the knowledge about the leukaemia risk associated with low-dose-rate exposure. Recent
studies did not suggest the existence of a threshold dose for the risk of radiation-induced leukaemia.
133. One main limitation is that most of these studies (except the UK occupationally-exposed worker
study, the Mayak worker study, the Ukrainian clean-up workers, the Eldorado study and the Techa
River cohort) consider mortality rather than incidence data. This is mainly due to the retrospective
nature of these studies, which include early periods during which no leukaemia incidence data wereavailable. The development of cancer registries in some countries or regions should allow thedevelopment of more incidence studies in the future.

D. Definition of scenario
134. A scenario of repeated exposure over the occupational lifetime of workers in the nuclear industry
was chosen, for which pertinent results have been published in recent years. As most risk models for
adult exposures only cover mortality, assessment of the risk of leukaemia mortality was considered.
This contrasts with the childhood analyses, which concentrated on incidence (see section II). Risk
assessment considered both the risk of mortality from leukaemia and leukaemia excluding CLL.

1.

Exposure scenario

135. The scenario was designed to reflect the characteristics of the INWORKS cohort [L7]. The
INWORKS was a mortality study. This cohort consisted of 85% males. Only those who weremonitored for external exposure for one year or more were included. The mean age was 29 at firstmonitoring and 43 at last monitoring (i.e. a mean duration of radiation monitoring of 14 years). The
mean duration of follow-up was 27 years and the mean age at the end of follow-up was 58. The mean
cumulative dose to the RBM was 16 mGy, accrued at very low dose rates (mean 1 mGy each year).
136. The defined scenario has the following characteristics:(a) United States population;(b) Population composed of males only;(c) Prolonged exposure from ages 30 to 45 (duration of 15 years);(d) Constant dose rate to the RBM of 13.3 mGy each year, leading to a cumulative dose of 200 mGy;(e) Alive at the first monitoring (i.e. at age 30);(f) Follow-up ranging from ages 30 to 60 (approximately the mean attained age at the end of thefollow-up) or 90 (approximately the maximum attained age at the end of follow-up).

58 UNSCEAR 2019 REPORT

2.

Reference data

137. For the calculation of the survival function and of the baseline risk of leukaemia, the following
sources of data were used:(a) For the calculation of the survival function, age- and sex-specific mortality rates were based ondata from the United States in 2000 [A4];(b) For the estimation of baseline mortality risk of leukaemia and leukaemia excluding CLL,
age- and sex-specific rates per 100,000 were based on data on leukaemia mortality derived for
the United States from the Surveillance, Epidemiology, and End Results (SEER) program of
the National Cancer Institute (NCI) for the period 2000–2005 [N6].
138. Leukaemia baseline mortality rates increase sharply with age. Figure III presents the evolution ofleukaemia mortality baseline rates in the United States over the age range considered in the scenario.
Figure III. Baseline rates of leukaemia (all leukaemia including CLL) mortality among males in the
United States in 2000–2005 per category of attained age [N6]
CLL: Chronic lymphocytic leukaemia, PY: Person–years

3.

Risk models

139. Assessment of radiation-associated risk was performed using the models for all leukaemia
mortality derived from the cohort of Japanese atomic bombing survivors by Kaiser and Walsh [K3].
The cohort included 86,611 persons, with 318 deaths from leukaemia observed between 1950 and 2003
[O5]. Both the ERR and EAR models were considered. The ERR model (denoted thereafter as the LSS
ERR model) considered a linear–quadratic function with the dose to the RBM and a modifying effect ofattained age [K3]:
ERR = (β1 d + β2 d2) ⋅ exp(α⋅ln(age/55))

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 59

where d is the dose to the RBM, and β1=1.38 Gy−1; β2=1.33 Gy−2; α=−1.63. In contrast to the ERR
model of Ozasa et al. [O5], the ERR model of Kaiser and Walsh [K3] provided a significant age
dependence which ensures a decreasing risk contribution to the CER with increasing attained age.
Without this age dependence the CER estimate would exceed plausible limits at old age.
140. The EAR model (denoted thereafter as the LSS EAR model) was linear–quadratic in dose with
log–linear effect modification depending on time since exposure (TSE), with different dose coefficientsfor males and females:
EAR = (β1 d + β2 d2) ⋅ exp(γ⋅ln(TSE/40)±αs )where d is the dose to the RBM, and β1=1.75 (Gy 10,000 PY)−1; β2=1.53 (Gy2 10,000 PY)−1; γ=−0.50
and αs=0.54 using the positive, negative sign for males and females, respectively. Since no EAR model
has been reported by Ozasa et al. [O5], the above EAR model has been developed by refitting the LSS
cohort data used in Kaiser and Walsh for the calculation presented in this annex; for the fit, thefunctional form of the baseline model like that one for the ERR model of Kaiser and Walsh has been
applied [K3]. The dependence of time since exposure was significant (p<10−2), but measured by the
AIC goodness-of-fit was about 10 points lower compared to the ERR model.
141. Risk was also assessed using ERR models derived from the INWORKS cohort, for all leukaemia
and leukaemia excluding CLL [L7]. The risk coefficient for all leukaemias obtained from a sensitivity
analysis on a restricted group with a dose range 0–300 mGy was also considered. The models
considered a linear association with dose to the RBM, with no modifying factor. Model parameters areindicated below:
ERR = β ⋅ dwhere d is the cumulative dose to the RBM. For all leukaemia, β was equal to 1.95 (90% CI: 0.50,
3.73) Gy−1 over the whole dose range and 2.52 (90% CI: 0.70, 4.73) Gy−1 over the restricted range
0–300 mGy. For leukaemia excluding CLL over the whole dose range, β was equal to 2.96 (90% CI: 1.17,
5.21) Gy−1.
142. For the purpose of this annex, an EAR model was also fitted from the full INWORKS cohort forleukaemia excluding CLL, using a baseline parametric model that was adjusted for country, sex, birthyear, birth year squared, log age and log age squared [R4]. The model considered a simple linear
association with dose to the RBM but did not allow assessment of the potential impact of modifying
factors. Model parameters are indicated below:
EAR = β ⋅ dwhere d is the 5-year lagged cumulative dose to the RBM, and β=2.25 (90% CI: 0.69, 4.1)
per 10,000 person–years Gy−1 over the whole dose range.
143. The variation of the risk coefficients with attained age, for a dose of 200 mGy received at the ageof 30 years is presented in figure IV. Based on the risk derived from the LSS, the ERR decreases
sharply with attained age, whereas no variation with attained age is considered in the ERR model
derived from the INWORKS.

60 UNSCEAR 2019 REPORT

Figure IV. Excess relative risk at 200 mGy as a function of attained age, for age at exposure of
30 years obtained from the LSS model [K3] and the INWORKS model [L7]4.

Risk-transfer methods

144. No correction was applied to extrapolate from high to low dose. Indeed, a reduction of thestrength of the dose–risk relationship at low dose is implicitly considered in the linear–quadratic shapeof the models derived from data on the atomic bombing survivors.
145. Because the follow-up of the cohort of atomic bombing survivors began only in 1950, no mortality
data are available for the period between 1945 and 1949. This lack of data led to a large uncertainty in theestimated risk for the years just following exposure, when models derived from the LSS were used. To
avoid this problem, risk coefficients were capped for the first five years after the bombings in the LSS
models, i.e. for each year between exposure and 5 years later the ERR value was the one estimated.
146. To transfer the data from the atomic bombing survivors to the scenario, both multiplicative (basedon an ERR model) and additive (based on an EAR model) risk transfers were used. For the applicationof all models (derived from the cohort of atomic bombing survivors or from the INWORKS cohort) to
the scenario, a minimum lag time was considered between exposure and risk. This lag time wasmodelled as a sigmoid function, varying between 0 and 2 years and centred on 1.5 year.

E. Results147. The results of the estimated cumulative leukaemia risk associated with the worker scenario for a followup to age 60 and 90 are presented in table 6. The survival fraction was 88% at age 60 and 15% at 90. The CBR
for leukaemia was 10 per 10,000 persons from ages 30 to 60 years (9.1 for leukaemia other than CLL), and 84
per 10,000 persons from ages 30 to 90 (65 for leukaemia other than CLL). The estimated CFR varied between
18 and 58%, using the ERR model, and up to 135% with the LSS EAR model and follow-up to age 60 years.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 61

148. For the INWORKS cohort, the CER obtained using the ERR model based on the restricted dose
range below 300 mGy is slightly higher than that obtained using the ERR model based on the full dose
range (a difference of about 30%). Application of the ERR model to leukaemia or leukaemia other than
CLL leukaemia makes little difference in the estimation of CER; slightly higher values are obtained in
the case of leukaemia other than CLL (a difference of about 30%).
149. For the follow-up from ages 30 to 60, the CER for leukaemia due to the cumulative dose to theRBM estimated from the EAR models was systematically higher than that estimated from the ERR
models for both the LSS and INWORKS cohorts, but were associated with a much wider confidence
interval. This difference was no longer observed for follow-up from ages 30 to 90.
150. For the follow-up to age 60, the estimated CERs for all leukaemias derived from the ERR modelfor the LSS and INWORKS cohorts were close and had similar confidence intervals (2.8 (95% CI:−0.1, 5.6) Gy−1 and 3.5 (95% CI: 0.8, 8.0) Gy−1, respectively). But when a follow-up to age 90 was
considered, the CER estimated from the ERR model for the LSS cohort was about half that estimatedfor the INWORKS cohort (15 (95% CI: 0.1, 31) Gy−1 and 32 (95% CI: 6.9, 73) Gy−1, respectively) andthe confidence interval for the INWORKS cohort was wide. This wide confidence interval may reflect
an overestimation of the risk to the INWORKS cohort, due to the absence of correction for themodifying effect of attained age.

Table 6. Cumulative risk of leukaemia mortality for a scenario of a population of workers receiving adose of 200 mGy to the RBM over their occupational life
CBR: Cumulative baseline risk; CER: Cumulative excess risk, estimated using the REIC methodology; CFR:
Cumulative fractional ratio; CLL: Chronic lymphocytic leukaemia; LSS: Life Span Study
Cumulative leukaemia risk
associated with exposure scenario b

CBR a
Model

All-causemortality per
10 000 persons

Leukaemia
incidence per
10 000 persons

CER per
10 000 persons(95% CI)

CFR(CER/CBR)PROTRACTED EXPOSURE FROM AGES 30 TO 45, FOLLOW-UP TO AGE 60 c d
LSS mortality models [K3]
ERR transfer—whole dose range—
all leukaemia
EAR transfer—whole dose range—
all leukaemia

1 294

2.8 (−0.1, 5.6)

0.27

14 (−3.0, 32)

1.35

3.5 (0.8, 8.0)

0.34

4.5 (1.1, 10)

0.43

4.6 (1.5, 9.6)

0.50

8.8 (2.3, 19)

0.97

10

INWORKS mortality model [L7]
ERR transfer—whole dose range—
all leukaemia
ERR transfer—restricted dose range
0–300 mGy—all leukaemia

1 294

10

ERR transfer—whole dose range—
leukaemia excluding CLL
EAR transfer—whole dose range—
leukaemia excluding CLL

1 294

9.1

62 UNSCEAR 2019 REPORT

Cumulative leukaemia risk
associated with exposure scenario b

CBR a
Model

All-causemortality per
10 000 persons

Leukaemia
incidence per
10 000 persons

CER per
10 000 persons(95% CI)

CFR(CER/CBR)PROTRACTED EXPOSURE FROM AGES 30 TO 45, FOLLOW-UP TO AGE 90 c d
LSS mortality models [K3]
ERR transfer—whole dose range—
all leukaemia

8 716

EAR transfer—whole dose range—
all leukaemia

15 (0.1, 31)

0.18

20 (−4.2, 43)

0.24

32 (6.9, 73)

0.38

42 (9.7, 93)

0.49

37 (12, 78)

0.58

16 (4.2, 35)

0.25

84

INWORKS mortality model [L7]
ERR transfer—whole dose range—
all leukaemia
ERR transfer—restricted dose range
0–300 mGy—all leukaemia
ERR transfer—whole dose range—
leukaemia excluding CLL
EAR transfer—whole dose range—
leukaemia excluding CLL

8 716

8 716

84

65

a

United States male population—without exposure—alive at exposure age.

b

Cumulative dose to RBM of 200 mGy due to external exposure at 13.3 mGy each year from ages 30 to 45.

cWorkers exposed to 13.3 mGy during 15 years from ages 30 to 45.

dUp to age 60 (90) means up to the 60th (90th) birthday.

F. Discussion of scenario calculations
1.

Sources of uncertainties(a) Selected populations
151. The scenario was designed to reflect the characteristics of the INWORKS cohort [L7]. The
INWORKS was a mortality study, and therefore the whole scenario was based on mortality: use of
mortality reference data for baseline rates and use of a risk model based on mortality from the LSS. As
85% of the INWORKS workers were males, the scenario was focused on males only. The pattern of
exposure (200 mGy cumulative over 15 years of activity) was considered to be realistic. The durationof exposure corresponds to the mean duration observed in the INWORKS cohort. The level of exposure
is clearly higher than the mean cumulative dose (16 mGy), however was chosen to be closer to therange where significant results had been observed. This level of dose is realistic, as some workers fromthe cohort received doses of more than 200 mGy.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 63

152. The LSS cohort included a large portion of the atomic bombing survivors who were within
2.5 km of the hypocentres at the time of the bombings, and an age- and sex-matched sample of people
who were between 2.5 and 10 km from the hypocentres. The people were recruited on the basis of the1950 Japanese National Census and of additional information collected by the Atomic Bomb Casualty
Commission. Comprehensive mortality follow-up began in October 1950. Unavoidably, leukaemia
cases occurring before 1950, the number of which could have been appreciable, were lost to the study[F1]. The number of persons included was 120,321 (82,214 from Hiroshima and 38,107 from
Nagasaki). Among them, 123 persons were unavailable for the study because of misidentification or
insufficient information, and another 7,058 survivors did not have dose estimates, mainly due toinsufficient or uncertain information on their location and shielding at the time of the bombings. About
27,000 were not in the city at the time of the bombings and, thus, were not exposed. The dose–risk
analyses were therefore based on a total of 86,611 persons [O5].
153. The INWORKS cohort consisted of the pooling of three pre-existing cohorts of occupationallyexposed workers in France, the United Kingdom and the United States. The selection criteria were thatthe workers should have been employed in the nuclear industry for at least one year and to have beenmonitored for external radiation exposure. The selection criteria were independent of the studied healthoutcome, namely the risk of leukaemia death. Workers entered the study either one year after the dateof first employment or on the date of first dosimetric monitoring, whichever was later. In France,
follow-up began only on 1 January 1968, as no information on individual causes of death is available
from the National Death Registry before. In France, workers were given the opportunity to refuse
participation, which is required by the French Data Protection Authority; however, none did. In the
United States, worker information was taken from existing records, with no direct contact with any
participants; because there is minimal risk to participants, the Institutional Review Board of the
National Institute for Occupational Safety and Health waived the requirements for informed consent. In
the United Kingdom, less than 1% of the workers refused to participate in the NRRW study.
154. It should be noted that the risk model derived from the INWORKS is based on a population with arange of age at exposure and attained age much narrower than the LSS. It is not sure that a risk model
derived from an LSS subset limited to adults at the time of the bombings would be able to quantify themodifying effect of age at exposure and attained age.

(b) Exposure assessment
155. The exposure parameters and doses considered in the present scenario are fixed and considered
without uncertainty. Nevertheless, uncertainties and measurement errors exist in the cohorts used toderive the scenario and the risk models. In the LSS, the main points to be considered while discussing
uncertainty in the exposure assessment are detailed in section II.F.1(b).
156. The reconstruction of cumulative doses due to occupational exposure is associated withuncertainties, detailed in [T6]. The absorbed dose to the RBM was calculated for each worker based on
the results of their individual monitoring for external exposure. The main sources of uncertainties were
due to incomplete dosimetric records, limit of detection of dosimeters, and non-consideration ofneutron and internal exposure. In the INWORKS, it was not possible to estimate doses due to neutron
or internal contamination, but it was possible to flag workers with such potential exposures. Sensitivity
analyses based on these flags proved not to change the main conclusions of the INWORKS [L7, S5].
Nevertheless, one cannot exclude the possibility that the highest doses were received by earlier workers
whose dose records could be most suspect and be most susceptible to missing doses. A thoroughinvestigation of the impact of neutron doses and doses from internal emitters, as well as the possibilityof unrecorded occupational external radiation doses has yet to be performed.

64 UNSCEAR 2019 REPORT(c) Health outcome assessment
157. In the scenarios, age- and sex-specific rates per 100,000 population for leukaemia mortality for
the United States were used. This choice was made to ensure coherence between the reference data andrisk models from the LSS and the INWORKS. Either rates for all leukaemias or for leukaemia
excluding CLL were used to coincide with the model used.
158. The total number of leukaemia deaths in the LSS cohort was 318. The sex-averaged ERR for all
leukaemia was 3.1 (95% CI: 1.8, 4.3) Gy−1 [O5]. Since only seven deaths were attributed to CLL (2%)
in the LSS, no separate dose–response analysis was performed for CLL [R3]. No model was derived forleukaemia mortality excluding CLL. Nevertheless, due to the small percentage of deaths, exclusion of
CLLs should have little impact on the estimated ERR.
159. The total number of leukaemia deaths in the INWORKS cohort was 669, including 138 CLL
deaths (21%). The estimated linear ERR for leukaemia mortality per unit absorbed dose was 1.9 (90%
CI: 0.5, 3.7) Gy−1 and 3.0 (90% CI: 1.1, 5.2) Gy−1 after excluding CLL [L7, L8]. The ERR per unit dose
for CLL was −1.1 (90% CI: not estimable, 1.8) Gy−1 [L7].

(d) Study design160. In the LSS, information on vital status and cause of death has been collected continually since thecohort’s inception in 1 October 1950 until 31 December 2000. Mortality follow-up was facilitated by
the family registry system (koseki), which covers the whole of Japan and is 99% complete.
Classification of decedents was by the underlying cause of death coded according to the International
Classification of Diseases. Only a very small number were lost to follow-up due to migration out of thecountry [O5, R3].
161. The INWORKS gathered data from three cohorts. Follow-up ranged from 1943 to 2005. In the
United Kingdom, follow-up commenced in 1955 with updates of mortality information obtained on an
ongoing basis from central registries for England, Wales and Scotland, as well as at intervals fromregional offices for workers resident in the Channel Islands, the Isle of Man or Northern Ireland. In the
United States, follow-up commenced with the start of operations at each facility (1944 for the earliest
nuclear facility in the United States), and cause of death was obtained from the National Death Index(from 1979 onwards, and from US State and multiple other sources before that year) [H1]. The cause ofdeath was confirmed through periodic searches of social security administration records conducted by
the US National Institute for Occupational Safety and Health. In France, follow-up commenced in 1968
because the French National Death Registry has only recorded information on individual causes ofdeath since 1968; follow-up is updated biannually with cause of death obtained from the French
National Institute for Medical Research. The number of deaths in the INWORKS was 66,632.
Underlying causes of death were coded according to the International Classification of Diseases.
Because information was obtained from employers and national registries, loss to follow-up is minimal:
only 0.22, 2.56 and 0.83% of employees were lost to follow-up or emigrated from the French, United
Kingdom and United States cohorts, respectively [H1, L6].

(e) Confounding factors
162. Risk factors for and causes of leukaemia are largely unknown. Therefore, there are few known
confounders of the association between leukaemia and radiation. Age and sex are controlled in theanalyses. Other known risk factors include genetic diseases, exposure to benzene and some

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 65

pesticides, and history of treatment for cancer. Smoking is associated with myeloid leukaemia;
however, the size of this association is relatively small and therefore would require large differencesin smoking frequency across levels of cumulative dose to cause substantial confounding of theradiation–leukaemia association.
163. In the LSS, differences in the baseline mortality rates for leukaemia were reported between
proximal and distal survivors, particularly in the first decade after the bombings [R3]. Adjustment for
proximal or distal location at the time of the bombings affected the estimates of the association between
radiation dose and leukaemia mortality. Differences in baseline mortality rates by location at the time of
the bombings may also reflect the selective survival among proximal survivors. Adjustment for baseline
differences in mortality rates between proximal and distal survivors minimizes the problems ofconfounding by proximal or distal location, but does not address concerns that the survivors of theatomic bombings may be a selected group of people who are relatively less susceptible to radiationinduced leukaemia than the general population [R3].
164. In the INWORKS, adjusting the risk analyses by socio-economic status was considered to
substantially reduce confounding by smoking. Adjustment for socio-economic status, however, resulted
in little change in the risk estimate for leukaemia excluding CLL. Exposure of nuclear workers to othercauses of leukaemia such as benzene cannot be excluded as a potential source of bias [L7]. A previous
analysis of the United States nuclear workers reported weak evidence of confounding by benzene
exposure in an analysis of the leukaemia risk associated with external radiation exposure [S5]. Benzene
exposure could not be assessed for the INWORKS cohort. Nevertheless, results from sensitivity
analyses and the use of indirect arguments suggest that confounding did not have a large impact on
estimates derived from INWORKS [S5].

(f) Statistical methods and model uncertainties
165. Many uncertainties can be linked to the risk models, including the shape of the dose–risk
relationship, the quantification of modifying effects (age, time since exposure), the latency period
between exposure and risk, and the type of risk transfer (EAR or ERR based transfer).
166. In the scenarios used, the statistical uncertainty associated with the risk coefficients wasconsidered through confidence intervals, and, where available, for both the EAR and ERR models.
167. The mortality risk models derived from the LSS are based on a large population (more than
86,000 persons) and long duration of follow-up (from 1950 to 2003). There were 318 registered deaths
from leukaemia. Uncertainty in the risk estimates at low doses was thought to originate from various
sources, including different estimates of risk at background levels, uncertainty in the dose estimates,
residual confounding and interaction, other risk factors, and exposure to residual radiation and/or
medical radiation [O6]. In addition, due to the late beginning of follow-up (1950, which was five years
after the bombings), a greater uncertainty is associated with the risk estimated in the first years after
exposure. This lack of information was managed by applying a 5-year cap to the risks estimated in thefive years after the bombings. Both the ERR and EAR models were considered. The models considereda linear–quadratic function with dose to the RBM and a modifying effect of attained age. There is noevidence for modifying effects of sex, age at exposure, or time since exposure [K3]. Neglecting suchpossible modifying effects may introduce a small uncertainty.
168. In the INWORKS cohort, additional analyses considered possible modification of the ERR bytime since exposure and age at exposure. Compared to the risk coefficient estimated using a fixed lag of
two years, the estimated ERR for non-CLL leukaemia increased nearly twofold using a fitted lag of

66 UNSCEAR 2019 REPORT

19 years (ERR per unit dose of 4.68 (90% CI: 1.26, 9.37) Gy−1). The analysis of a combined temporal
effect on the risk of non-CLL leukaemia due to radiation exposure showed that the highest significantly
positive ERR estimates were observed for age at exposure of 35 years or older and time since exposureof 20 or more years. Significant risk heterogeneity was observed for CML with time since exposure,with increased ERR estimates shortly after exposure (2–10 years) and again later (20–30 years).
Delayed effects were observed for AML although the estimates were not statistically significant.
Additional sensitivity analyses considered different lag times. Using a 10-year lag time, the estimatedlinear ERR for leukaemia mortality per unit absorbed dose was 2.5 (90% CI: 0.8, 4.5) Gy−1 (instead of
1.9 (90% CI: 0.5, 3.7) Gy−1 in the main analysis using a 2-year lag) [D1].

(g) Other sources of uncertainty
169. The estimation of baseline risk relied on the baseline mortality risk for leukaemia, age- and sexspecific rates per 100,000 population for the United States for the period 2000–2005. Baseline mortality
rates could change, if, for example, an effective treatment could be developed for some types ofleukaemia. Nevertheless, for the purpose of the present risk assessment, it was assumed that uncertainty
associated with baseline rates in the United States would be small. The survival function was also
considered known and not affected by uncertainties.
170. One major assumption was that the selected reference rates were considered stable over the whole
period of the risk assessment, which is here up to a period of 60 years (scenario of exposure at age 30
with a follow-up to age 90). Evolution of leukaemia mortality rates in the future is very difficult topredict. The assumption of stability is even more critical based on mortality than on incidence in viewof the evolution of treatment. Uncertainty exists in the level of reference rates, but also a potential shiftof rates with attained age cannot be excluded. This uncertainty applies to the estimation of both the
CBR and CER.
171. Another potential source of bias is related to the healthy worker effect. This effect, however, ismore applicable to solid cancer than to leukaemia risk and is discussed in detail in section IV.F.1(g).

2.

Preferred risk inference(a) Selection of the preferred risk inference
172. The preferred risk inference for the selected scenario of repeated exposure over the occupational
life of a United States male worker in the nuclear industry is the one derived from the INWORKS forleukaemia excluding CLL [L7]. For exposure from ages 30 to 45 and follow-up to 60 years of age, theestimated CER is 4.6 per 10,000 persons (95% CI: 1.5, 9.6) for a dose to the RBM of 200 mGy.
173. Given that the selected scenario refers to the exposure of a modern population of United States
male workers exposed over a prolonged period, the CERs based on the INWORKS are thought to be
more representative than the CERs obtained using models from the LSS. Indeed, this estimate isderived from a study which has been used as the basis to determine the characteristics of the scenario.
There is no need to transfer the exposure from another population, or extrapolate from high doses, ortranspose from a different dose-rate pattern. Therefore, this estimate is preferred to the one derived
from the LSS study for which assumptions for these three aspects are needed.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 67

174. As CLL does not seem to be associated with radiation exposure, the ERR for leukaemia excluding
CLL was the preferred one derived from the INWORKS [L7]. The confidence interval associated with
the CER is narrow, and of similar width to the one associated with the CER estimated for all
leukaemias, for both the full dose range and the restricted dose range below 300 mGy.
175. Estimation of risk up to 60 years of age is preferred to 90 years of age, as 60 years corresponds
approximately to the mean age at the end of follow-up in the INWORKS. As no age-dependency could
be derived for the ERR and EAR obtained from the INWORKS, the CERs cumulated up to age 90
probably lead to an overestimation of the risk, as they do not reflect a potential reduction of the strengthof the association with attained age (as illustrated by the results in table 6).
176. Up to now, only ERR models have been published from the INWORKS [L7]. Simple EAR
coefficients derived from the INWORKS were provided especially for this annex to allow comparison.
The estimated CER up to age 60 for leukaemia excluding CLL for the INWORKS cohort was higher
with the EAR model than that with the ERR model, and associated with a much wider confidence
interval. It is noted, however, that the quality of fit of the INWORKS EAR model was lower than thatof the ERR model. If multi-model inference was to be implemented, the results would be very close to
the risk estimates based on the ERR model with negligible influence of the EAR model. This is a resultof the negligible weight for the EAR model in multi-model inference.

(b) Discussion of the impact of sources of uncertainty
177. The main sources of uncertainties associated with this risk estimate are summarized in table 7.
The subsequent paragraphs give the reasons for the grading of the uncertainties (very small, small,moderate or large).
178. Selected populations: The INWORKS includes a contemporary population of mostly males (87%)
from several countries (France, United Kingdom and United States) with similar lifestyles to those in
the selected scenario (United States male workers). The potential impact of population selection istherefore considered to be very small.
179. Exposure assessment: The primary source of exposure of the members of the INWORKS cohort
was external low-LET radiation received at the workplace. However, some INWORKS workers had
additional exposures to neutrons at the workplace, internal emitters (caesium, tritium, plutonium),environmental sources (radon and decay products and terrestrial gamma radiation) and job-related
medical exposures (i.e. chest X-rays). Leuraud et al. showed that adjustment for neutron led to adecrease in the ERR estimated for leukaemia excluding CLL of about 40% [L7]. The other sources of
dosimetric uncertainties (e.g. missing dose records) were not assessed but are also expected to be small.
A reanalysis of Canadian nuclear energy workers allowed considering separate terms for tritium andgamma doses. The mean cumulative person-time weighted lung dose from tritium was about 3 mSv.
Risks were due solely to gamma doses and the addition of tritium doses did not improve the fit of themodel [Z5].
180. Health outcome assessment: The impact of sources of uncertainty related to outcome assessment is
considered to be small or very small because of the good quality of ascertainment of cancer cases. Choosing
all leukaemias instead of only leukaemia excluding CLL makes a difference of 24% in the estimated CER.
In the future, if possible, from available data, analysis by leukaemia subtypes is recommended.

68 UNSCEAR 2019 REPORT181. Study design: The completeness of follow-up was very good in the three countries involved in
the INWORKS. Certification of death was of good quality, and considered to be independent of dose.
The potential impact of study design on the estimated risk is therefore small to very small.
182. Confounding factors: Adjustment for socio-economic status resulted in a decrease of only 2% in
the estimated ERR for leukaemia excluding CLL in the INWORKS [L7]. Furthermore, no evidence ofconfounding by benzene exposure was suggested in a previous analysis of US nuclear workers [S5].
Thus, the potential impact of confounding is considered to be small to very small.
Table 7. Characterization of the main sources of uncertainty associated with the preferred risk
inference of leukaemia following prolonged exposure over the occupational life of a US male worker
in the nuclear industry
SourceCharacterization of source

Selected populations

Scenario based on the INWORKS

Exposure assessment

Exposures to radiation not specificallyaccounted for in the INWORKS

Health outcome assessment

Study design

Confounding factors

Statistical methods andmodel uncertainty

Other sources of uncertainty

Judged impact a
Very small

Neutrons

Small

Internal emitters

Small

Missed doses

Small

Job-required chest X-rays

Small

Uncertainty in external doses

Small

Quality of causes of death

Very small

Inclusion of CLL

SmallCompeting causes of death

Small

Loss to follow-up

Very small

Socio-economic status

Very small

Exposure to other leukaemogens at the work
place (benzene, hydrazine …)

Small

Assumed latency period

Small

Curvature of the dose–risk relationship

Small (at such level of doses)

Transfer (ERR versus EAR)

Small b

Absence of assessment of the modifying
effect of age and time

Small (as the scenario is like thestudy characteristics)

Hypothesis of stability of the baseline risk up
to the end of follow-up

Small

a

The impact of the different sources of uncertainty is classified into four categories according to the variation they are expected
to induce on the reported CER: very small—less than a factor of 1.1; small—between a factor of 1.1 to 1.5; moderate—between afactor of 1.5 to 2; and large—greater than a factor of 2.

b

The judged impact is “small” in all situations, except when using the INWORKS EAR model, which presents a limited qualityof fit compared to the INWORKS ERR model.

183. Statistical methods and model uncertainties: A sensitivity analysis of the impact of the lag period(10-year instead of 2-year lag) indicated an increase of 21% in the ERR per unit dose estimate forleukaemia excluding CLL [L7]. Furthermore, even though additional analyses of the INWORKS cohort
observed risk heterogeneity with age or time since exposure for CML or AML, they did not identify age attime of exposure, time since exposure or attained age dependencies for leukaemia excluding CLL [D1].

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 69

The risk models based on the INWORKS can be used to predict the risk for the population of interest in
this scenario for a follow-up period ending at age 60, as this range of ages represents the centre of the data
for the INWORKS cohort. The ERR per unit dose estimated for leukaemia in the INWORKS over thewhole dose range is very similar to the one obtained for the restricted dose range 0–300 mGy.
A difference of about a factor of 1.9 is observed between the CERs estimated using an ERR- and an EARbased model. The impact of the choice of model transfer therefore seems to be a source of uncertainty ofmoderate magnitude. However, due to the limited quality of fit of the INWORKS EAR model, its impact
on risk estimates based on multi-model inference would be negligible.
184. Other sources of uncertainty: In the present scenario, the estimated risk is valid under theassumption that the baseline rates apply without change to the whole period of the scenario. Thishypothesis of stability of baseline rates for a period of 30 years (from age 30 at first exposure toattained age 60) is indeed a strong hypothesis. Nevertheless, the evolution of leukaemia mortality rates
in the United States has shown a decrease of about 25% over the last two decades. It is therefore
unlikely that variation of baseline rates could have more than a small impact on the estimated risks.
Other sources of uncertainty not taken into account explicitly are expected to have only a minor impact
on the preferred estimates and credible intervals.

(c) Concluding remarks on the preferred risk inference
185. The selected risk estimate was applied to a scenario similar to the population from which it wasderived. The characteristic limits any uncertainties associated with transfer to a different population or
exposure situation.
186. Different sources of uncertainty apply to such cumulative analysis of the risk estimates. None of
the sources of uncertainty not considered in the calculations is expected to lead to a large or evenmoderate impact on the estimated radiation risk. Those considered include selection of the study
population, exposure assessment, health outcome classification, latency period, potential confounders,transfer of effect-per-unit-dose estimates to risk of the population in the scenario, and the evolution of
baseline rates.
187. Based on the analysis of the different sources of uncertainty presented in table 7, and thestatistical uncertainty estimated in table 6, the Committee judged the credible interval for the“preferred” risk estimate of the CER. Leukaemia mortality up to age 60 after occupational exposure
with a total whole-body dose of 200 mGy is estimated to be about 5 cases among 10,000 persons with a
95% credible interval from about 1 to about 10 cases (table 24).

G. Conclusions
188. For exposure to a dose to the RBM of 200 mGy from 30 to 45 years of age with a follow-up to
60 years of age, the INWORKS provides a much more pertinent source of information to assess
leukaemia mortality risk among male workers than the LSS. For the present scenario, the use of the
ERR per unit dose derived from the INWORKS cohort requires no extrapolation or transfer, whereasusing risk models derived from the atomic bombing survivors relies on uncertain assumptions (related,for example, to a different situation of exposure, higher doses and different dose rates). Based on the
INWORKS, a CER of about 5 per 10,000 persons with a credible interval from about 1 to 10 per
10,000 persons was derived.

70 UNSCEAR 2019 REPORT189. Calculation of cumulative leukaemia excess risk associated with radiation using risk models,
derived either from LSS or INWORKS ERR risk models, appears to be coherent, when scenarios focus
precisely on the characteristics of the INWORKS population (repeated low doses over several years).
This supports the validity of using risk models derived from the LSS to transfer risks to other exposed
populations. In the future, consideration of leukaemia subtypes in such comparisons of the ability ofdifferent risk models to predict risk for a similar scenario would be worthwhile.
190. The agreement in the estimated CER between the LSS ERR model and the INWORKS ERR
model with follow-up to age 60 was good. However, for follow-up to age 90, the agreement was lessgood. This may be due to the inability of the INWORKS model to estimate risk over a long lifespan.
Indeed, the mean age at the end of follow-up in the INWORKS is 58, and prediction* up to age 90 liesat the edge of the covered age span. In addition, the considered model did not include any modifying
effect of age.
191. In this analysis, it was considered that the preferred estimate was for the follow-up period up to
age 60, because the scenario was elaborated on the characteristics of the INWORKS population.
Nevertheless, it is likely that the excess risk for cancer before age 60 from occupational exposures
accounts for a relatively small portion (presumably, less than 25%) of the CER for cancer at age 90.
192. Currently, risk models derived from the INWORKS are not so reliable when a longer duration offollow-up is considered. For such a prediction, a risk model derived from the LSS and considering themodifying effect of age may be preferred. The limitations of the INWORKS should be reduced in the
future, because of an extended follow-up, which should allow a better assessment of the modifying
effect of age on the dose–risk relationship. Furthermore, comparison between cohorts will provide a
better knowledge about the coherence of the ERR and EAR risks estimated between countries.

IV. SOLID CANCER MORTALITY AFTER ACUTE AND
PROTRACTED EXPOSURE
A. Motivation
193. Studies of radiogenic solid cancer are essential to understand the societal impact on health fromexposure to ionizing radiation, and to develop risk assessment tools that can be used in retrospective orprospective analyses of radiation-exposure situations. After a uniform whole-body exposure to lowLET radiation, the excess lifetime risk of cancer is dominated by the excess lifetime risk of solidcancers (more than 90% in males and 95% in females, of the excess risk of all cancers combined [N9,U3]). Studies providing dose–response relationships for individual solid cancer types are, in particular,
useful for estimating the risk from non-uniform exposure (e.g. X-ray exposures of particular organs or
tissues, and radionuclides that accumulate in certain organs [U2, U3]). Studies of solid cancers as agroup (e.g. ICD-9 codes 140–199; ICD-10 codes C00–C80) are useful when all organs are exposed todoses of similar magnitude, which is often the case for workers or members of the public exposed
externally to penetrating gamma radiation. Dose–response relationships for solid cancers as a group arebased on a larger number of cases than for individual cancers, and they normally have lower
uncertainties. However, they represent a mixture of dose responses that could be biologically different
from cancer to cancer and could need different mathematical approaches.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 71

194. An important part of the mandate of the Committee is to analyse the risk of adverse health effects
for workers exposed to radiation during typical day-to-day activities. This section describes anevaluation of solid cancer mortality for a hypothetical scenario of workers in the United States assumed
to be exposed over a prolonged period to low doses of low-LET radiation. This evaluation is based on
the most recent relevant epidemiological studies for solid cancers, and is intended to:(a) Provide estimates of the magnitude of radiogenic risk for solid cancers at low doses and low
dose rates;(b) Provide a meaningful assessment of uncertainties in the risk estimates;(c) Enhance understanding of the application of risk models derived from one exposure situation(e.g. LSS) to estimate the risk in a different exposure situation (e.g. occupationally-exposed
workers).
195. The scenario described in this section focuses on solid cancer mortality at the borderline of low tomoderate dose exposures (100 mGy). Since the publication of the Committee’s previous report on thesubject (annex B of the UNSCEAR 2012 Report [U8]), two major epidemiological studies of all solidcancers for the LSS cohort have been published: Ozasa et al. [O5] for cancer mortality, and Grant et al.
[G8] for cancer incidence based on a revised dosimetry (DS02R1 [C10]). Furthermore, a significant
number of epidemiological studies of cancer incidence and mortality in workers and in members of the
public exposed to radiation have been published. All of these studies (see section IV.C) yieldsignificant developments and a sufficient amount of information to allow meaningful assessments ofradiological cancer risk and its uncertainties.

B. Recapitulation of previous UNSCEAR publications
196. The UNSCEAR 2006 Report, annex A [U3] reviewed dose responses for all solid cancers as agroup and for individual solid cancers. For all solid cancers in the LSS cohort, the report included
mortality data with follow-up until the end of 2000, using the DS02 dosimetry system. The report
included risk estimates for solid cancer derived from other cohort studies, mainly among workers in the
nuclear industry, for comparison. The UNSCEAR 2006 Report [U3] also presented a series of estimatesof lifetime cancer risk based on mortality and incidence data from the LSS cohort of the atomic
bombing survivors. Projected lifetime cancer mortality risks (i.e. the REID) were calculated at three test
doses: 0.01 Sv, 0.1 Sv and 1 Sv (assuming no threshold in dose), and they were reported separately formales, females and both sexes combined, for different ages at exposures, for populations offive countries/territories (China, Japan, Puerto Rico, United Kingdom and United States), and for
different ERR and EAR risk models with different dependencies on dose, age at exposure and timesince exposure. Risk estimates were presented as single values for each combination of factors (age,sex, population). Probability distributions of the REID obtained using Bayesian Markov Chain Monte
Carlo approaches (MCMC) were included for selected endpoints. Their main results were for a dose of
100 mSv (0.1 Sv). For the United States population (table 60 of [U3]), the point estimates* of the REID
from a test dose of 100 mSv from various models varied from 3.8 to 7.9% Sv−1 for males and from
5.0 to 12.9% Sv−1 for females.
197. The UNSCEAR 2012 Report, annex B [U8] focused on the characterization of uncertainties in theestimation of risk of cancer due to exposure to radiation and stated that proper estimation andcommunication of uncertainties is essential for gaining confidence among the public, decision-makers
and professionals. The report identified the main sources of uncertainty in risk estimation and separated

72 UNSCEAR 2019 REPORT

them into (a) uncertainties in different aspects of radio-epidemiology (dosimetry, health data, model
choice and statistical analysis); and (b) uncertainties associated with the application of epidemiological
data to different populations and exposure situations (different exposure rates or radiation types).
The report provided guidance regarding methods to propagate and combine uncertainties (e.g. Monte
Carlo techniques; multi-model inference) and provided uncertainty ranges for important parameters of
the risk assessment models.
198. The UNSCEAR 2012 Report, annex B [U8] developed an example of an assessment of risk ofsolid cancers in workers exposed to low-LET radiation from ages 30 to 44, at cumulative whole-bodydoses of 100 mGy above typical natural background exposure. The scenario was based on typical
exposures observed among occupationally-exposed workers included in the UK’s NRRW study [M12,
M13]. Lifetime expected cancer cases (baseline) and excess cases (radiation-associated), as well as
years of life lost and life expectancy, were estimated. Radiation-associated risks of cancer incidence
were determined using risk models derived from the NRRW study [M12, M13] and risk models derived
from studies of the atomic bombing survivors [N9]. The NRRW-based estimate of risk was 96 excess
cases in 10,000 persons (95% CI: 7, 197), while the LSS-based estimate of risk was 81 excess cases in
10,000 persons (95% CI: 32, 160).

C. Review of recent epidemiological literature
199. A significant number of epidemiological studies on the relationship between incidence ormortality of solid cancers and exposure to radiation have been published since the publication of the
UNSCEAR 2012 Report [U8]. A comprehensive literature search was performed to identify originalpeer-reviewed epidemiological studies using the quality criteria described in the UNSCEAR 2017
Report [U10]. The literature search covered the period from January 2012 to July 2017, producing a
total of 249 unique references. Since this section focuses on cohort studies dedicated to all solid cancers
as a group from (external) exposure of workers to low-LET radiation, selection criteria were set suchthat preference was given to studies that included exposures of healthy adults (representative of aworker population) and studies which report dose-dependent risk models for all solid tumours (ICD-9
codes 140–199). Studies reporting risks for all cancers excluding leukaemia were preserved as well
(ICD-9 codes 140–203). Studies of single cancer types (e.g. breast, lung), those dedicated only to
exposures to high-LET radiation (e.g. radon and its decay products), those concerned with exposures inchildhood, and those concerned with radiation treatment of people with cancer were not included. After
applying the selection criteria, a total of 21 publications were selected, some describing the same
epidemiological cohort. Several studies published since 2009 and after July 2017 were included after
the literature search was completed. The studies were grouped into three categories: (a) the LSS of theJapanese atomic bombing survivors; (b) studies of occupationally-exposed workers; and (c) studies ofmembers of the public. A summary of the studies of risk of solid cancer from exposure to low-LET
radiation is provided at the end of this section. References other than epidemiological studies(e.g. publications describing details of dosimetry for exposed workers) were used as supporting
documents and cited as needed.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 73

1.

Life Span Studies of the Japanese atomic bombing survivors(a) Cancer mortality in the Life Span Study cohort
1950–2003 follow-up
200. The publication of Ozasa et al. [O5] represents the 14th Radiation Effects Research Foundation(RERF) report on the LSS cohort. It provides an analysis of mortality due to cancer and non-cancer
diseases in the cohort for 1950–2003 (3,294,210 person–years). Among the 86,611 persons withestimated DS02 doses, 58% had died during the follow-up period, 80% of those under age 20 at thetime of exposure were still alive at the end of the period, and 99.6% of those of age 40 or older at thetime of exposure had died. This study includes an additional 6 years of follow-up since the previous
report, and provides substantially more epidemiological information, with 17% more deaths over theentire cohort and 58% more deaths among those under 10 years of age at the time of exposure.
201. Weighted doses were expressed in Gy and they were estimated as the gamma dose plus 10 times
the neutron dose, to allow for the greater biological effectiveness of neutron doses. DS02 doses havebeen estimated for 15 tissues, and analyses of mortality from all solid tumours combined have been
carried out using the dose to the colon as representative of the whole-body dose, as was done inprevious LSS studies. For individual dose estimates, shielded kerma estimates above 4 Gy
(317 persons) have been truncated at 4 Gy, because they are likely to reflect incorrect information onshielding or exact location. Out of 86,611 persons with known doses to the colon, 38,509 had doses
lower than 0.005 Gy (44.5%), 68,470 had doses lower than 0.1 Gy (79.1%), and 84,224 had doses
lower than 1 Gy (97.2%). The mean dose to the colon was 0.117 Gy [P12]. The dose estimates in the
LSS cohort have been corrected for classical errors using a statistical method of regression calibration
which assumed a plausible 35% multiplicative error [P3]. Pierce et al. [P3] found that the excess cancer
risk estimates in the LSS are 6–16% greater than estimates obtained without accounting for classicalerrors in dosimetry.
202. The risk of all causes of death was positively associated with radiation dose. The risk of all solidcancers combined was modelled with linear, linear–quadratic and quadratic dose–response
relationships, with the linear model providing the best fit for the full dose range, but with the linearquadratic model having a better fit when the dose range was limited to 0–2 Gy. The ERR and EAR
dose–response relationships for all solid cancers combined with modifiers for sex, age at exposure andattained age were developed (table 8). The EAR per unit dose for all solid cancers was found toincrease throughout life with a linear dose–response relationship, having a value of 26.4 (95% CI: 20.3,
32.8) excess cancer cases per 104 person–years per Gy at attained age 70 after an exposure at age 30,
based on a linear model. For the same ages, the sex-averaged ERR at 1 Gy was 0.42 (95% CI: 0.32,
0.53). These ERR and EAR models were obtained based on the analysis of the entire cohort (full rangeof doses and all ages; table 8) and are the models preferred by Ozasa et al. [O5].
203. The analyses of the LSS cohort data [O5] restricted to those who had received doses less than
1 Gy and 0.5 Gy indicate ERRs per unit dose (at attained age 70 after an exposure at age 30) that are
similar to those from the full dose-range analysis. However, the uncertainty in the ERRs at 1 Gy and in
the modifiers increases as the dose range becomes more restricted, and only an ERR model was capableof fitting the restricted LSS data (table 8).
204. Estimates of ERR at 1 Gy based on a simple linear model (i.e. without modifiers) were also
derived (table 9) by restricting the analysis to people in the LSS cohort aged 30 to 44 years at the timeof exposure [O5] (similar to the age range of workers included in the INWORKS cohort [R5]).

74 UNSCEAR 2019 REPORTThe ERR at 1 Gy for the restricted ages is compatible with that for the full range of doses in the wholecohort, but is more uncertain. The uncertainty in the ERR at 1 Gy increases even further if the cohort
aged 30 to 44 years is more restricted to persons with doses less than 1 Gy. However, when the cohortof people aged 30 to 44 years is restricted to persons receiving doses less than 0.5 Gy, the ERR at 1 Gy
is reduced significantly, with a wide uncertainty range in which the lower bound is negative and theupper bound is positive (table 9). Thus, the dose response for adults exposed to doses less than 0.5 Gycannot be used reliably for the purposes of risk assessment.
205. The risk of cancer mortality increased significantly for most major cancer sites, including thestomach, lung, liver, colon, breast, gallbladder, oesophagus, bladder and ovary [O5]. For these cancer
sites, dose responses with positive ERR at 1 Gy have been observed. Analyses of cancer mortality forrectum, pancreas, uterus, prostate and kidney parenchyma did not result in ERR values that indicate asignificantly increased risk to these organs.
206. Revised individual radiation dose estimates for the LSS (DS02R1) have been recently publishedby Cullings et al. [C10]. The DS02R1 system is the same as the previous DS02 system, but with
updated input parameters regarding the location of survivors and shielding, based on a thorough reviewof the original materials collected for dosimetry purposes. Although changes have been made for many
persons, about 90% of the survivors stayed in the same kerma category. Cullings et al. [C10] compareddose responses obtained using DS02 and DS02R1 individual dose estimates using the LSS mortality(not incidence) data for all solid tumours combined [O5]. For the full dose range, the estimated sexaveraged ERRs at 1 Gy based on a linear model were virtually identical; 0.42 (95% CI: 0.32, 0.53) in
the Ozasa et al. analysis and 0.43 (95% CI: 0.32, 0.53) in the Cullings et al. analysis. Estimates of the
ERR based on the full dose range were also very similar in the two analyses for males and females
separately, and when a linear–quadratic model was considered. Some differences in curvature between
males and females were observed when the data was analysed by restricting the doses to <2 Gy.
207. The LSS mortality data are one of the most comprehensive sources of information on radiationinduced effects, with a large cohort and number of cases, long follow-up, good cancer ascertainment,
good information about smoking, and in-depth analyses of dose–response relationships, with age andsex modifiers. The LSS cohort received a single acute exposure to radiation. This aspect is a strength, if
the risk from acute exposures is to be estimated, but it can be a weakness, if the risk from prolonged
exposures is required. Another possible weakness is the “healthy survivor effect” (i.e. cohort members
had to survive until the commencement of the LSS in October 1950), particularly for persons exposed
to high doses.

Table 8. Excess relative risk and excess absolute risk mortality risk models, with effect modifiers, for all solid cancers as a group, for the LSS cohort [O5]Dose range for LSS cohort
Risk model coefficient

SymbolFull dose range a b
Estimate<1.0 Gy c<0.5 Gy c

95% CI

Estimate

95% CI

Estimate

95% CIERR MODEL dβ0.42(0.32, 0.53)

0.40(0.24, 0.55)

0.41(0.14, 0.67)

Sex (ERR ratio: female/male)σ

2.1(1.4, 3.1)

1.7

(0.84, 4.2)

2.2

(0.72, 13.1)

Age at exposure

τ

−0.29

(−0.41, −0.17)−0.18(−0.4, 0.07)−0.35(−0.66, 0.05)

Attained age

ν

−0.86

(−1.6, −0.06)−0.73(−1.9, 0.67)

0.16(−1.66, 2.88)

Sex-averaged ERR per unit dose (Gy−1)
Modifiers

β26.4(20.3, 32.8)—e—eSex (EAR ratio: female/male)σ1.1(0.80, 1.74)—e—e

Age at exposure

–τ

−0.19

(−0.31, −0.07)—e—e

Attained age

ν

3.4(2.7, 4.1)—e—e

Sex-average EAR per unit dose (104 person–year Gy)
ModifiersaThis is the preferred model selected by Ozasa et al. [O5].

bAll persons in the cohort were included in the analyses, but 317 persons with shielded kerma greater than 4 Gy were assigned a truncated kerma of 4 Gy. Estimates of kerma of >4 Gy are likely to represent
misinformation on exposure factors such as shielding or exact location [O5].

c

Derived for this report base on the LSS data used by Ozasa et al. [O5].

Risk models are defined as ERR or EAR = βd ∙ exp(Τ E∗ + υ Ln(A∗ )) ∙ (1 + σ s) , where d is the dose, s is the sex, E ∗ = (E − 30)/10 and e is the age at exposure, A∗ = a/70 and a is the attained age, and σ, τ and ν
are coefficients for effect modification. For sex, variable s is −1 for males and +1 for females, and the sex ratio presented in the table relates to the coefficient σ, as follows: sex ratio = (1+σ)/(1-σ). For the ERR model,
coefficient β represents the sex-averaged ERR per unit dose (Gy) at attained age 70 after an exposure at age 30. For the EAR model, coefficient β represents the sex-averaged EAR per unit dose (104 person–year Gy) atdattained age 70 after an exposure at age 30.
eNo reliable estimates could be derived because of lack of convergence.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 75

EAR MODELd

76 UNSCEAR 2019 REPORT

Table 9. Excess relative risk per unit dose (Gy−1) for mortality due to all solid cancers as a group,
obtained using a simple linear model, with no effect modifiers, for the LSS cohort restricted to adults
exposed between 30 and 44 years of age [O5]Dose range for LSS cohort a b

ERR per unit dose (Gy−1)

95% CI

Full dose range

0.32(0.19, 0.47)<1.0 Gy

0.27(0.055, 0.50)<0.5 Gy

0.0048(−0.33, 0.38)

a

To obtain the ERR per unit dose reported in this table, the cohort was restricted to persons exposed between ages 30 and 44.
Then the cohort was further restricted to persons with doses to the colon of <1 Gy and <0.5 Gy [O5].

b

If persons of all ages at exposure are kept in the cohort, an ERR per unit dose for a simple model linear in dose of 0.56 (95%
CI: 0.15, 1.04) is obtained for a range of doses to the colon restricted to 0 to 0.2 Gy [O5].

(b) Cancer incidence in the Life Span Study cohort
1958–1998 follow-up
208. Analyses of the cancer incidence data from the LSS with a follow-up from 1958 to 1998 were
published by Preston et al. [P11]. These analyses were based on the DS02 dosimetry and included
17,448 first primary cancers (including non-melanoma skin cancer) diagnosed among 105,427 cohort
members, with individual dose estimates for those who were alive and not known to have had cancer
prior to 1958. The same data were analysed by the US National Academy of Sciences to derive the risk
models for different types of solid cancers included in the BEIR VII report [N9]. The BEIR VII risk
models estimated ERR and EAR for 11 cancer types: stomach, colon, liver, lung, breast, prostate,uterus, ovary, bladder, thyroid and leukaemia based on the cancer cases in the LSS cohort.
209. Berrington de González et al. [B5] provided an extension of the analyses of radiogenic cancer
incidence in the LSS cohort carried out by BEIR VII, to include risk models for seven additional cancer
types (oral, oesophagus, gallbladder, pancreas, rectum, kidney and brain/central nervous system (CNS)
cancers), using the same dataset and the same basic model formulation for the risk of solid cancer. The
new cancers included at least 100 incident cases available and had been evaluated in detail in the LSS
cancer incidence report [B5, P11].
210. The ERR and EAR models for solid cancers other than breast and thyroid have the form βs D exp(γ e*) (a*)η, where βs is the site-specific risk coefficient (for males or females), D is the dose in Gy, γ isthe age at exposure parameter, e* is (e–30)/10 for e <30 and zero for e ≥30, where e is age at exposurein years, η is the attained age parameter and a* is (a/60), where a is the attained age in years. For breast
and thyroid cancer, detailed pooled analyses were carried out based on a combination of data from the
LSS and from medically exposed populations. The risk of thyroid cancer was based on the ERR model
derived from the pooled data of seven studies described by Ron et al. [R8], as analysed by Land et al.
[L2] and modified for sex-dependency by BEIR VII [N9]. The ERR thyroid model is similar in form to
the standard model for solid cancers, however, depends only on age at exposure and not on attained age(i.e. η is equal to zero). The preferred model for breast cancer was the EAR model from the pooled
analysis of four cohorts by Preston et al. [P9]. That model has the same form as the standard model forsolid cancers, but e* is (e–25)/10 for all ages, e, at exposure and a* is (a/50).
211. A new risk model for a remainder category of cancers (different from that in the BEIR VII report)
was developed to include all solid cancers except the 17 solid cancers modelled separately [B5]. The

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 77

risk models for the new remainder category had a mathematical form similar to that applicable to mostsolid cancers and a sex-specific risk coefficient, β. When the total cancer risk is estimated, the risk forthe remainder category needs to be added to the risk estimates for individual cancer types.

1958–2009 follow-up
212. The third analysis of solid cancer incidence among the LSS cohort of atomic bombing survivors[G8] includes an additional 11 years of follow-up (since the previous analysis) in which 5,090 new
incident cancers were observed, an updated dosimetry system (DS02R1 [C10]) and improved
adjustments for smoking (similar to those from Furukawa et al. [F5]). The cohort included
111,917 persons (45,864 males and 66,053 females) who were alive and cancer free at the beginning of
the follow-up on 1 January 1958. After eliminating 6,473 persons for whom doses could not be
estimated, analyses have been carried out for a cohort of 105,444 eligible persons, which included
80,205 survivors with dose estimates and 25,239 persons who were not in either city at the time of thebombings. By the end of the follow-up (31 December 2009), 42,138 persons (37.7% of the cohort) werestill alive. This study provides 3,079,484 person–years of follow-up during the period 1958–2009.
213. After excluding lymphopoietic cancers and cancers diagnosed only at autopsy, a total of 22,538
eligible solid cancer cases were counted during the follow-up period (10,473 in males and 12,065 infemales), with 5,918 cases (26%) occurring in the 11 years (1999–2009) since the end of the previous
follow-up period. For 76.7% of the cases, cancer diagnosis was verified histologically.
214. Of all persons with known doses (80,205), 44.9% had weighted doses to the colon less than
0.005 Gy, 79.2% had doses less than 0.1 Gy and 97.4% had doses less than 1 Gy. Only 495 people
received doses higher than 2 Gy. Doses were the DS02R1 revised individual dose estimates [C10]. This
dosimetry system is unchanged from the previous DS02 system, but information regarding the locationof survivors and shielding had been refined based on a reanalysis of the original material. Althoughchanges had been made for many persons, about 90% of the survivors stayed in the same kerma category.
215. The modelled sex-averaged ERR per unit dose for all solid cancers was 0.50 (95% CI: 0.42, 0.59) Gy−1,
representative of an exposure at age 30 and follow-up to age 70. Only a small difference in the radiation
risk estimate occurred when account was taken of smoking through the use of a joint multiplicativesmoking–radiation model: ERR per unit dose of 0.47 (95% CI: 0.39, 0.55) Gy−1. These values are very
similar to that observed in the previous LSS analysis of 0.47 Gy−1 [P11].
216. Grant et al. [G8] estimated a significantly higher ERR per unit dose for females (0.64 (95% CI:
0.52, 0.77) Gy−1) than for males (0.27 (95% CI: 0.19, 0.37) Gy−1), when a linear model over the entire
dose range was applied. While for females, the dose response was consistent with linearity, for males, asignificant upward curvature was observed, and the modelled dose response was compatible with alinear–quadratic dependency on dose with an ERR of 0.2 (95% CI: 0.12, 0.28) at 1 Gy, and an ERR of
0.01 (95% CI: −0.0003, 0.021) at 0.1 Gy.
217. The EAR per 10,000 person–years at 1 Gy for males was estimated from the linear–quadratic
relationship provided by Grant et al. [G8] (table 10) as 21.7×1 Gy+21.2×1 Gy2=42.9. This value is rather
similar to the EAR at 1 Gy of 54.7 for females. However, the EAR for males at 0.1 Gy was only 2.4
compared to that for females of 5.5, because of the significant quadratic component of the male risk model.
218. The lowest dose range that showed a statistically significant dose response using the sex-averaged
linear ERR model was 0 to 100 mGy with an estimated ERR per unit dose of 0.49 (95% CI: 0.026,
1.01) Gy−1; p=0.038; table 11), virtually identical with the estimate of 0.50 over the full dose range[G8]. For the sexes combined, over the full dose range the linear–quadratic model fits the data better

78 UNSCEAR 2019 REPORT

than the pure quadratic model (p<0.001), indicating that there is a positive slope at low doses.
Statistical tests did not indicate a dose threshold significantly different from zero (p=0.18 for females,
p=0.49 for males).
219. The interpretation of the sex-related differences in curvilinearity is complex, and no mechanistic
explanation exists at this time. This result is surprising especially because such differences were notobserved in the previous analysis of the LSS incidence data [P11]. Statistically, the differences inmagnitude of risk between males and females could be attributed, in part, to the higher baseline cancer
rates in males. Another potential reason for the observed curvilinearity is the revision of individual
doses (DS02R1). Cullings et al. [C10] compared dose responses obtained using individual doses ofDS02 and DS02R1 and the LSS mortality (not incidence) for all solid tumours [O5]. For the full dose
range, no statistically significant curvature for either sex was observed, and the best estimates were
nearly identical for both original and updated doses. For the dose range 0–2 Gy, a statisticallysignificant curvature was obtained for females for the updated DS02R1 individual doses (p=0.02), but
not for the original DS02 doses. No curvature was observed for males for the dose range 0–2 Gy witheither the revised or the original dose estimates. These results are at odds with the results reported by
Grant et al. [G8] for new incidence data which indicated a dose–response curvature for males, but not
for females. Further studies are necessary to explain the curvilinearity observed by Grant et al. [G8] inthe data on the incidence of solid cancers, and to answer the more fundamental question of why theshape of the dose response for males should be different from that for females.
220. Aside from the differences in curvilinearity between males and females, the data for theperiod 1958–2009 on solid cancer incidence from the LSS cohort of atomic bombing survivors, with its
large number of person–years of follow-up and cancer cases, offers high statistical power for theestimation of radiation risk, and it provides evidence of a statistically significant dose response over thedose range of 0 to 100 mGy.

Table 10. Parameter estimates and 95% confidence intervals for the preferred excess relative risk and excess absolute risk models for all solid cancer incidence:
LSS cohort, 1958–2009 [G8]Males
Dose–response model

Dependency on dose a

Females

Both sexes

Attained age

Dependency on dose a

Attained age

Age at exposure b

LinearQuadraticPower

LinearPower

ERR

0.094 (<0.02, 0.23)

0.11 (0.04, 0.20)−2.70 (−3.58, −1.81)

0.64 (0.52, 0.77)−1.36 (−1.86, −0.84)−22% (−30%, −13%)EAR

21.7 (<−1.7, 47.7)

21.2 (6.8, 37.6)

2.89 (2.14, 3.68)

54.7 (44.7, 65.3)

2.07 (1.64, 2.53)−30% (−37%, −22%)a

The linear parameter is the ERR (per Gy) or the EAR per 10,000 person–years (per Gy), respectively. The quadratic parameter is the ERR or EAR per Gy2. Parameters represent the risk at attained age 70, from an
exposure at age 30.
b

Percentage change per decade increase in age at exposure (same value applies to both males and females).

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80 UNSCEAR 2019 REPORT

Table 11. Excess relative risk per unit dose (Gy−1) for incidence due to all solid cancers as a group,
obtained using a simple linear model: LSS cohort, 1958–2009 [G8]Dose range(Gy)
LSS cohort a b

Females

Males

Both sexes

ERR per unit
dose (Gy−1) a

95% CIERR per unit
dose (Gy−1) a b

95% CI

Full range

0.64(0.52, 0.77)

0.27(0.19, 0.37)

<2.0

0.65

(0.52, 0.78)

0.25(0.17, 0.36)

<1.0

0.58

(0.44, 0.47)

0.19(0.09, 0.30)

<0.5

0.53

(0.34, 0.75)

0.07(<−0.05, 0.22)

<0.25

0.55

(0.24, 0.92)

0.02(<−0.18, 0.25)

<0.1

0.39

(−0.27, 1.1)

0.33(<−0.10, 0.89)

ERR per
unit dose(Gy−1)

95% CI

0.49(0.026, 1.01)

a
Estimated sex-specific excess relative risks per Gy were based on a linear dose–response model that included radiation effectmodification by attained age (sex-specific) and age at exposure (common to both sexes) and adjusted for smoking using a
multiplicative ERR model for the joint effect of radiation and smoking [G8].
bStatistically significant curvature in the dose response was observed for males (table 10) over the full range of doses.
Statistically significant linear–quadratic dose responses for males were also determined for dose ranges 0–2 Gy and 0–1 Gy. For adose range 0–0.5 Gy, a negative range was observed. No linear–quadratic dose responses or significant curvatures were observed
for lower dose ranges [G8].

2.

Studies of workers exposed to radiation(a) Cancer mortality in the INWORKS cohort
221. The INWORKS [R5, R6] focused on mortality in the cohorts of occupationally-exposed workers
from France [M4], the United Kingdom [M12, M13] and the United States [S5]. INWORKS continued
the idea of pooling data of exposed workers which was previously used by the 15-country worker study[C4, C5], and did so with updated data from the three countries, as they hold much of theepidemiological information contained in the 15-country study. The INWORKS cohort included
308,297 workers in the nuclear industry (87% males) with detailed monitoring data for external
exposure to ionizing radiation and a total follow-up of 8.2 million person–years. Out of 66,632 known
deaths by the end of follow-up, 19,748 were identified to be due to cancer (17,957 solid cancers).
The follow-up period was from 1944 to 2005, the mean follow-up per worker was 27 years (median of
26 years), the mean attained age at the end of the follow-up was 58, the mean length of employment
was 15 years (median 12 years), and the mean age at the beginning of employment was 28.
222. External exposures of the workers in the INWORKS included photons of energies between
100 and 3,000 keV. Recorded doses from neutrons and internal emitters, including inadvertently missed
external gamma doses, were not quantified for all members of the cohort and were not used in theanalysis directly, although their potential impact on the dose response was assessed at least for some of
the missing components and judged to be modest (section IV.F.1). The ERR per unit dose for mortality
from solid cancers was estimated using absorbed doses to the colon. Among the 257,166 workers with apositive recorded dose, the average cumulative dose for the cohort was 20.9 mGy (France, 17.6 mGy;
United Kingdom, 22.5 mGy; United States, 20 mGy), and the distribution of doses was skewed (mediandose 4.1 mGy; 90th percentile, 53 mGy; maximum dose 1.3 Gy) [R5, T6].

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 81

223. Dose responses were quantified using a Poisson regression model for the relative rate, defined as
1+βD, where D is the cumulative dose in Gy, lagged by ten years, and β is the ERR per unit dose.
A statistically significant ERR at 100 mGy for solid cancers of 0.047 (90% CI: 0.018, 0.079) was
derived based on 17,957 cancer deaths in the entire cohort. One test for the effect of smoking on therisk estimate was performed by estimating the risk of solid cancer, other than lung cancer, and wasbased on 12,155 cases. The ERR at 100 mSv for this grouping was 0.046 (90% CI: 0.011, 0.085). Theseestimates do not include any dependencies on the age at exposure, age at diagnosis or time since
exposure. A separate analysis of risk dependency of age at exposure and time since exposure as
categorical variables [D1] was performed using piecewise constant models (age at exposure groups:<35, 35–50, 50+; time-since-exposure groups: 10–20, 20–30, 30+ years). Trends were not statisticallysignificant, although a decrease of the ERR per unit dose with increasing time since exposure wasobserved when a 10-year lag was imposed.
224. The preferred ERR at 100 mGy for solid cancers of 0.047 (90% CI: 0.018, 0.079) represents theentire dose range in the cohort, with the largest dose group having an average dose of 631 mGy [R5].
Dose responses for lower cumulative doses were not reported for solid cancers (ICD-9 codes 140–199),
but for all cancers other than leukaemia, as a cancer grouping (ICD-9 codes 140–203). The number ofdeaths in the cohort from all cancers other than leukaemia was 19,064. For the entire dose range, theERR at 100 mGy for this cancer grouping was 0.048 (90% CI: 0.02, 0.079). For the restricted dose ranges(table 12), the observed ERRs at 100 mGy for dose ranges 0–200 mGy, 0–150 mGy and 0–100 mGy
were 0.104 (90% CI: 0.055, 0.156), 0.069 (90% CI: 0.010, 0.13) and 0.081 (90% CI: 0.001, 0.164),
respectively. The uncertainty in the ERR per unit dose increases substantially with the decreasing upperbound of the dose range, but the statistical significance and the consistency of the estimated ERRs
indicate an approximately linear dose response.
Table 12. Estimates of excess relative risk at 100 mGy and excess absolute risk per 10,000 person–
years at 100 mGy for cancer mortality derived using data of specific dose ranges [R5]
Dose range (mGy)

ERR or EAR
Estimate

90% CISOLID CANCERS (ICD-9 CODES 140–199)
ERR at 100 mGy
Entire dose range

0.047(0.018, 0.079)
EAR per 10 000 PY at 100 mGy

Entire dose range

0.48(0.021, 1.03)

ALL CANCERS OTHER THAN LEUKAEMIA (ICD-9 CODES 140–203)
ERR at 100 mGy
Entire dose range

0.048(0.020, 0.079)

0–200

0.104

(0.055, 0.156)

0–150

0.069

(0.010, 0.130)

0–100

0.081

(0.001, 0.164)

225. Models for EAR for solid cancers were fitted using a baseline parametric model that was adjusted
for country, sex, birth year, birth year squared, log of age and log of age squared. The fits were basedon the full INWORKS cohort. The EAR coefficient resulting from the fit for solid cancers, based on a
5-year lagged dose to the colon, is 0.48 (90% CI: 0.02, 1.03) per 10,000 person–years per 100 mGy.

82 UNSCEAR 2019 REPORT226. Site-specific solid cancer mortality analyses for the INWORKS cohort by Richardson et al. [R6]
showed positive point estimates of ERR per unit dose for oral, oesophagus, stomach, colon, rectum,
pancreas, peritoneum, larynx, lung, pleura, bone and connective tissue, skin, ovary, testis and thyroid
cancer. In addition, negative point estimates of ERR per unit dose were observed for cancer of the liverand gallbladder, prostate, bladder, kidney and brain. However, the estimated coefficients exhibited
substantial imprecision. A second type of dose-response analysis (i.e. using hierarchical Poisson
regression) had little impact on the estimated associations for the most commonly observed outcomes.
However, for less frequent cancer types, the estimates obtained using this second method tended to takefewer extreme values and, thus, have less uncertainty than estimates obtained with maximum-likelihood
Poisson regression.
227. The INWORKS is based on a large cohort, with long follow-up and good dosimetry for external
doses. The dose–response analyses indicate a statistically-significant association of mortality from all
solid cancers combined with increasing dose. Estimates of ERR and EAR per unit dose for a linear dose
response can be considered reliable for assessing risk in workers exposed to low-LET radiation. The
INWORKS, however, includes mostly adult males (with only a relatively small fraction of females andno children), and thus cannot be used for cases of childhood exposure and is less reliable for assessing
radiogenic risk in adult females. Dose responses for all solid cancers are based on recorded external
gamma doses, with unaccounted or missed neutron and internal emitter doses potentially affecting
reported results. Mortality dose responses for site-specific solid cancers have been derived, but they areaffected by substantial imprecision.

(b) Cancer mortality in German uranium millers
228. Extensive uranium mining and milling were in operation from 1946 until 1990 in the southernpart of the former German Democratic Republic. The mining operations were conducted by a company
named “Wismut”. Workers were exposed primarily to radon decay products, external gamma radiation,
long-lived radionuclides from uranium ore, and silica and arsenic dust. Other occupational exposures
include asbestos, diesel, sulfuric acid and ammonia. In 1993, a cohort was established to include 58,982
male workers who had been employed by Wismut for at least six months between 1946 and 1989.
229. A study by Kreuzer et al. [K24] focused on a cohort of 4,054 uranium millers, a subgroup of the
Wismut workers, who had never worked in underground or opencast mining. Uranium millers differ
from uranium miners because they were exposed to (a) appreciably lower mean cumulative radon
daughter exposures (8 versus 328 WLM); (b) lower average cumulative exposures to external gamma
radiation (26 versus 50 mSv) and silica dust (4.9 versus 7 mg m−3 per year); and (c) additional exposure
to a variety of chemicals. In contrast, both millers and miners were exposed to similar amounts of longlived internally-deposited radionuclides (3.9 versus 4.1 h kBq m−3).
230. The cohort of German uranium millers includes males who worked between 1946 and 1989.
Workers were employed for an average of 15 years (range 0.5–44). The mean age at first employment
was 25 years (range 14–61). Mortality was followed up from 1946 to 2008 (62 years), accumulating
158,383 person–years at risk. The mean duration of follow-up was 39 years.
231. Their cumulative exposure included: (a) inhalation of radon decay products (mean 8; median 5;
maximum 127 WLM); (b) inhalation of long-lived radionuclides (mean 3.9; median 0.97; maximum
132.1 h kBq m−3); (c) inhalation of silica dust (mean 4.9; median 2.5; maximum 37 mg m−3 per year);and (d) exposures to external gamma radiation (mean 26; median 11; maximum 667 mSv). Effective
doses from external exposure to gamma radiation were calculated based on measurements of 226Ra inrocks. Only 4.2% of the cohort had received external doses larger than 100 mSv. The mean, median andANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 83

maximum duration of exposure was 11, 8 and 43 years, respectively. Exposures to silica occurred overa longer period (mean 15; median 12; maximum 43 years).
232. Statistical analyses of the cohort included (a) external comparisons with a compatible generalmale population in Eastern Germany expressed as standard mortality ratios (SMR); and (b) determinationof dose responses expressed as relative rates, using Poisson regression, with both RR and ERR being
reported. A linear dose response was used independently for each contaminant of interest (radon, longlived radionuclides, gamma, silica) and the exposure was lagged by five years to account for theminimum latency period.
233. The external comparisons, for a follow-up from 1970 to 2008, showed that all-cause mortality
was lower in the exposed cohort (SMR=0.85; 95% CI: 0.81, 0.90; O=1,539; statistically significant).
All-cancer mortality in the exposed cohort was also lower than in the general population (SMR=0.92;
95% CI: 0.8, 1.00; O=437; not statistically significant).
234. Although the average cumulative radon daughter exposure was rather low, the ERR for all solidcancers combined was significant (ERR at 100 WLM=1.74; 95% CI: 0.24, 3.23) with the largest
contributor being lung cancer (ERR at 100 WLM=3.39; 95% CI: −0.01, 6.78). The central estimate ERR
per unit exposure for lung cancer is much larger than the value derived for the complete Wismut cohort(ERR at 100 WLM=0.19; 95% CI: 0.16, 0.22) [W3], although the confidence limits overlap. However,
the two central estimates become compatible when taking into account that the ERR per unit exposurefor the full Wismut cohort was strongly modified by the exposure rate, with the ERR per unit exposure
being about 10 times lower at higher exposure rates than at low exposure rates. Also, among 2,148 Port
Hope uranium processors in Canada with low radon exposure levels, similar to those in the present
report, a lower mortality risk was reported (n=78; ERR at 100 WLM=0.39; 95% CI: <−1.22, 4.52) [Z3].
235. No significant increase in risk of all solid tumours as a group was observed for exposures to longlived radionuclides: ERR at 100 h kBq m−3 of −0.04 (95% CI: −0.80, 0.72), although a relatively high,but not statistically significant ERR at 100 h kBq m−3 was observed for stomach 2.61 (95% CI: −1.60,
6.81) and kidney 7.38 (95% CI: −11.2, 26.0). Exposures to silica indicated a low ERR per unit exposure
for all solid tumours (mg m−3 per year) of 0.011 (95% CI: −0.010, 0.032) for all solid cancers as a group.
236. The dose response for all solid tumours from exposure to external gamma radiation had an ERR
per unit dose of 1.86 (95% CI: −0.08, 3.8) Sv−1; p=0.06, with the highest contributors to the risk being
stomach cancer: ERR per unit dose of 10 (95% CI: −2.65, 22.6) Sv−1; p=0.12, and lung cancer: ERR per
unit dose of 2.55 (95% CI: −0.97, 6.07) Sv−1; p=0.16. Given that the members of the cohort were also
exposed to radon, silica and long-lived radionuclides, analyses were carried out to adjust the ERR fromgamma radiation for the effect of radon or silica. When adjusted for the effect of radon, the ERR at 1 Sv
for solid tumours decreased from 1.86 to 0.26 (95% CI: −2.47, 2.98) and the ERR at 1 Sv for lungcancer was reduced from 2.55 to −1.1. No analyses have been carried out to adjust for both radon andsilica. Furthermore, no adjustments of the ERR from gamma radiation due to the effect of exposure to
long-lived radionuclides were carried out.
237. The results of this study provide only suggestive evidence for a dose response for solid tumours
for external gamma exposure to doses under 100 mSv. The study has a number of limitations asfollows: (a) low statistical power due to low exposures, small population size (4,054 males) and a small
number of deaths in specific subgroups (457 from cancer; 717 cardiovascular diseases; 111 non-malignant
respiratory disease); (b) magnitude of the observed risks influenced by several correlated sources of
exposure; (c) unaccounted for uncertainties in exposure assessment, including missing-dose
calculations; (d) missing information on potential confounders such as smoking and chemicals; and(e) some issues with ascertainment of the cause of death (e.g. cause of death was missing for a small

84 UNSCEAR 2019 REPORT

number of persons (n=47) who died prior to 1970; however sensitivity analyses showed that results
restricted to 1970–2008 period are similar to results for the full follow-up period).

(c) Cancer mortality in German nuclear power plant workers
238. A study of risk of cancer mortality in a cohort of workers from 11 nuclear power plants inGermany was conducted by Merzenich et al. [M3]. This study included 8,972 workers and was anextension of an earlier study of 4,844 workers from 10 power plants in Germany. The extended cohort
comprised 8,746 males (97.5%) and 226 females (2.5%) who (a) were employed on 1 January 1991 orstarted employment before 31 December 2008; (b) were employed for at least three months in a job
category with exposures expected to exceed an annual effective dose of 6 mSv; (c) were monitored forexternal exposure; and (d) were directly employed by the nuclear power plant.
239. The follow-up of vital status started on 1 January 1991. The end of follow-up was defined as thedate of death, last information date (for workers lost to follow-up) or 31 December 2008. The average
age at the end of the 17 years of follow-up was 50. The average follow-up duration per worker was
14.6 years. By 2009, 310 males, but no females, had died (120 persons had died from a solid cancer),
70 persons (67 males) were lost to follow-up, and 8,592 persons (8,369 males) were alive on
31 December 2008. The mortality follow-up accumulated 130,737 person–years at risk (128,570
person–years at risk for males).
240. The workers were monitored with film badges for external photon radiation (X-rays and gamma
radiation), with detection thresholds varying from 0.1 to 0.4 mSv during different time periods ofmeasurement. Effective annual doses for males ranged from 0 to 31.8 mSv, with 98% being below
6 mSv. At the end of the follow-up period, the mean cumulative effective dose for males was 29.5 mSv(median 5.7 mSv), with 48% of the population having doses less than 5 mSv and 90% of the population
having doses less than 100 mSv. The mean cumulative dose among the 226 females was 0.64 mSv(median 0.0 mSv). Given that, for females, the number of persons was small, doses were very low, andno deaths have been recorded during the follow-up period, the dose–response analysis was limited tothe male subgroup of the cohort.
241. Based on a representative general population of males from Germany for the period 1991–2008,
the expected number of deaths would have been 627.1 with 188.7 from solid tumours. The SMR for thecohort of workers is 0.5 (95% CI: 0.45, 0.56) for all causes of death and 0.64 (95% CI: 0.50, 0.81) for
solid cancers. It was not possible to adjust for smoking and alcohol consumption for this cohort.
Instead, analyses were carried out by grouping causes of deaths related to these two risk factors. Thus,
the SMR for tobacco-related solid cancers was 0.50 (95% CI: 0.35, 0.71), the SMR for solid cancers
excluding tobacco-related cancers was 0.80 (95% CI: 0.58, 1.10) and the SMR for alcohol-associatedcauses was 0.31 (95% CI: 0.19, 0.50).
242. The statistical analysis was based on Cox regression models fitted to estimate hazard ratios as afunction of cumulative radiation dose for selected causes of death. A latency period of two years forleukaemia and 10 years for solid cancers was assumed. Poisson regressions were not carried out andERR or EAR dose responses have not been derived. A hazard ratio per mSv for all solid cancers of
0.999 (95% CI: 0.996, 1.001) was determined, with hazard ratios per mSv for individual cancer sites
being almost indistinguishable from 1.0.
243. The cohort of German nuclear power plant workers is still young, with a relatively small numberof persons 8,972 workers compared to 308,297 workers included in the INWORKS [R5];59,201 workers in the French cohort [M4]; 119,195 workers in the United States cohort [S5]; 174,541

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 85

workers in the United Kingdom cohort [M12]; and a small number of observed deaths due to solidcancers with 120 deaths compared to 17,957 in the INWORKS; 2,312 in the French cohort; 10,877 in
the United States cohort from all cancers except leukaemia; and 8,107 in the United Kingdom cohort.

(d) Cancer incidence in nuclear power plant workers in the Republic of Korea
244. A large-scale epidemiological investigation of nuclear power workers was launched in the
Republic of Korea in 1992 [J4]. Between 1992 and 2005, workers participated in questionnaire surveys
and clinical health check-ups. Given the relatively small number of females in the workforce, theepidemiological investigation was focused on males. A cohort of 16,236 male workers was formed,
with persons identified as radiation workers (8,429) or non-radiation workers (7,807). The first category
included persons who were issued with thermoluminescent dosimeters (TLD) and who have external orinternal exposure data recorded in a nationwide database. The second category comprised persons withno dosimetry records. Given that radiation monitoring of nuclear workers is strictly applied in the
Republic of Korea, persons with no dosimetry records were assumed to have had negligible radiation
doses. The survey results indicated that 1,995 persons (23.7%) never smoked, 1,846 persons (21.9%)
were former smokers, and 4,553 persons (54.0%) were current smokers. The remaining 35 persons did
not provide responses regarding smoking history.
245. The radiation workers (8,429) and non-radiation workers (7,807) accumulated 63,503 and 48,301
person–years at risk, respectively, calculated from the date of the questionnaire or the first exposure
date, whichever occurred later. The average length of follow-up was 7.53 years for radiation workers
and 6.19 years for non-radiation workers. The mean ages at the end of follow-up for the exposed andunexposed categories were 41.3 and 46.2, respectively. A total of 99 cancer incidence cases and
43 deaths were observed among the radiation workers, compared to 104 incident cases and 39 deaths in
the non-radiation workers. Cancer ascertainment relied on data from the Korean Central Cancer
Registry, based on ICD-10 codes. The linkage was deterministic based on an identification number
unique to each person. Cancer and vital status were checked for the period between 1992 and 2005 [J4].
246. The dose recorded in the database for the Korean workers included in this study [J4] was, inprinciple, a total effective dose from both external and internal exposure. The majority of recorded
doses came from external exposures to high-energy photons (>100 keV). External exposure to neutrons
occurred for 7.8% of the radiation workers (657 persons), who had received less than 10% of their total
dose from neutrons. The mean dose from neutrons was 0.44 mSv. Internal exposures were recorded for
25.9% of the radiation workers (2,182 persons; mean dose 0.82 mSv). Doses from neutrons and internal
emitters were considered too small for separate dose–response analyses. The main indicator selected toassess risk of cancer was the total cumulative dose estimated from the recorded total annual doses
between the date of first exposure and the date of exit (date of cancer diagnosis, death or study end,
whichever occurred first). Total cumulative doses ranged from 0 to 480.5 mSv, with a mean dose of
19.9 mSv (median 2.93 mSv). Doses to 19.9% of the radiation workers (1,678) were recorded as zeroand 95% had doses less than 100 mSv. The cumulative dose was lagged by two years for leukaemia and10 years for solid cancers. The mean dose lagged for leukaemia was 18.4 mSv and the mean dose
lagged for solid cancers was 12.05 mSv [J4].
247. Poisson regression was used to determine the ERR as a linear function of the cumulative laggeddose, after adjusting the baseline risk for age, birth year and smoking status. Standardized incidence
ratios (SIR) were derived for both radiation and non-radiation workers by comparisons with thenational baseline rates. For leukaemia, and stomach, lung, thyroid, all cancers (as a group) and allcancers excluding leukaemia (as a group), the SIRs were greater than 1.0, indicating higher rates ofcancer incidence in the radiation workers than in the general population, although this was not

86 UNSCEAR 2019 REPORT

statistically significant. For all cancers excluding leukaemia, the SIR was 1.064 (95% CI: 0.86, 1.29)
for the radiation workers, compared to an SIR of 0.86 (95% CI: 0.70, 1.05) for the non-radiation
workers. The ERR at 1 Sv for all cancers excluding leukaemia was positive at 2.06, but with a large
uncertainty range (95% CI: −1.91, 9.0). For all cancers combined, the ERR at 1 Sv was 1.69 (95% CI:−2.07, 8.21) [J4].
248. Although the study by Jeong et al. [J4] was well organized and conducted, the size of the cohort
was small, leading to a low statistical power. Dosimetry relied on TLD measurements for each person,
but similar to other studies no consideration was given to medical exposures or to prior exposures inother workplaces, and no attempt was made to correct for potential underestimation of doses recorded
as zero below the limit of detection (0.1 mSv for TLD badges).

(e) Cancer incidence in Chinese medical diagnostic X-ray workers
249. The Chinese medical X-ray workers study included 27,011 persons (radiologists and radiological
technicians) who were employed between 1 January 1950 and 31 December 1980 at major hospitals in
24 provinces of China [S16]. The median duration of employment was 26 years and the mean age atentry into the cohort was 26.4. Another cohort comprising 25,782 physicians (surgeons,otolaryngologists etc.), who did not use X-ray equipment, was established as a control. The mean age atentry of this population was 25.1.
250. Cancer ascertainment relied on information through 1995, which included date, diagnosis andother details related to diagnosis from medical records. Histological confirmation was available for
70% of all cancer cases. All neoplasms (excluding leukaemia) were included and cancers wereclassified according to ICD-9 codes. The vital status of the cohort was checked first in 1980, then in1985 and again in 1990 and the follow-up ceased in 1995. Out of the 27,011 persons in the cohort(21,571 males and 5,440 females), 95% were still alive on 31 December 1995. The mean follow-up forthe cohort was 25.7 years (683,425 person–years at risk). The mean follow-up for the comparison
population was 29.6 years. A total of 1,643 of solid cancer cases were observed during the follow-up
period among all workers (52,793 persons), with 795 in the exposed group and 848 in the unexposedgroup [S16].
251. Most, if not all, members of the cohort were exposed to 25 to 40 keV X-rays, with no or
negligible exposures to other types of radiation. Estimated doses represent annual averages, for eachyear from 1949 until 1995. For calendar years prior to 1949, it was assumed that doses were equal tothe annual average for 1949. Doses were estimated by simulating measurements for multiple types of
X-ray machines, workplaces and working conditions, including the use of lead aprons and work history
for 3,805 of the workers (14% of the cohort). Mathematical models were used to determine yearspecific personal dose equivalent (Sv) at a tissue depth of 10 mm, Hp(10), from the dosimetermeasurements. The dose to the colon, used as a proxy for the dose to all organs, was determined byconverting Hp(10) using conversion coefficients representing exposures to 35 keV X-rays in ananterior-posterior geometry. The mean reconstructed cumulative dose to the dosimeter was 0.25 Gy(250 mGy) with a median of 0.12 Gy (120 mGy). The distribution of cumulative doses to the colon wasvery skewed, with 60% of the medical workers having a dose to the colon less than 0.05 Gy (50 mGy)and 99% of workers having a dose less than 0.5 Gy (<500 mGy). The mean cumulative dose to thecolon was 0.086 Gy (86 mGy), while the median dose was 0.042 Gy (42 mGy) [S16].
252. Poisson regression methods were used to fit ERR and EAR models for all cancers excluding
leukaemia, based on 5-year lagged doses to the colon. The ERR and EAR models were set as functionsof dose (linear response), sex, age at exposure and attained age. The ERR at 1 Gy for an attained age ofANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 87

50 was 0.87 (95% CI: 0.48, 1.45) for both sexes, 0.82 (95% CI: 0.46, 1.32) for males and 0.93 (95% CI:
0.35, 1.84) for females. The EAR for an attained age of 50 was 22 (95% CI: 14, 32) per 10,000 person–
years Gy for both sexes, 25 (95% CI: 16, 36) per 10,000 person–years Gy for males, and 18 (95% CI:
8, 33) per 10,000 person–years Gy for females. Neither the ERR nor the EAR models seem to indicate adependency of risk on age at exposure, a fact that is not surprising since the workers were adults, withmany of similar ages at the time of employment. However, both the ERR and the EAR risk models
seem to indicate a statistically significant increase of risk with increasing attained age. While this isnormal for an EAR model, such behaviour is curious for ERR and is at odds with the dependencies on
attained age observed in the LSS cohort [S16].
253. The study by Sun et al. [S16] included a large number of persons with similar exposures (i.e.
X-rays only), had a long follow-up and used cancer incidence rather than mortality. However, the study
has a number of limitations. There are several issues related to dosimetry: (a) group-level data wereused to impute dosimetry parameters, since individual data were not available for more than 14% of thecohort; (b) dose to the colon was used as a proxy for doses to specific organs; and (c) assumptions
made about the energy of X-rays being constant over the years and about prevalent exposure orientation(e.g. anterior-posterior). These are important sources of uncertainty. While the limited potential for
substantial confounding by other occupational exposures than radiation is a strength of this study, thecompleteness of cancer ascertainment is uncertain, with 30% of cancers not confirmed histologically.
Finally, the reported attained-age dependency dose–response relationship is unclear, and, for the ERR
model, it predicts an increase of ERR with attained age.

(f) Cancer mortality and incidence in the United Kingdom’s BNFL workers
254. Gillies and Haylock [G6] provided an updated analysis of cancer mortality and incidence innuclear workers formerly employed by BNFL in the United Kingdom. The BNFL cohort is only aportion of the larger NRRW cohort, representing around 50% of the collective dose in the NRRW study[M12]. The cohort comprises 64,956 employees, out of which 136 were excluded because ofincompleteness of data. Thus, analyses were performed on 64,820 workers (53,821 males, 83% and
10,999 females, 17%) who accumulated 1,894,069 person–years at risk. Cohort members wereclassified either as radiation workers or as non-radiation workers, according to whether they weremonitored for external radiation exposures using film badges. The cohort had 42,431 radiation workers(38,785 males, 91% and 3,646 females, 9%) and 22,389 non-radiation workers (15,036 males, 67% and7,353 females, 33%).
255. The radiation workers were separated into those only exposed externally and those also exposedinternally, according to the source of radiation exposure for which they were monitored. Those exposedinternally were exposed to systemically distributed plutonium, tritium, or uranium or a combination of
the three elements, in addition to external exposure to gamma radiation and possibly neutrons. These
workers, monitored for internal exposures, tended to accumulate higher external doses than workers
exposed only externally, because they had, in general, longer lengths of service, longer exposure
histories and job assignments prone to higher external exposures. The mean annual external dose for theentire cohort was 4.9 mSv. The cumulative external dose among all radiation workers had a mean of
53 mSv and a median of 12 mSv, and the 99th percentile of the dose distribution was 590 mSv. The
average cumulative external dose was 90 mSv for workers exposed either to plutonium or to tritium,
and 31 mSv for workers exposed to uranium, while workers exposed to multiple internally depositedradionuclides received an average external cumulative dose of 212 mSv.
256. Workers in the cohort were employed between the beginning of 1946 and the end of 2002. The
vital status was followed to 31 December 2005 starting from 1 January 1971 for cancer incidence

88 UNSCEAR 2019 REPORT(i.e. 34 years of follow-up; 1,894,069 person–years). Altogether 5,511 solid cancers were diagnosed
among the radiation workers (2,535 in the externally exposed group and 2,976 in the internally exposedgroup). Non-radiation workers had 3,524 solid cancer diagnoses during the period of follow-up. The
number of recorded cancer deaths from solid cancers was 3,026 in the group of radiation workers(1,363 in the externally exposed group and 1,663 in the internally exposed group) and 2,223 in the
non-radiation workers.
257. The SIRs for solid cancers were 0.95 (95% CI: 0.91, 0.98) for those externally exposed, 1.01 (95%
CI: 0.97, 1.05) for those internally exposed, and 1.00 (95% CI: 0.97, 1.04) for the non-radiation workers.
The SMRs were 0.86 (95% CI: 0.81, 0.90) for those externally exposed, 0.94 (95% CI: 0.89, 0.98) for
those internally exposed, and 0.98 (95% CI: 0.94, 1.03) for the non-exposed workers.
258. Poisson regression was used to fit ERR models for incidence and mortality, as a function of thetotal cumulative external radiation dose lagged by 10 years. For solid cancer incidence, the ERR at 1 Sv
for all radiation workers was 0.28 (90% CI: 0.08, 0.49). Estimates of the ERR for smoking-related
cancers and for non-smoking related cancers were 0.11 (90% CI: −0.22, 0.50) and 0.35 (90% CI: 0.11,0.62), respectively. When the cohort was analysed separately, the ERR at 1 Sv of cumulative external
dose (lagged by 10 years) was 0.87 (90% CI: 0.36, 1.44) for the externally exposed workers and0.13 (90% CI: −0.07, 0.36) for those exposed internally.
259. For solid cancer mortality, the ERR at 1 Sv for all radiation workers was 0.29 (90% CI: 0.02,0.59). Estimates of the mortality ERR for smoking-related cancers and for non-smoking-related cancers
were very similar to the ERR for all solid cancers, but with slightly larger uncertainty ranges. When thecohort was analysed separately for externally and internally exposed workers, the ERR at 1 Sv was
1.03 (90% CI: 0.37, 1.81) for those exposed externally and 0.06 (90% CI: −0.02, 0.37) for those
exposed internally, for external dose lagged by 10 years.
260. The BNFL cohort is an important component of the larger NRRW cohort. Possible limitations of
this study are: (a) radiation doses from internally incorporated radionuclides have not been included
explicitly; (b) unrecorded neutron doses could be a source of bias in the reported dose responses; and(c) external doses for workers at the plutonium plant in Sellafield could have been overestimated fromthe 1960s onwards, leading to a potential underestimation of the risk per unit dose. Further work would
be necessary to investigate the full effect of internal radiation worker exposure.

(g) Cancer mortality among nuclear workers in Japan
261. A cohort study of Japanese nuclear workers was started in 1990 by the Institute of Radiation
Epidemiology of the Radiation Effects Association [A1]. The cohort included 200,583 male Japanese
workers followed up from 1991 to 2000, with an average follow-up duration of 6.8 years. The cohort
had an accumulated 1,373,000 person–years at risk. A total of 2,636 deaths from cancers other than
leukaemia were observed during the follow-up period, a value very close to that expected for in thegeneral population. About a quarter of the members of the cohort (48,281 males) responded to aquestionnaire survey used to determine occupational history and lifestyle characteristics such as alcoholand tobacco consumption.
262. The radiation exposure of the Japanese nuclear workers in this cohort was almost exclusively
external. Individual external doses were recorded using film badges, and in recent years, TLD. The
mean individual cumulative dose was 12.2 mSv, with 75.4% of the cohort having received doses lessthan 10 mSv and 97.4% of the cohort received doses less than 100 mSv. Poisson regression was used toderive the ERR per unit dose for leukaemia and for other cancer mortality. Statistical analyses of theANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 89

dose response were carried out using cumulative doses lagged by 2 years for leukaemia and 10 years
for cancers other than leukaemia.
263. The ERR at 1 Sv for all cancers except leukaemia was 1.26 (95% CI: −0.27, 3.0). To investigate
the potential confounding effect of alcohol consumption, a dose–response analysis was carried out bygrouping alcohol-related cancers. The ERR at 1 Sv for alcohol-related cancers only was 4.64 (95% CI:
1.13, 8.91), while the ERR at 1 Sv for all cancers excluding those related to alcohol and excluding
leukaemia was 0.2 (95% CI: −1.42, 2.09). The ERR at 1 Sv for smoking-related cancers was also
increased 1.9 (95% CI: −0.28, 4.47), compared to the ERR for all cancers except leukaemia.
264. Alcohol consumption and smoking are suspected to have been important confounding factors that
may have influenced the results. The risk of death from alcohol-related cancers is strongly related to the
cumulative dose. Mortality from liver cancer was not found to be statistically related to the cumulative
dose. However, no unequivocal explanation is available to clarify why. Furthermore, a more recent
study by Kudo et al. [K27] examined the confounding effect of smoking in a differently constructedcohort of Japanese nuclear workers followed up from 1999 to 2010 and determined that, when adjustingfor smoking, the ERR at 1 Sv for all cancers except leukaemia declined compared to the ERR at 1 Sv
obtained without adjusting for smoking.

(h) Cancer incidence and mortality in Canadian uranium processing workers
265. The retrospective cohort study of uranium workers from Port Hope, Ontario, Canada included
3,000 males and females with dates of first employment between 1932 and 1980, who were followed up
for cancer mortality from 1950 to 1999 and for cancer incidence from 1969 to 1999 [Z3]. The cohort
included workers that processed uranium (2,472) and radium (528), however did not have a history ofwork in uranium mines. The average age at first employment was 30 and the average duration of
employment was 6.4 years. Out of all the workers 2,645 were males of whom, 2,148 processeduranium, and 497 processed radium. Given the low number of females and their low exposures,
analyses were carried out for males only. This study provides 82,999 person–years of follow-up for
mortality and 55,493 person–years for incidence (males only).
266. The members of this cohort were exposed to external gamma radiation and to radon decay
products, but also to long-lived radionuclides (e.g. concentrated forms of uranium sulphites, nitrates,oxides and fluorides) and other chemicals. Exposure was characterized separately for external gamma
radiation and radon decay products, with external exposures being represented by effective doses(reported in mSv) and internal exposures represented in terms of WLM. Gamma radiation was theprimary type of radiation exposure at Port Hope. Measured doses by individual dosimeters wereavailable for all workers starting after 1970. For earlier years, gamma doses were calculated using
estimated average dose rates and time on the job. Exposures to radon decay products were determinedbased on monitoring data including radiochemical analyses of urine and whole-body counting. Dosesfrom external gamma exposure to the end of follow-up, for males, had a mean of 116.4 mSv(range 0–5,099 mSv). Radon decay products produced an exposure, for males, with a mean of
13.3 WLM (range 0–628 WLM) [Z3].
267. Cancer incidence and mortality were lower among the members of this cohort when compared to
the general Canadian population. A total of 418 cancer cases were observed during the period of
follow-up, when 453.52 cases were expected (SIR=0.92; 95% CI: 0.84, 1.01). Similarly, 266 cancer
deaths were observed compared to 282.5 expected cancer deaths (SMR=0.94; 95% CI: 0.83, 1.06).
Analyses using a linear dose response resulted in a mortality ERR per unit dose for solid cancers of
0.12 (95% CI: <−0.35, 0.98) Sv−1. Because radon decay products primarily affect the lung tissue,

90 UNSCEAR 2019 REPORT

separate dose–response analyses were carried out for lung cancer, resulting in a mortality ERR at
1 WLM of 0.21 (95% CI: <−0.45, 1.59) and an incidence ERR at 1 WLM of 0.77 (95% CI: <−0.19,
3.4). Other dose responses for either incidence or mortality were not statistically significant [Z3].
268. The study by Zablotska [Z3] included both cancer incidence and mortality and a relatively long
follow-up period (especially for mortality). However, the cohort was small (2,645 males) and thestatistical power low. Dose responses were provided separately for external gamma radiation and radon
decay products and analyses for cancer other than lung were performed, all showing non-significant
dose responses. A confounding effect of exposure to radon decay products on cancers other than lungcould not be addressed by the existing data.

(i) Cancer incidence and mortality in Chernobyl emergency and recovery
workers269. Emergency workers from the Russian Federation who responded after the 1986 Chernobyl
accident have been followed up to assess radiological health effects. Solid cancer incidence andmortality data were collected by the Russian National Medical and Dosimetric Registry. A retrospective
epidemiological study of solid cancers has been carried out on a cohort of 67,568 male workers who
worked in the Chernobyl Exclusion Zone in the period 1986–1987 [K5]. The follow-up period of thecohort for this study was 1992–2009. A total of 4,002 solid cancers were diagnosed and a total of 2,442
solid cancer deaths were recorded during the follow-up period. However, many of the cancers werediagnosed as a result of the mandatory annual health examinations that started in 2003. The study has afollow-up of 972,659 person–years for cancer incidence, and 993,423 person–years for mortality.
270. The members of the cohort were aged between 18 and 70 at the time of entry into the exclusionzone, with a mean age of 34 years (median 35). Dosimetry was based on recorded dates of arrival anddeparture from the Chernobyl Exclusion Zone. The majority of doses (85%) were measured using
individual dosimeters, while the remaining doses were determined from group dosimeters or estimated[P7]. Cumulative absorbed doses to each group member from external exposure to gamma radiation
during the working period ranged from 0.0001 to 1.24 Gy (median 0.102 Gy; mean 0.132 Gy), with
46% of the persons having received doses less than 0.1 Gy and 99% of the persons’ doses less than
0.3 Gy. Uncertainties in individual doses ranged between factors of 0.5 to 3, depending on the method
used (individual dosimeter, group dosimeter or calculations based on dose rate).
271. Cancer incidence was about 18% larger in the emergency workers, compared to the baseline rates
among males in the Russian Federation, as indicated by the observed SIR of 1.18 (95% CI: 1.15,1.22).
However, this result could have been influenced by the screening effect introduced by the mandatory
annual health examinations. Cancer mortality, on the other hand, was not statistically different from themortality baseline cancer incidence rate (SMR=0.95; 95% CI: 0.92, 0.99).
272. Statistically significant linear dose responses were observed for both incidence and mortality. The
ERR per unit dose for solid cancer incidence was 0.47 (95% CI: 0.03, 0.96) Gy−1, and this value wasobtained when an attempt was made to adjust for the screening effect that may have produced theincreased cancer incidence compared to the baseline risk (SIR=1.18). For solid cancer mortality, theERR per unit dose was 0.58 (95% CI: 0.002, 1.25) Gy−1.
273. The strengths of this study include a large cohort with 85% of doses measured by individual
dosimeters; and availability of both incidence and mortality data. However, this study (a) has a fairly young
cohort, with a relatively short follow-up period 1992–2009 available to date; (b) does not have information

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 91

on smoking or alcohol consumption; (c) includes no investigation of non-linear dose responses; and (d) has
results possibly influenced by the screening effect of mandatory annual medical examinations.

(j) Cancer incidence and mortality in Mayak workers
274. The Mayak Production Association located in Ozyorsk in the Chelyabinsk oblast of the RussianFederation was an industrial complex dedicated to the production of plutonium for the Soviet nuclear
weapons programme. The Mayak Production Association began operations in 1948 and has included
nuclear reactors, radiochemical and plutonium production plants and a number of auxiliary
departments. Studies of radiological health effects in Mayak workers started in the 1980s when the
Mayak workers cohort was first established. Occupational radiation exposure was complex as membersof the Mayak cohort worked at five different facilities, where operations and radiological protection
measures had changed with time (resulting mostly in a reduction of dose over time). Workers
experienced external exposure to gamma radiation, internal exposure due, mostly, to inhalation of
plutonium and other alpha emitters, or a combination of both external and internal exposure. Lung, liverand bone are particularly affected by the internal exposure to plutonium and other alpha emitters andthey have been studied separately [G2, G3, G5, K15, K16, S13]. This section focuses on the newest
studies of risk from external exposure to low-LET radiation for incidence and mortality of solid cancers
other than lung, liver and bone (“non-LLB cancers” [H6, S11, S12]).
275. Dosimetry for studies of incidence [H6] and mortality [S12] of solid cancers other than lung, liverand bone was based on the 2008 version of the Mayak worker dosimetry system (MWDS-2008) [K12].
External doses were mainly from gamma radiation and were monitored by the Radiation Protection
Department of the Mayak Production Association. Estimates of doses to specific organs from externalsources were obtained either from individual film badge measurements or from reconstructed badge
readings based on individual work histories. Film badge measurements were corrected to account forenergy and angular variation, using information on the nature of the radiation fields at various
workplaces. Measurements of cumulative external exposures are expressed as personal equivalent
dose* at a tissue penetrating depth of 10 mm.
276. The cohort for analysis of solid cancer incidence [H6] includes 22,366 workers (75% males;
25% females) first employed between 1948 and 1982, with 6,699 workers with known plutonium
exposure, 5,154 workers confirmed to have had no plutonium exposures, and 10,513 potentially
exposed to plutonium (but unmonitored). The cohort is restricted to Ozyorsk residents, as data on
diagnosed cases could not be collected for persons who migrated out of the area. The follow-up period
includes 535,932 person–years.
277. For the incidence study cohort, the mean cumulative external dose was 0.51 Gy (range of 0 to
6.8 Gy; mean dose to males of 0.54 Gy; mean dose to females of 0.44 Gy). The mean dose wassubstantially higher for people who started to work between 1948 and 1958 (0.81 Gy) compared tothose who started to work between 1959 and 1982 (0.15 Gy).
278. A total of 1,447 solid cancers other than lung, liver and bone were diagnosed during the follow-up
period. Non-melanoma skin cancer was not included among these cases because of incomplete registration.
Dose–response analyses for cancer incidence were conducted using Poisson regression, with cohort dataclassified by sex, attained age, calendar year, smoking status, alcohol consumption, plant where the person
received the exposure, and estimated cumulative dose. Different latency periods (0, 5, 10, 15 and 20 years)
were considered. An ERR model linear in dose was selected as the main type of dose response for cancer
incidence, with a linear–quadratic fit as a potential alternative. Dose responses for incidence of all solidcancers except lung, liver and bone as a group and cancer-specific dose responses were investigated.

92 UNSCEAR 2019 REPORT279. A statistically borderline significant relationship with cumulative dose from external gamma
exposure was observed for non-LLB solid cancer incidence on a basis of a zero-year lag for cumulative
doses (ERR per Gy=0.07; 95% CI: 0.01, 0.15; p=0.06) after adjusting for age, sex and smoking status.
A non-statistically significant association was obtained after adjusting for internal plutonium exposure(ERR per Gy=0.06; 95% CI: −0.01, 0.14; p=0.12). Alternative lag periods (5, 10, 15 and 20 years) wereused, but the resulting estimates or their statistical significance did not change substantially. There wasalso no evidence of non-linearity with external dose, based on a linear–quadratic model.
280. The mortality cohort [S11, S12] includes 25,757 persons, of whom 25% are females. Cohort
members started employment in the period 1948–1982 and they have been followed up for cancer
mortality from 1948 until 2008. The mean age of entry into the cohort was 24.8 and the mean age ofexit was 61.7 (average follow-up of 36.9 years). The follow-up period includes 950,896 person–years.
281. Cigarette smoking was recorded among the mortality cohort, with 50% of the cohort being
smokers, 37% never smokers and the remaining 14% having an unknown smoking status. Smoking wasmore prevalent in males than in females. Among males, 65% were smokers and 22% were neversmokers, compared to 3% smokers and 80% never smokers among females.
282. Epidemiological analyses of mortality for solid non-LLB cancers were carried out using 5-year
lagged cumulative external doses to the colon up to the end of follow-up. Doses ranged from zero to
more than 3 Gy. The mean external dose was 0.354 Gy [S11]. People with doses to the colon of lessthan 100 mGy contributed 54% of the follow-up expressed in person–years, while those with doses lessthan 1 Gy contributed 90% of this.
283. Statistical analyses for the mortality cohort were carried out using Poisson regression. Analysedmodels included linear and non-linear dose responses, with and without attained age modifiers. The
studies also analysed the possible effect of internal exposures to alpha particles on the mortality risk ofsolid, non-LLB cancers as a function of the doses from external gamma exposure. These analyses werecarried out for the entire cohort using time-dependent indicators of exposure to alpha particles [S11]and for subcohorts of persons who did not work in areas where intakes of alpha-emitting radionuclides
were likely (table 13) [S12].
284. Linear dose responses with no age modifiers produced the best statistically significant fits to thedata. The ERR per unit dose for mortality from all solid cancers other than lung, liver or bone fromexposure to external gamma radiation was 0.16 (95% CI: 0.07, 0.26) Gy−1, when unadjusted forplutonium exposure and 0.12 (95% CI: 0.03, 0.21) Gy−1, when adjusted for plutonium exposure. These
results represent the dose response for the entire cohort. The dose response for a subcohort that does notinclude plutonium workers produced a similar ERR per unit dose: 0.14 (95% CI: 0.04, 0.25) Gy−1,
while the dose response for reactor workers only was higher: 0.25 (95% CI: 0.01, 0.59). Analyses forother subcohorts (table 13) also produced comparable ERRs, indicating that the observed dose response
from exposure to external gamma radiation was not influenced by the exposures to alpha particles.
Male and female smokers showed significantly elevated risks of death from non-LLB compared tonon-smoking males and females with estimated ERRs per unit dose of 1.53 (95% CI: 1.33, 1.76) Gy−1
and 1.60 (95% CI: 1.01, 2.40) Gy−1, respectively [S12].
285. The major strength of the Mayak studies is a relatively large cohort with long follow-up of goodquality, with individual doses from external exposure from measurements and detailed work histories.
Multiple dose–response models have been studied and the effect of smoking has been assessed. Alcohol
consumption has not yet been accounted for explicitly. The current risk models for the Mayak cohort
are based on the 2008 version of the dosimetry system (MWDS-2008). A new, updated Mayak workers
dosimetry system, labelled MWDS-2013, has recently been published [N3, V4] and is already being

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 93

updated by MWDS-2016 [V5]. The MWDS-2013 dosimetry system incorporates a Bayesian approachto determining plutonium intake from historical urinalysis samples, and a revised set of models for theestimation of doses from external sources of radiation, adapted and expanded to account for
uncertainties in the exposure scenarios, measurements and other parameters. Revised dose–response
analyses based on MWDS-2013 or MWDS-2016 have not been published.
Table 13. Excess relative risk per Gy from external gamma exposure for solid cancers other thanlung, liver and bone observed in the Mayak worker cohort [H6, S11, S12]
Cohort/subcohort

Number ofworkers

Cases aERR per Gy

95% CI

1 447

0.07

0.01, 0.15

No adjustment for effect of 239Pu

0.16

0.07, 0.26

Adjusted for effect of 239Pu

0.12

0.03, 0.21

INCIDENCE
Full cohort

22 366MORTALITY
Full cohort

25 757

1 825

No plutonium workers

17 944

1 281

0.14

0.04, 0.25

No plutonium or radiochemical workers

8 800

593

0.20

−0.0002, 0.46

Reactor workers only

5 416

405

0.25

0.01, 0.59

NA

NA

0.19

0.022, 0.39

16 957

1 232

0.15

0.063, 0.25

Plutonium versus non-plutonium workers
Reactor + auxiliaryRadiochemical + plutonium productionaIncident cases or death cases, respectively, from solid cancers other than lung, liver and bone.

(k) Cancer mortality in Rocketdyne workers
286. Rocketdyne was a rocket engine testing facility in the United States. In 1950, Rocketdyne mergedwith Atomics International. It was located in California at the Santa Susana Field Laboratory, Canoga
Park and Do Soto sites, where ten research reactors and seven criticality facilities were operated. Other
radiation-related activities included fabrication of enriched uranium and plutonium fuel (from 1958 to1983) for research, space and power reactors, decladding spent nuclear fuel, storing radioactive
material, and disassembling and decontamination of reactor facilities.
287. A cohort of Rocketdyne workers has been set up to analyse radiogenic cancer mortality. The first
analysis followed up the cohort from 1948 until 1999 [B6], and an updated analysis added nine years offollow-up (1948–2008) [B8]. The cohort now includes 46,970 workers, out of which 5,801 wereinvolved in radiation-related activities. While at Rocketdyne, workers were monitored for external
exposure (3,569), internal exposure (58) or both external and internal exposures (2,174). There were1,833 (32%) workers who were monitored for radiation either before or after employment at
Rocketdyne. Among the 2,232 workers monitored for internal exposures, 87% (1,941) had negligible
intakes. The remaining 41,169 members of the cohort were engaged in activities not involving radiation
exposure. Most of the workers were males (92%). Vital status of workers has been ascertained from
1 July 1948 (or six months after the date of first hire, whichever came later) up to 31 December 2008(or age 95, whichever came first), producing a total of 196,674 person–years of follow-up.

94 UNSCEAR 2019 REPORT

288. Whole-body doses from external gamma exposure were obtained in two ways: from abstracted
dosimeter records from Rocketdyne/Atomics International radiation files or by linking Rocketdyne
workers with various nationwide databases to determine exposures before and after employment at
Rocketdyne [B7]. Doses were cumulated through end of follow-up for those alive and to the date ofdeath for those who died. The mean dose from external exposure was 13.5 mSv with a maximum of
1 Sv, with approximately 87% of persons having doses less than 100 mSv. A small number of workers(665 workers) received neutron doses from Rocketdyne/Atomics International or other employment.
The mean dose from neutrons was 1.2 mSv, with 93% having received doses less than 5 mSv and 98%
less than 30 mSv (maximum 55.8 mSv). Internal doses to 16 organs from intakes of 14 radionuclides
were estimated based on bioassay data recorded on 11 different bioassay forms. The most important
contributors to internal exposure were isotopes of uranium (234U, 235U and 238U), plutonium (239Pu),
strontium (90Sr), thorium (232Th), polonium (210Po), americium (241Am) and cerium (144Ce). Lung was
the organ with the largest internal exposure. The mean dose to lung from external and internal radiation
combined was 19 mSv.
289. Out of the 5,801 monitored persons, 2,382 died during the period of observation, with 684 deaths
due to malignant neoplasms [B8]. A number of 778.2 solid cancer deaths from radiation were expected,
indicating an SMR of 0.88 (95% CI: 0.81, 0.95). Relative risks were estimated by Cox proportional
hazard modelling techniques. The relative risk at 100 mSv for all cancers except leukaemia fromexposure to external radiation was 0.98 (95% CI: 0.82, 1.17). This converts into an ERR at 1 Sv of −0.2
(95% CI: −1.8, 1.7). The RR at 100 mSv for lung cancer was 1.01 (95% CI: 0.89, 1.16) or an ERR at
1 Sv of 0.1 (95% CI: −1.1, 1.6). Relative risks from internal exposures for organs other than lung havebeen calculated. As for solid tumours and lung, no statistically significant increases in risk were seen
for any individual solid cancer types.
290. Strengths of this study include the cohort design with a large number of non-exposed workers
from the same facilities, estimation of doses both before and after employment at Rocketdyne and along period of observation of up to 60 years (mean: 33.9 years). Limitations include (a) a small numberof exposed workers (n=5,801), leading to low statistical power; (b) no possibility of analysing the joint
effect of external and internal exposures, because of small number of cases (only six lung cancers)among workers with more than 5 mSv from both external and internal exposures; (c) possible errors in
dosimetry which are unaccounted for; (d) no analyses of non-linear dose responses; and (e) incomplete
knowledge about confounding factors such as smoking.

3.

Studies of members of the public exposed to radiation(a) Cancer incidence and mortality in Techa River cohort
291. From 1949 to 1956, the radiochemical plant that operated at the Mayak Production Association in
support of the Soviet nuclear weapons programme released large amounts of radionuclides into the
Techa River, creating significant exposures of a population of about 30,000 residents of 41 ruralvillages along 250 km of this river. The exposed residents are members of the Techa River cohort
which includes males and females of all ages. This cohort has been followed to study cancer mortality
from 1950 to 2007 [S4] and cancer incidence from 1956 to 2007 [D3], accumulating 927,743 person–
years for mortality and 472,788 person–years for incidence.
292. The cohort for cancer mortality includes 29,730 persons, out of which 42% are males (12,487)
and 58% are females (17,243), with 80% Slav and 20% Tatar and Bashkir ethnicities. Of all membersof the cohort, 40% were exposed before age 20, 28% at ages 20–40 and 32% after age 40.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 95

293. The cohort for cancer incidence includes 17,435 persons, out of which 43% are men (7,521) and
57% are females (9,914), with 68% Slav (11,810) and 32% Tatar and Bashkir (5,625) ethnicities. The
distribution of the cohort by age at entry is: 7,353 (less than 20 years of age), 5,876 (20–40 years ofage) and 4,206 (more than 40 years of age).
294. Members of the cohort were exposed externally to penetrating gamma radiation from river
sediments and flood-plain soil, and internally through ingestion of water and food products
contaminated primarily with 89Sr, 90Sr and 137Cs, but also with 144Ce, 95Zr and 95Nb. Estimates of thedoses to organs were based on the most recent Techa River Dosimetry System (TRDS)-2009 [D4].
Analyses of incidence and mortality for solid cancer as a group were performed using the 5-year lagged
cumulative doses to the stomach, which varied from 0 to 960 mGy.
295. Analyses of solid cancer mortality based on 2,303 solid cancer deaths (1,188 males and 1,115
females) indicated a statistically significant linear trend with an ERR at 100 mGy of 0.061 (95% CI:
0.004, 0.127). No EAR analyses have been carried out for this study. The reported ERR per unit dose
represents a total of 927,743 person–years of follow-up, out of which 92.3% were for persons thatreceived less than 100 mGy (99.9% less than 500 mGy) [S4] with a mean dose to the cohort of 35 mGy.
296. Analyses of solid cancer incidence based on 1,933 cases (963 males and 970 females), with amean age at diagnosis of 63, indicated a linear dose response with an ERR at 100 mGy (adjusted for theeffects of smoking) of 0.077 (95% CI: 0.013, 0.15). The ERR at 100 mGy unadjusted for the effects ofsmoking was similar: 0.09 (95% CI: 0.02, 0.16). The reported ERR represents 472,788 person–years offollow-up, out of which 89.1% were for persons who had received less than 100 mGy (97.7% less than
300 mGy). The mean dose to the stomach was reported as 52 mGy, with a mean dose to the persons
diagnosed with cancer (dose to the cases) of 55 mGy.
297. The ERRs observed for the Techa River cohort are not suitable to estimate risks in a population of
adult workers, because it includes children (40% of the cohort received exposures under age 20). No
ERR values are available for adults only.
298. The Techa River studies provide important insights into the radiation-related health risks in thecase of accidental or routine prolonged radiation exposure of the general population (of both sexes, allage groups and different health status). The strength of these studies is the relatively large andunselected cohort of males, females and children with a long follow-up. The cohort includes persons
who lived along the river at some time during the period 1950–1960, out of which about 75% havedied. For the mortality study, a limitation is introduced by the lack of complete histopathologicalverification of the cancer deaths and by missing cause of death for about 9% of deaths. The results ofboth mortality and incidence studies could have been affected by the relatively large fraction (~20%) of
the population lost to follow-up because of migration out of the area, by the potential medical screening
bias, by unaccounted radiation exposure due to medical diagnostic examinations (e.g. fluoroscopy) andby issues with cancer ascertainment [D3, K10, K17, S4]. In addition, there are still concerns about
uncertainties related to the estimated doses [D3, S4].
299. A study to revise the Techa River Dosimetry System (TRDS)-2009 with the purpose ofquantifying the uncertainties in dosimetry is ongoing and revised dose–response analyses accounting
for the uncertainties in dose are expected.

96 UNSCEAR 2019 REPORT(b) Cancer incidence in India’s high natural background area
300. Epidemiological studies on cancer in high level natural radiation areas provide useful information
on chronic low dose and low dose rate radiation exposure on human populations. The population
residing in Karunagappally Taluk of Kerala state in southwest India gives a unique opportunity toevaluate health effects of low dose and low dose rate chronic exposure prevailing in this area becauseof its vast population size and varied range of background radiation exposure due to the patchy
distribution of 232Th containing monazite sand. Radiation levels in this area vary from less than 1.0 to
45 mGy per year and some places are reported to be as high as 70 mGy per year.
301. A comprehensive survey was conducted in this area and a population cancer registry wasestablished in 1990. Data on cancer incidence and cancer deaths were collected during 1990–1996 andthe preliminary study did not reveal any increase in cancer incidence due to the level of external gamma
radiation [N1].
302. A more recent study [N2] was conducted that examined 69,958 residents (32,085 males and37,873 females) with age range between 30−84 years, an average follow-up period of 10.5 years, andwhich accumulated 736,586 person–years of observation. A total of 1,349 cases of cancer (747 males
and 602 females) and 30 leukaemia cases were identified by the end of 2005. Data obtained were
evaluated with respect to sex, attained age, follow-up interval, sociodemographic factors and smoking.
303. Cumulative radiation doses to colon were estimated for all members of the cohort, lagged by
10 years for analyses of risk of solid tumours. Doses to bone marrow lagged by two years were used forleukaemia analyses. Doses are based on TLD measurements in indoor and outdoor environments
combined with occupancy factors describing the fraction of time spent in each environment andconverted to colon or bone marrow dose. For solid tumours, the portion of the cohort including persons
with a cumulative dose to colon less than 50 mGy was used as reference. Persons with doses less than
500 mGy account for 98% of all solid cancer cases (21% of cases in the reference group with doses lessthan 50 mGy, and 77% of cases for the group with doses between 50 and 500 mGy). Also, persons withdoses less than 500 mGy account for 99% of person–years of follow-up, while those with doses lessthan 100 mGy account for 60% of the person–years.
304. The ERR per Gy for incidence of all cancers except leukaemia was estimated to be −0.13 (90%CI: −0.58, 0.46; p>0.5) [N2]. The incidence of leukaemia excluding CLL was not significantly related
to high level natural radiation. The cancer incidence among the residents in high level natural radiation
areas of Karunagappally Taluk in Kerala state, India suggests that the ERR per unit dose for solidcancer after chronic radiation exposure is significantly lower than that associated with acute exposure inother studies [N2].
305. A recent follow-up study on the Karunagappally cohort showed that bidi smoking (larger amountsof bidi smoked a day and longer durations) increases the risk of hypopharyngeal and laryngeal cancer.
Tobacco chewing was found not related to the risk of hypopharynx or larynx cancer [J3].
306. This study includes persons exposed to very low doses who have been followed up only for a
relatively short time period. Thus, the study may not have adequate power to detect a dose response,
despite the relatively large size of the exposed population. Environmental and social factors such as
tobacco use and bidi (local tobacco) smoking, income difference, access to health care and migration of
population may also affect the dose response [N2]. Overall, this study indicates that it is unlikely thatthe risk from exposure at these levels of natural radiation is substantially higher than currently believed.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 97(c) Cancer mortality in China’s high natural background area
307. The Yangjiang region in the Guangdong province, China, is known for its high levels of
background radiation, with an average annual dose from external radiation from natural sources,
including thorium, of 2.1 mSv, compared to levels of 0.77 mSv in a control area. Yangjiang has twohigh background radiation areas, Tongyou and Donganling, with 21,838 and 44,786 residents,
respectively, during the 1979 census. The control area is located in the eastern part of Yangjiang andhad 25,924 residents during the same census.
308. To study the effect of the high background radiation, a cohort of 31,604 persons (16,045 males
and 15,559 females) was established [T3]. The members of the cohort attained the age of 30 to 74 years
during the follow-up period 1979–1998. It was decided to exclude persons younger than 30 years of age
because the baseline cancer risk is low for those in this age group. Persons aged 75 years or older were
not included because they were less likely to seek medical care for cancer or other diseases, and this
could result in inaccurate diagnosis and cause of death. The mean attained age of persons from the highbackground area was 54, while the mean attained age in the control group was 57.
309. In the follow-up period 1979–1998, the study accumulated 736,942 person–years at risk (528,429
in the high background radiation group and 208,513 person–years in the control group). A total of
6,005 deaths were recorded, with 956 deaths from cancer (941 from cancers other than leukaemia, and15 deaths from leukaemia). The most common cancer types resulting in death were liver (202 males, 73
females) and oropharynx (159 males and 49 females) followed by lung, stomach, colon-rectum anduterus. To determine cancer ascertainment, trained local census workers visited hospitals in the study
area and reviewed the medical records of the deceased persons and extracted the relevant information.
310. Doses were based on indoor and outdoor measurements of gamma dose rates (at a height of 1 m,with the contribution from cosmic rays being subtracted) and on sex- and age-specific occupancy
factors, the fraction of time spent indoors. Outdoor measurements were taken in each village andcommunity, on main roads, in alleys, in open recreational areas, on rice paddies, in areas adjacent towells, and on banks of ponds. Indoor measurements were carried out in about one third of homes ineach village or community, 8,028 households in total. House occupancy factors were based on aquestionnaire survey of 5,291 persons (aged 0–92 years, mean age 32.3) in 88 villages andcommunities. Cumulative doses to the colon lagged by 10 years were used in the risk analysis. The
mean individual cumulative dose was 21.6 mGy in the control area. In the Tongyou and Donganlinghigh background areas, the mean doses were 88.5 and 81.7 mGy, respectively. Among the 941 cases ofcancer death (excluding leukaemia), 81% received cumulative doses less than 100 mGy, and 95.6%
received doses less than 125 mGy. Among all members of the cohort, 28,009 persons (88.6%) received
doses less than 100 mGy.
311. Statistical analyses were carried out by Poisson regression using linear dose–response models
with no age modifiers [T3]. The ERR per unit dose for all cancers excluding leukaemia was −1.01(95% CI: −2.53, 0.95) Gy−1. Liver cancer was the most commonly observed cancer type leading todeath, but there were difficulties of accurately diagnosing and establishing liver cancer as a cause ofdeath, and the liver cancer data are suspected to have contained errors. Only six cases of liver cancer
deaths had pathological confirmation. Thus, an analysis was also carried out for all cancers excluding
both leukaemia and liver cancer. The resulting dose response was positive, however had a negativelower bound: ERR per unit dose of 0.19 (95% CI: −1.87, 3.04) Gy−1.
312. The strengths of this study [T3] include a stable population with exposed and controls from areas
with similar life styles. The limitations include (a) the dosimetry does not include contributions frominternal doses (due to inhalation of radon decay products), a contribution that would affect at least the

98 UNSCEAR 2019 REPORT

risk of lung cancer; (b) indoor measurements have been carried out in an only limited number ofhouseholds, although they did show a rather uniform distribution; and (c) cancer ascertainment of livercancer is questionable, and this is important because this cancer type is responsible for most of thedeaths in the cohort.

4.

Summary of studies of solid cancer risk from low-LET radiation

313. A significant number of epidemiological studies of risk of solid cancers from exposure to lowLET radiation have been published since the UNSCEAR 2012 Report [U8], covering a wide variety of
exposure situations (table 14). Cancer incidence and mortality studies of the Japanese atomic bombing
survivors have been updated with a longer follow-up of the LSS cohort and a revised dosimetry system(DS02R1, fully applied to the solid cancer incidence data so far). New or updated cancer incidence andmortality studies of radiation workers from nine countries (Canada, China, France, Germany, Japan,
Republic of Korea, Russian Federation, United Kingdom and United States) cover nuclear power plant
workers, uranium processing workers, X-ray technicians, emergency workers and others.
314. The reviewed studies have several common characteristics. Low-LET radiation (X-rays and gamma
radiation) was the primary cause of exposure for all cohorts, although exposure to high-LET radiation(alpha particles, neutrons) has occurred in some cohorts and this may have induced biases in thereported dose response for low-LET exposures. Aside from the Japanese atomic bombing survivors(LSS cohort), the other studied cohorts received chronic or fractionated exposures, at low doses anddose rates. For all cohorts, more than 50% of the members received doses less than 100 mGy, withmost cohorts having the majority of their members being exposed to doses lower than 100 mGy (i.e.
~90% or more). Table 14 presents the risk as the ERR at 100 mGy (or mSv), instead of the customary
ERR at 1 Gy, since risks at ~100 mGy (or mSv) are of interest for radiation protection purposes andthey are relevant for the discussed cohorts.
315. In most studies, the best estimate of the ERR at 100 mGy (or mSv) was greater than zero and lessthan 0.1. However, not all positive ERRs per unit dose were statistically significant. Many of thestudies are small (i.e. small number of people and small number of diseases) and have negligible power
to detect radiogenic risk (at the present), but studies that are well conducted should be followed up in
the future, as they may be informative as the workers age. In addition, smaller studies should be pooledwith other studies to create larger cohorts. The worker studies often showed a healthy worker effectindicated by cancer incidence or mortality rates in the exposed group that were lower than those in acomparable general population (i.e. the SIRs or SMRs were less than 1.0); thus, internal analyses within
the exposed group are required to assess a risk per unit dose, and determine if the risk for workers withlow doses is different from the risk for workers with high doses.
316. The LSS cohort is unique because it includes a large number of persons of all ages and both sexes, it
has been carefully characterized and followed up, and it relies on high quality dosimetry. The LSS studies
provide information on the age dependence of radiogenic health effects, allowing derivation of dose
responses that depend explicitly on age at time of exposure, time since exposure and attained age.
317. Among the workers studies, the recent information from the INWORKS is exceptional because of
the long observation time (more than 8 million person–years), the large number of solid cancer deaths
analysed (nearly 18,000), and the high quality of data. Worker studies provide data that are relevant toexposure of adults, mostly males. Thus, dose responses from worker studies cannot be reliably applied
to childhood exposure (e.g. children exposed as members of the public, or paediatric medical
exposures) or to exposure of adult females. None of the worker studies reviewed provided reliable

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 99

information on the dependency of risk with age at exposure or attained age within the adult age group.
However, the dose response from the worker studies could be applied to the estimation of risk in otherworkers exposed to radiation.
318. The ERR at 100 mGy for mortality from all solid tumours from studies of the LSS and
INWORKS cohorts are compatible with the ERR at 100 mGy from the rest of the studies (figure V).
While some of the studies have statistically non-significant ERRs, the central values are within a rangeof 0 and 0.07 per 100 mGy (0 to 0.7 per Gy) for all studies except those on the Rocketdyne workers,
which has a slightly negative ERR at 100 mGy.
Figure V. Excess relative risk at 100 mGy for solid tumours from recent mortality studies of cohorts
with prolonged, long-term exposures, except the LSS cohort which represents an acute exposure
ERRs for LSS [O5], INWORKS [R5], BNFL [G6], Mayak [S11], Chernobyl [K5], Port Hope [Z3], Rocketdyne [B8] andWismut [K24] represent exposures of adults only, while for Techa River [S4] and Yangjiang region [T3] children
are included. The study of nuclear workers in Japan [A1] is not included because of strong confounders (alcoholand smoking)Included are studies published between 2009 and 2017. Previous studies are included in previous UNSCEAR reports
Study
JAPAN
Mortality
Atomic bombing survivors [O5]

CohortExposed(DS02 doses)
86 611 persons
35 687 males
50 924 females

Cases
Exposed(DS02 doses)

Follow-up
Total follow-up
1950–2003(53 years)

Person–years
Exposed(DS02 doses)
3 294 210 PY

50 620 deaths
10 929 deaths fromsolid cancer

Dosimetry aExternal exposure
Mean weighted dose tocolon=117 mGy
79.1% of cohort had
doses <100 mGy

Entire cohort
120 321
persons
50 175 males
70 146 females

Dose response(95% CI) b
ERR at 100 mGy
Sex-averaged
0.042 (0.032, 0.053)
Males
0.028 (0.018, 0.039)
Females
0.057 (0.042, 0.072)

All ages
JAPAN
Incidence
Atomic bombing survivors [G8]Exposed(DS02R1 doses)
80 205 persons
Entire cohort
105 444 persons
42 910 males
62 534 females

Exposed(DS02R1 doses)
22 538 cases
10 473 in males
12 065 in females

Total follow-up
1958–2009(51 years)

Exposed(DS02R1 doses)
2 317 915

External exposure
Mean weighted dose tocolon=114 mGy

Entire cohort
3 079 484 PY

79.2% of cohort had
doses <100 mGy

ERR at 100 mGy
Sex-averaged, linear
0.050 (0.042, 0.059)
Males, linear quadratic ERR at
1 000 mGy=0.20 (0.12, 0.28)
ERR at 100 mGy=0.01(−0.0003, 0.024)

All ages
Females, linear
0.064 (0.052, 0.077)
Sex-averaged, linear
With smoking adjustment
ERR at 100 mGy=0.047 (0.039,
0.055)

100 UNSCEAR 2019 REPORT

Table 14. Summary of recent studies of solid cancer incidence and mortality

StudyUNITED KINGDOM, FRANCE,
UNITED STATESMortality INWORKS
Occupationally-exposed workers[R5]

GERMANY

CohortEntire cohort
308 297 workers
268 262 males
40 035 females

Cases
Solid cancer
17 957 deaths
All cancers
19 748 deaths
All causes
66 632 deaths

4 054 males

Mortality
Uranium millers [K24]

Solid cancers
457 deaths
CardiovasculardiseaseTotal follow-up
1944–2005(61 years)

Person–years

Dosimetry a

Entire cohort
8.2 million PY

External gamma exposure
Dose to colon (10-year lag)
Mean=20.9 mGy
Median=4.1 mGy

Mean follow-up duration
27 years

Total follow-up
1946–2008(62 years)

158 383 PY

Mean follow-up duration
39 yearsMortality
Nuclear power plant workers [M3]

Males
Solid cancers
120 deaths

Total follow-up
1991–2008(17 years)

All causes
310 deaths

Mean follow-up duration
14.6 years

Females
No deaths

ERR at 100 mGy
0.047 (90% CI: 0.018, 0.079)

Effective dose fromexternal gamma radiation(5-year lag)
Mean=26 mSv
Median=11 mSv

External gamma exposure
ERR at 100 mSv

95.8% of cohort had
doses <100 mSv

Adjusted for radon
0.026 (−0.247, 0.298)

Radon daughter exposure
Mean=8 WLMMedian=5 WLM

Radon daughter exposure
Solid cancers
ERR at 1 WLM=0.0174(0.0024, 0.0323)

Long-lived radionuclides
exposure
Mean=3.9 h kBq m−3
8 972 workers
8 746 males
226 females(95% CI) b

90th percentile=53.4 mGy

Non-malignant
respiratory disease
111 deaths

GERMANY

Dose responseMales
128 570 PY
Entire cohort
130 737 PY

Effective dose fromexternal gamma exposure(10-year lag)
Mean=29.5 mSv
Median=5.7 mSv
90% of cohort had
doses <100 mSv

Solid cancers
0.186 (−0.008, 0.38)

Adjusted for gamma
ERR at 1 WLM=0.0163(0.0034, 0.036)
No reported
ERR or EAR dose response
Hazard ratio/mSv
Solid tumours
0.999 (0.996, 1.001)
All cancers excluding leukaemia
0.999 (0.996, 1.001)

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 101

717 deaths

Follow-upKOREA, Republic of
Incidence
Nuclear power plant workers [J4]CohortExposed8 429 workers

Cases

Follow-up

Person–years

Males
Exposed workers
96 cases

Total follow-up
1992–2005(13 years)

Exposed63 503 PY

All workers
197 cases

Mean follow-up duration
7.53 years
exposed workers

Entire cohort
111 804 PY

Total follow-up
1950–1995(45 years)

Exposed683 425 PY

Males only
Entire cohort
16 236 workers

Incidence
Medical X-ray technicians [S16]

Exposed27 011 workers
21 571 males(80%)
5 440 females(20%)
Unexposed
25 782 workers

Exposed795 cases
Unexposed
848 cases

Mean follow-up duration
25.7 years

Entire cohort
1 643 cases

Total effective doses(external and internal)(10-year lag)
Mean=19.9 mSv
Median=2.93 mSv
95% of cohort had
doses <100 mSv

All cancers
excluding leukaemia

CHINA

Dosimetry a

Unexposed
762 950 PY
Entire cohort
1 446 375 PY

Dose response(95% CI) b
ERR at 100 mSv
All cancers excluding leukaemia
0.21 (−0.19, 0.9)
All cancers
0.17 (−0.21, 0.82)

External gamma exposure
Dose to colon (10-year lag)
Mean=86 mGy
Median=42 mGy

ERR at 100 mGy
0.087 (0.048, 0.145)

60% of cohort had
doses <50 mGy

EAR at 100 mGy
2.2 (1.4, 3.2) per 10 000 PY Gy

At attained age 50 years

99% of cohort had
doses <500 mGy

Entire cohort
52 793 workers
UNITED KINGDOM
Incidence and mortality
BNFL nuclear workers [G6]Exposed42 431 workers
38 785 males(91%)
3 646 females(9%)
Unexposed
22 389 workers
Entire cohort
64 820 workers

Exposed5 511 cases
3 026 deaths
Unexposed
3 524 cases
2 223 deaths

Total follow-up
1971–2005(34 years)

Exposed1 164 960 PY
Unexposed
729 109 PY
Entire cohort
1 894 069 PY

External gamma exposure(10-year lag)
Mean=53 mSv
Median=12 mSv
99% of cohort had
doses <590 mSv

ERR at 100 mSv
Solid cancers
Incidence
0.028 (90% CI: 0.008, 0.049)
Mortality
0.029 (90% CI: 0.002, 0.059)

102 UNSCEAR 2019 REPORT

StudyStudyJAPAN

CohortCases

200 583 males

All cancers
excluding leukaemia
2 636 deaths

Mortality
Nuclear workers [A1]

Follow-up
Total follow-up
1991–2002(11 years)

Person–years
Entire cohort
1 373 000 PY

Mean follow-up duration
6.8 years

Dosimetry a
Effective dose fromexternal gamma exposure(10-year lag)
Mean=12.2 mSv
97.4% of cohort had
doses <100 mSv
75.4% of cohort had
doses <10 mSv

Dose response(95% CI) b
ERR at 100 mSv
0.126 (−0.027, 0.30)
All cancers excluding leukaemia
0.020 (−0.142, 0.21)
Excluding alcohol-related
cancers and leukaemia
0.464 (0.113, 0.891)
Alcohol-related cancers only

CANADA
Incidence and mortality
Port HopeUranium millers [Z3]3 000 workers
2 645 males
355 females

418 cases
266 deaths

Analyses formales only

Total follow-up

Entire cohort

Incidence
1969–1999(30 years)

Mortality
82 999 PY

Mortality
1950–1999(49 years)

RUSSIAN FEDERATION
Incidence and mortality
Chernobyl emergency workers [K5]67 568 males

4 002 cases
2 442 deaths

Incidence
55 493 PY

Total follow-up

Entire cohort

Incidence
1992–2009(17 years)

Mortality
993 423 PY
Incidence
972 659 PY

Effective dose fromexternal gamma exposure(5-year lag)
Mean=116.4 mSv
Range=0–5 099 mSv
Radon decay products
Mean=13.3 WLM
Range=0–628 WLMExternal gamma
ERR at 100 mSv
Mortality, solid cancers
0.012 (<−0.035, 0.098)
Radon decay products
ERR at 1 WLM, lung cancer
Incidence: 0.0077 (<−0.0019,
0.034)
Mortality: 0.0021 (<−0.0045,
0.016)

External gamma exposure
Whole-body doses
Mean=132 mGy

ERR at 100 mGy

46% of cohort had doses<100 mGy

Mortality: 0.058 (0.0002, 0.125)

Incidence: 0.047 (0.003, 0.096)

Screening effect99% of cohort had
doses <300 mGy

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 103−0.002 (−0.265, 0.304)
Excluding smoking-related
cancers and leukaemia

RUSSIAN FEDERATION
Incidence
Mayak workers [H6]

Cohort22 366 workers
16 679 males
5 687 females

Cases
1 447 cases
All solid cancers,
except lung, liverand bone;

Follow-up
Total follow-up
1948–2004(56 years)

Person–years
Entire cohort
535 932 PY

Dosimetry aExternal gamma exposure
Hp(10) doses (0-year lag)
Mean=510 mGy
Range: 0–6 800 mGy
90% of person–years had
doses <1 000 mGy

Non-melanoma skin
not included

Dose response(95% CI) b
ERR at 100 mGy
Solid cancers
Excluding lung, liver and bone
Adjusted for age, sex, smoking
status (0-year lag)
0.007 (0.001, 0.015)
Unadjusted for Pu exposure
0.006 (−0.001, 0.014)
Adjusted for Pu exposure

RUSSIAN FEDERATION
Mortality
Mayak workers [S11, S12]25 757 workers
19 318 males
6 439 females

1 825 deaths
All solid cancers,
except lung, liverand bone

Total follow-up
1948–2008(60 years)

Entire cohort
950 896 PY

External gamma exposure
Dose to colon (5-year lag)
Mean=354 mGy
54% of person–years had
doses <100 mGy

Mean follow-up
duration
36.9 years

90% of person–years had
doses <1 000 mGy
UNITED STATESMortality
Rocketdyne nuclear workers [B8]

Exposed5 801 workers
5 335 males
466 females
Unexposed
41 169 workers
31 677 males
9 492 females
Entire cohort
46 970 workers

684 deaths

Total follow-up
1948–2008(60 years)
Mean follow-up duration
33.9 years

Exposedworkers
196 674 PY

ERR at 100 mGy
Solid cancers
Excluding lung, liver and bone
0.016 (0.007, 0.026)
Unadjusted for Pu exposure
0.012 (0.003, 0.021)
Adjusted for Pu exposure

External gamma exposure
Whole body dose
(10-year lag)
Mean=13.5 mSv

ERR at 100 mSv

External and internal
dose to the lung
Mean=19.0 mSv

Lung cancer
0.01 (−0.11, 0.16)

87% of cohort had
doses <100 mSv

All cancers excluding leukaemia−0.02 (−0.18, 0.17)

104 UNSCEAR 2019 REPORT

StudyStudyRUSSIAN FEDERATION
Incidence and mortality
Members of public
Techa River region
Incidence [D3]Mortality [S4]CohortIncidence
17 435 persons
7 521 males
9 914 females

Cases
1 933 cases
2 303 deaths

Follow-up
Total follow-up
Incidence
1956–2007(51 years)

Mortality
29 730 persons
12 487 males
17 243 females

Person–years
Incidence
472 788 PY

Mortality
927 743 PY

Mortality
1950–2007(57 years)

Dosimetry a

Dose response(95% CI) b

External gamma + internalbeta exposure
Doses to stomach(5-year lag)

ERR at 100 mGy

Incidence
Mean=60 mGy

Mortality
0.061 (0.004, 0.127)

Incidence
0.077 (0.013, 0.15)

89.1% of person–years
had doses <100 mGy

Age at entry <20
years for 40% ofcohortMortality
Mean=35 mGy

INDIA
Incidence
Members of publicHigh background radiation area [N2]

Incidence
69 958 persons
32 085 males
37 873 females

All cancers
excluding leukaemia

Total follow-up
10.5 years

1 349 cases
747 males
602 females

Entire cohort
736 586 PY

External gamma
Doses to colon (10-year
lag)
99% of person–years had
doses <500 mGy
60% of person–years had
doses <100 mGy

ERR at 1 000 mGy
Incidence all cancers excluding
leukaemia−0.13 (90% CI: −0.58, 0.46)

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 105

92.3% of person–years
had doses <100 mGy

CHINA
Mortality
Members of publicHigh background radiation areas[T3]

CohortCases

Highbackgroundgroup
31 604 persons
16 045 males
15 559 females

All cancers
excluding leukaemia
941 deaths

Control group25 924 persons
Adults only
Ages 30–74

All causes
6 005 deaths

Follow-up
Total follow-up
1979–1998(19 years)

Person–years
Highbackgroundgroup
528 429 PY
Control group
208 513 PY
Entire cohort
736 942 PY

Dosimetry aExternal gamma exposure
Doses to colon (10-year
lag)

Dose response(95% CI) b
ERR at 100 mGy

High background area
Mean=84.8 mGy

All cancers excluding leukaemia
and liver0.019 (−0.187, 0.304)

Control area
Mean=21.6 mGy

All cancers excluding leukaemia−0.101 (−0.253, 0.095)

88.6% of cohort had
doses <100 mGy
81% of the 941 deaths had
doses <100 mGyaDoses from studies involving acute or short-term exposures (e.g. LSS cohort) represent the total dose received during the exposure event. Doses from studies involving prolonged exposures represent the total
cumulative dose lagged by either 5 or 10 years.
b

95% confidence interval, unless otherwise specified.

106 UNSCEAR 2019 REPORTStudyANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 107

D. Definition of scenario
1.

Exposure scenario

319. The purpose of this scenario is to understand the long-term risk of solid cancer mortality forworkers exposed to low doses and low dose rates of low-LET radiation and to assess the predictive
capabilities of the risk models, including a meaningful assessment of the uncertainties in the estimatesof risk. This section provides the details of the exposure scenario, discusses the models andmethodology used to estimate risk and describes the sources and magnitude of the uncertainties
considered in this risk assessment.
320. A scenario involving a hypothetical case of occupational exposure to low-LET radiation for maleworkers from the United States population was considered. The exposure is assumed to have occurred
over a period of 15 years, starting at age 30 and ending at age 45. The scenario assumes that workers
are alive and cancer free at the beginning of the period of exposure (i.e. at age 30). The risks of cancer
mortality are estimated up to age 60 and 90 (i.e. over the first 30 years and 60 years, respectively, after
the beginning of exposure). The total cumulative uniform whole-body dose is assumed to be 100 mGy,
received in annual increments of about 6.67 mGy.
321. This exposure scenario is similar to the exposure situation encountered by the occupationallyexposed workers included in the INWORKS [H1, R5, T6], who had a mean duration of employment of
15 years, starting at an average age close to 30. The mean attained age at the end of the follow-up formembers of the INWORKS cohort was approximately 60, with a maximum attained age at the end of
the follow-up of 90.
322. The majority of the members of the INWORKS cohort are males (87% males, 13% females) andthe persons with the highest doses are males as well. Thus, the dose response provided by the
INWORKS is representative for males and therefore representative for the given exposure scenario.

2.

Reference data

323. The population of the United States workers was described by baseline rates of cancer obtained
from the Surveillance, Epidemiology, and End Results Program of the US National Cancer Institute(NCI) for 2000–2005 and by survival functions based on the US Decennial Life Tables for the period
1999–2001 [A4]. The baseline rates and survival functions are presented in figure VI. By definition, theUnited States baseline rates and survival functions are considered to be representative for the
population of workers for which the risk is estimated, with no uncertainties.

108 UNSCEAR 2019 REPORT

Figure VI. Baseline mortality rates for all solid cancers in the United States (2000–2005) [N6]3.

Risk models

324. The risk models from the mortality study of the INWORKS workers [R5] and from the mortality
analyses of the LSS cohort [O5] are considered to be the most relevant for the estimation of risk of allsolid cancer mortality for the occupational exposure scenario defined above. These two studies are welldesigned and include large cohorts, large numbers of cancer-related deaths, long follow-up, highstatistical power, good dosimetry and good case ascertainment.
325. Estimates of risk were obtained using the linear risk model from the INWORKS for the full dose
range described by an ERR at 100 mGy of 0.047 (90% CI: 0.018, 0.079) for all solid cancers. A second
set of risk estimates based on the INWORKS was estimated using the ERR at 100 mGy of 0.081 (90%
CI: 0.001, 0.164) derived from the result obtained by restricting the dose range to 0–100 mGy (thisERR is for all cancers except leukaemia) [R5]. These ERR values were assumed to apply to all ages at
exposure and all attained ages (figure VII). Weibull probability distribution functions were used, withthe mode set equal to the best estimate and the 5th and 95th percentiles set to the limits of the reported
90% confidence interval.
326. Risk estimates were obtained using the preferred Ozasa et al. linear models for all solid cancers[O5], based on the LSS data for the full dose range (table 8). These ERR and EAR models account forthe dependency of risk on age at exposure and attained age. The ERR and EAR models are defined as:
ERR or EAR = β 𝑑𝑑 ∙ exp(τ e∗ + υ ln(a∗ )) ∙ (1 + σ s)

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 109

where d is the dose, s is the sex (with s=−1 for males), e∗ = (e − 30)/10 and e is the age at exposure,a∗ = a/70 and a is the attained age, and σ, τ and ν are coefficients for effect modification. The
parameter values and uncertainty distributions are presented in table 8. For the ERR model, theparameter β represents the ERR per unit dose (Gy) at attained age 70 after an exposure at age 30(0.42 per Gy sex-averaged; 0.27 per Gy males and 0.57 per Gy females; female:male ERR ratio=2.1).
For the EAR model, the parameter β represents the sex-averaged EAR per unit dose (104 person–year
per Gy) at attained age 70 after an exposure at age 30 (26.4 per 104 PY/Gy sex-averaged; 24.6 per 104
PY/Gy males; 28.2 per 104 PY/Gy female; female:male EAR ratio=1.1). Figure VII shows ERR at
100 mGy and EAR per 10,000 person–years at 100 mGy obtained from the INWORKS and LSS
studies, as a function of attained age, for adult males.
327. The REID was estimated using the methods described in annex A of the UNSCEAR 2006 Report(appendix B) [U3], with the difference that the risk was cumulated up to age 60 and 90, as opposed tobeing cumulated up to the end of the expected lifetime. The reported CBR and CER represent all solidcancers. In a few cases, the risk for all cancers except leukaemia was estimated, as noted in table 15.
Figure VII. Excess relative risk at 100 mGy and excess absolute risk per 10,000 person–years at
100 mGy for all solid cancer mortality from the INWORKS [R5] and from the LSS (males; [O5])110 UNSCEAR 2019 REPORT

4.

Risk-transfer methods

328. The term “risk transfer” refers to the application of the risks observed in a given population(e.g. the LSS cohort) to another population (e.g. United States workers). The risk of cancer in the
population of the United States workers was estimated using both a purely multiplicative projection anda purely additive projection of risk [U8].
329. The purely multiplicative projection of risk assumes that the excess risk of cancer due to exposure
to radiation is proportional to the baseline cancer rates in the population of workers. That is, themultiplicative projection is obtained by multiplying the baseline rates by the ERR indicated by eachrisk model for the dose of interest. Age- and sex-specific mortality baseline rates for all solid cancers
were used to estimate the REID based on the models from Ozasa et al. [O5] and the INWORKS [R5].
330. The purely additive projection assumes that the excess risk of cancer is given directly by the EAR
indicated by each risk model, thus being independent of the baseline rates of cancer for the populationof workers. The differences observed between the risks predicted by the multiplicative and the additive
projections represent the magnitude of uncertainty in the risk transfer model.
331. Risk estimates based on both the multiplicative and the additive projections were produced using
risk models from the INWORKS [R5] and the LSS [O5] cohorts. The two projections for each model
provide a range of possible values of risk based on the data from the two cohorts [U8].

5.

Accounting for the minimum latency period of cancer

332. In an epidemiological study, the minimum latency period is accounted for by using a lag time
during which no health effect is expected. In the INWORKS, their preferred estimate for the ERR at
100 mGy for all solid cancers mortality was obtained using an assumed fixed lag of 10 years [D1, R5].
Sensitivity analyses were carried out by allowing the lag time to vary. The best fit was three years [D1]and, for this fitted lag time, the observed ERR at 100 mGy was similar to that observed for a fixed
10-year lag.
333. Ivanov et al. [I6] estimated a minimum latency period of four years (95% CI: 3.3, 4.9) for the
induction of solid cancers as a group based on studies of 59,770 emergency workers followed up during
the first 10 years after the Chernobyl accident. The minimum latency period is expected to differ withcancer type, and perhaps to change with age at exposure, but information is limited to only a few
cancers. For the incidence of thyroid cancer after the Chernobyl accident, a minimum latency period of
three years was suggested by Heidenreich et al. [H3] based on childhood thyroid cancer incidence inBelarus. A minimum latency period to the time of surgery (not diagnosis) of about six years wasobserved by Williams et al. [W12] in children from Ukraine and Belarus. For lung cancer in the
Colorado Plateau uranium miners, Gasparrini [G1] modelled the hazard ratio as an increasing functionof lag time, reaching half of its maximum value at about five years and the maximum value at about
10 years. For breast cancer, Norton [N7] estimated an average of eight years of growth from one cell toclinical recognition size of one to five billion cells. Shorter times are now expected for the diagnosis ofcancer, given the existence of more advanced diagnostic techniques, such as mammography.
334. The BEIR VII report [N9] used a minimum latency period of five years in their risk assessment
for both cancer incidence and mortality of any solid tumours, which they described as a “threshold
function”: that is, the risk was assumed to be zero for the first five years after exposure and changed
abruptly to a non-zero risk after that. More realistic predictors of the dynamics of tumour growth are
Logistic- or Gompertz-type S-shaped functions [L1, W13]. For risk assessment, Berrington deANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 111

González et al. [B5] adopted an S-shaped function that increases from 0 to 1 with increasing time after
exposure. This function is used to adjust the risk estimated during the first years after exposure.
335. For the solid cancer scenario addressed in this section, the risk was phased using an uncertain
adjustment factor described as an S-shaped function that increases with increasing time after exposure.
The risk reaches 99% of its full value at about seven years after exposure and 99.9% of its full value
about 10 years after exposure, compared with five years used in the BEIR VII report [N9]. The overall
timespan of the minimum latency period is 2 to 7 years (1 and 99% of the full risk). The midpoint,
defined as the time after exposure where risk is one half of its maximum value, is considered uncertain
and varies from 3 to 4 years (i.e. described by a uniform probability distribution function between 3 and4 years). This function is consistent with findings and assumptions used in the INWORKS. In the
INWORKS, the preferred ERR risk model was derived using a 10-year lag time, but, when the lag time
was allowed to vary and it was fitted to maximize the likelihood* function, lags as short as three years
were indicated.

E. Results
336. The cumulative excess risk (CER) of solid cancer mortality in adult male workers was assessed
based on the most recent epidemiological studies. The REID for all solid cancers for United States
workers was estimated assuming an exposure to low-LET radiation, delivered from ages 30 to 45 with a
total dose of 100 mGy (table 15). Risks were estimated using ERR and EAR models developed fromthe INWORKS, which included workers from France, the United Kingdom and the United States, andfrom studies of the LSS cohort of atomic bombing survivors. They were reported as risks cumulated up
to age 60 and 90 years. The application of the ERR models from the INWORKS or LSS represents a
multiplicative risk projection, as the ERR is multiplied by the baseline rates of cancer mortality in the
exposed population of workers. The application of the EAR models assumes that the excess risk due toradiation is independent of the baseline rates of cancer mortality, and is given directly by the EAR fromeach respective risk model.
337. The results based on the ERR model from the INWORKS for all solid cancers and the full dose
range [R5] give values of CER of 11 (95% CI: 3.1, 19.3) chances in 10,000 persons when cumulated up
to age 60, and 86 (95% CI: 24, 151) chances in 10,000 persons when cumulated up to age 90 (table 15).
These excess cases represent fewer than 5% of the number of solid tumour deaths expected to occur inthe absence of exposure to radiation, for the same range of ages. The range of the confidence interval isabout a factor of 1.5 larger than the central estimate.
338. The results based on the EAR model from the INWORKS for all solid cancers and the full dose
range indicate a CER of 11 (95% CI: −1.1, 25) chances in 10,000 persons when cumulated up to
age 60. The central estimate is similar to that obtained from the ERR risk model from the INWORKS.
However, the confidence interval is considerably wider. When cumulated up to age 90, the CER is only
20 (95% CI: −1.9, 44) chances in 10,000 persons, substantially lower than the risk produced by using
the ERR model. The ERR and EAR models from the INWORKS do not include attained age modifiers,
and they represent average risks from the mean age at the beginning of employment (age 28) up to themean age at the end of follow-up (age 58). Thus, the CER cumulated up to age 90 and obtained basedon the ERR model from the INWORKS is likely to be an overestimate, while that obtained based on the
EAR model is likely to be an underestimate. However, the CER cumulated from age 30 up to age 60
are consistent between the ERR and EAR models and they are reliable estimates.

112 UNSCEAR 2019 REPORT339. The estimates based on the ERR model from the LSS [O5] give risks from an exposure to
100 mGy of 6.4 (95% CI: 4.0, 9.8) chances in 10,000 persons when cumulated up to age 60, and39 (95% CI: 25, 58) chances in 10,000 persons when cumulated up to age 90 (table 15). The CER
predicted based on the EAR risk models are larger: 12 (95% CI: 8.1, 18) when cumulated up to age 60
and 52 (95% CI: 34, 75) when cumulated up to age 90. These results were obtained without making any
adjustments for differences in dose and dose rate between the exposure condition of the LSS cohort andthat of the workers assumed for this scenario (i.e. no DDREF adjustment was used). The uncertainties
in such estimates are a factor of 2 to 3.

Table 15. Cumulative risk of all solid cancers mortality for United States workers exposed to a total dose of 100 mGy of low-LET radiation delivered from ages 30 to 45,
estimated using risk models from two epidemiological studies
The confidence intervals include only uncertainties explicitly discussed in the risk modelling section. The impact of further sources of uncertainties is considered in section IV.F
CBR: Cumulative baseline risk; CER: Cumulative excess risk, estimated using the REID/C methodology; CFR: Cumulative fractional ratio; CI: Confidence interval; LSS: Life Span Study. Unlessotherwise noted, excess risks represent all solid cancers as a group; DDREF: Dose and dose-rate effectiveness factor
Risk models

Cumulative mortality baseline risk
All causes (per 10 000)

Excess risk associated with exposure to radiation

Cancer only (CBR) (per 10 000)

FOLLOW-UP TO AGE 60 YEARS(CER (95% CI) a) (per 10 000)CFR (CER/CBR)b

LSS mortality models [O5]Full dose range, all solid cancers
ERR transfer
EAR transfer

1 294

229

0.028
0.054

11 (3.1, 19.3)
11 (−1.1, 25)

0.048
0.048

18.8 (−2.6, 41)0.082

39 (25, 58)
52 (34, 75)

0.022
0.028

86 (24, 151)
20 (−1.9, 44)

0.046
0.011

148 (−20, 320)

0.080

INWORKS mortality models [R5]
Full dose range, all solid cancers
ERR transfer
EAR transfer

1 294

229

Dose range 0–100 mGy, all cancers except leukaemia
ERR transfer

FOLLOW-UP TO AGE 90 YEARS b
LSS mortality models [O5]Full dose range, all solid cancers
ERR transfer
EAR transfer

8 716

1 844

INWORKS mortality models [R5]
Full dose range, all solid cancers
ERR transfer
EAR transfer

8 716

1 844

Dose range 0–100 mGy, all cancers except leukaemia
ERR transfera

No DDREF was applied. The uncertainty ranges were obtained using Monte Carlo methods for uncertainty propagation, using 1,000 iterations with values produced by Latin Hypercube sampling.

b

Risks are integrated up to 60th and 90th birthday, respectively.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 113

6.4 (4.0, 9.8)
12 (8.1, 18)

114 UNSCEAR 2019 REPORT

F. Discussion of scenario calculations
1.

Sources of uncertainties

340. The uncertainties in risks for solid cancer mortality were estimated using the Monte Carlo method
for error propagation. This method is described in annex B (appendix A) of the UNSCEAR 2012
Report [U8]. The uncertainty in each component of the risk calculation was represented as a probability
density function. In the Monte Carlo process, a computer algorithm carried out 1,000 simulations, and,in each simulation, one value of CER was estimated based on one sample obtained for the probability
density function of each component. The CER and its uncertainty range were derived by statisticalanalysis of all Monte Carlo samples.
341. A comprehensive description of the components and sources of uncertainties that can potentiallyaffect the results of any radiation risk assessment are described in annex B of the UNSCEAR 2012
Report [U8]. This section discusses the limitations and sources of uncertainties originating from theepidemiological studies used for estimation of the risk of mortality for all solid cancers as a group, for
the worker exposure scenario.

(a) Selected populations
342. The LSS cohort [O5] includes 86,611 persons with estimated DS02 doses (35,687 males and50,924 females) selected from among the atomic bombing survivors who were within 2.5 km of the
hypocentres at the time of the bombings, combined with the age- and sex-matched sample of people
who were between 2.5 and 10 km from the hypocentres. The cohort also includes approximately 27,000
additional people registered as residents of Hiroshima and Nagasaki in 1950, but who were not in thecities at the time of the bombings. The members of the cohort were selected from the 1950 Japanese
National Census and include persons who had supplementary questionnaires about exposures to theatomic bombings and participated in surveys conducted by the Atomic Bomb Casualty Commission in1950 and 1951 and in the resident surveys of Hiroshima and Nagasaki in 1953 and 1950, respectively.
The entire cohort accounts for 120,321 persons of all ages (50,175 males and 70,146 females).
A potential limitation of the LSS cohort is that the persons survived physical injuries and burns fromthe bombings, biological injuries due to the deterministic effects,* and poor nutrition and hygiene in the
post-war period. These conditions could have led to selective exclusion of vulnerable people.
Nevertheless, estimation of the magnitude of stochastic health effects* such as solid cancer is not likely
to be affected by such selection bias as supported by negligible discrepancies in the dose–response
curves between the early and late periods for all solid cancers [O5, P11].
343. The INWORKS is a retrospective cohort study of workers employed by: the CEA, AREVA
Nuclear Cycle (formerly COGEMA) and the EDF in France; five installations from the Departments ofEnergy and Defense in the United States; and the nuclear industry in the United Kingdom (i.e. those in
the NRRW). The INWORKS cohort includes 308,297 workers in the nuclear industry (87% males)with detailed monitoring data for external exposure to ionizing radiation [H1, R5]. The inclusion
criteria were based on the completeness, quality of monitoring and dose data, and the start of facility
operations. Workers who were employed in the nuclear industry for less than one year were excluded
from the study. Uranium miners and private contractors were not included. The mean age at the
beginning of employment was 28 years and the mean age at the end of the follow up was 58 years.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 115(b) Exposure assessment
344. Doses for the members of the LSS cohort went through several major revisions. Doses for themembers of the LSS cohort were estimated for 15 organs/tissues and represent the gamma dose plus
10 times the neutron dose, to allow for the greater biological effectiveness of neutrons. Analyses of allsolid cancer mortality were carried out using the doses to the colon [O5]. An important source of error
in dosimetry is the exact location and orientation of each survivor, because this information was
obtained from interviews that took place five to ten years after the bombings [C10]. Shielded kerma
estimates were truncated at 4 Gy (this applies to 317 persons), because they are likely to reflectmisinformation on location and shielding. The doses were adjusted for possible biases based on a
statistical method of regression calibration with a plausible error model assuming a 35% multiplicative
error, to eliminate a 6–16% possible bias in the estimated excess risk [P3]. The current dosimetricapproach deals with both classical errors in doses (originating from uncertainty in each survivor’s
location and shielding), and Berkson errors (related to assumed input parameters used to estimate theeffectiveness of shielding [P5, P6]. Most recently, a mixed classical-Berkson error* model has been
introduced to account for uncertainties in the true dose* distribution, and applied to the cancerincidence analyses [M9]. The effect of shared errors in parameters common to many survivors (heightof the two detonations, yield of the two bombings, or location of the hypocentres) is considered to be anegligible source of uncertainty in the estimated risks. The potential effect of internal contamination ofsurvivors with radionuclides deposited in the environment after the bombings was analysed, but noclear evidence of long-term health effects was found [S1].
345. Workers in the INWORKS cohort were exposed mainly to external radiation, usually gamma
radiation, and doses were measured routinely using individual dosimeters, with records being kept as
early as the 1940s [H1, R5, T6]. The main sources of errors in the recorded doses from external
exposure were quantified to account for the evolution of technology and differences in practice between
facilities and countries. Correction factors were derived by dosimeter type and applied to eliminate
biases. The recorded doses were converted into doses to organs. Analyses of risk for all solid tumours
were carried out using the absorbed dose to the colon. The analyses included workers with potential
exposure to neutrons and adjustment was made for neutron monitoring status when possible.
Measurement of neutron doses has been a challenging task, because the energy of neutrons could range
from 1 eV to around 20 MeV and detectors were not able to measure all energies, especially during theearlier years. Thus, it was not possible to estimate doses from neutron exposures for all workers andsome persons might have had unrecorded or missed neutron exposures. For these reasons, estimates ofrisk in relation to external radiation dose were analysed by grouping workers with regard to neutronmonitoring status (16,651 deaths among workers with no neutron dose, 1,570 deaths among people
with a neutron dose less than 10% of the gamma dose, and 843 deaths among people with a neutron
dose greater than 10% of the gamma dose). The ERR for all cancers except leukaemia was 0.048 at
100 mGy for the entire cohort, but 0.055 for persons with no recorded neutron exposure, 0.036 forpersons who had neutron doses less than 10% of their gamma doses, and 0.062 for persons who hadneutron doses greater than 10% of their gamma doses. Although the number of persons with positive
neutron records is small, accounting for neutron exposures demonstrates an additional uncertainty in the
ERR per unit dose from the INWORKS. However, this uncertainty is small, being less than 30% aboveand below the reported ERR per unit dose [R5].
346. In addition, among the members of the INWORKS cohort there was the possibility of unrecorded
doses and of “missed” photon doses in the early years of operations due to high limits of detection,
frequent change of dosimeters, and doses below the limit of detection being recorded as zero. Excluded
doses (due to neutron and internal exposure) and “missed” doses could be positively correlated with

116 UNSCEAR 2019 REPORT

recorded photon doses, but the level of correlation is likely to be different among different sources of
missed or unaccounted exposures (neutrons, internal emitters and missed gamma radiation).
347. The effect of exposure to internal emitters, environmental exposures (radon and terrestrial
external exposures) and job-related medical exposures (e.g. screening chest X-rays; fluoroscopy inearly years) was studied by Fournier et al. [F2] among French workers, who are part of the INWORKS
cohort. No substantial effect on the reported ERR per unit dose was found for exposures to internal
emitters and for environmental exposures. Job-related medical exposures are not well known, especiallyfor early time periods, but several medical exposure scenarios have been investigated, indicating that
the ERR risk coefficient may be reduced by medical exposures by up to 30 to 55%, depending on theassumed scenario.

(c) Health outcome assessment
348. The outcome of interest for the scenario discussed in this section is all solid cancers as a group.
Epidemiological studies for all solid cancers as a group include a larger number of cases (or deaths, inmortality studies) than the number of cases for any individual cancer type in the same cohort and thus
are expected to produce risk estimates with lower uncertainties than those for individual cancer types
from the same cohort. However, large cancer groupings, such as all solid cancers, cannot be used todetermine the risk from highly non-homogenous exposures (e.g. exposure from a CT scan with anarrow field). Also, it is unlikely that all cancers included in the all solid cancers grouping will have thesame dose response. This could be problematical if populations with different spectra of cancer types
are being compared.
349. The members of the LSS cohort have been followed up to 31 December 2003 [O5]. Mortality
ascertainment was facilitated by the family registry system (koseki) which covers the whole of Japanand is more than 99% complete. The cause of death for cohort members was classified by trained staff
in the ABCC/RERF according to the international classification of diseases (ICD). During the long
follow-up period of the LSS cohort, the 7th to 10th editions of the ICD have been used. The all solidcancer grouping analysed by Ozasa et al. [O5] includes cancers with ICD-7, -8 and -9 codes 140–199,
and with ICD-10 codes C00–C80.
350. In the INWORKS, the vital status was ascertained through 2001, 2004 and 2005 for the United
Kingdom, French and United States cohorts, respectively. Information on the underlying cause of death
was abstracted and coded according to the international classification of diseases, 9th revision (ICD-9).
Currently, the main ERR estimate reported by the INWORKS is for all solid cancers (ICD-9 codes
140–199) and was obtained based on the entire range of doses in the cohort [R5]. For subgroups of thecohort with doses restricted to less than 100 mGy or less than 200 mGy, the INWORKS reports the
ERRs only for all cancers except leukaemia (ICD-9 codes 140–203). However, a sensitivity analysis
carried out for the entire cohort (i.e. entire dose range) showed no significant differences between the
ERRs for all cancers, all cancers except leukaemia, all solid tumours, and all solid cancers other thanlung (table A4 in [R5]).

(d) Study design
351. The study of mortality from all solid cancers in the Japanese atomic bombing survivors is notexpected to include any significant bias due to individuals lost to follow-up. That is, the LSS cohort
included 120,321 members, among whom 123 were excluded because of misidentification or
insufficient information. Doses based on the DS02 system were available for 86,611 persons. A total of

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 117

50,620 deaths were observed up to the end of the follow-up period in 31 December 2003. Only a verysmall number of persons were lost to follow-up due to migration out of the country and were censoredat the time of emigration [O5]. Nineteen persons were born before 1900 and were presumed to be alive
as of 1 January 2004 (104 years of age and older). However, only five of them were documented as
being alive and the rest were deleted from the database, had migrated out of the area, or had no
information. Survivors with doses less than 5 mGy were selected as the control group, while the26,529 persons who were not in the cities at the time of the bombings were not included in the analysesof risk because of concerns of compatibility of their mortality rates with those in the zero-dose cohort[O5]. The effect of selection of the control group for the analyses of risk in the LSS cohort has been analysed
by French et al. [F4] for the incidence data. That study showed less than 10% variation in the estimated
ERRs when the control group was changed from the not in the cities group to the zero-dose group.
352. For the INWORKS cohort, information on demographic variables, including sex and date of birth,as well as race for the United States workers, was obtained from the employment records. Information
also included the period of radiation work, job titles, facilities of employment and classificationaccording to socio-economic status. While information on date and cause of death was carefullycollected, information on other factors that may have affected the health of the workers could not berecorded. The follow-up started in 1968 in France, 1955 in the United Kingdom and 1944 in the UnitedStates. Information about persons obtained from employers and national registries was reliable andproduced a minimal loss to follow-up: only 0.22%, 2.56% and 0.83% of workers were lost to follow-up
or emigrated from the French, the United Kingdom and the United States cohorts, respectively [H1].

(e) Confounding factors
353. Smoking, alcohol consumption and other factors can affect the rates of solid cancer incidence andmortality either by themselves or in interaction with exposures to radiation. A strong interaction
between radiation and smoking was observed in the incidence of lung cancer in the LSS cohort [C1,F5]. The reported ERRs for smoking-related cancers might be too high because of such an interaction.
The incidence of lung cancer increased with the degree of smoking as expressed in terms of the numberof cigarette pack-years (equal to one packet of 20 cigarettes every day for one year) and decreased withtime since quitting smoking at any level of radiation exposure. The ERR per unit dose for lung cancer
was significantly higher for low to moderate smokers than for heavy smokers, with little evidence ofany radiation-associated excess risk in heavy smokers [C1, F5]. Significant smoking effects wereobserved for both laryngeal and other respiratory cancers, but there was little evidence of a radiation
effect for laryngeal cancer. A non-significantly elevated risk of other respiratory cancers was observed[C1]. All solid cancers as a group include cancers that are not associated with smoking and thus theeffects of smoking on the dose response for this grouping are expected to be smaller than the effects on
smoking-related cancer sites.
354. Analyses of the INWORKS cohort [R5] indicate that smoking did not have a significant effect in thereported ERR for all solid cancers. Although lung cancer incidence is affected by smoking, the ERR for
all solid cancers group of 0.047 at 100 mGy (90% CI: 0.018, 0.085), is virtually identical to the ERR
observed for all solid cancers other than lung of 0.046 at 100 mGy (90% CI: 0.01, 0.085). Eliminating all
cancers that are potentially related to smoking (oral cavity and pharynx, oesophagus, stomach, colon,
rectum, liver, gallbladder, pancreas, nasal cavity, larynx, lung, cervix, ovary, bladder, kidney and ureter)
yielded an estimated excess relative rate of 0.37 per Gy (90% CI: −0.14, 0.95). This large group ofsmoking-related cancers constituted 70% of solid cancer deaths, and thus resulted in a reduced magnitude
and precision of the estimated ERR per Gy. The effect of alcohol consumption was not assessed118 UNSCEAR 2019 REPORT

specifically, however, it can be inferred as being small from analyses of the effect of stratification bysocio-economic status of the INWORKS cohort, which, arguably, is correlated with alcohol consumption.

(f) Statistical methods and model uncertainties
355. Statistical uncertainties refer to the magnitude of uncertainty in the ERR or EAR estimated fromthe results of the epidemiological studies, as described by the uncertainty ranges in the coefficients of
the risk model. For the risk assessment described in this section, the reported 90% or 95% CI of the risk
coefficients (ERR or EAR per unit dose, with age and sex modifiers) were used to describe statisticaluncertainties. The risks estimated by a typical epidemiological study can be affected by selection ofcohort members, uncertainty in dosimetry, assessment of outcome, study design, or statistical methods
employed. The two main studies (LSS and INWORKS) used in the risk assessment scenario for solidcancers were selected because the uncertainties associated with the results of these epidemiologicalstudies and the statistical methods employed are not substantial relative to other studies.
356. Multiple models were used in this section of the annex to estimate the CER for solid cancers inworkers. Values of the CER were estimated using linear ERR and EAR dose responses based on thefull range of doses for the INWORKS or for the LSS (i.e. the entire cohort). This approach has thelowest statistical uncertainties, given that it is based on the largest number of cases. Estimates of risk
(i.e. the ERR) for restricted dose ranges are presented in table 8 for the LSS and table 12 for the
INWORKS. Table 9 presents estimates of risk for the LSS cohort for both restricted ranges of dose andrestricted ranges of age at exposure. Statistically significant central-value estimates of risks for
restricted ranges of dose seem to be within a factor of two of the risk estimates obtained for the fullrange of doses. Uncertainties in the CER estimates for restricted ranges of dose increase as the range
becomes more limited.
357. To further investigate the effect of other risk models, organ-specific ERR and EAR risk models
based on the LSS cancer-incidence data with a follow-up from 1958 to 1998 [B5, N9, P11] were used
to generate organ-specific risks of cancer, which were then converted into risks of mortality using
adjustments for lethality of cancer. Risk estimates were then summed across cancer sites to obtain the
CER estimates for all solid cancers. Age- and sex-specific baseline rates for cancer incidence andmortality for specific organs were used to produce risk estimates using these risk models. Because thesemodels represent risk of cancer incidence, estimates of CER were obtained by applying an adjustment
for cancer lethality derived for each organ as the ratio of mortality to incidence baseline risks cumulated
from the last exposure to radiation until the age limit (i.e. age 60 or 90). Lethality fractions estimatedbased on risks cumulated starting at any other of the 15 ages at exposure have very similar values to thelethality fractions obtained from risks cumulated starting at the last of the 15 exposures. Lethality
fractions based on the risks from the last exposures were used in this exercise.
358. The approach based on the LSS cancer-incidence data for the period 1958–1998 described above
produced CER values (for a dose of 100 mGy) of: (a) 11 (95% CI: 7.4, 15) chances in 10,000 persons
when cumulated up to age 60; and (b) 54 (95% CI: 36, 78) chances in 10,000 persons when cumulated
up to age 90. These estimates are comparable with those based on mortality data in the INWORKS [R5]and LSS [O5] (table 15).
359. A model uncertainty that is explicitly included is the approach for the transfer between
populations, where multiplicative and additive projections of risk are used. While the uncertainty
ranges for the multiplicative and additive projections overlap (table 15), the differences between thecentral values of the two projections vary by factors less than two for risks cumulated up to age 60, but
by factors up to four, for risks cumulated up to age 90. In the latter case, the differences between

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 119

predictions using multiplicative and additive models from the INWORKS are not entirely due to thetransfer between populations, but they are strongly affected by the lack of an attained age modifier. The
transfer between populations can be an important source of uncertainty in the estimated CER, dependingon the selection of models (INWORKS versus LSS) and on the age up to which the risk is cumulated.

(g) Other sources of uncertainty
360. The exposure parameters and dose rates received by workers were considered constant for the
given exposure scenario. That is, the age of workers at the beginning (30 years) and end (45 years) of
the defined exposure period, the total cumulative dose (e.g. 100 mGy), the annual dose rate (6.67 mGy
per year for all years), and baseline rates of cancer mortality and survival functions were consideredknown and not affected by uncertainties.
361. Evolution of future baseline rates: The REID for solid cancers was estimated using the baseline
rates for the period 2000–2005 and associated survival functions for the United States population.
These REID estimates are representative for a population of exposed workers and followed up starting
during the decades before and continuing after the period 2000–2005. However, for a prospective study
which would assume that workers will be exposed and experience a radiation risk in the future,additional uncertainties associated with baseline rates need to be considered, because it is likely that
future baseline rates will be different from the currently available rates (i.e. the 2000–2005 rates
represent a modern existing population but not necessarily a future population). For example, future
cancer mortality rates are expected to decrease with increasing treatment success, and be affected bychanges in smoking habits. A similar situation would occur if a risk assessment were to be performed
for historical exposures (e.g. patients or medical personnel exposed to X-rays in the 1930s and 1940s),
meaning that additional uncertainty is introduced if current-day baseline rates were applied to earlier
populations. Finally, uncertainties in baseline rates need to be considered in cases of risk assessmentsfor populations for which reliable baseline-rate data is scarce (e.g. the population of Marshall Islands),
as rates from nearby or surrogate populations need to be used in the calculations.
362. Healthy worker effect: The cumulative (e.g. lifetime) risk of cancer (mortality or incidence) froman exposure at given age and to a given dose depends on three elements: (a) the radiation dose response
described by the ERR and EAR models; (b) the baseline rates of cancer (B); and (c) a survival
function (S), which depends on the probability of death from all causes in the population of interest.
The risk estimates presented in this section were obtained assuming the population of interest(i.e. United States workers) has baseline rates for all solid tumours and probabilities of death(i.e. survival functions) similar to those observed in the general United States population. The healthy
worker effect occurs for a population of workers who are expected to be healthier and live longer than
the general population. That is, the healthy worker effect accounts for a reduction in the baseline rates
of cancer (B) and an increase of the probability survival (S) in the population of interest (United States
workers in the example scenario), compared to the general (United States) population.
363. An indicator of the state of health in a selected population (e.g. occupationally-exposed workers)
is the SMR, defined as the ratio of observed to expected deaths for an occupational cohort, where thenumber of expected deaths is determined by applying age–sex–birth cohort-specific person–years offollow-up to the relevant national mortality rates for the same age, sex and calendar period. SMRs are
the most reliable metric for worker mortality when an internal control group of non-exposed employees
is not available. SMR can be estimated using the number of deaths from all causes, deaths due to allcancers, deaths due to solid cancers as a group or deaths due to site-specific cancers (stomach, lung,etc.). Similarly, standardized incidence ratios (SIR) can be used as indicators of disease incidence.
Section IV.C provides estimates of SMR and SIR from different studies of workers published since120 UNSCEAR 2019 REPORT

2012. Other summaries of SMR for the purpose of evaluating the healthy worker effect can be found in
the literature (e.g. [P2]).
364. If only the survival function (S) were to be adjusted for the healthy worker effect, estimates of the
CER will be larger than estimates of cumulative risk unadjusted for the healthy worker effect. This
would happen because healthier persons survive longer (longer lifespan) and thus they are alive for alonger period to experience the risk from radiation. However, if only the baseline rates (B) were to be
adjusted for the healthy worker effect, estimates of the cumulative risk obtained using a multiplicative
projection (ERR transfer) will always be lower than estimates of risk unadjusted for the healthy worker
effect. This happens because healthier persons have lower baseline rates of cancer than the general
population. When both S and B are adjusted for the healthy worker effect, the adjusted cumulative risk
based on the ERR transfer can either increase or decrease. The decreasing effect introduced by thelower baseline rates (B) is generally stronger than the increasing effect introduced by the survival
function (S). Thus, cumulative risks based on a multiplicative projection for which both B and S havebeen adjusted are expected to be lower than the unadjusted cumulative risks in most cases.
365. When the cumulative risk is estimated using an additive projection (EAR transfer), the excess
absolute risk from the epidemiological study (e.g. the INWORKS) is assumed to apply directly to the
population of interest (e.g. United States workers in the example scenario considered in this section),and thus the resulting cumulative risk does not depend on the differences between the baseline rates ofcancer mortality in the population of interest and those in the general population. Thus, it is expectedthat resulting cumulative risk is affected at most by changes in the survival functions between the
population of interest (United States workers) and the general population, with the adjusted risk for thehealthy worker effect being higher than the unadjusted EAR-projected risk.
366. It is important to note that the impact of the healthy worker effect on estimated cumulative risk ofcancer mortality or incidence depends on sex and on the age at time of exposure to radiation. The
healthy worker effect refers to differences in rates of cancer between workers and general population
observed during the adult, work-productive years of life (~30–60 years of age). No differences inbaseline rates between the population of interest and the general population due to the healthy worker
effect are expected for children and teenagers, and differences are expected to decrease towards zero for
older ages (>60).

2.

Preferred risk inference(a) Selection of the preferred risk inference
367. Cumulative excess risks (CER) of solid cancer mortality were calculated in this annex for typicalmale workers in the United States who were exposed from age 30 to 45, to an assumed total cumulative
dose of 100 mGy. Risks cumulated up to age 60 and up to age 90 were estimated using models obtained
from epidemiological studies of the LSS and of the INWORKS cohorts, for different dose ranges andfor two types of assumed transfer between populations (ERR or EAR; table 15).
368. For this scenario, the preferred risk inference is the CER cumulated up to age 60 obtained using
the ERR transfer from the INWORKS [R5], which is equal to 11 chances in 10,000 persons (95% CI:
3.1, 19.3). Given that this scenario refers to the exposure of a modern population of United States maleworkers subject to prolonged exposure, the CERs based on the INWORKS are thought to be more
representative than the CERs obtained using models from the LSS because (a) members of LSS cohort

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had been exposed to acute, high-dose-rate radiation; and (b) the LSS cohort is representative of a mid20th century Japanese population with different cancer rates, who survived the atomic bombings in1945 and the subsequent difficult living conditions.
369. Among the risk estimates based on the INWORKS, the CERs cumulated up to age 60 are consideredmore reliable than the CERs up to age 90 because no age-dependency could be derived for the ERR andEAR per unit dose obtained from the INWORKS. The constant ERR and EAR per unit dose values are
representative for attained ages 30 to 60, because this is the dominant age range in the current follow-up
for the INWORKS cohort. In the calculation of the CERs cumulated up to age 90, it was assumed that
the ERR and EAR per unit dose representative for ages 30 to 60 apply without modification. Based on
the experiences in the LSS, it is expected that, when attained age increases from 60 to 90, the ERR
would decrease, while the EAR would increase (figure VII). Thus, the CERs up to age 90 from the
INWORKS (table 15) are expected to be too large for the transfer of the ERR and too low for thetransfer of the EAR to the United States population, respectively.
370. Estimates of risk based on the INWORKS were obtained using either the ERR and EAR models
derived for solid tumours using the full dose range, or the ERR derived for all cancers less leukaemia
based on persons exposed to doses less than 100 mGy (no EAR estimates were available for solidcancer in this group). The CERs for solid cancers based on the full dose range are preferred because(a) the scenario considered in this section refers to solid cancers; (b) even when the full dose range isused, more than 95% of the INWORKS cohort has doses less than 100 mGy (90th percentile of dose in
the cohort was 53 mGy [R5]), an exposure situation similar to the assumed exposure scenario; and(c) the CER for all cancers except leukaemia based on doses less than 100 mGy has a negative lower
2.5th percentile, although the ERR for the INWORKS cohort has a positive lower bound at the
5th percentile (table 12).
371. The CERs based on the INWORKS cumulated up to age 60 were obtained using either an ERR oran EAR transfer (or projection) of risk to the United States population. The central values of the two
CERs are identical indicating that, in this case, the transfer from the INWORKS cohort to the UnitedStates population does not represent a major source of uncertainty, and that the INWORKS cohort is agood choice for performing risk assessments for the United States male worker population. Given that
the INWORKS CER based on the EAR transfer has a negative lower bound, the INWORKS CER
based on the ERR transfer and cumulated up to age 60 is the preferred risk inference for the scenario
selected in this case.

(b) Discussion of the impact of sources of uncertainty
372. The main sources of uncertainties associated with this risk estimate are summarized in table 16.
The subsequent paragraphs give the reasons for the grading of the uncertainties (very small, small,moderate or large).
373. Selected populations: The INWORKS cohort includes a population of mostly males (87%) fromcountries (France, United Kingdom and United States) with a lifestyle rather similar to that of the
population of interest (United States male workers). The risk estimates obtained using the multiplicative(ERR) or additive (EAR) transfer models (table 15) are very similar, indicating that, in this case, theuncertainty associated with the transfer between populations is very small. Since the INWORKS cohort
includes 87% males and 13% females, a potential bias may occur when the INWORKS ERR or EAR
risk models are applied to a population of male workers. To assess the magnitude of such a bias, the
CER was estimated using the LSS mortality data [O5] for a population with a mixture of 87% males
and 13% females and was compared to the CER for 100% males for the LSS. The difference in CERs

122 UNSCEAR 2019 REPORT

was less than 4% in this case, with the risk for the mixed population being larger than the risk for males(due to the slightly larger risk for females indicated by the LSS cohort). Accounting for the fact that the
INWORKS cohort is different from the LSS cohort, it is expected the CER based on the INWORKS
cohort to be different from the risk in male workers by no more than 5–10%.
374. Exposure assessment: The primary source of exposure of the members of the INWORKS cohort
was external low-LET radiation received in the workplace. However, some INWORKS workers also
had exposure to neutrons in the workplace, exposure to internal emitters (caesium, tritium, plutonium),environmental exposure to radon and decay products and terrestrial radiation and exposure to jobrelated medical examinations (i.e. chest X-rays). Workers who were active during the early periods oftime and who accumulated the highest doses are more prone to missed gamma, neutron or internal
doses. The effect of exposure to neutrons has been evaluated based on the neutron monitoring status byRichardson et al. [R5], who found that, when accounting for neutron exposure, the ERR differed by lessthan 30% compared to the main reported ERR (table A5 in [R5]). A number of analyses of the effect ofother exposures on the main reported ERR per unit dose were carried out for the French workers, who
are part of the INWORKS cohort. The effect of exposure to internal emitters was studied by Fournier et
al. [F2], based on a qualitative indicator of exposure, and their analysis indicated that no significant
effect is expected on the observed ERR per unit dose. Fournier et al. [F3] analysed the effect ofenvironmental exposures (radon and terrestrial external exposures) and the effect of job-related medical
exposures (e.g. screening chest X-rays; fluoroscopy in early years) under different exposure scenarios.
Adjusting for environmental radiation exposure did not substantially modify the ERR risk coefficientfor the French cohort, but it was attenuated by medical exposure by 30 to 55% depending on theassumed exposure scenario.
375. Dose estimates rely largely on the results of individual monitoring. The errors associated withthese measurements are unshared from person to person, and are of the classical as opposed to Berkson
type [G4, S8]. However, doses could be larger if missed doses from external or other exposure types areto be explicitly included. The effect of uncertainties in the estimated external doses has not yet beenaddressed for the INWORKS cohort.
376. Health outcome assessment: Although outcomes are based on abstracted death certificates which
can be inaccurate on a cancer by cancer basis, the uncertainty related to outcome assessment is
expected to be very small when identifying all solid cancers as a group, given that whole-body
irradiation can induce cancer at most, if not all sites. Also, no additional uncertainty related to theoutcome assessment is introduced, because the preferred risk inference is based on the analysis of solidcancer mortality from the INWORKS, which matches the risk assessment selected for the scenario.
377. Study design: The INWORKS cohort comprises the French, United Kingdom and United Statescohorts which are among the largest, oldest and most informative groups of occupationally-exposed
workers in the world. They include workers who have been monitored for external exposure toradiation using individual dosimeters and have been followed up over decades to collect information on
causes of death [R5]. While information on date and cause of death was carefully collected, informationon other factors that may have affected the health of the workers or contributed to their death could not
be recorded. Smoking, alcohol consumption and exposure to asbestos as potential causes of death aretreated separately as confounding factors. The effects of any remaining cohort-wide causes of death that
may compete with radiation have not been assessed. However, information about persons obtained
from employers and national registries was reliable and produced a minimal loss to follow-up: only
0.22%, 2.56% and 0.83% of workers were lost to follow-up or emigrated from the French, the United
Kingdom and the United States cohorts, respectively, with an average of 1.54% for the entire cohort[H1]. Thus, uncertainties in the ERR per unit dose reported by the INWORKS due to the minimal loss
to follow-up are judged to be small.

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378. Confounding factors: Smoking, alcohol consumption, and exposure to other carcinogens arepotential confounding factors. In the INWORKS cohort, asbestos was identified as a possible
carcinogen of concern. The effects of smoking and exposure to asbestos have been analysed [R5] andthey were found to be less than 10% for smoking and less than 20% for asbestos. Effect of alcohol
consumption was not assessed specifically, but the INWORKS cohort has been stratified by socioeconomic status, which, arguably, is correlated with alcohol consumption. Given that consumption of
alcohol is not known to be a major lifestyle consideration for the INWORKS cohort and given that thecohort has already been stratified by socio-economic status, it is expected that this impact of this sourceof uncertainty to be small.
379. Statistical methods and model uncertainties: An analysis of the dose responses for 5-year and15-year lag for individual doses, in addition to the nominal 10-year lag [R5], indicate a potential
variation in the risk estimate of less than 25%. The dose response for solid cancer mortality in the
INWORKS cohort was linear, and no other dose response improved the fit to the data [R5].
380. Although attempted, analyses of the INWORKS cohort [D1] did not identify clear age at time of
exposure, time after exposure or attained age dependencies. However, risk estimates based on the
INWORKS are reliable to predict risk for the population of interest in this scenario for a follow-up
period ending at age 60, as this represents the centre of the range of ages for the INWORKS cohort.
381. Other sources of uncertainty: The reported CERs are based on baseline rates for solid cancers
averaged over a 5-year period (2000–2005), although the CERs represent the risk cumulated for
30 years (from ages 30 to 60). A potentially important source of uncertainty is the representativeness of
the 5-year averaged baseline rates to the 30-year period of risk accumulation. This may be especially
important for risk projection (as opposed to retrospective risk assessments), as the evolution of baseline
rates in the future is unknown (e.g. cancer mortality rate may decrease with future increased
effectiveness of treatment). The effect of this source of uncertainty will depend on the magnitude of therate of change in baseline rates: a small expected change in baseline rates would translate into a smallchange in the estimated CER. An inspection of age-adjusted mortality rates for all cancers combined
and for all solid cancers in the United States, as reported by SEER [N6], indicates variations less than
50% (less than a factor of 1.5) during time periods from 1970 to 2015. Thus, the uncertainty in thepreferred CER due to evolution of baseline rates of solid cancer mortality is expected to be less than
50% (a factor of 1.5).
382. The CER estimates for this scenario are representative of a population of United States workers
assumed to have baseline mortality rates and survival probabilities similar to those in the general
United States population. Workers are often healthier and live longer than the general population.
Lower cancer mortality rates and higher probabilities of survival will affect the estimated CERs. The
impact of the healthy worker effect was assessed for the preferred risk inference (i.e. CER cumulated
up to age 60 based on the ERR model from the INWORKS), by comparing the CER unadjusted for thehealthy worker effect (11 chances in 10,000 persons (95% CI: 3.1, 19.3)), with a CER adjusted for thehealthy worker effect. The adjustment for the healthy worker effect was based on the assumption thatthe baseline mortality rates of all solid cancers could be lower by an average of 20% for ages 20 to 60
in the population of interest compared to the rates in the general United States population (i.e. an SMR
for all solid tumours of 0.8; range 0.6 to 1.0 [M12, P2]; uncertainty described by a triangular
distribution, minimum=0.6, mode=0.8, maximum=1.0), with differences decreasing linearly to 0% (i.e.
an SMR for all solid tumours of 1.0) for ages less than 15 and greater than 90. This adjustment issimilar to that used by the UNSCEAR 2012 Report [U8]. In addition, the adjustment for the healthy
worker effect also assumed that the mortality rates from all causes in the population of interest (which
affect the survival function) could be lower by about 30% between ages of 20 and 60 compared to thegeneral United States population (i.e. an SMR for all causes of death of 0.7; range 0.5 to 1.0,

124 UNSCEAR 2019 REPORT

uncertainty described by a triangular distribution), with differences decreasing linearly to 0% (i.e. an
SMR for all causes of death of 1.0) for ages less than 15 and greater than 90. The CER cumulated up to
age 60 and adjusted for the healthy worker effect was reduced by about 20% to 9 chances in
10,000 persons (95% CI: 2.5, 16). The uncertainty range (measured as the upper 97.5th percentile
divided by the mean) increased from 1.77 in the CER unadjusted for the healthy worker effect to only
1.81 in the adjusted CER. Interestingly enough, the CER cumulated up to age 90 was unaffected by thehealthy worker effect, because the decrease in risk introduced by the lower baseline rates of cancer
mortality were largely offset by the assumed longer survival.
383. Overall credible interval for the preferred risk inference: The uncertainty in the CER cumulated
up to age 60 obtained using the ERR transfer from the INWORKS, which is equal to 11 chances in
10,000 persons (95% CI: 3.1, 19.3; table 15), is dominated by the statistical uncertainty in the ERR per
unit dose reported from epidemiological analyses performed for the INWORKS cohort [R5]. Thissection and table 16 summarize 19 other sources of uncertainties that may affect the estimated CER.
These additional sources of uncertainty are small or very small and they can be considered statisticallyindependent. Their combined effect is judged not to exceed a factor of 1.5 above and below thepreferred estimate. Small shifts towards lowering the CER may be attributed to the effect of medical
job-required chest X-rays [F3] and the healthy worker effect [P2]. To develop uncertainty intervals thatwould account for all envisioned sources of uncertainty, these additional sources of uncertainty werecombined with the statistical uncertainty in the CER using a semi-analytical method for uncertainty
propagation. The additional sources of uncertainty were characterized by a multiplicative judgementbased probability distribution for all additional uncertainties combined (e.g. defined by a factor of 1.5)
and was considered to be probabilistically independent to the statistical CER. The limits and preferred
estimate of the resulting credible intervals were rounded up or down to 1, 2, 3 or 5, or their productswith 10 or 0.1. The most representative 95% credible interval for the CER up to age 60 for solid
tumours was judged to be 2 to 20 chances in 10,000 persons, with a preferred estimate of 10 chances in
10,000 persons. The resulting uncertainty intervals are referred to as “credible” intervals.

(c) Concluding remarks on the preferred risk inference
384. Considering the additional uncertainties in table 16, the 95% credible interval of the preferred risk
inference ranges from a factor of the preferred estimate of about a half to about two (i.e. +/− 100% fromthe preferred estimate, on a linear scale). These estimates may be compared to the credible interval of
lifetime risk of radiation-induced solid cancer incidence of 81 (95% CI: 32, 160) chances in
10,000 persons estimated in the UNSCEAR 2012 Report [U8] for United Kingdom workers exposed to
100 mGy delivered from ages 30 to 44. This corresponds to a credible interval that ranges from a factor
of about a third to about two (i.e. about –60% to +100% from the preferred estimate, on a linear scale).
Roughly the ranges of the credible intervals are comparable, with a tendency that the lower boundary of
the present preferred risk inference is a bit farther from the preferred estimate than in the UNSCEAR
2012 Report, and that there is a good agreement in the upper estimate (figure VIII). In the UNSCEAR
2012 Report, the Committee noted that “whether or not the credible interval is too narrow cannot beanswered presently”. The preferred risk inference has confirmed the width of the credible interval.
Also, the present lifetime risk estimate (up to age 90) is consistent with the result of the UNSCEAR
2012 Report [U8].

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Table 16. Characterization of the main sources of uncertainty associated with the preferred risk
inference of solid cancers
SourceSelected populations

Exposure assessment

Judged impact a

Transfer between populations

Very small

Sex mixture (87% males in the INWORKS)

Small

Exposures to radiation not specifically accounted for in the
INWORKS
Neutrons

Small

Internal emitters

Very small

Missed dose

Small

Job-required chest X-rays

Small

Uncertainty in external doses

Small

Health outcome
assessment

Representativeness of outcome

Negligible

Case ascertainment

Very small

Study design

Competing causes of death

Small

Loss to follow-up

Very small

Smoking

Very small

Alcohol consumption

Small

Exposure to other carcinogens in the workplace,e.g. asbestos

Small

Assumed latency period

Small

Absence of assessment of modifying effect of age and time

Very small

Evolution of future baseline rates

Small

Healthy worker effect

Small

Confounding factors

Statistical methods andmodel uncertainties
Other sources of
uncertaintyaCharacterization of source

The impact of the different sources of uncertainty is classified into four categories according to the variation they are expected
to induce on the reported CER: very small—less than a factor of 1.1; small—between a factor of 1.1 to 1.5; moderate—between afactor of 1.5 to 2; and large—greater than a factor of 2.

126 UNSCEAR 2019 REPORT

Figure VIII. Comparison of the magnitude of the credible intervals from the preferred risk inference(CER up to age 60 based on the INWORKS) and the credible interval for the lifetime risk from the
UNSCEAR 2012 Report
The magnitude of the credible interval is depicted as the ratio between the upper and lower bounds to therespective preferred estimates

G. Conclusions
385. Cumulative risk of all solid cancer mortality in a population of United States male workers
subject to prolonged exposure to low-LET radiation was estimated based on recently publishedepidemiological studies. Estimates of risk were obtained using risk models from the INWORKS andfrom the most recent mortality studies of the LSS cohort.
386. The preferred risk inference for the considered exposure scenario was the CER obtained using the
ERR risk model obtained from the INWORKS. Data on workers from France, United Kingdom andUnited States have been successfully pooled in the INWORKS, leading to a large cohort with a long
follow-up. The INWORKS is particularly suitable for estimation of risk of solid cancers in
occupationally-exposed male workers because (a) the population of interest has similar characteristics
to the INWORKS cohort; and (b) the population of interest was exposed to low-LET radiation at low
doses and low dose rates.

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387. The strengths of the LSS mortality study are: (a) a large, representative sample across all age
groups of the atomic bombing survivors, using stratified sampling to enrich the higher-dose portion of
the sample; (b) reasonably precise estimates of individual doses; (c) a wide range of doses in the cohort;(d) complete ascertainment of mortality and cause of death using the koseki system; and (e) a longobservation period with a large number of deaths. Those strengths provide a high-quality, informative
epidemiological study.
388. Pooling all solid cancers offers the advantage of large numbers to enhance the statistical precisionin the assessment of the dose response, especially at low doses, and investigating effect modification of
the radiation risk by age, time, sex and other factors. Pooling of all solid cancers is particularly relevant
for the atomic bombing survivors, who received whole-body exposure and among whom radiation
effects have been observed for almost all major organ sites. Aggregated solid-cancer risks havetraditionally been reported in terms of both cancer incidence and cancer mortality from the LSS andother cohorts. However, there are also limitations to such an approach, because there may be realdifferences in the magnitude of the radiation risk and the nature of effect modification across different
cancer sites. When developing a risk assessment system, it is advantageous to derive risk models andestimate risks on a cancer type by cancer type basis. As opposed to risk models for all solid tumours
combined, a risk assessment system that relies on cancer-specific risk models would be suitable tohandle cases of non-homogeneous exposures to radiation, such as medical diagnostic exposuresinvolving CT scans of specific areas of the body (e.g. CT scan of the head).
389. Aside from the LSS, recently published epidemiological studies for all solid cancers rely on
cohorts of (a) occupationally-exposed workers; or (b) members of the public from different countries.
These cohorts include mostly males (except for the Techa River, the Yangjiang and the Kerala cohorts,
which includes females). Many of these cohorts are either small or still young (short follow-up) andexperienced small radiation doses leading to studies with low statistical power. It is expected that
selected cohorts will be further followed up in the next decades allowing for accumulation of a larger
number of cases over much of the lifespan of exposed persons. Cohorts including persons exposed as
children have a better potential for detecting dose–response relationships.
390. Studies of workers or adult members of the public report radiation risks from prolonged or
fractionated exposures. Typically, the risks are described in terms of the ERR per unit dose, and they
are representative for the entire exposure period of the members of the cohort, without modifiers forage at exposure or attained age. Thus, these risks do not apply to exposures in childhood, andextrapolation of the risk to older ages needs to be interpreted carefully.
391. Risks obtained from epidemiological studies of workers or members of the public who have been
subject to prolonged exposures can be directly applied to other groups of similar age and sex who haveexperienced chronic exposures to radiation of similar dose rates. However, they would need to be appliedcautiously to estimate the risk for persons with acute exposures, even if such persons are of similar age
and sex to the members of the studied cohort, as further adjustments may be necessary to account forpossible differences between responses at high doses and dose rates and low doses and dose rates.
392. Epidemiological studies of persons subject to prolonged exposures can be further improved if
designed to include larger groups of workers, and be combined (or pooled) with cohorts of members of
the public, and other exposed groups (patients with long-term fractionated exposures).

128 UNSCEAR 2019 REPORT

V. THYROID CANCER RISK AFTER EXPOSURE DURING
CHILDHOOD

A. Motivation
393. Probably the strongest evidence for the statistical association between internal radiation exposureand thyroid cancer (ICD-10:C73) has been produced by studies of children and adolescents who
ingested radioiodine deposited after the Chernobyl accident in April 1986. Updated time trends forincidence of thyroid cancer in Ukraine and Belarus are published in [U9]. Kazakov et al. [K9] reported
early evidence of the radiogenic origin of thyroid cancer six years after the accident. Other early studies
were published about a decade after the event and these already observed a marked increase of thyroidcancer incidence (reviewed in [C6]). In later studies on about 13,000 persons of the UkrainianAmerican (UkrAm) cohort, which were exposed below age 19 and were subject to enhanced medical
surveillance since 1998, the ERR per unit dose to the thyroid dropped from about 5.3 (95% CI: 1.7, 28)
for prevalence at a mean age at operation of 16 to 1.91 (95% CI: 0.43, 6.34) for the incidence at themean age at operation of 24 [B12, T11]. The estimate for a similar cohort from Belarus (BelAm cohort)
is compatible with this decreasing trend of the relative risk with attained age [Z1]. Increased incidence
in the LSS for exposure in childhood persisted for more than 50 years. For exposure in adulthood, therates among the Japanese atomic bombing survivors are just marginally elevated [F6]. In the post
Chernobyl cohorts UkrAm and BelAm “age at operation” of pathologically ascertained thyroid cancer
cases determines age-risk patterns, while risk estimates are commonly related to “attained age”.
394. After the accident at the Fukushima Daiichi nuclear power station in March 2011, the Committee
undertook an assessment of the health-related consequences. Radiation doses to the population were
found to be much lower than those in the Chernobyl accident [U7]. A comprehensive summary of latehealth effects after exposure of children, which included the radiogenic origin of thyroid cancer, wasprovided in the UNSCEAR 2013 Report, annex B [U6]. A summary of radiation risk coefficients frompertinent thyroid cancer studies published until 2013 is given in table B7 of that report.

B. Recapitulation of previous UNSCEAR publications
395. In the UNSCEAR 2012 Report [U8] an assessment of risk and uncertainties for thyroid cancer
after childhood exposure was performed for a hypothetical group of Ukrainians irradiated at age 10
with a dose to the thyroid of 200 mGy from ingested 131I. The attributable risk (AR) was calculated forage 28 in April 2004 and for the whole life. The choice of age 28 was possibly motivated by the mean
age of operation of 27 in the study of Brenner et al. [B12]. Because thyroid cancer does not lead todeath in most cases, the lifetime attributable risk (LAR) closely approximates the REIC [B2]. Two
approaches for risk projection were applied.
396. The first approach was guided by risk estimates from studies conducted after the accident andused estimates from the LSS [P11] for risk projection to a hypothetical Ukrainian population. BothERR and EAR models were applied for sex-specific transfer, but a thorough analysis of uncertainties
was not performed. A transfer of ERR model estimates yielded considerably smaller AR and LAR thanthat using EAR model estimates.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 129

397. The second approach was based on studies before the Chernobyl accident and did not take into
account post-accident risk studies. The risk transfer was performed with the ERR model for thyroid
cancer given in the BEIR VII report [N9]. The EAR for the Ukrainian population was generated bymultiplying the recorded baseline incidence of thyroid cancer in Ukraine in 2009 and the ERR value of
BEIR VII report. The BEIR VII model is based on the pooled analysis of Ron et al. [R8], which
included the atomic bombing survivors’ cohort and six other cohorts. Doses from 131I were delivered asa prolonged exposure which prompted the application of the BEIR VII DDREF in risk transfer. The
uncertainty in the LAR is dominated by the statistical uncertainty in the BEIR VII estimate of the ERR,
followed by the BEIR VII uncertainty in the DDREF. However, DDREF was not applied in the present
annex (see also section I.4).
398. A recent UNSCEAR white paper gives the latest data on the incidence of thyroid cancer after the
Chernobyl accident for the most affected regions (defined as the whole of Belarus and Ukraine and thefour most contaminated regions of the Russian Federation) [U9]. It contains a summary of the pertinent
literature and provides an estimate of the fraction of the observed incidence that could possibly be
attributed to radiation exposure. During the period 2006–2015, the crude incidence rate increased
monotonically in all age groups for persons from the most affected regions who were younger than
19 years of age in 1986. Some 20,000 cases of thyroid cancer were recorded in this cohort during theperiod 1991–2015. The observed increase was attributed to (a) an increase in the sporadic incidence inthis ageing cohort; (b) enhanced risk awareness after the accident; (c) enhanced effectiveness of
medical surveillance; and (d) radiation exposure. The fraction of cases attributable to radiation exposure
was estimated to be 0.25 with an uncertainty range from 0.07 to 0.5.

C. Review of recent epidemiological literature
399. The selection criteria developed by the Committee in its UNSCEAR 2017 Report [U10] wereapplied for the thyroid scenario. A literature search in PubMed with the query string “Search(((((thyroid cancer[Title/Abstract]) AND radiation[Title/Abstract])) AND (""2011/01/01""[Date Publication] : ""3000""[Date - Publication]))) AND cohort[Title/Abstract]” resulted in 88 entries.
However, not all of these 88 publications were relevant for the aim of this annex.
400. Furukawa et al. [F6] analysed long-term trends in the risk of thyroid cancer among the Japaneseatomic bombing survivors up to 60 years after exposure. Using a linear dose–response model, the ERR
for thyroid cancer at a dose of 1 Gy was estimated as 1.28 (95% CI: 0.59, 2.70) at age 60 after an acuteexposure at age 10. The risk decreased sharply with increasing age at exposure and there was little
evidence of increased rates of thyroid cancer for those exposed after age 20.
401. After the Fukushima accident, a large screening study for thyroid cancer in Fukushima Prefecture
comprising about 300,000 residents below age 19 on 1 April 2011 was undertaken. The effort put inthyroid screening after the Fukushima accident was considerably higher than after Chernobyl [Y1]. At
the onset of the screening study, an estimate of the prevalence of 0.027% (95% CI: 0.010%, 0.050%)
for the first screening campaign was made by Jacob et al. [J1]. The underlying risk model for theprevalence estimate was derived from a reassessment of the incidence data for thyroid cancer in the
LSS [P11]. The most recent observational study reported a prevalence of 0.037% from screening
examinations between October 2011 and June 2015. The risk of thyroid cancer was not found to be
associated with the radiation doses estimated by the Fukushima Health Management Survey and by
WHO experts [I4, O1, W9]. This finding is at variance with claims that the excess thyroid cancers foryears after the accident cannot be explained by ultrasound screening alone [T12]. Wakeford et al. [W2],130 UNSCEAR 2019 REPORT

Williams [W11] and Suzuki [S17] disagreed with the conclusions of the study by Tsuda et al. [T12] forunderestimating the impact of screening when comparing incidence rates of the Fukushima Prefecturewith incidence rates in the general Japanese population.
402. An updated pooled analysis by Veiga et al. [V2] of 12 studies of thyroid cancer patients
comprised 1,070 malignant thyroid tumours (66% females, 79% with papillary histology). The patientswere exposed between 1920 and 2000 and followed up from 1935 to 2005. The time-related covariables
were mean age at exposure of 5 (range 0–19), mean time since exposure 30 years, and mean age at
diagnosis of 41 years. Patients were exposed to radiation with a mean (median) dose to the thyroid of
0.71 (0.07) Gy with a maximal dose of 76 Gy. Their study focused on the characterization of the shapeof the dose response. Effect modifications of attained age, age at exposure and time since exposure
were calculated in categories without considering combinations of age and time-related modifiers.
Veiga et al. [V2] found an ERR at 1 Gy of 2.76 (95% CI: 0.94, 4.98) consistent with the result ofBrenner et al. [B12]. The companion study of Lubin et al. [L18] investigated the same data sets but was
especially interested in the shape of the dose response relation below 0.2 Gy. Lubin et al. reported nodeviation from linearity with an ERR at 0.2 Gy of 2.2 (95% CI: 1.3, 3.3). In line with the findings of
Furukawa et al. [F6], the ERR remains elevated more than 50 years after exposure in the studies of
Veiga et al. [V2] and Lubin et al. [L18].
403. In contrast to the LSS cohort of Japanese atomic bombing survivors with maximal thyroid doses
below 4 Gy, much higher doses up to 10 Gy have been reported in post Chernobyl studies of thyroid
dosimetry [L9, L10, L11]. In the LSS risk reduction at higher doses with an exponential attenuation
factor was marginally significant (p-value 0.07) [F6]. A significant risk attenuation for doses up to
10 Gy has been also observed in the Chernobyl studies [K4, Z1]. Veiga et al. [V2] observed a decreaseof the ERR estimate only for doses above 20 Gy.
404. An analysis by Kaiser et al. [K4], using all of the 115 patients with papillary thyroid cancers in
the UkrAm cohort, and a biologically based model of pathogenesis, reported an estimate for the ERR at
1 Gy of 1.6 (95% CI: 0.67, 2.6) for an attained age of 27, again in line with the result of Brenner et al.
[B12]. However, the estimate of Kaiser et al. [K4] for the EAR at 1 Gy of 5.1 (95% CI: 3.5, 7.5) per 104
person–years was markedly higher compared to that in the reference [B12]. But it was close to an older
estimate from Ron et al. [R8] of 4.4 (95% CI: 1.9, 10.1) per 104 person–years, which had been obtainedin 1995 from a pooled analysis of seven studies of thyroid cancer patients as part of the updated
analysis of Veiga et al. [V2]. The difference in the estimates of Brenner et al. and of Kaiser et al. is
mainly related to the approach of adjusting for the effect of strongly varying incidence rates in different
Ukrainian regions. Whereas Brenner et al. [B12] adjusted only the baseline rate, Kaiser et al. [K4]applied the adjustment on the total hazard, which resulted in a marked improvement in AIC based
goodness-of-fit.
405. Tronko et al. [T10] reported an ERR estimate of 1.36 (95% CI: 0.39, 4.15) Gy−1 for the fifth cycleof thyroid screening of the UkrAm cohort during the period 2012–2015. Nearly 30 years after theaccident, 47 cases were detected at a mean age of screening of 35. The ERR estimate follows thedecreasing trend with attained age, which was observed in the studies of Tronko et al. [T11] andBrenner et al. [B12]. Using an age dependence that is proportional to age−4 Kaiser et al. [K4] estimatedan ERR of 0.75 (95% CI: 0.26, 1.3) Gy−1 for a mean age of screening of 35. Their result obtained byextrapolation is compatible with the estimate of Tronko et al. [T10].

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 131

D. Definition of scenario
1.

Exposure scenario

406. The design of the present scenario was inspired by the situation of the UkrAm cohort. The
intention was to consider issues that had not been addressed in the previous scenario for thyroid cancer
analysed in the UNSCEAR 2012 Report [U8]. Compared to the UNSCEAR 2012 scenario, whichassumed an exposure of 200 mGy, it focuses on thyroid doses of about 500 mGy. The higher dose waschosen because the risk uncertainties appeared very large in the previous scenario. The risk wasevaluated for ages 1 and 10 at exposure and ages 30 and 40 for the end of follow-up, in line with thescenario for leukaemia risk in children. Follow-up started at the first birthday and ended at the 30th and40th birthday, respectively. In the UkrAm cohort [B12, K4], the oldest persons were operated on atage 35 in 2008. Provision of stable iodine and contamination of drinking water with nitrate are
discussed below (see section V.F.1(e)) as possible confounding factors on risk estimates. In order toavoid any biases due to both factors, the target cohort was assumed to be the same as the UkrAm cohort
in these aspects.

2.

Reference data

407. To transfer the ERR estimates to the Ukrainian population, the combined incidence rates forthyroid cancer pertaining to the period 2001–2007 were applied, which were available in age groupsof five years from the National Cancer Registry of Ukraine (figure IX). Data for 2005 on the
Ukrainian population and deceased persons in age groups of five years were used from the State
Statistics Service of Ukraine [U1]. With these data, the sex-specific and sex-averaged survival curves
were calculated for age groups of five years up to age 75 in the standard way as negative
exponentials of the cumulative incidence.

132 UNSCEAR 2019 REPORT

Figure IX. Thyroid cancer incidence in Ukraine for the period 2001–2007 [U1]3.

Risk models

408. Risk models derived from the LSS and UkrAm cohorts were applied for risk projection in the
thyroid scenario. The most recent LSS incidence data set with follow-up from 1958 to 2005 has been
analysed by Furukawa et al. [F6] for thyroid carcinoma >10 mm applying the ERR model:
ERR = β*dose * exp(αa*log(age/60) + αe*(agex-10)/10) * (1+αs*msex)
where β=1.28, αa=−1.27, αe=−0.769 and αs=0.327;
and the EAR model:
EAR = β*dose * exp(αa *log(age/60) + αe*(agex-10)/10) * (1+ αs *msex)
where β=2.95*10−4, αa=1.03, αe=−1.19 and αs=0.729; msex= −1, +1 for males, females, respectively.
Covariables, age, agex and dose denote attained age, age at exposure and absorbed dose to the thyroid,
respectively.
409. Jacob et al. [J1] have derived risk estimates from the LSS for all malignant tumours without size
restrictions in the period 1958–1998 based on the ERR model:
ERR = β*dose * exp(αa*log(age/60) + αe*(agex-20)/10 + αs*msex)
where β=1.13, αa=−0.688, αe=−0.658 and αs=0.115;

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 133

and the EAR model:
EAR = β*dose* exp(αa*log(age/60) + αe*(agex-20)/10 + αs*msex)
where β=1.58*10−4, αa=1.04, αe=−0.802 and αs=0.697, msex=−1, 1 for males, females, respectively.
To calculate the CER from the risk models derived for the UkrAm cohort, the study of Kaiser et al.
[K4] was used. They developed an EAR model:
EAR = β*dose* exp(αexp*dose + αs*msex)
where β=6.5*10−4, αexp=−0.0891 and αs=0.429; msex=−1, 1 for males, females, respectively, andbaseline h0 = b0*exp(bage *log(age/25)) where b0=0.935*10−4 and bage=3.82.
410. The EAR model of Kaiser et al. [K4] was used for the present analysis. It yielded almost identicalresults compared to those from their biologically based model. A multiplicative ERR model wasderived from the EAR model by multiplication of the parametric baseline model for the UkrAm cohort.
411. The ERR estimates applied in thyroid scenario calculation for age at exposure of 10 are shown infigure X for attained age between 10 and 30. In this age interval, the majority of cases from the studies
of Brenner et al. [B12] and Kaiser et al. [K4] have been recorded. As discussed below, the EAR models
are less suitable in risk transfer calculations due to their higher sensitivity to differential screening
regimes.
412. In the LSS, tumours of size <10 mm have been detected mainly in autopsy studies before 1970.
The main difference between the UkrAm and LSS cohorts is the screening regime. With the LSS
cohort, medical surveillance was not as intensive as with the UkrAm cohort even for persons
participating in the adult health study. To assess the influence of tumour size on the risk projections, themodels of the studies from Furukawa et al. [F6] and Jacob et al. [J1] were applied. Complete model
parameter estimates and correlation matrices are given in the appendix. The sex dependence of the EAR
estimates differed markedly between the LSS and UkrAm cohorts with female/male ratios of 6.4 (LSS)
versus 2.4 (UkrAm) [K4] which may be related to ethnicity. Thus, to facilitate comparability only
sex-averaged risk estimates are discussed in line with the scenario for leukaemia from CT scans inchildren. The studies of Tronko et al. [T11] and Brenner et al. [B12] report only sex-averaged risk
estimates as their main results due to the small number of cases in each study.
413. The pooled study of Veiga et al. [V2] provides an alternative set of risk estimates. However,age-related dose–effect modifiers were presented in a form which did not allow risk estimates to be
extracted that would be suitable for the scenario calculations.
414. For the CER calculation, a latency period of three years after exposure was assumed [H3]. During
this period, the excess risk was set at zero. Smoothing of the latency period has been applied in the
IREP software package [K13], but had negligible effect on the CER.
415. In the LSS, follow-up for thyroid-cancer incidence started in 1958, 13 years after exposure.
Beyond a three-year latency period, the risk estimates for the 10 years before 1958 had to be inferred.
This inference was accomplished by extrapolating the risk estimates pertaining to the time since
exposure below 13 years based on age-related parameter estimates derived from cohort data for timesince exposure exceeding 13 years.

134 UNSCEAR 2019 REPORT

Figure X. Sex-averaged excess relative risk estimates and 95% confidence intervals for the LSS
cohort by Furukawa et al. [F6] (LSS1), by Jacob et al. [J1] (LSS2) and by Kaiser et al. [K4] for the UkrAm
cohort using a latency period of three years after exposure at 10 years of age

4.

Risk-transfer methods

416. Risk transfer was performed with an average risk coefficient for a population with an equal
number of males and females. Sex-specific risk assessment was avoided for the thyroid scenario sincesex-specific risk estimates and the baseline risk differed markedly between Japan and Ukraine.
417. The CER calculations with the ERR models were performed by applying the sex-averaged
recorded incidence rates for thyroid cancer in the Ukrainian population for the period 2001–2007,
which are shown in figure IX.

E. Results
418. In table 17, the estimates of the CBR, maximum-likelihood estimates (MLEs) and confidence
intervals of the CER related to the scenario are given for the transfer of the ERR and the EAR at 0.5 Gy
in the LSS based on the models in the references of Furukawa et al. [F6] and of Jacob et al. [J1], andfrom the UkrAm cohort based on the models of Kaiser et al. [K4]. To put disease-specific risks into
perspective, cumulative risks for overall mortality are shown as well.
419. Although Furukawa et al. [F6] excluded tumours <10 mm from the analysis and Jacob et al. [J1]included all reported thyroid cancer cases, the central estimates and confidence intervals for the CER
were very similar for all four combinations of age at exposure and age at the end of follow-up.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 135

Table 17. Overall mortality in the follow-up period, CBR, maximum-likelihood estimates and 95% CIsof the sex-averaged CER of thyroid cancer incidence
Risks calculated per 10,000 persons exposed at age 1 or 10 to a thyroid dose of 500 mGy; LSS: Life Span Study;
CBR: Cumulative baseline risk; CER: Cumulative excess risk; CFR: Cumulative fractional ratio; CI: Confidence
interval
Overall mortality and CBR
Method of transfer

Mortality
per 10 000

CBR
per 10 000

CER associated with the exposure
scenario and CFR (i.e. CER/CBR)
CER per 10 000(95% CI)CFR

EXPOSURE AT AGE 1, FOLLOW-UP TO AGE 30, DURATION OF FOLLOW-UP 29 YEARSLSS incidence models
ERR transfer [J1]

383

3.3

13 (3.7, 40)

4.0

EAR transfer [J1]

383

3.3

31 (12, 90)

9.5

ERR transfer [F6]

383

3.3

16 (3.4, 59)

4.7

EAR transfer [F6]

383

3.3

30 (11, 91)

9.1

UkrAm incidence models
ERR transfer [K4]

383

3.3

19 (3.2,119)

5.6

EAR transfer [K4]

383

3.3

74 (46, 101)23

EXPOSURE AT AGE 1, FOLLOW-UP TO AGE 40, DURATION OF FOLLOW-UP 39 YEARSLSS incidence models
ERR transfer [J1]

831

7.4

25 (8.6, 69)

3.4

EAR transfer [J1]

831

7.4

55 (27, 119)

7.5

ERR transfer [F6]

831

7.4

26 (7.2, 79)

3.5

EAR transfer [F6]

831

7.4

53 (23, 126)

7.6

UkrAm incidence models
ERR transfer [K4]

831

7.4

20 (4.4, 118)

2.7

EAR transfer [K4]

831

7.4

101 (62, 138)

14

EXPOSURE AT AGE 10, FOLLOW-UP TO AGE 30, DURATION OF FOLLOW-UP 20 YEARSLSS incidence models
ERR transfer [J1]

258

3.3

6.8 (1.8, 19)

2.1

EAR transfer [J1]

258

3.3

13 (4.3, 33)

3.9

ERR transfer [F6]

258

3.3

7.0 (1.6, 23)

2.1

EAR transfer [F6]

258

3.3

8.6 (3.1, 24)

2.6

UkrAm incidence models
ERR transfer [K4]

258

3.3

7.7 (2.4, 18)

2.4

EAR transfer [K4]

258

3.3

49 (31, 66)

15

136 UNSCEAR 2019 REPORT

Overall mortality and CBR
Method of transfer

Mortality
per 10 000

CBR
per 10 000

CER associated with the exposure
scenario and CFR (i.e. CER/CBR)
CER per 10 000(95% CI)CFR

EXPOSURE AT AGE 10, FOLLOW-UP TO AGE 40, DURATION OF FOLLOW-UP 30 YEARSLSS incidence models
ERR transfer [J1]

712

7.4

13 (4.5, 37)

1.8

EAR transfer [J1]

712

7.4

25 (11, 52)

3.3

ERR transfer [F6]

712

7.4

12 (3.2, 33)

1.7

EAR transfer [F6]

712

7.4

17 (7.6, 36)

2.3

UkrAm incidence models
ERR transfer [K4]

712

7.4

9.3 (3.5, 20)

1.3

EAR transfer [K4]

712

7.4

76 (49, 103)10420. The central CER estimates based on the transfers of the ERR at 0.5 Gy in the LSS and UkrAm
cohort agree well. However, estimates based on the transfer of the EAR at 0.5 Gy from the LSS cohort
are markedly lower than those from the UkrAm cohort. This may be related to the intensive screeningof the UkrAm cohort. The ERR estimates have been shown to be less sensitive to screening procedures
than the EAR estimates when radiation-induced and sporadic thyroid cancers grow to similar size insimilar timelines [K2].
421. Figure X shows that the ERR estimates from the UkrAm largely exceed those from the LSS
cohort up to age 20. This discrepancy does not affect the CER calculations, since it is suppressed by the
low thyroid cancer incidence at young age (figure IX). The width of the confidence intervals of the
CER estimates are dominated by those of the transferred risk quantities. In the model of Kaiser et al.
[K4], the EAR has a quite narrow confidence interval. The ERR derived from the EAR and the baseline
risk has a huge uncertainty at age at exposure of 1 and a small one at age at exposure of 10. The small
number of baseline cases at young age is driving this uncertainty.

F. Discussion of scenario calculations
1.

Sources of uncertainties(a) Selected populations
422. The LSS cohort includes about 93,000 atomic bombing survivors in Hiroshima and Nagasaki andabout 27,000 people who were not in the cities at the time of the bombings. The incidence of cases ofthyroid cancer in this cohort was identified from a study of the cancer registries in both cities.
Histological materials collected from area hospitals and pathology laboratories were assessed by apanel of pathologists for diagnostic confirmation and histologic classification. The population-based
cancer registries have been in operation since 1958 with a follow-up until 1998 [P11] or 2005 [F6].

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 137

423. For the UkrAm cohort, a list of persons born between 26 April 1968 and 26 April 1986 (the dateof the accident), and who had had measurements made on the thyroid during May and June 1986 in the
Chernihiv, Zhytomyr, or Kyiv oblasts in Ukraine was compiled. A sample of 32,385 persons was
selected from this list. This number was reduced by applying several exclusion criteria and through loss
to follow-up. Tronko et al. [T11] analysed 13,127 persons who were actually screened in the first cycle
1998–2000. Brenner et al. [B12] considered 12,514 persons who underwent up to four thyroidscreening examinations between 1998 and 2007. Tronko et al. [T10] considered 10,073 persons for therisk assessment in the fifth screening cycle.
424. The population in the scenario was assumed to have similar living conditions to those of the
UkrAm cohort. Thus, the risk calculations based on a transfer of effect per unit dose estimates in the
UkrAm cohort are not biased. On the other hand, there are possible biases in the transfer of effect perunit dose estimates in the LSS, because, for example, the provision of stable iodine is certainly different.

(b) Exposure assessment
425. The dose uncertainties in the LSS cohort have already been accounted for by the adjustment of thedose estimates (see section II.F.1(b)). For the UkrAm cohort, individual doses to the thyroid from 131I
and their uncertainties were estimated from the combination of thyroid measurements, data on dietaryand lifestyle habits, and environmental transfer models using a Monte Carlo approach [L9, L10, L11].
426. Little et al. [L14] performed a thorough analysis of the impact of dose uncertainties on the risk
estimates for the UkrAm cohort by applying two different methods of regression calibration and Monte
Carlo simulation. The unadjusted ERR estimate per unit dose obtained by Tronko et al. [T11] was
5.3 (95% CI: 1.7, 28) Gy−1. The first regression-calibration method yielded an ERR per unit dose of
5.8 (95% CI: 1.9, 27) Gy−1, about 7% higher than the unadjusted ERR estimate. The second regressioncalibration method gave an ERR per unit dose of 4.8 (95% CI: 1.6, 20) Gy−1, about 11% lower than theunadjusted value. The Monte Carlo maximum-likelihood method produced an excess odds ratio* perunit dose of 4.9 (95% CI: 1.7, 20) Gy−1, about 8% lower than the unadjusted value. The authors
discussed the reason for the unexpected decrease of the ERR and pointed to the large contribution ofBerkson errors to the overall dose uncertainties whereas classical errors would increase ERR estimates.
For the BelAm cohort the Bayesian Markov Chain Monte Carlo method yielded a maximum posterior
ERR per unit dose of 1.2 (95% CI: 0.20, 4.3) Gy−1 [L15]. The central estimate is 23% lower than theunadjusted value from the same study. As the dose estimates for the BelAm cohort were based on aradioecological model of exposure pathways rather than direct measurements as for the UkrAm cohort,the dose uncertainties have a larger impact on the risk estimate.

(c) Health outcome assessment
427. During the period 1958–2005, Furukawa et al. [F6] identified 371 thyroid cancer cases(299 papillary carcinomas, 15 follicular carcinomas, 12 anaplastic carcinomas, 3 medullary carcinomas
and 42 other carcinomas) with a first primary of size >10 mm in the cancer registry catchment area.
Jacob et al. [J1] analysed 471 malignant thyroid tumours of all sizes and histologic types recorded in
the LSS during the period 1958–1998.
428. To analyse the incidence of thyroid cancer in the UkrAm cohort, each listed member was
screened for thyroid cancer either by a mobile team visiting the local area or at a screening centre at the
Research Institute of Endocrinology and Metabolism in Kyiv, Ukraine. The procedure consisted ofultrasonography by an ultrasonographer and independent clinical examination and palpation by an138 UNSCEAR 2019 REPORT

endocrinologist. Questionnaires were used to determine demographic and medical characteristics anditems relevant to dose estimation such as residential history and milk consumption during the period
May–June 1986. An initial assessment of any thyroid pathology was provided by the endocrinologist at
the time of the screening. Cohort members with peculiar diagnosis were referred to the clinic in Kyiv
for possible fine needle aspiration for nodules >5 mm in size and/or possible surgery [T11].
429. Primary risk analyses for the UkrAm cohort were performed with 45 prevalent cases from the firstscreening cycle [T11], 65 new cases from the second to the fourth screening cycles [B12] and 47 cases
from the fifth cycle [T10]. In those studies, all histological types of papillary, follicular and medullary
thyroid cancer were combined for the main risk analysis. The thyroid cancer cases among the UkrAm
cohort were verified by an international panel of experts so that uncertainty from diagnosis of thyroid
cancer is negligible.
430. The focus of the study by Kaiser et al. [K4] was on molecular carcinogenesis and was only
concerned with the development of papillary thyroid cancer. To achieve maximal statistical power, all
115 cases of papillary thyroid cancer in the UkrAm cohort, which had undergone operation up to 2008,
were included.

(d) Study design
431. Epidemiological studies of thyroid cancer are often influenced by screening procedures.
A characteristic trait of the UkrAm cohort is an efficient screening protocol for all members of thecohort and for the whole study period up to 2015, the end of the last period of examinations. In thesecond to the fifth screening cycles, cases had developed within cycles of two to three years. Since thetime period for cancer development is relatively short, cases from these screening cycles can beconsidered as incidental. Such a screening regime will produce a markedly higher number of cases
compared to the general population without periodic thyroid examinations. However, due to the designof this study, the impact of a screening bias on the risk estimates is negligible.
432. The bias introduced by transfer of a risk estimate from a dedicated radioepidemiological study tothe population of interest is difficult to quantify. To mitigate ambiguity, transfer procedures often apply
multiplicative and additive models with different weights, where the weighting is decided by expert
judgement. In addition, epidemiological studies of thyroid cancer may be burdened by a bias because ofdifferential screening in systematically studied and general populations. For example, in the UkrAm
study, the screening bias is under adequate control due to the application of the same screening protocol
to all cohort members. The general Ukrainian population is not subject to periodic screening, so that theestimated radiation effects on thyroid cancer incidence in the UkrAm cohort are not fully transferable.
To quantify a possible transfer bias for the special case of paediatric thyroid cancer risk after the
Chernobyl accident in the Ukrainian oblast of Kyiv, Chernihiv and Zhytomir, Kaiser et al. [K2]performed a simulation study. They estimated a moderate screening bias of about 20% for the ERR
from ecological risk studies of post-accident thyroid cancer. Of more importance for the present
scenario is the ratio of the ERR from a hypothetical cohort study to the ERR in the population ofinterest, which was estimated to be 0.91 (95% CI: 0.37, 4.96) [K2]. It is noted that simulated estimatesof both the ERR and the EAR in the hypothetical cohort of Kaiser et al. [K2] are close to those of
Kaiser et al. for the UkrAm cohort [K4].

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 139(e) Confounding factors
433. Screening conditions in the general population and the cohort are different. Thus, it is more
appropriate to transfer the estimated ERR rather than the estimated EAR which is much morestrongly affected by differential screening strategies [K2]. Therefore, the CER estimates frommultiplicative transfer should be more consistent between different cohorts than the CER estimatesfrom additive transfer.
434. In the LSS, confounding by screening can be estimated by comparing the baseline rates for theparticipants in the AHS to those for non-participants. The AHS participants were under a regime ofenhanced medical surveillance from the late 1950s to the early 1970s. The AHS baseline rates were
estimated to be higher by a factor of 1.7 compared to those for non-participants ([J1] table A4).
435. In November 2000, a workshop of the Ukrainian-Belarus-USA study group addressed the issue ofiodine nutrition in the region and how this might have influenced the risk of thyroid tumour incidence[R7]. The study group comprised experts involved in establishing both the UkrAm and the BelAm
cohort. Tronko et al. [T9] reported the results of an iodine excretion study in the UkrAm cohort where
urinary iodine levels were found to be lower in rural than urban areas. They concluded that iodine statusneeds to be considered when evaluating the risk of thyroid cancer. Cardis et al. [C3] attempted toquantify the impact of iodine levels on the radiation risk estimates. Compared to the unadjusted
estimate, self-reported intake of iodine prophylaxis was associated with a three times lower risk ofradiation-related thyroid cancer (ratio of excess relative rates per unit dose of 0.34 (95% CI: 0.1, 0.9),
for consumption of potassium iodide versus no consumption). Residence within iodine-deficientterritories at the time of the accident was associated with a three times higher risk of radiation-related
thyroid cancer (ratio of excess relative rates per unit dose of 3.2 (95% CI: 1.9, 5.5)). This result is
supported by an earlier study of Shakhtarin et al. [S6]. Brenner et al. [B12] observed similar trends but
emphasized that their data were not strong enough to support a modifying effect of either iodine
prophylaxis or iodine deficiency. The Committee noted that “individual measurements of the iodine
status at the time of the accident are not available and approximations derived … from soil or urine
10 years after the accident, have to be considered with caution”. Nevertheless, the uncertainty fromneglecting the influence of a varying iodine supply could be large. The UkrAm cohort stems partly
from an iodine-deficient area; a small fraction of the cohort members took iodine prophylaxis. It isnoted that the transfer of the ERR per unit dose in the LSS gives very similar results to those from thetransfer from the UkrAm cohort (table 17), even though the Japanese population has an iodine-rich diet.
436. Drozd et al. [D6] observed markedly different rates of paediatric thyroid cancer incidence in the
Belarussian regions of Mogilev and Brest. Whereas the estimated doses to the thyroid from 131I werecomparable in the two regions, nitrate contamination of drinking water trended with the rate difference.
They hypothesized that high nitrate levels which range between 60−500 mg/liter in open wells could
modulate the carcinogenic effect of radiation on the thyroid but did not quantify the impact on risk
estimates. The Committee notes that baseline thyroid cancer incidence rates vary considerably in
different regions. For example, in the Ukrainian regions of Kyiv and Chernihiv baseline incidence wasfound by a factor of 2.5 higher than in Zhytomir oblast [K4]. There is also considerable variation in
Ukrainian and Japanese baseline rates [J1, K4]. The reasons for this variability are not fully understood,
and further research is needed in this area.

140 UNSCEAR 2019 REPORT(f) Statistical methods and model uncertainties
437. It is common practice to apply a single model of choice to estimate the risk although several
similar models describe the observational data almost equally well. This has been discussed by Walshand Kaiser [W4], who recommended the application of multi-model inference by using the averages of
the risk estimates from selected models weighted by their AIC. Furukawa et al. [F7] applied a Bayesiansemi-parametric approach for the same reason of avoiding a characterization of the dose response basedon a single model. In the present analysis, both the EAR and ERR models from Jacob et al. [J1] andFurukawa et al. [F6] were applied for risk transfer. The risk estimates from both studies were in good
agreement. Therefore, the uncertainties from the choice of an LSS risk model are considered small anddo not justify an in-depth multi-model inference analysis.
438. Kaiser et al. [K4] found a descriptive model and a mechanistic model providing the same
goodness-of-fit and the same risk estimates of both the EAR and the ERR. In this special situation,model uncertainty need not be considered for risk estimation. It is important to recall here that central
lifetime risk estimates are reported as MLEs calculated with model parameter estimates which
minimize the Poisson likelihood. Confidence intervals for the lifetime risk estimates were calculated
exclusively from the Wald-based standard errors and correlations of those model parameters, whichpertain to the radiation risk. In Monte Carlo simulations, multivariate normal probability distributionsof 10,000 samples are generated under the constraint of conserving the covariance matrix for theseparameters. From the resulting distribution of 10,000 lifetime risk estimates confidence intervals are
picked as 2.5 and 97.5% quantiles.

(g) Other sources of uncertainty
439. The impact of uncertainties in the recorded baseline rate of the thyroid cancer incidence in thetarget population was tested by allowing for Poisson-distributed fluctuations in the counting for thenumber of cases, but these were found to be negligible in the present thyroid scenarios. However, whenbaseline incidence rates for the period 2001–2007 are applied in the CER predictions withmultiplicative ERR models, secular trends of thyroid cancer incidence are not taken into account. Dueto the enhanced medical surveillance, incidence rates are likely to increase in the future. Hence, the
CER estimates, which make use of recorded baseline incidence rates, might possibly come out too low,
although this effect is hard to quantify. Consequently, in the present scenario calculations, constantsecular trends are implicitly assumed.
440. The transfer of effect-per-unit-dose estimates in the LSS to protracted exposure situations introduces
an uncertainty additional to those considered explicitly in the risk calculations performed in the present
report. In the scenario of a single uptake, the mean duration of exposure from 131I can be calculated by:0ᶴ∞

t exp{−[t½/ln(2)]t} dt/(t½/ln(2)) = t½/ln(2)where t½ is the effective half-life of 131I in the thyroid. The biological half-life of iodine in the thyroidfor children aged 1 and 10 is 15 and 58 days, respectively ([U6] annex B, table A13). When account istaken of the radioactive decay of 131I, the effective half-life in the thyroid is about 5 and 7 days,
respectively. For a dose to the thyroid of 500 mGy, the mean dose rate is therefore about 3 and 2 mGy/hfor children exposed at ages 1 and 10, respectively. The Committee considers dose rates <6 mGy/h as
low [U4]. So, the average dose rates in the scenarios are low, and there is an uncertainty in the transferof the effect-per-unit-dose estimate in the LSS with a high dose rate, additional to those considered
explicitly in the calculation of the present analysis.

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441. The dose to the thyroid in the scenario (0.5 Gy) is somewhat smaller than the arithmetic meandose 0.65 (geometric mean 0.20) Gy in the UkrAm cohort. A linear dose response was observed with aslight down-bending in the upper dose range [K4]. At 0.5 Gy the correction factor would be 0.96 but
this correction has already been included in the CER estimates from the UkrAm cohort.

2.

Preferred risk inference(a) Selection of the preferred risk inference
442. For the thyroid scenario, the preferred risk inference was chosen in view of the following
conditions. Screening has a large influence on the observed thyroid cancer incidence. The scenario wasdefined for contemporary screening conditions in Ukraine. These are better reflected by the product ofan ERR per unit dose and the baseline incidence in Ukraine rather than by an EAR per unit dose in a
population with different screening conditions. Therefore, an ERR transfer was performed. The ERR
for age at exposure of 10 has considerably lower uncertainty than the ERR for age at exposure of one
and is thus more informative. Basing the ERR estimate on the UkrAm cohort does not need the use of
uncertain risk transfers from acute to protracted low-dose-rate exposure, or between populations withdifferent living habits and environmental conditions (e.g. intake of stable iodine with food). Duration offollow-up was restricted to 19 years since the data on thyroid cancer incidence in the UkrAm cohort
had not been published at the time of the analysis. For exposure at age 10 to a dose to the thyroid of
0.5 Gy, a follow-up until age 30, and based on the ERR per unit dose in the UkrAm cohort, the CER
was estimated to be 7.7 (95% CI: 2.4, 18) chances per 10,000 persons. The estimated impact of sourcesof uncertainty not taken into account in the calculations is indicated in table 18 and discussed below. In
the preferred risk inference, the preferred estimate of the CFR is 2.4 (table 17).

(b) Discussion of the impact of sources of uncertainty
443. The main sources of uncertainties associated with this risk estimate are summarized in table 18.
The subsequent paragraphs give the reasons for the grading of the uncertainties (very small, small,moderate or large).
444. Selected populations: The cohort members of the UkrAm cohort were selected for age <19 at thetime of accident under the condition that they possessed direct measurements of the dose to the thyroid
over a plausible range. This condition does not question the applicability of the “preferred” estimate ofrisk to the general Ukrainian population aged <19. On the other hand, the cohort members resided inthree northern regions of Ukraine and in Kyiv, which might not be representative for the population of
the whole of Ukraine with respect to iodine intake and nitrate contamination of drinking water(see sections V.F.1(a),(e)).
445. The preferred scenario involves Ukrainian children who are exposed at age 10 to a dose to thethyroid of 500 mGy and the number of thyroid cancers is calculated up to age 30. This scenario is
closely related to the actual situation with the UkrAm cohort in which the mean age at exposure was 10
and the number of thyroid cancers up to the end of the fourth screening was determined [B12]. Thus,
the CER estimates for the preferred scenario possess confidence intervals with the lowest achievable
range. For ages at exposure below 10, and for follow-up periods of interest longer than 20 years, theconfidence interval of the risk estimate would markedly increase.

142 UNSCEAR 2019 REPORT446. Exposure assessment: The exposure assessment, which might be prone to dose measurement
errors [L14], is identical for all considered scenarios based on risk estimates from the UkrAm cohort(see sections V.F.1(a),(b)).
447. Health outcome assessment: The assessment of health outcome, which might suffer from caseascertainment problems [B12, T10, T11], is identical for all considered scenarios based on UkrAm
cohort data (see section V.F.1(c)).
448. Study design: In the simulation study of Kaiser et al. [K2], the ratio of the ERR per unit dose for ahypothetical cohort study to that for the general population was estimated to be about 0.91 (95% CI:
0.37, 4.96). The central estimate of the hypothetical cohort study, which was inspired by the study
design of the UkrAm cohort, closely approximates to the expected “true” value in the general target
population. The range of the confidence interval is a factor of five larger than the central estimate. Sucha large range was also observed in the UkrAm cohort by Tronko et al. [T11]. It reflects the “natural”
statistical uncertainty introduced by the underlying Poisson distribution of case counts and does not
introduce additional uncertainty for the risk transfer. Results of the simulation study suggest that the
CER estimates from the preferred scenario represent the “true” value in the target population well.
449. Confounding factors: The potential impact of iodine deficiency on thyroid cancer risk after
exposure to radioiodine has been investigated in a number of studies [B12, C3, S6, T9]. The CER
estimates in the preferred risk calculation are only valid under the assumption that the average iodinesupply in contemporary Ukraine does not differ substantially from that in the UkrAm cohort. Even
under this condition, there is a remaining uncertainty: In the UkrAm cohort, there was potentially an
anti-correlation between dose to the thyroid (high in rural areas) and iodine supply (low in rural areas).
Thus, the radiation effect per unit dose in the UkrAm cohort might have been larger than in a
population without such a correlation. Therefore, there is a potential for a small systematic error
resulting in too high CER values in table 17.
450. Nitrate contamination of drinking water has been identified as an additional confounder forthyroid cancer incidence in Belarus, but the impact on risk estimates could not be quantified [D6]. As in
the case for stable iodine intake, the nitrate contamination of drinking water for the target cohort andthe UkrAm cohort are assumed not to be markedly different. By assuming similar conditions in risk
transfer for both confounders, they do not introduce additional uncertainty in the risk estimate for the
preferred scenario.
451. Statistical methods and model uncertainties: Since the preferred thyroid scenario concentrate on
the centre of the incidence data in cohort strata with many cases, plausible risk models are expected toyield similar risk estimates. In this case, the influence of model uncertainty can be neglected(see section V.F.1(f)).
452. Other sources of uncertainty: The use of risk estimates obtained from the UkrAm cohort study forthe preferred scenario eliminates several sources of uncertainty. Corrections for the influence of dose
rate effects on the CER estimate are not necessary, if risk estimates are derived for the same exposure
situation. Ethnicity is excluded as a confounder with unknown impact. Additional uncertainty fromextrapolation of the LSS risk estimates for time since exposure of less than 13 years (i.e. before 1958)
can be avoided.
453. To integrate molecular data into their study, Kaiser et al. [K4] performed their analysis with thehistological type of papillary thyroid cancer, whereas most other studies produced risk estimates for all
histological types (papillary, follicular, medullary) combined. Papillary thyroid cancers constitute morethan 90% of all thyroid cancers in the studies of Tronko et al. [T10, T11] and Brenner et al. [B12].

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Brenner et al. published estimates of the ERR and EAR for both all histological types combined andpapillary thyroid cancer alone. Central estimates of both the ERR and EAR for papillary thyroid cancer
alone were found to be a factor of 0.8 smaller compared to estimates for all histological types combined.
Table 18. Characterization of the main sources of uncertainty associated with the preferred risk
inference of thyroid cancer
SourceCharacterization of source

Judged impact a

Selected population

Selection of cohort members from thegeneral population

Very small

Exposure assessment

Dose measurement errors

Small overestimation

Health outcome assessment

Case ascertainment

Very small

Study design

Risk transfer from the UkrAm cohort to
the general population

Very small underestimation

Confounding factors

Iodine supply

No impact, since distribution of stableiodine supply in the scenario is thesame as in the UkrAm cohort

Confounding factors

Nitrate contamination of drinking water

No impact, since distribution of nitratecontamination of drinking water inthe scenario is the same as in the
UkrAm cohort

Statistical methods andmodel uncertainty

Model uncertainty minimized byapplying CER estimates from the centreof the data

Very small

Other sources of uncertainty

Using papillary thyroid cancer (PTC)
instead of all histological subtypes

Small underestimation

Other sources of uncertainty

Development of future baseline rates

Not quantifiable, part of the scenario
assumptiona

The impact of the different sources of uncertainty is classified into four categories according to the variation they are expected
to induce on the reported CER: very small—less than a factor of 1.1; small—between a factor of 1.1 to 1.5; moderate—between afactor of 1.5 to 2; and large—greater than a factor of 2.

(c) Comparison with other estimates of lifetime risk and attributable fractions
454. Two independent studies were published with lifetime risk estimates of thyroid cancer afterchildhood exposure by the Committee [U8] and by WHO [W9]. Recently, an UNSCEAR white paper
estimated the attributable fraction of thyroid cancer incidence in the period 1991–2015 in the regions
affected by the Chernobyl accident [U9]. Table 19 compares the results of the present report for the
preferred thyroid scenarios with the earlier estimates of lifetime risk from the studies of the Committee[U8] and of WHO [W9].

144 UNSCEAR 2019 REPORT

Table 19. Thyroid cancer incidence (cases per 10,000 persons) after exposure at age 10 with athyroid dose of 500 mGy. Preferred estimates and 95% credible intervals as far as available
CBR: Cumulative baseline risk; CER: Cumulative excess risk; EAR: Excess absolute risk; ERR: Excess relative risk

SourceWHO [W9] a

UNSCEAR [U8] aThis report

15 years

18 years

20 years

2.0

3.5

3.3

Preferred model

9.6

7.2 (1.3, 21) b

7.7 (2.4, 18) c

Transfer of ERR from LSS

7.1

12 d

7.0 (1.6, 23) c

Transfer of EAR from LSS

12

15 d

8.6 (3.1, 24) c

Follow-up time
CBR
CER

a

Incidence averaged over both sexes and CER scaled to a dose to the thyroid of 500 mGy assuming a linear dose relationship.

b

Calculations based on the BEIR VII model.

c

See text for extension of credible interval due to sources of uncertainty not taken into account.

dCalculations took into account post-accident studies and were based on LSS data.

455. In the UNSCEAR 2012 Report, annex B [U8], simulation calculations were performed for a
scenario similar to the present one: exposure of Ukrainians at age 10 to a dose to the thyroid of
200 mGy and follow-up until age 18. Based on the BEIR VII model, the sex-averaged CER was
estimated to be 7.2 cases per 10,000 persons after linear scaling to 500 mGy. For a slightly shorter
follow-up, the credible interval is larger by a factor 1.3. In view of the independent approaches in the
UNSCEAR 2012 Report and in the present report, this agreement of the central estimates and credibleintervals is likely due to the relative short follow-up period. The ERR of the BEIR VII is constant withtime since exposure and will produce steadily increasing age-integrated risk estimates driven byincreasing baseline incidence rates. This behaviour is likely to generate higher CER estimates for long
follow-up compared to the present approach.
456. The WHO 2013 report summarized expected health effects after the Fukushima accident. Closely
related to the preferred scenario are the CER estimates of thyroid cancer for Japanese children exposed
at age 10 for the first 15 years after exposure (termed Cumulative Attributable Risk AR15 in WHO
2013, annexes, table 44). Risk transfer was based on both EAR and ERR models for the LSS [P11]separately and in combination with equal weight. Confidence intervals were not reported. Doseestimates were given for different villages around the Fukushima power plant for the first year ([W9],table 6). If a linear dose response is assumed, the CER estimates can be scaled to a dose of 500 mGy.
The model chosen by WHO yields a sex-averaged CER of 9.6 cases in 10,000 Japanese children
exposed at age 10. Taking into account the shorter follow-up period, the WHO result is about a factorof two larger than the preferred estimate in the present preferred risk estimation. In view of the wide
confidence interval, this is considered to be a good agreement.
457. A recent white paper to guide the Committee’s future programme of work assessed in a simplified
approach the fraction of the thyroid cancer incidence during the period 1991–2015 in areas affected by
the Chernobyl accident that can be attributed to radiation [U9]. Based on an average dose to the thyroidof non-evacuated children and adolescents of 170 mGy, the attributable fraction was assessed to be
25%, with a credible interval of at least 7 to 50%. A comparison with the present calculation is basedon the results in table 17 for the transfer of the ERR per unit dose in the UkrAm cohort to the scenario
with age at exposure of 10 and a follow-up over 30 years (i.e. up to age 40). Multiplying the CFR by ascaling factor of 170/500, which accounts for different test doses in the present scenario and in the

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white paper, gives an ERR estimate for a dose to the thyroid of 170 mGy. The result is expected to beslightly larger than the estimate in the white paper, because the follow-up starts two years earlier, aperiod in which the attributable fraction is particularly high. Indeed, the corresponding attributablefraction is then assessed to be 30%, with a credible interval of at least 14 to 48%, which confirms thesimplified approach used in the white paper [U9].

G. Conclusions
458. Estimation of lifetime radiation risks based on an effect-per-unit-dose estimate in anotherpopulation involves a number of steps and each step is likely to add to the overall uncertainty. In thepresent scenario addressing calculations for thyroid cancer, the contribution from each source of
uncertainty has been discussed and the impact estimated.
459. For the “preferred” risk estimation, most sources of uncertainty not considered in the calculations
for table 17 are assessed to be small or very small. However, there is potential overestimation of therisk in the Ukrainian population after the Chernobyl accident, because the anti-correlation of dose to thethyroid and iodine supply in the UkrAm cohort probably does not apply to the general Ukrainian
population. As has already been noted above, cohort members from rural areas of iodine deficiency
received higher thyroid doses and iodine prophylaxis was not an important covariable in the cohort.
Further, the preferred risk inference is only valid, if (a) the baseline thyroid cancer incidence rate in the
Ukrainian population under consideration is comparable with that in the period 2001 to 2007; and(b) the average iodine supply in the Ukrainian population is comparable with that in the UkrAm cohort.
Also, there is some suspicion that nitrate contamination of drinking water might influence thyroid
cancer risk. If this is true, then also the distribution of nitrate in drinking water has to be comparable.
Further research is needed on such risk factors for thyroid cancer.
460. In summary, under the conditions listed above, the CER up to age 30 after exposure at age 10
with a dose to the thyroid of 0.5 Gy is estimated to about eight with a credible interval from about 2 to
20 chances among 10,000 persons. In the thyroid scenarios considered, cancer incidence is dominatedby the radiation effect as the estimated values of CFR exceed one. The CFR of 2.4 in the “preferred”
risk estimation corresponds to an attributable fraction of about 70%.
461. By choosing the “preferred” risk estimation as close as possible to the Ukrainian target
population, important sources of uncertainty have been neutralized. These sources include dose rate
effects, and extrapolation to small times since exposure and other ethnicity, which would have occurred
if the risk models from the LSS had been used.
462. Model uncertainties could be avoided to a large extent, since the preferred scenario was based on
central cohort strata with large numbers of cases. In this region of the data, any credible model judged
by goodness-of-fit will yield a similar risk estimate.
463. The preferred model of WHO [W9] is the mean of the results based on transfers of ERR and EAR
in the LSS, while the Committee did not choose a preferred model [U8]. Table 19 summarizes thecalculation result of the Committee based on the BEIR VII model and calculations taking into accountpost-accident studies based on LSS data. For the conditions of the preferred scenario, the CER estimate
given in the UNSCEAR 2012 Report [U8], is lower than that given in WHO [W9] by a factor of about
0.7. The present preferred risk inferences give an intermediate value of the CER with a credible intervalembracing the preferred estimates of the two previous studies. Compared to the BEIR VII results [N9],the credible interval in the preferred risk inference is somewhat narrower, mainly because the146 UNSCEAR 2019 REPORT

uncertainty associated with transfer from an acute to a low-dose-rate exposure situation could be
avoided, i.e. no DDREF was applied in the present scenario (see section I.4).
464. To conclude, risk models for thyroid cancer from the LSS provide adequate central CER estimatesin risk transfer calculations for the general Ukrainian population. For the preferred scenario, however, anarrower credible interval can be achieved by using effect estimates from the UkrAm cohort.

VI. RISK OF CIRCULATORY DISEASES AFTER RADIATIONEXPOSURE
A. Motivation
465. Circulatory diseases are the leading cause of death in many countries. While exposure to ionizing
radiation with doses greater than a few grays is known to damage circulatory systems and consequently
increase the risk of circulatory diseases, effects are less clear at lower levels of exposure (<1 Gy) [D2,
W14]. In the past decade, increasing evidence of an association of circulatory diseases with moderateradiation doses has been emerging in epidemiological studies of the atomic bombing survivors andother populations exposed occupationally, environmentally or medically [K23, L13, L16]. Reports ofassociations between circulatory diseases and radiation exposure are much fewer in number than thoseon cancer and radiation exposure, and those that have been published present rather inconsistentfindings. While the limited statistical power, heterogeneity in the potential confounders and uncertainty
in the dose assessment play similar roles to those in the assessment of cancer risk, the relatively large
effects of risk factors other than radiation, and presumably substantial errors in the diagnoses andclassifications of the diseases constitute additional challenges. Nevertheless, radiation effects on
circulatory diseases could be of great public health concern because the underlying rates of these
diseases are so high that even small increases in relative risks could lead to considerable absolute risks inthe population, and these might be of a similar order of magnitude to those of radiation-associated cancers.

B. Recapitulation of previous UNSCEAR publications
466. The UNSCEAR 2006 Report, annex B [U3] reviewed the evidence accumulated from a range ofstudies and concluded that given the inconsistent epidemiological data and the lack of a biologicallyplausible mechanism, the present scientific data were not sufficient to establish a causal relationshipbetween ionizing radiation and cardiovascular disease at doses of less than about 1 to 2 Gy.
467. The UNSCEAR 2010 Report, section III.C [U5] briefly summarized the current status of researchand recognized the emerging evidence from recent epidemiological studies indicating elevated risks of
non-cancer diseases at low to moderate doses and the difficulty of estimating effects at low doses andestablishing plausible mechanisms.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 147

C. Review of recent epidemiological literature
468. A number of epidemiological studies have been conducted in the past decade on radiationassociated circulatory diseases. In this review, a literature search was performed in PubMed using the
keywords “((radiation[Title/Abstract])) AND risk AND (incidence OR mortality) AND (noncancer[Title] OR circulatory[Title] OR heart[Title] OR cerebrovascular[Title] OR stroke[Title] OR
cardiovascular[title]) AND (cohort OR case-control) AND ("2006"[Date - Publication] :"3000"[Date Publication]).” Of the 113 papers derived from the search, this review was limited to about 40 papers
that presented original epidemiological results for populations in which persons had been exposed tomoderate or low doses and from which quantitative dose–risk relationships for one or more of thecirculatory diseases (heart diseases and cerebrovascular diseases) had been derived.

1.

Life Span Studies of the Japanese atomic bombing survivors

469. The effects of radiation exposure on circulatory diseases have been evaluated among the LSS andAHS cohorts. Using information obtained from death certificates, Shimizu et al. [S7] analysed mortality
data from cardiovascular diseases among 86,611 persons (mean dose of about 0.1 Gy) in the LSS
cohort in the period 1950–2003. Significant dose-dependent increases were observed in an LNT model
for mortality from all circulatory diseases with an ERR per unit dose of 0.11 (95% CI: 0.05, 1.7) Gy−1,
from cerebrovascular disease (stroke) of 0.09 (95% CI: 0.01, 0.17) Gy−1 and from heart disease of 0.14
(95% CI: 0.06, 0.23) Gy−1. There was no strong evidence for non-linearity in the dose–response curvesin these analyses. However, a pure quadratic model for stroke, which suggests relatively little risk atlower doses, nominally provided a better fit than the linear model. The form of the dose–response
relationship was uncertain at doses less than 0.5 Gy. In analyses of subtypes of heart disease in the LSS,
the mortalities from hypertensive heart disease, rheumatic heart disease and heart failure showedsignificant associations with radiation dose with ERRs per unit dose of 0.37 (95% CI: 0.08, 0.72) Gy−1,
0.86 (95% CI: 0.25, 1.72) Gy−1 and 0.22 (95% CI: 0.07, 0.39) Gy−1, respectively. However, formortality from ischaemic heart disease, the association was small and not statistically significant (ERR
per unit dose of 0.02 (95% CI: −0.10, 0.15) Gy−1). There was no indication of confounding by smoking,alcohol consumption, education, occupation or obesity, nor indication of variation of excess risk by sex,time since exposure, or age at exposure, although there was a slight indication of decreasing trends ofthe ERR with attained age.
470. With an extended follow-up in the period 1950–2008, Takahashi et al. [T2] analysed the LSS
mortality data for heart disease subtypes. Significant positive associations of radiation dose withmortality from valvular heart disease, hypertensive organ damage and heart failure were observed with
ERRs per unit dose of 0.45 (95% CI: 0.13, 0.85) Gy−1, 0.36 (95% CI: 0.10, 0.68) Gy−1 and 0.21 (95%
CI: 0.07, 0.37) Gy−1, respectively. No significant association was observed for other subtypes including
ischaemic heart disease with an ERR per unit dose of 0.03 (95% CI: −0.08, 0.15) Gy−1. Misclassification,*background incidence of disease, and accuracy of diagnosis seem to have contributed to considerable
variations of the subtype-specific risk estimates. The effect on rheumatic valvular heart disease wasparticularly high and significant in the earliest period of follow-up (1950–1968) in contrast to that on
non-rheumatic valvular heart disease, which was high and significant in the later period, in which
diagnosis was more reliable.
471. Based on information obtained through biennial clinical examinations for the members of the AHS
cohort, Tatsukawa et al. [T4] reported an increased incidence of cerebrovascular disease or myocardial
infarction for those exposed at childhood (age <10) with an ERR per unit dose of 0.72 (95% CI:
0.24, 1.40) Gy−1, while no significant increase in these diseases was observed for those exposed in utero.

148 UNSCEAR 2019 REPORT472. Due to the limited statistical power, it is generally difficult to detect a departure from linearity inthe dose–response relationship for circulatory diseases. As an alternative to the standard approach basedon a single preferred model, Schöllnberger et al. [S2] applied a multi-model inference approach to thedose–response analysis for mortality from cardiovascular disease in the LSS cohort. A combined set ofplausible models fitted equally well. With this combined set of models, they found a dose response that
was about one third weaker than that with the LNT model at doses below 0.6 Gy. The dose response at
higher doses was stronger. More recently, the multi-model inference approach was applied to mortality
in the LSS cohort from cerebrovascular and heart disease [S3]. For cerebrovascular disease, thedose–response curve estimated by multi-model inference was below the LNT model at low to medium
doses (0–1.4 Gy) while at higher doses, a higher risk was estimated compared to the LNT fit. Similarly,
a sublinear dose response was found for heart disease at doses of 0 to 3 Gy. The estimated confidencebounds indicated no conclusive answer for an increased risk below 0.75 Gy for cerebrovascular disease
and 2.6 Gy for heart disease.
473. While the data from the Japanese atomic bombing survivors have been providing one of the mostimportant sources of information to evaluate the late health effects of radiation exposure, care should betaken in interpreting the LSS results for circulatory diseases and applying them to other exposed
populations. In general in Japan, hypertension is considered to be the major risk factor for circulatory
diseases, and thus the risk of radiation-related circulatory disease may have increased largely owing to
the increased risk of the radiation-related hypertension [O7, T1]. This may not be the case in Western
populations, where hypercholesterolemia or atherosclerosis is a major risk factor for these diseases.

2.

Other studies

474. A number of studies following up workers employed at the Mayak Production Association in the
Russian Federation have provided relatively strong evidence for radiation effects on circulatory
diseases [A6, A7, A8, A9, A10, A11, A12, A13, M10, S9, S10]. Azizova and colleagues have reported
radiation-associated incidence and mortality from cerebrovascular disease [A10] and ischaemic heartdisease [A12] in an extended cohort of workers first employed in the period 1948–1982 with follow-up
to 2008. These cohort studies contained 22,377 workers who had received prolonged external exposure
to low-LET radiation (mean cumulative dose 0.54 Gy) and internal exposure to high-LET radiation(mean total absorbed alpha-particle dose to the liver of 0.23 and 0.44 Gy for males and females,
respectively). Generally, the estimated risks for incidence from these analyses of the Mayak cohort
were much higher than those for mortality. These results might be partly due to the increased
surveillance of workers exposed to higher doses and the relatively small numbers of mortality cases. It
should be also noted that incidence data were confined to workers residing in Ozyorsk, which is thedormitory town of Mayak, whereas Mayak worker mortality studies considered all members of thewhole cohort since until 2004 data on causes of death were available for all Mayak workers including
those who had left Ozyorsk for another place of residence. However, these data became unavailable inrecent years because of new regulations on personal data protection. The ERR per unit dose associatedwith external exposure was estimated as 0.10 (95% CI: 0.04, 0.17) Gy−1 from the incidence ofischaemic heart disease, while that from the mortality data for ischaemic heart disease was 0.06 (95%
CI: <0, 0.15) Gy−1. Similarly, the ERR per unit dose for the incidence of cerebrovascular disease was
0.46 (95% CI: 0.37, 0.57) Gy−1, while that for the mortality from cerebrovascular disease was small andinsignificant with 0.05 (95% CI: −0.03, 0.16) Gy−1. All of these analyses were adjusted for smoking,alcohol consumption and internal alpha-particle exposure, and showed no indication of non-linearity inthe relationship. The ERRs associated with internal alpha-particle exposure were significant for
mortalities for both ischaemic heart and cerebrovascular disease. However, these results need to beinterpreted cautiously, due to the dose uncertainty, e.g. related to the considerable heterogeneity ofANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 149

internal exposure from plutonium, and a revised dose-response analysis based on the recently updated
dosimetry system [N3] is awaited. With a subcohort restricted to those with negligible internal exposurein the Mayak cohort, Simonetto et al. [S10] conducted a detailed analysis for cerebrovascular disease.
Using multi-model inference, they showed that the dose response for the incidence of cerebrovasculardisease appeared to be sublinear at low doses.
475. A study of the NRRW found suggestive evidence for an increased mortality from all circulatory
diseases combined, with the ERR at 1 Sv of 0.25 (95% CI: −0.01, 0.54) [M12]. An earlier study of thecohort of BNFL workers, which included the large group of workers at the Sellafield nuclear complex,
and was largely subsumed into the NRRW study, had found some evidence for an association between
radiation dose and circulatory disease mortality, while significant heterogeneities observed in risk
estimates by employment status were suggestive of influences other than radiation [M2]. Data on
potential confounders were not available for these studies. Most recently, the association between
mortality from heart disease and cumulative radiation dose from external sources was studied among
174,541 persons of the NRRW cohort (with a mean 10-year lagged lifetime external dose of 23.2 mSv)
followed up until the end of 2011 [Z7]. Among the subtypes of heart disease, the increasing mortalities
with cumulative dose were observed for ischaemic heart disease with an ERR per unit dose of 0.32
(95% CI: 0.04, 0.61) Sv−1 and other heart diseases with 1.08 (95% CI: 0.03, 2.45) Sv−1. For ischaemic
heart disease, the increased mortality appeared at least 20 years after the first exposure (with a peak
between 30 and 40 years).
476. Azizova et al. [A14] jointly analysed the mortality from circulatory diseases in the Mayak Worker
Cohort (MWC, n=22,374) and the UK Sellafield Worker Cohort (SWC, n=23,443). A common
methodology was used to estimate exposure from external gamma radiation and internal alpha radiation
to the liver; the mean cumulative external Hp(10) dose was 0.52 and 0.07 Sv for the MWC and SWC,
respectively, while the mean cumulative internal dose was 0.19 and 0.01 Gy for the MWC and SWC,
respectively. The dose responses for circulatory disease, ischaemic heart disease and cerebrovasculardisease in relationship to internal exposure to alpha radiation did not differ significantly from zero for
either the MWC, the SWC or the pooled plutonium worker cohort. The ERR per unit dose (Sv) for
external exposure was significantly increased for both cohorts for circulatory disease and ischaemic
heart disease (but not for cerebrovascular disease), but differed significantly between the two cohorts,
the estimate for the SWC being about ten times greater than that for the MWC. In a pooled analysis of
the two cohorts (for the later first-employment periods), the estimated ERRs per unit dose were0.22 (95% CI: –0.01, 0.49) Sv−1 for circulatory disease, 0.22 (95% CI: –0.06, 0.57) Sv−1 for ischaemic
heart disease and 0.24 (95% CI: –0.17, 0.80) Sv−1 for cerebrovascular disease. It should be noted that thesignificant heterogeneities mentioned in the previous paragraph would be present in the Sellafield data.
477. Studies of the emergency and recovery operation workers of the Chernobyl accident have reported
significant evidence for radiation-associated circulatory diseases [I5, K6, K7]. Most recently,
Kashcheev et al. [K6, K7] studied a cohort of 53,772 recovery operation workers who arrived in thezone of the Chernobyl accident within the first year after the accident (with the mean and maximum doses
from external exposure of 0.161 and 1.42 Gy, respectively). During the follow-up period 1986–2012, a
significant dose–response relationship was observed for the incidence of cerebrovascular disease(23,264 cases) with the ERR per unit dose of 0.45 (95% CI: 0.28, 0.62) Gy−1 [K6] and for the incidenceof cardiovascular disease other than cerebrovascular disease (27,456 cases) with the ERR per unit doseof 0.47 (95% CI: 0.31, 0.63) Gy−1 [K7]. These estimates tended to vary by the duration of the workers’
stay in the zone, with the highest risk observed for liquidators in the first year after arrival in the
Chernobyl zone, with accumulated doses above 0.15 Gy and duration of work less than six weeks.
In this study, information on factors that might cause circulatory diseases was limited other thanradiation and some concomitant disease. Also, a large concern remains on the dose uncertainty and its
impact on the risk estimation.

150 UNSCEAR 2019 REPORT478. A cohort study of occupationally-exposed workers in 15 countries was conducted to determine
whether mortality from non-cancer diseases is related to external exposure to low doses of ionizing
radiation [V6]. The analyses included 275,312 workers with adequate information on socio-economic
status, with an average cumulative radiation dose of 20.7 mSv. With 11,255 deaths from non-cancer
diseases, the ERR per unit dose was 0.24 (95% CI: −0.23, 0.78) Sv−1 for mortality from all non-cancer
diseases and 0.09 (95% CI: −0.43, 0.70) Sv−1 for circulatory diseases. Increased risks were observed
among the younger workers (attained age <50) for all groupings of non-cancer causes of death.
However, there are problems with the Canadian worker data used in this study due to inconsistencies indose information in early periods, which suggests that little reliance can be placed on results includingthese data [A5, Z5].
479. The INWORKS followed up a part of the 15-country occupationally-exposed worker study [V6]with about 310,000 workers from France, the United Kingdom and the United States who were exposed
to low-dose radiation accumulated at low dose rates (average cumulative external photon dose of
25.2 mSv). Gillies et al. [G7] reported statistically significant associations between radiation dose andmortalities from circulatory disease (ERR per unit dose=0.22 (90% CI: 0.08, 0.37) Gy−1), cerebrovasculardisease (ERR per unit dose=0.50 (90% CI: 0.12, 0.94) Gy−1), and ischaemic heart disease (ERR per unit
dose=0.18 (90% CI: 0.004, 0.36) Gy−1). No significant departure from linearity in the dose response
was observed for mortalities from circulatory diseases and ischaemic heart disease. However, the dose
response for cerebrovascular disease mortality was better described by a linear–exponential model(p=0.02), with increased risks at lower doses and a flattening of risk at doses above 200 mSv. The
authors also noted heterogeneities in the risk estimates that precluded firm conclusions to be drawn.
480. The association between prolonged external exposure to low doses of ionizing radiation andmortality was studied in a cohort of workers (22,393 persons with a median age of 48 at the end offollow-up) at EDF and followed up to 2003 [L4]. Based on a total of 874 deaths, no significant
association between cause of death and dose was found, except for cerebrovascular diseases (p=0.01).
However, this study was based on only 22 cases. The cohort is still relatively young and further followup would be necessary to confirm the findings. The EDF cohort is a part of the INWORKS [G7] andthe 15-country study [V6].
481. The Wismut cohort of 58,982 uranium miners has been periodically studied for evidence of an
association between exposure to external gamma radiation (mean cumulative dose of 47 mSv with amaximum of 909 mSv) and death from cardiovascular diseases [K20, K21, K22], but no statisticallysignificant results have been found. Most recently, during the follow-up period 1946–2008, the ERR
per unit dose was estimated to be −0.13 (95% CI: −0.38, 0.12) Gy−1 for all cardiovascular diseases(9,039 deaths), −0.03 (95% CI: −0.38, 0.32) Gy−1 for ischaemic heart disease (4,613 deaths) and 0.44
(95% CI: −0.16, 1.04) Gy−1 for cerebrovascular disease (2,073 deaths) [K22].
482. A Canadian cohort of 337,397 persons occupationally exposed to ionizing radiation and includedin the National Dose Registry has been studied to assess the risk of cardiovascular disease mortality[Z8]. The cohort consisted of nuclear workers as well as medical, dental and industrial workers (meanwhole-body doses of 8.6 and 1.2 mSv for males and females, respectively). During the study period
1951–1995, a significant and strong dose response was observed based on 3,533 deaths fromcardiovascular disease, with the ERR per unit dose of 1.22 (95% CI: 0.47, 2.10) Gy−1 for males and7.37 (95% CI: 0.95, 18.1) Gy−1 for females. However, the potential bias introduced by dosimetric
uncertainties, possible record linkage errors and lack of adjustment for non-radiation risk factors are ofconcern, so that the results cannot be considered reliable.
483. A cohort of 17,660 Eldorado uranium workers was studied to assess the dose response of
exposure to radon decay products and gamma-ray doses and mortality from circulatory diseases [L3].

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 151

The cohort consisted of workers first employed during the period 1932–1980 and followed up to 1999.
No association was observed between exposures to radon decay products and gamma-ray doses andischaemic heart disease, stroke or other cardiovascular diseases.
484. The cohort of French uranium miners (5,086 persons) was studied to determine whether any
association existed between exposure to high-LET radiation due to radon decay products and mortality
from various cancers and non-cancer diseases during a follow-up from 1946 to 2007 [R1]. An
association between cumulative exposure to radon decay products and cerebrovascular risk with an
ERR at 100 WLM of 0.41 (95% CI: 0.04, 1.03) was one of a number of significant associations found.
485. The Newfoundland fluorspar miners cohort (n=2,070) in Canada was studied for the relationshipbetween cumulative exposure to radon decay products and circulatory disease mortality [V3]. While theradon daughter exposure levels in this cohort were relatively high (the mean cumulative exposure of
348 WLM, and about one quarter of the miners had average annual exposures that exceeded 60 WLM
in a year), no significant association was observed between exposure to radon decay products and therisk of death from coronary heart disease. This finding did not change after adjusting for the lifetimesmoking status (available for about half of the cohort). Similarly, the cumulative radon daughter
exposure was found to be unrelated to deaths of the circulatory system, acute myocardial infarction andcerebrovascular disease.
486. Another Canadian cohort of Ontario uranium miners (n=28,546 male miners with a mean cumulative
radon daughter exposure of 21.0 WLM) was studied for non-cancer mortality including ischaemic heartdisease and cerebrovascular disease [N4]. No increased risk from cumulative exposure to radon decay
products was suggested for any of non-cancer diseases in the follow-up period 1954–2007.
487. A follow-up study over the period 1950–2003 of the Techa River cohort included about
30,000 persons who had been subjected to prolonged exposure due to radioactive discharges into the
Techa River from Mayak Production Association. This study has provided some evidence for radiationassociated increases of mortality from all circulatory disease (ERR per unit dose of 0.36 (95% CI: 0.02,0.75) Gy−1) and ischaemic heart disease (ERR per unit dose of 0.56 (95% CI: 0.01, 1.19) Gy−1) with a
15-year lag [K19]. The total cohort-average absorbed dose to muscle due to external and internal
exposure was 35 mGy, with the maximum dose of 510 mGy.
488. A study of the cohort of Kazakhstan residents exposed to fallout from the nuclear weapons tests at
the Semipalatinsk nuclear test site (with a follow-up for the period 1960–1999) reported highly
significant dose-related increases in the incidence of cardiovascular disease (ERR per unit dose of
3.15 (95% CI: 2.48, 3.81) Gy−1), heart disease (ERR per unit dose of 3.22 (95% CI: 2.33, 4.10) Gy−1)
and cerebrovascular disease (ERR per unit dose of 2.96 (95% CI: 1.77, 4.14) Gy−1) with the cohort
including unexposed residents [G9]. However, when account was taken of the difference in baseline
rates between the exposed and unexposed groups, no statistically significant dose–response relationshipwas observed for any of the end points.
489. The effects of high natural background radiation on mortality were evaluated with a cohort of
31,604 males and females aged 30–74 years living in Guangdong Province, China, with a follow-up forthe period 1979–1998 [T3]. The cumulative external radiation dose was estimated for each person
based on hamlet-specific indoor and outdoor doses, and sex- and age-specific house occupancy factors.
Mean cumulative radiation doses from radiation in the high natural background and control areas were
84.8 and 21.6 mGy, respectively. The mortality due to any non-cancer diseases was not significantly
related to exposure in the high background area, with the ERR per unit dose of 0.54 (95% CI: −2.65,
6.13) Gy−1 for ischaemic heart disease (221 deaths) and 0.44 (95% CI: −0.88, 2.08) Gy−1 forcerebrovascular disease (1,302 deaths).

152 UNSCEAR 2019 REPORT490. The possible association of mortality from circulatory disease with medical diagnostic exposure
has been studied in cohort studies of tuberculosis patients who had received fluoroscopic procedures in
Canada [Z6] and in Massachusetts [L16]. In the Canadian study (63,707 persons), with absorbed doses
to the lung from fluoroscopy that were individually determined (mean cumulative person–year
weighted lung dose of 0.79 Gy, maximum dose of 11.6 Gy, and median dose fractionation of 0.36 Gy
per year), the ERR per unit dose was estimated to be 0.176 (95% CI: 0.011, 0.393) Gy−1 for ischaemic
heart disease mortality with a significant dose-rate effect (a higher ERR per unit dose was found for
those with the fewer fluoroscopic procedures in a year, p=0.02) [Z6]. In the Massachusetts study(13,568 persons), there was no evidence for such an inverse dose-fractionation pattern or significant
excess risk, but there were some indications of excess deaths from circulatory disease at doses below
0.5 Gy [L16]. By pooling these two cohorts, Tran et al. [T8] observed radiation-associated increases in
the risk under 0.5 Gy for all circulatory diseases (n=10,209; ERR per unit dose of 0.246 (95% CI:
0.036, 0.469) Gy−1) and for ischaemic heart disease (n=6,410; ERR per unit dose of 0.267 (95% CI:
0.003, 0.552) Gy−1). These risks tended to reduce with increasing time since exposure (p<0.005). Overthe entire dose range, however, negative trends between dose and mortality from all circulatory disease(p=0.014) and from ischaemic heart disease (p=0.003) were found, possibly due to competing causes ofdeath. Because the data on the well-known lifestyle and medical risk factors for circulatory disease
were limited, potential confounding of the dose trend could not be excluded.

3.

Synthesis of studies

491. A meta-analysis of 11 epidemiological studies (including those of the LSS and cohorts ofoccupationally and environmentally exposed populations) with moderate to low doses (whole-bodydoses less than 0.5 Sv) gave estimated ERRs per unit dose of 0.10 (95% CI: 0.05, 0.15) Sv−1 forischaemic heart disease, 0.12 (95% CI: −0.01, 0.15) Sv−1 for heart disease apart from ischaemic heartdisease and 0.20 (95% CI: 0.14, 0.25) Sv−1 for cerebrovascular disease [L12]. The results alsosuggested that the ERRs per unit dose do not exhibit a heterogeneity among the studies for various
types of circulatory disease. For ischaemic heart disease and non-ischaemic heart disease, there was no
significant heterogeneity in risks between the various studies; however, this was not the case for
cerebrovascular disease and other circulatory diseases. These combined risk estimates were largely
influenced by a few studies of large, significant effects. In particular, exclusion of Mayak data
decreased the ERR estimates by 30% for ischaemic heart disease and by 40% for cerebrovascular disease.
492. While an increasing number of reports have been recently published on the radiation-associatedcirculatory diseases, there is a considerable inconsistency in the estimated associations. Aside from thecommon issues such as the limited statistical power, potential confounders and dose uncertainty,
relatively large effects of risk factors other than radiation, and presumably substantial errors in the
diagnoses and classifications of the diseases would constitute additional challenges in the circulatory
disease risk assessment.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 153

D. Definition of scenario
1.

Exposure scenario

493. Circulatory diseases are multifactorial with the underlying rates varying considerably withlifestyles, socio-economic status and other personal factors, while their associations with radiation
exposure are expected to be smaller than those for cancers. Consequently, assessment of the circulatory
disease risk and determination of the dose–response relationship would be more challenging than thatof the radiation-associated cancers. Additional concerns that are more relevant to non-cancer diseases
than to cancers include potential sources of bias related to diagnostic misclassifications, with the degreeof accuracy varying over the period, and the selection of the exposed populations (the impact of thehealthy worker or healthy survivor effect is difficult to avoid in the selection) [P10, U3]. These issues
are likely to contribute to the inconsistency in the assessed risks, and complicate comparison andtransfer of the risks between populations of fairly different characteristics that might be associated withdifferent exposure–disease mechanisms.
494. To provide a basis for the assessment of the risk of circulatory disease from radiation exposure,
the risk estimated from the LSS was considered for application to the calculation of the lifetime risk fora general population in Japan, rather than transferring it to another population with different
characteristics. The exposure scenario was for a population of equal numbers of males and females who
received an acute exposure to a dose of 1.5 Gy at age 30 (which is roughly the mean age at exposure in
the LSS cohort) to be followed up to age 60 or 90. Because the existence of effects at low doses is lessclear for circulatory diseases than for solid cancers, the effect of an exposure to a relatively high dose
was evaluated. In view of the fact that uncertainty about the shape of the dose response has less impact
on the risk estimates for doses ranging from 1 to 2 Gy, compared to those for low to moderate doses,
the effect of exposure at a dose of 1.5 Gy was evaluated.

2.

Reference data

495. For the use of the life-table method, the cause-specific mortality rates in 2014, which wereavailable from the Statistics and Information Department of the Japanese Ministry of Health, Labourand Welfare [M8], were used to calculate the baseline risks of circulatory diseases and the survival
function, which were age and sex specific. Figure XI presents these sex-specific baseline mortality ratesof (a) cerebrovascular disease and (b) heart disease for age 30–90.

154 UNSCEAR 2019 REPORT

Figure XI. Baseline mortality rate of (a) cerebrovascular disease and (b) heart disease in Japan in 2014 [M8]3.

Risk models

496. The models for the current risk evaluation were derived from the LSS follow-ups for strokemortality (ICD-9 codes 430–438) during the period 1950–2003 [S7] and for heart disease mortality(ICD-9 codes 393–429) during the period 1950–2008 [T2]. Because of concern about diagnostic
accuracy [O7, T2], rheumatic valvular heart disease (242 cases) was excluded from the heart disease.
497. The data set consisted of numbers of cases and person–years cross-classified by city, sex,
radiation dose, follow-up period, attained age and age at exposure. The dose categories were defined interms of the estimated weighted absorbed dose to the colon based on DS02 dosimetry with 22 cut
points at 0, 0.005, 0.02, 0.04, 0.06, 0.08, 0.1, 0.125, 0.15, 0.175, 0.2, 0.25, 0.3, 0.5, 0.75, 1, 1.25, 1.5,
1.75, 2, 2.5 and 3 Gy. As for many other analyses of the LSS, the data were analysed using Poisson rate
regression with the mortality rate of each end point modelled as λ0(s,a,c,b){1+ERR(d)}, where λ0
describes the baseline mortality rate as a function of sex (s), city (c), attained age (a) and birth-year (b),and the ERR describes the radiation-associated excess relative risk. Conventionally, the ERR ismodelled by a simple parametric form, such as the linear, linear–quadratic or pure quadratic function ofdose (d), which may be multiplied by a function to allow for the variation of the dose effects by effectmodifiers such as sex, attained age and/or age at exposure.
498. When the risk is associated with substantial uncertainty, as is usually the case for circulatory
diseases, greater emphasis needs to be given to the determination of the uncertainty. While most of theactual, unknown dose–response relationships are expected to be simple in nature, parametric estimators
generally produce much tighter confidence bands owing to the additional structure imposed by theparametric model, but if the assumptions behind the parametric structure are incorrect, the results—
including their precision—are likely to be misleading [F7]. As an alternative to the standard parametricapproach, a Bayesian semiparametric model has been applied to determining the dose response. Withno particular assumption about the dose–response shape, this model can produce smooth and flexible
dose–response curves while reasonably handling the uncertainty in the risk at low doses and elsewhere[F7]. Here, this model was applied to the dose–response estimation for circulatory diseases from theobservations of the LSS.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 155

499. The Bayesian semiparametric dose–response model consists of a connected piecewise linear
function defined over a set of C+1 dose cut points {δ0=0,δ1,…,δC},ρ (d ) = ∑k =1 β k hk (d ), hk (d ) = {min(d , δ k ) − δ k −1}I (d >δ k −1 ), k = 1,..., CC

where I(.) is the indicator function such that I(A)=1 if A is true and 0 otherwise. For the LSS data, thecut points may be those used for the person–year table categorization. The slope of each dose category{βk, k=1,…,C} is assumed to be random* and conditionally specified by[βj|βj-1] ~ N(βj-1,σ2), j = 2, …,Cwhich can be regarded as prior distributions under the Bayesian framework (with a non-informative
prior on β1). The parameter, σ, controls the degree of smoothness of the dose–response curve and may
be estimated from the data (the fitted curve converges to a straight line as σ→0, while a more
complicated shape can be described with a sufficiently large σ). With non-informative priors on theother unknown parameters, inference for the dose–response curve (and other parameters) can bederived from the posterior distribution, which can be obtained iteratively through the Markov Chain
Monte Carlo algorithm.
500. There is great uncertainty about how the radiation effect on circulatory diseases can vary withother factors. While none of the current analyses observed any statistically significant effectmodification, the inclusion of effect modifiers was considered to determine how they might affect therisk evaluations. Three models for effect modification were considered: (a) constant (no effectmodification); (b) sex; and (c) sex and attained age, for the ERR with a linear dose–response function.

4.

Risk-transfer methods

501. The life-table method was used to estimate the radiation-associated CER (i.e. the REID) of
mortality from each of the outcomes of interest (cerebrovascular and heart disease) under the defined
scenario. A multiplicative risk transfer of the ERR estimated from the observations of the LSS wasapplied to the baseline mortality of the general Japanese population for the periods since exposure atage 30 up to the 60th and 90th birthdays under an additional assumption of a five-year latency period.
The five-year latency was used because the LSS risks were estimated based on the data followed up for
five years after the exposure. Alternatively, a longer latency of 10 years was considered to check the
impact of this assumption.
502. While the radiation-associated circulatory disease risks may be estimated in terms of the EAR,
additive risk transfer with an EAR model was not considered for the current evaluation. The EAR
model fits were consistently worse than the ERR counterparts, and larger uncertainties were associatedwith the parameter estimates, in particular, of effect modifiers, which could lead to fairly extreme andunreasonable estimates of the lifetime risk.

E. Results
503. The fitted ERR dose–response curves of selected models are presented in figure XII for(a) cerebrovascular disease and (b) heart disease (excluding rheumatic valvular heart diseases). The
solid curves represent the central estimates and the dashed curves their associated 95% confidence or156 UNSCEAR 2019 REPORT

credible interval for the linear model (yellow), the quadratic model (blue) and the semiparametricmodel (red). All of these curves were based on the fitted models with no effect modification:(a) For cerebrovascular disease, the quadratic ERR model was the best in AIC (−2 compared withthe linear ERR model) among the considered parametric models with an ERR at 1.5 Gy of
0.12 (95% CI: 0.04, 0.24). A slight indication of decreasing trends of the ERR with attained age
was found (p=0.08), but the ERR did not differ by sex (p>0.5). The estimated linear model fit was
similar to the quadratic fit at 1.5 Gy with ERR=0.13 (95% CI: 0.01, 0.25) but consistently higher
than the other model fits at doses lower than 1 Gy. Overall, the curve fitted by the semiparametricmodel was comparable to the quadratic fit with ERR at 1.5 Gy of 0.15, but with a wider uncertainty
interval (95% CI: 0.01, 0.30) such that the risk was not significant in the dose range up to about 1.3 Gy;(b) For heart disease, the AIC values of the linear and quadratic ERR models were fairly similar
within a difference of one. Under the linear model, the ERR at 1.5 Gy was estimated to be
0.17 (95% CI: 0.05, 0.30) with no significant indication of departure from linearity (p=0.20 in thelikelihood ratio test versus the linear–quadratic model). Neither of the effect modifications (by sex
or by sex and age) was statistically significant (p>0.1). Under the semiparametric model, the ERR
at 1.5 Gy was estimated to be 0.18 (95% CI: 0.03, 0.33), with the minimum dose for which the risk
was significant being about 1.2 Gy. At doses below 1 Gy, the semiparametric and linear models
were fairly comparable at the central estimates, but the linear model fit had a narrower confidence
interval indicating a significant risk even at the lowest doses which may be misleading.
Figure XII. Excess relative risk for (a) cerebrovascular disease mortality and (b) heart disease(excluding rheumatic valvular heart disease) mortality from the LSS, in relation to radiation exposure
estimated according to several models (legend) at doses of 0–2.5 Gy
Dashed curves represent the estimated 95% confidence or credible intervals. The closed circles represent theestimated ERR for each of the individual dose categories

504. The cumulative baseline mortalities calculated from the reference data for the Japanese population
are presented for cerebrovascular disease mortality in table 20 and for heart disease mortality in table 21.
The cumulative all-cause mortality for those who were alive at age 30 and followed up to age 60 was 569
per 10,000 persons, and that for those who were followed up to age 90 was 6,691 per 10,000 persons. The

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 157

cumulative baseline risks of death from cerebrovascular disease were 40 per 10,000 persons at age 60, and557 per 10,000 persons at age 90. Those of heart disease death were 64 per 10,000 persons at age 60, and934 per 10,000 persons at age 90.
505. Table 20 presents the CERs associated with the exposure scenario for cerebrovascular disease.
The estimated risks were relatively consistent with the time-constant ERR models with no effectmodification by age. Among them, the semiparametric model gave an estimated CER of 5.8 (95% CI:
0.4, 12) per 10,000 persons to age 60 and 80 (95% CI: 5.9, 162) per 10,000 persons to age 90. These
interval estimates were slightly wider than those of the other parametric models. The cumulative
fractional ratios were estimated to be 12–15% with the time-constant ERR models. The ERR modelwith effect modification by age yielded larger CERs at age 60 and smaller CERs at age 90 compared tothose estimated with the time-constant ERR models. This difference is due to a decreasing trend in therelative risk estimate with attained age, which tended to increase the risk at earlier ages and decrease atolder ages. Also, the difference between the baseline in the LSS in the last century and the baseline
nowadays in Japan may have partly contributed to the variation.
506. Table 21 presents the CERs associated with the exposure scenario for heart disease. The estimated
CERs were comparable among the considered ERR models. The semiparametric model gave an
estimated CER of 11 (95% CI: 1.9, 21) per 10,000 persons to age 60 and 160 (95% CI: 28, 294) per
10,000 persons to age 90. Inclusion of effect modification by age and/or sex did not have much impact
on the CER. Overall, the CERs of heart disease deaths at age 90 varied from 13 to 17% of the baseline
cumulative risk among the considered models. The issue regarding the difference in the baseline risk inthe current Japanese population and that in the LSS would also apply to the heart disease risk, but the
impact was not as much as for the cerebrovascular disease.
Table 20. Cumulative risk of cerebrovascular disease mortality for a scenario of a person in a general
Japanese population exposed to 1.5 Gy at age 30
CBR: Cumulative baseline risk; CER: Cumulative excess risk, estimated using the REID methodology;
CFR: Cumulative fractional ratio; CI: Confidence interval; LSS: Life Span Study

Model (effect modification)
estimated with the LSS data
[S7]

Cumulative risk associated withthe exposure scenario

CBRAll-cause mortality
per 10 000 persons

Cerebrovasculardisease mortality
per 10 000 persons

CER
per 10 000 persons(95% CI)

CFR(%)

CUMULATIVE RISK UP TO AGE 60
Semiparametric(none)

5.8(0.4, 12)0.147

Quadratic(none)

4.6(1.8, 7.5)

0.116

Linear(none)

4.9(1.5, 8.4)

0.124

569

40

(sex)

5.3

(0.3, 10)0.134

(sex, age)11(−4.5, 23)

0.273

80

(5.9, 162)

0.143

65

(28.4, 102)

0.117

68

(22.7, 114)

0.122

(sex)

70

(5.4, 135)

0.126

(sex, age)43(−5.3, 90)

0.076

CUMULATIVE RISK UP TO AGE 90
Semiparametric(none)

Quadratic(none)

Linear(none)

6 691

557

158 UNSCEAR 2019 REPORT

Table 21. Cumulative risk of mortality of heart disease (excluding rheumatic valvular heart disease)
for a person in a general Japanese population exposed to 1.5 Gy at age 30
CBR: Cumulative baseline risk; CER: Cumulative excess risk, estimated using the REID
methodology; CFR: Cumulative fractional ratio; CI: Confidence interval; LSS: Life Span Study

Model (effect modification)
estimated with the LSS data
[T2]

Cumulative risk associated withthe exposure scenario

CBRAll-cause mortality
per 10 000 persons

Heart diseasemortality
per 10 000 persons

CER
per 10 000 persons(95% CI)

CFR(%)

CUMULATIVE RISK UP TO AGE 60
Semiparametric(none)

11(1.9, 21)0.175

Quadratic(none)

8.1(3.0, 13)

0.127

Linear(none)

11(4.3, 17)

0.167

(sex)

7.0

(−1.6, 16)

0.110(sex, age)

8.7(−2.1, 28)

0.137

569

64

CUMULATIVE RISK UP TO AGE 90
Semiparametric(none)

160(28, 294)

0.171

Quadratic(none)

120(54, 183)

0.128

Linear(none)

157(78, 234)

0.168

6 691

934

(sex)

145

(31, 256)

0.155(sex, age)

150

(28, 280)

0.160

F. Discussion of scenario calculations
1.

Sources of uncertainties(a) Selected populations
507. In the current risk evaluation, the estimated risks of radiation-associated circulatory disease in theJapanese atomic bombing survivors were transferred to a population with the baseline cause-specificmortality rate given in the latest statistics for Japan. While this is expected to be less problematic thantransfer to another population of fairly different characteristics regarding circulatory disease
pathogenesis, there is also likely to be an intrinsic difference in the lifestyle and other characteristics
that could affect the baseline circulatory disease rate between the LSS cohort and the target population.
In particular, due to the extended lifespan of the Japanese population in recent decades, the baseline
mortality rates now are lower than they were in the LSS cohort at relatively younger ages (<75) andthen much higher at older ages after age 75.

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508. In addition to radiation, some aspects of the atomic bombing survivors, such as having sufferedfrom bomb-related injuries and malnutrition during and right after the war, might have affected theircirculatory disease development in later life. Also, the construction of the LSS cohort five years after
the bombings may have led to an inherent bias in the composition of the cohort members (the healthy
survivor effect). This effect appeared to have a larger impact on the risk assessment of non-cancer
diseases than on that of cancers, especially in the earlier years of the follow-up [P10]. This would
introduce additional uncertainty even in the application of the risk obtained from the LSS data to a
population of similar characteristics.

(b) Exposure assessment
509. As in the other evaluations in this chapter, the risk estimates were derived from the data from the
LSS with the estimated individual doses being those using the DS02 dosimetry system. The issues
regarding the uncertainty in the DS02 doses are described elsewhere (see section II.F.1.(b)). It is
unlikely that the recent revision of the individual doses for the LSS (DS02R1) [C10] would have aremarkable impact on the current risk evaluation.
510. While the risks of circulatory diseases were conventionally estimated using the dose to the colon
as a surrogate for the dose to the whole body, it is a matter of debate as to whether this organ is mostappropriate for the assessment of the risk of radiation-associated circulatory diseases. Clarification of
the underlying disease mechanism and the target tissue for radiation-associated circulatory disease
would be required in order to make an appropriate choice of organ. Other possible organs include thelung, stomach and heart, although it is unlikely that replacing the colon with any of these would have amajor impact on the current risk evaluation.

(c) Health outcome assessment
511. The lack of precision in the information on non-cancer diseases compared to that on cancer has
frequently been noted. While misclassification of causes of death could increase the bias in the risk
evaluation, it is usually not formally incorporated into the analysis. In the LSS mortality follow-up for
circulatory diseases, there were major changes in disease coding in Japan owing to the introduction of
ICD-10 in the mid-1990s, after which, heart failure was much less frequently diagnosed while
diagnoses of ischaemic heart disease and cerebral infarction increased [O7, O8].
512. In the current risk evaluation of heart disease, rheumatic valvular heart disease was excluded due
to concerns over the diagnostic accuracy and an unusually high radiation-associated risk estimate
despite the small number of cases [T2]. It should also be noted that rheumatic heart disease is related to
infection in childhood. This exclusion resulted in a slight decrease in the ERR estimates but littlechange in the cumulative risk estimates.
513. Heart failure is included in the current evaluation. The number of heart failure cases is about athird of the overall number of heart disease cases (9,303), and exclusion of heart failure would have had
a non-negligible impact on the risk projection; if heart failure had been excluded, the risk estimate
would have been much smaller (the ERR per unit dose=0.07 Gy−1 without heart failure, compared to0.12 Gy−1 with heart failure).

160 UNSCEAR 2019 REPORT(d) Study design
514. The LSS is a cohort study with a reasonably complete follow-up of about 86,000 persons exposedat the time of the bombings. Because the proximal survivors (typically defined to be those exposed
within 3 km of the hypocentre) and distal survivors differ considerably with respect to socio-economic
status, lifestyle and other risk factors related to circulatory diseases, especially in the earlier periods ofthe follow-up, selection of the control group in the risk evaluation is known to affect the shape of thedose response for non-cancer diseases which may vary with the period, tending to negligible as thefollow-up proceeded [P10].

(e) Confounding factors
515. In the LSS, the effects of some non-radiation factors that are closely related to the distance fromthe hypocentre, such as the severity of bomb-related injuries, lifestyle and socio-economic status, areoften considered to be potential confounders in the determination of radiation-associated health effects.
This fact, along with the healthy survivor effect, could complicate the risk evaluation. Caution istherefore required in the use of the mortality data over the period of observation in estimating thechange in risk with dose.
516. The current risk evaluation did not account for lifestyle factors that are often considered to affectthe risk of circulatory disease, although there has been a substantial change in the Japanese lifestyleover recent decades. In the evaluations of the risk of circulatory disease from the data obtained in the
LSS, there has been no clear evidence of statistically significant confounding by smoking, alcohol
consumption, education, occupation or obesity [S7, T2].

(f) Statistical methods and model uncertainties
517. In addition to statistical uncertainty in the estimates of the risk parameters, uncertainty in themodel selection was accounted for by using a semiparametric model that assumed no specific form of
the dose–response function as an alternative to the conventional parametric models. The central ERR
estimates at 1.5 Gy were comparable between the semiparametric model and the linear model and theresulting cumulative risks of the two approaches agreed well. However, the semiparametric method did
not support a significant effect on cerebrovascular disease at 1 Gy, and gave a borderline significant
effect for heart diseases, indicating that the uncertainty intervals derived with the help of specificmodels, like the linear or the quadratic model, might be over-optimistic (figure XII). These results aresupported by multi-model inference [S3]. Thus, the LSS data only give limited evidence for circulatory
diseases being caused by a radiation dose of 1 Gy.
518. Models of different effect modifiers were considered in order to determine the impact of theuncertainty surrounding the risk modification on the lifetime risk evaluation. Neither of the effectmodifying factors was statistically significant and the impact of inclusion of a modifying factor did nothave a large impact among the considered models under the multiplicative transfer.
519. Aside from the model uncertainty, the current risk evaluation was likely to be associated withother sources of uncertainty from unverifiable assumptions on the risk transfer. While someassumptions, such as the latency period, may not have a large impact, others, such as the choice oftransfer method (additive or multiplicative transfer along with effect modifiers), might considerably
affect longer-term risk evaluations. Mainly due to the statistical power issue, which did not allow for
estimation with sufficient precision of effect modifiers in the EAR models, a rigorous investigation of

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 161

the potential impact due to uncertainty involved in the risk transfer approach was not possible in thecurrent evaluation.

(g) Other sources of uncertainty
520. The age- and sex-specific baseline mortality rates of the target population were fixed with noassumed uncertainty in the current risk evaluation, which is likely to have a marginal impact on theestimated risks.
521. While combined groups of disease were desirable for risk evaluations in order to improve thestatistical power, radiation-associated circulatory diseases in the LSS appeared to vary among thedifferent subtypes. This suggests heterogeneity in radiation-induced pathogenesis. Thus, the risk estimatesfor broader categories of diseases (such as overall heart disease) need to be interpreted with caution.

2.

Preferred risk inference(a) Selection of the preferred risk inference
522. For both cerebrovascular disease and heart disease (excluding rheumatic valvular heart disease), therisk at a dose of 1–1.5 Gy is more evident than that at lower doses for circulatory diseases, but uncertainty
in the dose–response shape might not be adequately accounted for by the linear dose–response model fit.
Also, the uncertainty associated with risk modifications (neither of which was statistically significant)might be a concern in risk projection over a longer period. These considerations lead to giving a greater
weight to the results based on the ERR transfer of the risk estimate from the semiparametric model,
applied for the follow-up to age 60. Thus, it was concluded that these results would be most reliable forboth cerebrovascular disease and heart disease.
523. The preferred risk inferences are summarized below:(a) The radiation-induced mortality of cerebrovascular disease cumulated up to age 60 after acuteexposure to dose of 1.5 Gy to Japanese citizens at age 30: the estimate based on the LSS mortality
risk estimate is 5.8 (95% CI: 0.4, 12) cases per 10,000 persons;(b) The radiation-induced mortality of heart disease (excluding rheumatic valvular heart disease)
cumulated up to age 60 after acute exposure to a dose of 1.5 Gy to Japanese citizens at age 30: an
estimate based on the LSS mortality risk estimate is 11 (95% CI: 1.9, 21) cases per 10,000 persons.

(b) Discussion of the impact of sources of uncertainty
524. The main sources of uncertainties associated with this risk estimate are summarized in table 22.
The subsequent paragraphs give the reasons for the grading of the uncertainties (very small, small,moderate or large).
525. Selected populations: The impact of population selection is likely to be small since the LSS risk
was transferred to the target population with baseline rates that are relatively similar to those of the
LSS. The difference in the baseline rates between the current Japanese statistics and the estimates forthe youngest cohort of the LSS cohort is relatively small before age of about 75, while the increase of
the current population rate is much more rapid than the LSS at older ages (>75), which suggests larger
uncertainties in the risk evaluations at age 90.

162 UNSCEAR 2019 REPORT526. Exposure assessment: The uncertainty associated with exposure has been well accounted for in
the LSS doses, and its impact on the risk evaluation should be very small.
527. Health outcome assessment: The uncertainty in diagnostic accuracy with change in disease coding
is a concern. Although it is difficult to quantitatively assess the impact of such uncertainty in outcome
assessment, the uncertainty would be larger for heart disease, which received a greater impact from thecoding change, than for cerebrovascular disease.
528. Study design: While there are concerns on the study design issues of the LSS, the potential impactof uncertainty in the study design on the estimated risk is considered to be small.
529. Confounding factors: None of the potential confounders examined was influential on theassessment of risk of circulatory disease in the LSS, but other unaccounted factors (e.g. severity ofbomb-related injuries, lifestyle and socio-economic status) cannot be ruled out as confounders thatmight have non-negligible impact on the current risk evaluations.
530. Statistical methods and model uncertainties: The impact of choice on latency period was small if thelatency was extended from 5 to 10 years. The current risk evaluation using models with different forms ofeffect modification indicated that uncertainties linked to the choice of effect modification might havemoderate impacts, although neither of the effect modifiers considered was statistically significant.
531. Other sources of uncertainty: The impact of the assumption of the fixed baseline risk would beminor unless there was a significant change in the baseline risk. Use of combined groups of diseases offairly different characteristics may not have a large impact unless the target population has a fairly
different distribution in subtypes of circulatory disease from that of the LSS. The distributions of typesof disease varied over time in Japan, which might have some impact if risks differ between these types.
Table 22. Characterization of the main sources of uncertainty associated with the preferred risk
inference of circulatory diseases
SourceaCharacterization of source

Judged impact a

Selected populations

Difference in the baseline between the LSS and thetargeted Japanese populations

Small

Exposure assessment

Dose errors and use of dose to the colon

Very small

Health outcome
assessment

Diagnostic accuracy

Moderate for heart disease
Small for cerebrovasculardiseaseStudy design

Selection of control group, healthy survivor bias

Small

Confounding factors

Unaccounted potential confounders (severity ofbomb-related injuries, lifestyle and socio-economic
status)Moderate

Statistical methods andmodel uncertainty

Latency (5 years or 10 years)
Risk modification

Small
Moderate

Other sources of
uncertainty

The fixed baseline mortality
Use of combined groups of diseases of fairly different
characteristics

Small
Small

The impact of the different sources of uncertainty is classified into four categories according to the variation they are expected
to induce on the reported CER: very small—less than a factor of 1.1; small—between a factor of 1.1 to 1.5; moderate—between afactor of 1.5 to 2; and large—greater than a factor of 2.

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G. Conclusions
532. Based on the excess risk derived from the study of the Japanese atomic bombing survivors, thelifetime risk of radiation-induced circulatory disease was evaluated for the entire population of Japan in
the year 2014. The results indicated that the best estimate of the cumulative excess deaths due toexposure to a dose of 1.5 Gy at age 30, and followed up to age 60 might be about six deaths with aconfidence interval of about 0.4 to 12 per 10,000 people from cerebrovascular disease and about 11
with a confidence interval of about 2 to 20 per 10,000 persons from heart disease excluding rheumatic
valvular heart disease. These estimates were based on the multiplicative transfer of the dose–effectrelationship estimated by the semiparametric dose–response model with no effect modification.
533. In the meta-analysis of Little et al. [L12], the estimated dose–effect relationship was transferred topopulations of various countries to estimate the lifetime risk. For Japan, the CER per 10,000 persons
per unit dose was estimated to be 219 (95% CI: 24, 414) Sv−1 for cerebrovascular disease, 57 (95% CI:
25, 88) Sv−1 for ischaemic heart disease and 80 (95% CI: −125, 285) Sv−1 for non-ischaemic heartdisease. The high risk estimated for cerebrovascular disease seems to have reflected the fairly highpooled ERR per unit dose of 0.20 Sv−1 compared to the risk estimated from the LSS (ERR per unit doseof 0.08 Gy−1 under the linear dose–response model). The high value for the ERR per unit dose in themeta-analysis is related to the inclusion of the high estimate of 0.44 Gy−1 in the incidence data, and theexclusion of the low estimate of 0.03 Gy−1 in the mortality data for the Mayak workers.
534. Much of the uncertainty that has been only roughly accounted for in the current risk evaluation
seems to be related to diagnostic accuracy, potential confounding and uncertainty in the age–risk
pattern. To reduce these limitations, further studies are anticipated to accumulate evidence to allow an
improved assessment on the nature of the risk and to clarify the underlying mechanism for radiationassociated circulatory disease, which are likely to be different among the disease subtypes and also
between low and high exposures. It is widely accepted that high doses cause circulatory tissue damage
that leads to an increased risk of cardiovascular diseases, so the findings of increased risks following
high doses (including after radiotherapy) are not unexpected. However, at low doses, cell killing is notexpected to be a major phenomenon, so if there is any effect at low doses then it presumably occurs via
a different mechanism. Unless it is assumed that radiation-induced cardiovascular diseases are astochastic process (even at high doses), the finding at high doses is unlikely to be generalized for low doses.

VII. RESEARCH NEEDS
535. Radiation epidemiology has considerably improved in the past decade mainly due to improved
health registries, longer follow-up periods and increased international collaborations. Recent studies ofcancer risk due to CT examinations during early childhood, occupational exposure and exposure to 131I
during childhood allow a more reliable assessment of radiation risk. Recommendations to reduce
specific limitations associated with these studies are proposed in the conclusions of each respective
section. Routes of potential improvement for the future also include collection of information on otherrisk factors in addition to radiation (e.g. other sources of exposure) and use of morbidity data in
addition to mortality.
536. Epidemiological studies of patients exposed to CT-scans during childhood or of workers exposed
to radiation involve rather young cohorts. These cohorts should continue to be followed up, as they are
expected to provide more information on the risk of leukaemia, all solid cancers and selected individual

164 UNSCEAR 2019 REPORT

cancer types. Especially, extension should improve the assessment of the modifying effect of age at
exposure and attained age on the dose–risk relationship. Further epidemiological follow-up and updated
risk information are expected from LSS studies. Both incidence and mortality epidemiological data areuseful and used for describing and understanding health effects of exposure to radiation.
537. To improve the predictions of risk of leukaemia and all solid cancers in workers exposed to low
doses of radiation, epidemiological studies of workers (e.g. INWORKS) should be continued by(a) extending the follow-up of the cohort until the end of lifetime of the cohort; (b) better assessing andaccounting for radiation doses from medical procedures received by workers; (c) better assessing andaccounting for radiation doses (including missing and unrecorded doses) from all sources ofoccupational exposures (doses from employment at other facilities, neutrons, internal emitters);(d) better assessing and accounting for the effect of possible non-radiation confounding factors(e.g. smoking, alcohol, exposure to other chemicals); and (e) considering to include other workforces(e.g. other United States nuclear sites) in INWORKS.
538. The impact of uncertainties in exposure assessment on estimation of radiation effects inepidemiological studies remains challenging but needs to be further quantified. Various approaches arebeing developed and applied. These range from the relatively simple regression calibration to accountfor unshared stochastic errors to Bayesian methods based on multiple realizations of individual dose
estimates aiming at capturing the effects of shared and unshared errors. There is a need to further developthese methods and tools that allow an application of these methods to more epidemiological studies.
539. In general, studies of radiation epidemiology include humans with a range of ages at exposure,doses, ages under follow-up and other parameters. A number of studies demonstrated that the estimatesof health effects in the upper range of the dose distribution and in the centre of the other parameters are
relatively independent of the approach used to analyse the data. However, risk estimates at lower doses,
and for more peripheral values of other parameters may depend strongly on the approach. Methods arebeing developed to deal with this model uncertainty, among them multi-model inference, semiparametricapproaches and Bayesian model averaging. These approaches need to be further developed and madeavailable to the broader community.
540. In the optimal setting, assessments of health risks from radiation exposures are based on
epidemiological studies and on an understanding of how the biological effects of radiation affect thebiological processes of the genesis of the disease and of the radiation action on its genesis. The
development of cancer and of circulatory diseases is a complex and multifactorial process. In the past
decade, it has the complexity of the biological processes of carcinogenesis and induction of circulatory
diseases become increasingly obvious and the understanding of the radiation action could not keep up
with this increasing complexity. It is of utmost importance to improve the knowledge of radiobiologicalprocesses contributing to radiation-induced carcinogenesis and induction of circulatory diseases andintegrate them in a modelling of effects in radiation epidemiology and risk inference.
541. Major uncertainties are linked to the transfer of risk from one population to another. Replicationof epidemiological studies in different populations, with as similar as possible design, would enhance
the understanding of the interactions between radiation and other risk factors. It should help
consolidating the choice of the weighting scheme between the EAR and ERR transfer models, andfinally improve the determination of the transfer of risk between populations. As outlined in theprevious paragraph, it is only through deeper knowledge of biological mechanisms that reliable
inferences of interactions between radiation and other factors can be gained, thus answering thequestion how to transfer risk from one population to another properly.

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542. Many sources of uncertainty challenge the reliability and precision of the estimates of radiationrelated health risks. While for very specific scenarios that are closely related to recent large
epidemiological studies the impact of uncertainties may be considered as small, for other exposure andfollow-up conditions, their impact could be much larger. Methods to better assess the impact of
uncertainties on the CER need to be developed. One approach could be to develop better methodology
for deriving estimates (and their uncertainties) from pooled analyses which account for both sampling
and non-sampling errors.
543. As long as mortality rather than incidence data are available there is a source of uncertainty intreatment success, which could be large in some instances. More effort should be devoted to cancer
registration data to avoid this source of uncertainty. It is very likely that different cancers have different
dose responses, modifying factors, etc., and it would be valuable to assess particular solid cancers, suchas breast cancer and colon cancer when sufficient data become available rather than combine all solidcancers. Finally, it is important to continue to gather high quality data for epidemiological studies, andthat large studies remain feasible through appropriate data linkage availability.

VIII. GENERAL CONCLUSIONS
544. The objective of this work was to perform quantitative risk evaluations of radiation effects on
cancer in specific exposure situations with low to moderate doses and on circulatory diseases insituations with higher doses. For each of the five scenarios (leukaemia during childhood, leukaemia
during adulthood, all solid cancers, thyroid cancer and circulatory diseases) calculations of cumulative
excess risk were performed using risk models derived from a study based on a population with similar
characteristics as that considered in the scenario. Parallel calculations of cumulative excess risk were
performed using risk models derived from the LSS, to allow comparison to be made of the results(again with the exception of circulatory diseases, where only LSS risk models were used). An important
goal was to go beyond purely statistical uncertainties and to consider, as far as possible, other sourcesof uncertainty, e.g. exposure assessment, outcome assessment, confounding factors and study design.
545. In the present analysis, it was considered that the preferred risk inference was the one that fits best
the characteristics of the considered scenario, based on an expert judgement on the magnitude of theuncertainties associated with it.

A. Health effects
546. In recent years, new results have been published from studies of leukaemia incidence amongpeople exposed to radiation during childhood and adolescence. These studies consolidated theknowledge about leukaemia risk related to low dose exposure and confirmed the fact that, for the samedose, the strength of the effect is higher if dose is received during childhood than during adulthood.
Among these studies, those on children undergoing CT scan diagnostic examinations are the mostpowerful.
547. Regarding leukaemia incidence and mortality associated with exposure to external radiation
during adulthood, several studies have been published in recent years which complement to the LSS
study. The results are mostly coherent in demonstrating a dose–effect relationship for leukaemia after
exposure during adulthood, with a lower slope than estimated in studies considering exposure during

166 UNSCEAR 2019 REPORT

childhood. Among these, the most powerful study was the INWORKS combined analysis ofoccupationally-exposed workers, which provided an estimate of the ERR per unit dose for leukaemia
mortality with a small confidence interval.
548. A large number of epidemiological studies dealing with solid cancer incidence and mortality fromexposure to low-LET radiation have been published in the last decade, including studies of the LSS
cohort and of occupationally-exposed workers from nine countries (i.e. nuclear power plant workers,
uranium processing workers, X-ray technicians, emergency workers and other occupationally-exposed
workers). For all worker cohorts, more than 50% of the members received doses less than 100 mGy,
with most cohorts having the majority of their members being exposed to doses lower than 100 mGy(i.e. ~90% or more). In most studies, the best estimate of the ERR at 100 mGy (or mSv) was greaterthan zero, but not all dose responses were statistically significant, especially for studies which involved
small cohorts or a short follow-up and which did not have sufficient statistical power (at the present) to
detect radiogenic risk. Worker studies provide dose responses relevant for long-term, prolonged
exposures of adults that can be applied for prospective and retrospective risk analyses of other workers
exposed to radiation. Among the occupational studies, the recent INWORKS is outstanding, because of
the long observation time (more than 8 million person–years), the large number of solid cancer deaths
analysed (nearly 18,000) and the high quality of data.
549. Estimates for radiation-induced thyroid cancer after exposure during childhood or adolescencehave been derived in the UkrAm cohort study with a well-defined screening regime in combination
with individual dose estimates. The LSS provides estimated doses to the thyroid of similar quality, but
the screening conditions were not fully controlled for the whole cohort. The large cohort of about
300,000 persons who were residents under age 18 in 2011 in the Fukushima Prefecture benefited fromscreening conditions similar to the UkrAm cohort (although with more modern equipment), but doses
to the thyroid were considerably lower and not known on an individual basis. Compared to the
Fukushima cohort, the UkrAm cohort fulfils important quality criteria and thus provides a sound basis
for the assessment of thyroid cancer risk after radiation exposure.
550. While an increasing number of epidemiological results have been reported on radiation-associatedcirculatory diseases in the past decade, the evidence on potential effects is largely inconsistent andinconclusive, in particular at low to moderate doses. The estimated radiation effects on circulatory
diseases in the LSS were mostly smaller than those observed for cancers, and the biologicalmechanisms behind such effects are unclear, which makes it difficult to evaluate the dose–response
relationship and its variation with age and other factors (effect modification). Issues specifically
relevant to the assessment of circulatory disease risk, such as potentially large effects of the non-radiation
risk factors and errors in diagnoses and classifications, complicate the comparison of the reported
effects between the studies of different populations at different periods.

B. Risk assessments
1.

Risk assessment with statistical uncertainties

551. The Committee assessed the risks of leukaemia, all solid cancer and thyroid cancer in scenarios of
exposure to ionizing radiation based on recent large epidemiological studies and on effect-per-unit-dose
estimates in the LSS. The best estimates of the two approaches (recent large study and the LSS
estimate) agreed well for conditions that corresponded closely to those of the recent studies (table 23).
For these specific scenarios, the recent studies provided a more reliable risk estimate than a transfer ofANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 167

effects from the LSS to the scenario. The latter approach is burdened by major sources of uncertainty
due to the transfer from moderate/high dose to low dose conditions, from acute high-dose-rate exposure
to protracted low-dose-rate exposure conditions, between populations with different background rates,
and from catastrophic after-war living conditions for the survivors of the atomic bombings to a normal
contemporary population. It should be noted that the Committee did not apply any corrections to theeffect-per-unit-dose estimates from the LSS to the scenarios involving low dose rates or low dose. The
good agreement of the two approaches, when applied to the conditions of the recent study, gives someconfidence that the LSS-based risk estimates can be used for ages at exposure or follow-up periods
other than those in the specific scenarios.
552. The Committee also assessed the mortality risks due to circulatory diseases in a scenario of aJapanese population exposed to ionizing radiation. There are considerable differences in thefrequency of the various types of circulatory diseases and their risk factors between the population in
the LSS and a modern Japanese population, and between Japanese and Western populations. The
pathogenesis of radiation-related circulatory diseases at low or moderate dose is not well understood.
Even after an acute exposure to a dose as high as 1 Gy, there are large uncertainties in the risk
estimates. A modern statistical approach was applied to assess these uncertainties for heart and for
cerebrovascular diseases (table 23).
Table 23. Cumulative cancer and circulatory disease risks due to radiation exposure in specificscenarios. Preferred estimates and statistical 95% confidence intervals
Additional information for each scenario (minimum latent period, description of source data for risk
models, baseline rates, etc.) are in sections II to VI. CER: Cumulative excess risk; LSS: Life Span Study
CER (per 10 000 persons)based on ERR transfer

Cumulative
dose

Follow-up

Four CT scans at age 1

20 mGyto the RBM

Occupational protracted
external exposure, age
30–45

Exposure scenario

Current study
corresponding to
the specific scenario

LSS

Leukaemia incidence up to
age 305.3 (0.6, 18.3)

5.1 (−0.8, 30)

200 mGy
to the RBM

Leukaemia a, males
mortality up to age 60

4.6 (1.5, 9.6)

2.8 (−0.1, 5.6)

100 mGy
to the colon

All solid cancer, males
mortality up to age 60

11 (3.1, 19.3)

6.4 (4.0, 9.8)

131

I-internal exposure atage 10500 mGy
to thethyroidThyroid cancer incidence
up to age 30

7.7 (2.4, 18)

7.0 (1.6, 23)

Acute external exposure
at age 30

1.5 Gyto the colon

Cerebrovascular diseasemortality up to age 60

5.8 (0.4, 12)Heart disease b mortality
up to age 60

11 (1.9, 21)aExcluding chronic lymphoid leukaemia.

bExcluding rheumatic valvular heart diseases.

168 UNSCEAR 2019 REPORT553. Using ERR models derived from the UK CT-scan study or from the LSS, consistent results wereobtained for leukaemia incidence up to age 30 after CT scans at age 1 with a total dose to the RBM of
20 mGy. Among 10,000 persons with an assumed cumulative baseline incidence of 9 cases, acumulative excess incidence of about 5 cases was estimated. Even though the statistical confidence
intervals were large, the interval obtained using the model from the UK CT-scan study was half of thatobtained using the LSS model.
554. The INWORKS provides a pertinent risk model to assess leukaemia mortality risk among maleworkers with a dose to the RBM of 200 mGy in the age range from 30 to 45 with a follow-up untilage 60. Among 10,000 persons with an assumed cumulative baseline mortality of 10 leukaemia deaths,
a cumulative excess mortality of about five leukaemia deaths was estimated, with an uncertainty range
from about 1 to about 10. For this scenario, the use of the ERR per unit dose derived from the
INWORKS cohort requires no extrapolation or transfer, whereas the use of risk models derived fromthe atomic bombing survivors relies on uncertain assumptions related to the situations of exposure
being different, higher doses and different dose rates.
555. The cumulative risk of all solid cancer mortality in a population of workers subject to prolonged
exposure to low-LET radiation was estimated based on risk models from the INWORKS and from themost recent studies of the LSS cohort. The specific scenario assumed exposures of United States maleworkers between ages of 30 and 45, to a total whole-body dose of 100 mGy. The preferred risk
inference for the considered exposure scenario was the CER up to attained age 60, obtained using the
ERR risk model from the INWORKS. The CER estimated based on the INWORKS risk model for all
solid cancers and the full dose range is 11 (95% CI: 3.1, 19.3) cases in 10,000 persons when cumulated
up to age 60 (table 23). These excess cases represent fewer than 5% of the total number of solid cancer
deaths (about 230 cases) expected to occur in the absence of exposure to radiation, for the same age
range. The range of the confidence interval is about a factor of 1.5 larger than the central estimate.
While epidemiological studies of adult workers are an excellent source of data regarding radiation risks
from prolonged or fractionated exposures, the reported risk models often do not include modifiers forage at exposure or attained age. Thus, extrapolation of risk to attained ages greater than the average age
at the end of follow-up needs to be interpreted carefully. For such cases, estimates of risk obtained using
models from the LSS cohort are still more reliable, although they need to account for differences inbaseline rates between LSS and the populations for which the risk is assessed.
556. For the preferred thyroid scenario with age at exposure 10, the sex-averaged CER estimates basedon the ERR models derived from the UkrAm cohort and the LSS cohort, agree very well. It is noted,
however, that at very young age, the ERR estimates for the UkrAm cohort are markedly higher thanthose for the LSS cohort. However, this difference does not influence the CER estimates, because atyoung age, baseline incidence of thyroid cancer is very low.
557. Based on estimates of radiation-related effects per unit dose derived from the LSS analysis, thelifetime risk of radiation-induced circulatory disease was evaluated for the general population in Japan.
The results indicated that the best estimate of the cumulative excess deaths due to exposure to 1.5 Gy at
age 30, and followed up to age 60 might be 5.8 (95% CI: 0.4, 12) and 11 (95% CI: 1.9, 21) deaths per
10,000 from cerebrovascular disease and heart disease, respectively. These values were based on thetransfer of the ERR estimated by the semiparametric dose–response model.
558. Inferences* of risk values for age at exposure, dose and follow-up periods other than thosedescribed in the preceding paragraphs are burdened by additional uncertainties related to a transfer ofthe risk quantities from the centre of the data in the new epidemiological studies to other conditions.
Most of these are currently not possible to be quantified reliably. However, the good agreement of thetwo approaches in the preferred risk (i.e. the risk models derived from the LSS for a cohort

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 169

corresponding to the scenario) reassures the methodology for the estimation of risks based on effects
observations in the LSS. This confirms that the LSS remains a major source of information for such risk
estimations scenarios as are considered here, but possibly also for other scenarios and health outcomes.
An important question remains: what is the best risk quantity to be used in the risk transfer? In
principle, this question could be addressed by an understanding of the interaction of the radiation
exposure with other factors causing the genesis and development of the disease. However, present
knowledge on this interaction is limited. In view of this, the choice of approach on how to transfer
radiation risk models from the LSS remains a major source of uncertainty in the assessment of healthrisks in other exposure conditions and populations.

2.

Risk assessment considering additional sources of uncertainty

559. The risk estimates for the specific scenarios based on recent large epidemiological studies werepreferred, because any major additional stochastic sources of uncertainties apart from the statisticaluncertainties relating to the exposure and follow-up conditions are small. The credible intervals of therisk estimates were assessed using the approach described in appendix A, considering all known
sources of uncertainties. The credible intervals presented in table 24 are thought to reflect both thestatistical uncertainty (reflected by the 95% confidence intervals in table 23) and the potential impact ofadditional sources of uncertainty discussed above (listed in the sections F.2(b) in the chapters II to VI ofthe single scenarios). In the scenarios for cancer risks, the additional stochastic sources of uncertainty
are very small or small, and most are considered to be multiplicative, but an additive component has
been considered as well. It was assumed that the impact of the additional sources of uncertainty may beapproximated by the maximal value of a moderate uncertainty (factor of less than two) and up to apotentially large impact for age at exposure in the CT-scan scenario (factor potentially higher than two).
A Monte Carlo approach of propagating uncertainties was applied (see appendix A).
Table 24. Rounded values of cumulative cancer risk due to radiation exposures in the preferred risk
inferences
The credible intervals are based on expert judgement, considering known sources of uncertainty

Exposure scenario

Cumulative dose

Four CT scans at age 1

20 mGy to the RBM

Occupational external
exposure, age 30–45

I-internal exposure
at age 10

131

Follow-up

Cumulative excess risk(per 10 000 persons)Preferred risk
inference

95% credibleinterval

Leukaemia incidence up to
age 305

0 to 20200 mGy to the RBM

Leukaemia mortality up to
age 60 excluding CLL

5

1 to 10100 mGy to the colon

All solid cancer mortality
up to age 60

11

2 to 20500 mGy to thethyroidThyroid cancer incidence
up to age 30

82 to 20560. The sources of uncertainties associated with the preferred risk inference for leukaemia after
exposure during early childhood and follow-up until young adulthood are considered to be very small(less than a factor of 1.1), small (less than a factor of 1.5), moderate for the inclusion of MDS (less than170 UNSCEAR 2019 REPORTa factor of two) and even potentially large for age at exposure (higher than a factor of two).
Nevertheless, the sources of uncertainties listed above are considered to essentially compensate eachother (upwards for the inclusion of MDS in the risk model and the inclusion of patients with previous
cancer, and downwards for the age at exposure) so that the preferred estimate value of the ERR per unit
dose was not modified. The credible interval, however, is affected by these sources of uncertainty andis considered to range from 0 to 20 leukaemia cases among 10,000 persons exposed, meaning thatbased on the credible interval the preferred risk inference is not significant.
561. The additional sources of uncertainties associated with the preferred risk inference for leukaemia
after exposure during adulthood and follow-up until age 60 are considered to be very small (less than afactor of 1.1) or small (less than a factor of 1.5). Using this estimate of a small impact, the credibleinterval (table 24) is therefore only enlarged by about 11% compared to the 95% confidence interval(table 23).
562. For the exposure scenario focusing on the risk of all solid cancers combined, the largest source of
uncertainty in the preferred risk inference is the statistical uncertainty in the ERR per unit dose from the
INWORKS-effect model. Other sources of uncertainty are expected to have a lower contribution to theuncertainty in the preferred estimate of cumulated risk of all solid tumours combined. The 95% credibleinterval is judged to range from 2 to 20 cancer mortalities among 10,000 persons exposed.
563. Credible intervals (table 24) for the thyroid scenario were inferred to be close to those for the
preferred scenario (table 23), although there is some evidence for a possible confounding by intake ofstable iodine or nitrate in drinking water. In order to be able to assign a credible interval regardless ofthese sources of uncertainty, the scenario was defined by assuming a similar amount of stable iodine
and nitrate in the drinking water as in the UkrAm cohort. In the thyroid scenario, cancer incidence isdominated by the radiation effect. The CER in the preferred risk inference corresponds to an
attributable fraction of 70%. By preferring the risk estimate closely related to the Ukrainian target
population important sources of uncertainty were avoided. Risk models for thyroid cancer from the LSS
provide adequate CER estimates in risk transfer calculations for the general Ukrainian population.
However, as in the other scenarios, risk estimates from a population closer to the target population
provide more reliable CER estimates since some sources of uncertainty have been excluded.
564. Important to keep in mind that the preferred risk inferences in table 24 refer to specific conditions
as exposure scenario, age range of follow-up, cancer specification and baseline risk in the populationconsidered. These conditions were chosen in order to avoid major assumptions on the transfer of theeffect per unit dose in an epidemiological study to the risk per unit dose in the scenario. The
epidemiological studies, on which the preferred risk inferences are based, are less informative for otherconditions. The more the conditions deviate from those of the preferred risk inference, the more
important becomes a transfer of the effect per unit dose in the LSS, which increases uncertainties due tothe assumptions needed in this transfer.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 171

ACKNOWLEDGEMENTS
The Committee acknowledges with gratitude the work of the members of the Expert Group involved in
preparing this annex, in particular, W.-U. Müller (Germany) who chaired the Expert Group and
P. Jacob (Germany) who acted as Senior Technical Adviser. The Committee would like also to thank
R. Wakeford (United Kingdom) and D. Pawel (United States) for critically reviewing the manuscript.
The views expressed in the annex remain those of the Committee and do not necessarily represent theviews of the United Nations or its Member States.

Members of the Expert GroupD. Laurier (France), J.C. Kaiser and W.-U. Müller (Germany), K. Furukawa (Japan) and I. Apostoaei(United States).

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 173

APPENDIX A. JUDGEMENT OF CREDIBLE INTERVALS
A1. This appendix deals with unbiased and not-shared stochastic errors. Systematic errors, biases,
confounding factors and shared errors are dealt with in the main text of the annex. These include:(a) Non-exclusion of patients with unreported previous cancer, possible overestimation of
myelodysplastic syndromes (MDS) risk by the high MDS excess rate in the UK CT-scan study, andapplication of an average excess relative risk (ERR) per unit dose to age at exposure of one in thechildhood CT-scan scenario;(b) Unaccounted exposure to radiation (neutron exposures, internal contamination, missed
external gamma doses, job-related chest X-rays) in the scenarios based on the INWORKS study;(c) Surveillance (screening) of the thyroid, and the possible influence of iodine supply and nitratein the drinking water on the excess risk in the scenario based on the UkrAm study.
A2. In the following, the cumulative excess risks (CERs) for the preferred risk inference (table 23) are
given in the unit chances among 10,000 persons. The CERs have been curve-fitted using Weibull
probability distribution functions (table A1) that pass through the best estimate (maximum-likelihood
estimate, MLE) and through the lower and upper limits of the 95% confidence intervals.
Table A1. Cumulative excess risk (CER) of cancer in 10,000 persons

Preferred risk inferenceaCER(95% CI)

Parameters of Weibull aprobability distribution function
Location (γ )Scale (η )Shape (β )Leukaemia incidence up to age 30 after CT scans atage 1 with RBM dose of 20 mGy

0.6–18.3−0.553

9.167

1.805

Leukaemia mortality up to age 60 after occupational
exposure from age 30 to 45 with RBM dose of
200 mGy

1.5–9.6

0.415

5.243

2.350

All solid cancer mortality up to age 60 after
occupational exposure from age 30 to 45 withcolon dose of 100 mGy

3.1–19.3−1.250

13.66

3.206

Thyroid cancer incidence up to age 30 afterincorporation of 131I at age 10 with thyroid dose of
500 mGy

2.4–18

0.841

9.424

2.078

Weibull probability distribution function shifted by the amount indicated by the location parameter.

A3. In addition to the uncertainties taken into account in the Monte Carlo calculations for estimating
confidence intervals of CER (e.g. table 23), there are several sources of very small or small stochastic
uncertainties (tables 4, 7, 16 and 18). The uncertainty range of CER judged based on its confidence
interval and the additional sources of uncertainty is expressed by a 95% credible interval.

174 UNSCEAR 2019 REPORT

A4. The additional uncertainties are assumed to be mainly multiplicative, but an additive component
is considered as well. The additive component is modelled by one additive uncertainty normally
distributed with a 95% range from −0.1*MLE to 0.1*MLE, where MLE is the maximum-likelihood
estimator of CER. The multiplicative component is modelled by one small uncertainty of maximum
size (factor 1.5, i.e. 95% credible range from 2/3 to 3/2). This source of uncertainty is assumed to havea log-normal distribution with mean one and to be independent of the sources of uncertainty expressedby the confidence interval of CER.
A5. The impact of additional uncertainties on the distribution of CER is evaluated by Monte Carlosimulation based on 10,000 samples per density function. Results are presented here only for the
95% credible intervals.
A6. The results of the Monte Carlo calculations for the credible interval of CER are depicted intable A2 and figure A-I. In the final results in the main text of this annex, best estimates of CER are
rounded to one-digit, lower bounds of the credible intervals to one of the digits 0, 1 and 2.
Table A2. Credible intervals and 95% confidence intervals of cumulative excess risk of cancer in
10,000 persons

Preferred risk inference

Confidence
interval(95%)

Leukaemia incidence up to age 30 after CT scans atage 1 with RBM dose of 20 mGy

Calculated credible interval

Roundedcredibleinterval

Small
multiplicative
uncertainty

Very small
additive
uncertainty

0.6–18.3

0.6–19.8

0.7 a–18.4

0 b–20

Leukaemia mortality up to age 60 after
occupational exposure from age 30 to 45
with RBM dose of 200 mGy

1.5–9.6

1.4–10.6

1.5–9.8

1–10

All solid cancer mortality up to age 60
after occupational exposure from age 30 to 45
with colon dose of 100 mGy

3.1–19.3

2.9–21.7

3.1–19.4

2–20

Thyroid cancer incidence up to age 30
after incorporation of 131I at age 10
with thyroid dose of 500 mGy

2.4–18

2.3–20.3

2.4–18.6

2–20

a
The higher value of the 2.5% percentile of the credible interval compared to the confidence interval is caused by statisticalfluctuations due to the limited number of Monte Carlo simulation.
b

The possibility of an overestimation of the risk coefficient (due to the inclusion of patients with previous cancer in the UK
childhood CT-scan study or the inclusion of MDS cases in the risk model) has been considered in fixing the lower bound of thecredible interval to zero.

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 175

Figure A-I. Three-parameter Weibull distribution of cumulative excess risk (CER) and its broadening
due to a small multiplicative uncertainty (*smu) and a very small additive uncertainty (+vsau)
The density function of the undisturbed Weibull3 distribution is expressed by the exact formula, the disturbed
density functions are simulated with sample size N=10,000; ASC=all solid cancers

ANNEX A: EVALUATION OF SELECTED HEALTH EFFECTS AND INFERENCE OF RISK [...] 177

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