Impact of uncertainties in exposure assessment on estimates of thyroid cancer risk among Ukrainian children and adolescents exposed from the Chernobyl accident

A Historical Survey of Key Epidemiological Studies of Ionizing Radiation Exposure

In this article we review the history of key epidemiological studies of populations exposed to ionizing radiation. We highlight historical and recent findings regarding radiation-associated risks for incidence and mortality of cancer and non-cancer outcomes with emphasis on study design and methods of exposure assessment and dose estimation along with brief consideration of sources of bias for a few of the more important studies. We examine the findings from the epidemiological studies of the Japanese atomic bomb survivors, persons exposed to radiation for diagnostic or therapeutic purposes, those exposed to environmental sources including Chornobyl and other reactor accidents, and occupationally exposed cohorts. We also summarize results of pooled studies. These summaries are necessarily brief, but we provide references to more detailed information. We discuss possible future directions of study, to include assessment of susceptible populations, and possible new populations, data sources, study designs and methods of analysis.

Recommendations on statistical approaches to account for dose uncertainties in radiation epidemiologic risk models

PURPOSE

Epidemiological studies of stochastic radiation health effects such as cancer, meant to estimate risks of the adverse effects as a function of radiation dose, depend largely on estimates of the radiation doses received by the exposed group under study. Those estimates are based on dosimetry that always has uncertainty, which often can be quite substantial. Studies that do not incorporate statistical methods to correct for dosimetric uncertainty may produce biased estimates of risk and incorrect confidence bounds on those estimates. This paper reviews commonly used statistical methods to correct radiation risk regressions for dosimetric uncertainty, with emphasis on some newer methods. We begin by describing the types of dose uncertainty that may occur, including those in which an uncertain value is shared by part or all of a cohort, and then demonstrate how these sources of uncertainty arise in radiation dosimetry. We briefly describe the effects of different types of dosimetric uncertainty on risk estimates, followed by a description of each method of adjusting for the uncertainty.

CONCLUSIONS

Each of the method has strengths and weaknesses, and some methods have limited applicability. We describe the types of uncertainty to which each method can be applied and its pros and cons. Finally, we provide summary recommendations and touch briefly on suggestions for further research.

A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.

A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated risk. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method. The quasi-2DMC+BMA method performs well when a linear model is assumed, but poorly when a linear-quadratic model is assumed. FMA performs as well as quasi-2DMC+BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC+BMA or FMA methods, particularly for the linear-quadratic model. The bias of predicted relative risk at a variety of doses is generally smallest for ERC, and largest for quasi-2DMC+BMA and FMA, with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.

Minimum latency effects for cancer associated with exposures to radiation or other carcinogens

BACKGROUND

In estimating radiation-associated cancer risks a fixed period for the minimum latency is often assumed. Two empirical latency functions have been used to model latency, continuously increasing from 0. A stochastic biologically-based approach yields a still more plausible way of describing latency and can be directly estimated from clinical data.

METHODS

We derived the parameters for a stochastic biologically-based model from tumour growth data for various cancers, and least-squares fitted the two types of empirical latency function to the stochastic model-predicted cumulative probability.

RESULTS

There is wide variation in growth rates among tumours, particularly slow for prostate and thyroid cancer and particularly fast for leukaemia. The slow growth rate for prostate and thyroid tumours implies that the number of tumour cells required for clinical detection cannot greatly exceed 106. For all tumours, both empirical latency functions closely approximated the predicted biological model cumulative probability.

CONCLUSIONS

Our results, illustrating use of a stochastic biologically-based model using clinical data not tied to any particular carcinogen, have implications for estimating latency associated with any mutagen. They apply to tumour growth in general, and may be useful for example, in planning screenings for cancer using imaging techniques.

A generalisation of the method of regression calibration and comparison with the Bayesian 2-dimensional Monte Carlo method

For many cancer sites it is necessary to assess risks from low-dose exposures via extrapolation from groups exposed at moderate and high levels of dose. Measurement error can substantially alter the shape of this relationship and hence the derived population risk estimates. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, much attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. In this paper we test a Bayesian model averaging method, the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, that has been fairly recently proposed against a very newly proposed modification of the regression calibration method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. We also compared both methods against standard regression calibration and Monte Carlo maximum likelihood. The Bayesian 2DMC method performs poorly, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5%, particularly when the magnitudes of classical and Berkson error are both moderate to large (20%-50%). The method also produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. By comparison the extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the Bayesian 2DMC and all other methods. The bias of the predicted relative risk at a variety of doses is generally smallest for extended regression calibration, and largest for the Bayesian 2DMC method (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between the other two methods.

Tumor Inflammatory Microenvironment of the Thyroid Cancer: Relationship between Regulatory T-Cell Imbalance, and p-NFΚB (p65) Expression-A Preliminary Study

BACKGROUND

Inflammatory microenvironment is an essential component of all tumors, including thyroid cancer. Autoimmune thyroid diseases are often associated with thyroid cancer. CD25, expressed in Treg cells and B cells, has been found to be associated with autoimmune thyroid diseases and the NFkB pathway is critical to tumor formation, regulating immune-related genes, and pro-inflammatory cytokine.

METHODS

Protein expression of CD25 and NFkB and its phosphorylated form was analyzed by immunohistochemistry in 80 patients with thyroid cancer (10 cases of cancers with Hashimoto's thyroiditis and 70 cases without).

RESULTS

CD25 was mainly detected in the nucleus of the inflammatory cells such as in the thyrocytes and neoplastic cells. Protein staining was detected in the T-lymphocytes of the outermost zone of the lymphoid follicles. Moreover, in all cancer alterations, there were a higher level of p-NFkB than in the surrounding tissues. Again, p-NFkB staining was evident in neoplastic cells but not evident in inflammatory cells.

CONCLUSIONS

Strong inflammatory infiltrate in the tumor microenvironment is correlated with an invasive phenotype. CD25 and p-NFkB levels were statistically significantly overexpressed in cancer cells.

A generalisation of the method of regression calibration

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient [Formula: see text] are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient [Formula: see text] are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for [Formula: see text] that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient [Formula: see text] are substantially upwardly biased.

A generalisation of the method of regression calibration

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient \(\alpha\) are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient \(\beta\) are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for \(\beta\) that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient \(\beta\) are substantially upwardly biased.

Dose estimates and their uncertainties for use in epidemiological studies of radiation-exposed populations in the Russian Southern Urals

Many residents of the Russian Southern Urals were exposed to radioactive environmental pollution created by the operations of the Mayak Production Association in the mid- 20th century. There were two major releases: the discharge of about 1x1017 Bq of liquid waste into the Techa River between 1949 and 1959; and the atmospheric release of 7.4 * 1016 Bq as a result an explosion in the radioactive waste-storage facility in 1957. The releases into the Techa River resulted in the exposure of more than 30,000 people who lived in riverside villages between 1950 and 1961. The 1957 accident contaminated a larger area with the highest exposure levels in an area that is called the East Urals Radioactive Trace (EURT). Current epidemiologic studies of the exposed populations are based on dose estimates obtained using a Monte-Carlo dosimetry system (TRDS-2016MC) that provides multiple realizations of the annual doses for each cohort member. These dose realizations provide a central estimate of the individual dose and information on the uncertainty of these dose estimates. In addition, the correlation of individual annual doses over realizations provides important information on shared uncertainties that can be used to assess the impact of shared dose uncertainties on risk estimate uncertainty.This paper considers dose uncertainties in the TRDS-2016MC. Individual doses from external and internal radiation sources were reconstructed for 48,036 people based on environmental contamination patterns, residential histories, individual 90Sr body-burden measurements and dietary intakes. Dietary intake of 90Sr resulted in doses accumulated in active bone marrow (or simply, marrow) that were an order of magnitude greater than those in soft tissues. About 84% of the marrow dose and 50% of the stomach dose was associated with internal exposures. The lognormal distribution is well-fitted to the individual dose realizations, which, therefore, could be expressed and easily operated in terms of geometric mean (GM) and geometric standard deviation (GSD). Cohort average GM for marrow and stomach cumulative doses are 0.21 and 0.03 Gy, respectively. Cohort average dose uncertainties in terms of GSD are as follows: for marrow it is 2.93 (90%CI: 2.02-4.34); for stomach and the other non-calcified tissues it is 2.32 (90% CI: 1.78-2.9).

Exposure to the Thyroid from Intake of Radioiodine Isotopes after the Chornobyl Accident. Report I: Revised Doses and Associated Uncertainties for the Ukrainian-American Cohort

Thyroid doses from intake of radioiodine isotopes (131I, 132Te+132I, and 133I) and associated uncertainties were revised for the 13,204 Ukrainian-American cohort members exposed in childhood and adolescence to fallout from the Chornobyl nuclear power plant accident. The main changes related to the revision of the 131I thyroid activity measured in cohort members, the use of thyroid-mass values specific to the Ukrainian population, and the revision of the 131I ground deposition densities in Ukraine. Uncertainties in doses were assessed considering shared and unshared errors in the parameters of the dosimetry model. Using a Monte-Carlo simulation procedure, 1,000 individual stochastic thyroid doses were calculated for each cohort member. The arithmetic mean of thyroid doses from intake of 131I, 132Te+132I, and 133I for the entire cohort was 0.60 Gy (median = 0.22 Gy). For 9,474 subjects (71.6% of the total), the thyroid doses were less than 0.5 Gy. Thyroid doses for 42 cohort members (0.3% of the total) exceeded 10 Gy while the highest dose was 35 Gy. Intake of 131I contributed around 95% to internal thyroid exposure from radioiodine isotopes. The geometric standard deviation of individual stochastic thyroid doses varied among cohort members from 1.4 to 4.3 with an arithmetic mean of 1.6 and a median of 1.4. It was shown that the contribution of shared errors to the dose uncertainty was small. The revised thyroid doses resulted, in average, in around 40% decrease for cohort members from Zhytomyr Oblast and an increase of around 24% and 35% for the cohort members from Kyiv and Chernihiv Oblast, respectively. Arithmetic mean of TD20 doses for the cohort was around 8% less than that estimated in TD10, 0.60 Gy vs. 0.65 Gy, respectively; however, global median of TD20 doses somewhat increased compared to TD10: 0.22 Gy vs. 0.19 Gy, respectively. The difference between TD10 and TD20 was mainly due to a revision of the individual 131I thyroid activity measured in the cohort members.

Impact of uncertainties in exposure assessment on thyroid cancer risk among cleanup workers in Ukraine exposed due to the Chornobyl accident

A large excess risk of thyroid cancer was observed among Belarusian/Russian/Baltic Chornobyl cleanup workers. A more recent study of Ukraine cleanup workers found more modest excess risks of thyroid cancer. Dose errors in this data are substantial, associated with model uncertainties and questionnaire response. Regression calibration is often used for dose-error adjustment, but may not adequately account for the full error distribution. We aimed to examine the impact of exposure-assessment uncertainties on thyroid cancer among Ukrainian cleanup workers using Monte Carlo maximum likelihood, and compare with results derived using regression calibration. Analyses assessed the sensitivity of results to various components of internal and external dose. Regression calibration yielded an excess odds ratio per Gy (EOR/Gy) of 0.437 (95% CI - 0.042, 1.577, p = 0.100), compared with the EOR/Gy using Monte Carlo maximum likelihood of 0.517 (95% CI - 0.039, 2.035, p = 0.093). Trend risk estimates for follicular morphology tumors exhibited much more extreme effects of full-likelihood adjustment, the EOR/Gy using regression calibration of 3.224 (95% CI - 0.082, 30.615, p = 0.068) becoming ~ 50% larger, 4.708 (95% CI - 0.075, 85.143, p = 0.066) when using Monte Carlo maximum likelihood. Results were sensitive to omission of external components of dose. In summary, use of Monte Carlo maximum likelihood adjustment for dose error led to increases in trend risks, particularly for follicular morphology thyroid cancers, where risks increased by ~ 50%, and were borderline significant. The unexpected finding for follicular tumors needs to be replicated in other exposed groups.

Assessment of internal exposure to 131I and short-lived radioiodine isotopes and associated uncertainties in the Ukrainian cohort of persons exposed in utero

This study revised the thyroid doses for 2582 Ukrainian in utero cohort members exposed to Chornobyl fallout (the Ukrainian in utero cohort) based on revision of: (i) 131I thyroid activity measured in the Ukrainian population, (ii) thyroid dosimetry system for entire Ukraine, and (iii) 131I ground deposition densities in Ukraine. Other major improvements included: (i) assessment of uncertainties in the thyroid doses considering shared and unshared error, and (ii) accounting for intake of short-lived radioisotopes of tellurium and iodine (132Te+132I and 133I). Intake of 131I was the major pathway for thyroid exposure, its median contribution to the thyroid dose was 97.4%. The mean prenatal and postnatal thyroid dose from 131I was 87 mGy (median = 17 mGy), higher than previous deterministic dose of 72 mGy (median = 12 mGy). For 39 individuals (1.5%) the dose exceeded 1.0 Gy, while the highest dose among the cohort members was 2.7 Gy. The geometric standard deviation (GSD) of 1000 individual stochastic doses varied from 1.9 to 5.2 with a mean of 3.1 and a median of 3.2. The lowest uncertainty (mean GSD = 2.3, median GSD = 2.2) was found for the subjects whose mothers were measured for 131I thyroid activity, while for individuals, whose mothers were not measured, the mean and median GSDs were 3.4. Uncertainties in thyroid doses were driven by shared errors associated with the parameters of the ecological model.

Review of the risk of cancer following low and moderate doses of sparsely ionising radiation received in early life in groups with individually estimated doses

BACKGROUND

The detrimental health effects associated with the receipt of moderate (0.1-1 Gy) and high (>1 Gy) acute doses of sparsely ionising radiation are well established from human epidemiological studies. There is accumulating direct evidence of excess risk of cancer in a number of populations exposed at lower acute doses or doses received over a protracted period. There is evidence that relative risks are generally higher after radiation exposures in utero or in childhood.

METHODS AND FINDINGS

We reviewed and summarised evidence from 60 studies of cancer or benign neoplasms following low- or moderate-level exposure in utero or in childhood from medical and environmental sources. In most of the populations studied the exposure was predominantly to sparsely ionising radiation, such as X-rays and gamma-rays. There were significant (p < 0.001) excess risks for all cancers, and particularly large excess relative risks were observed for brain/CNS tumours, thyroid cancer (including nodules) and leukaemia.

CONCLUSIONS

Overall, the totality of this large body of data relating to in utero and childhood exposure provides support for the existence of excess cancer and benign neoplasm risk associated with radiation doses < 0.1 Gy, and for certain groups exposed to natural background radiation, to fallout and medical X-rays in utero, at about 0.02 Gy.

Association between exposure to radioactive iodine after the Chernobyl accident and thyroid volume in Belarus 10-15 years later

BACKGROUND

While there is a robust literature on environmental exposure to iodine-131 (131I) in childhood and adolescence and the risk of thyroid cancer and benign nodules, little is known about its effects on thyroid volume.

METHODS

To assess the effect of 131I dose to the thyroid on the volume of the thyroid gland, we examined the data from the baseline screening of the Belarusian-American Cohort Study of residents of Belarus who were exposed to the Chernobyl fallout at ages ≤18 years. Thyroid dose estimates were based on individual thyroid activity measurements made shortly after the accident and dosimetric data from questionnaires obtained 10-15 years later at baseline screening. During baseline screening, thyroid gland volume was assessed from thyroid ultrasound measurements. The association between radiation dose and thyroid volume was modeled using linear regression where radiation dose was expressed with power terms to address non-linearity. The model was adjusted for attained age, sex, and place of residence, and their modifying effects were examined.

RESULTS

The analysis was based on 10,703 subjects. We found a statistically significant positive association between radiation dose and thyroid volume (P < 0.001). Heterogeneity of association was observed by attained age (P < 0.001) with statistically significant association remaining only in the subgroup of ≥18 years at screening (P < 0.001). For this group, increase in dose from 0.0005 to 0.15 Gy was associated with a 1.27 ml (95% CI: 0.46, 2.07) increase in thyroid volume. The estimated effect did not change with increasing doses above 0.15 Gy.

CONCLUSIONS

This is the first study to examine the association between 131I dose to the thyroid gland and thyroid volume in a population of individuals exposed during childhood and systematically screened 10-15 years later. It provides evidence for a moderate statistically significant increase in thyroid volume among those who were ≥ 18 years at screening. Given that this effect was observed at very low doses and was restricted to a narrow dose range, further studies are necessary to better understand the effect.

Thyroid doses in Ukraine due to 131I intake after the Chornobyl accident. Report I: revision of direct thyroid measurements

The increased risk of thyroid cancer among individuals exposed during childhood and adolescence to Iodine-131 (131I) is the main statistically significant long-term effect of the Chornobyl accident. Several radiation epidemiological studies have been carried out or are currently in progress in Ukraine, to assess the risk of radiation-related health effects in exposed populations. About 150,000 measurements of 131I thyroid activity, so-called 'direct thyroid measurements', performed in May-June 1986 in the Ukrainian population served as the main sources of data used to estimate thyroid doses to the individuals of these studies. However, limitations in the direct thyroid measurements have been recently recognized including improper measurement geometry and unknown true values of calibration coefficients for unchecked thyroid detectors. In the present study, a comparative analysis of 131I thyroid activity measured by calibrated and unchecked devices in residents of the same neighboring settlements was conducted to evaluate the correct measurement geometry and calibration coefficients for measuring devices. As a result, revised values of 131I thyroid activity were obtained. On average, in Vinnytsia, Kyiv, Lviv and Chernihiv Oblasts and in the city of Kyiv, the revised values of the 131I thyroid activities were found to be 10-25% higher than previously reported, while in Zhytomyr Oblast, the values of the revised activities were found to be lower by about 50%. New sources of shared and unshared errors associated with estimates of 131I thyroid activity were identified. The revised estimates of thyroid activity are recommended to be used to develop an updated Thyroid Dosimetry system (TD20) for the entire population of Ukraine as well as to revise the thyroid doses for the individuals included in post-Chornobyl radiation epidemiological studies: the Ukrainian-American cohort of individuals exposed during childhood and adolescence, the Ukrainian in utero cohort and the Chornobyl Tissue Bank.

Analysis of Cataract in Relationship to Occupational Radiation Dose Accounting for Dosimetric Uncertainties in a Cohort of U.S. Radiologic Technologists

Cataract is one of the major morbidities in the U.S. population and it has long been appreciated that high and acutely delivered radiation doses of 1 Gy or more can induce cataract. Some more recent studies, in particular those of the U.S. Radiologic Technologists, have suggested that cataract may be induced by much lower, chronically delivered doses of ionizing radiation. It is well recognized that dosimetric measurement error can substantially alter the shape of the radiation dose-response relationship and thus, the derived study risk estimates, and can also inflate the variance of the estimates. In the current study, we evaluate the impact of uncertainties in eye-lens absorbed doses on the estimated risk of cataract in the U.S. Radiologic Technologists' Monte Carlo Dosimetry System, using both absolute and relative risk models. Among 11,345 cases we show that the inflation in the standard error for the excess relative risk (ERR) is generally modest, at most approximately 20% of the unadjusted standard error, depending on the model used for the baseline risk. The largest adjustment results from use of relative risk models, so that the ERR/Gy and its 95% confidence intervals change from 1.085 (0.645, 1.525) to 1.085 (0.558, 1.612) after adjustment. However, the inflation in the standard error of the excess absolute risk (EAR) coefficient is generally minimal, at most approximately 0.04% of the standard error.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2-More complex methods of adjustment and advanced topics

We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.

Radio-biologically motivated modeling of radiation risks of mortality from ischemic heart diseases in the Canadian fluoroscopy cohort study

Recent analyses of the Canadian fluoroscopy cohort study reported significantly increased radiation risks of mortality from ischemic heart diseases (IHD) with a linear dose-response adjusted for dose fractionation. This cohort includes 63,707 tuberculosis patients from Canada who were exposed to low-to-moderate dose fractionated X-rays in 1930s-1950s and were followed-up for death from non-cancer causes during 1950-1987. In the current analysis, we scrutinized the assumption of linearity by analyzing a series of radio-biologically motivated nonlinear dose-response models to get a better understanding of the impact of radiation damage on IHD. The models were weighted according to their quality of fit and were then mathematically superposed applying the multi-model inference (MMI) technique. Our results indicated an essentially linear dose-response relationship for IHD mortality at low and medium doses and a supra-linear relationship at higher doses (> 1.5 Gy). At 5 Gy, the estimated radiation risks were fivefold higher compared to the linear no-threshold (LNT) model. This is the largest study of patients exposed to fractionated low-to-moderate doses of radiation. Our analyses confirm previously reported significantly increased radiation risks of IHD from doses similar to those from diagnostic radiation procedures.

Methods to account for uncertainties in exposure assessment in studies of environmental exposures

BACKGROUND

Accurate exposure estimation in environmental epidemiological studies is crucial for health risk assessment. Failure to account for uncertainties in exposure estimation could lead to biased results in exposure-response analyses. Assessment of the effects of uncertainties in exposure estimation on risk estimates received a lot of attention in radiation epidemiology and in several studies of diet and air pollution. The objective of this narrative review is to examine the commonly used statistical approaches to account for exposure estimation errors in risk analyses and to suggest how each could be applied in environmental epidemiological studies.

MAIN TEXT

We review two main error types in estimating exposures in epidemiological studies: shared and unshared errors and their subtypes. We describe the four main statistical approaches to adjust for exposure estimation uncertainties (regression calibration, simulation-extrapolation, Monte Carlo maximum likelihood and Bayesian model averaging) along with examples to give readers better understanding of their advantages and limitations. We also explain the advantages of using a 2-dimensional Monte-Carlo (2DMC) simulation method to quantify the effect of uncertainties in exposure estimates using full-likelihood methods. For exposures that are estimated independently between subjects and are more likely to introduce unshared errors, regression calibration and SIMEX methods are able to adequately account for exposure uncertainties in risk analyses. When an uncalibrated measuring device is used or estimation parameters with uncertain mean values are applied to a group of people, shared errors could potentially be large. In this case, Monte Carlo maximum likelihood and Bayesian model averaging methods based on estimates of exposure from the 2DMC simulations would work well. The majority of reviewed studies show relatively moderate changes (within 100%) in risk estimates after accounting for uncertainties in exposure estimates, except for the two studies which doubled/tripled naïve estimates.

CONCLUSIONS

In this paper, we demonstrate various statistical methods to account for uncertain exposure estimates in risk analyses. The differences in the results of various adjustment methods could be due to various error structures in datasets and whether or not a proper statistical method was applied. Epidemiological studies of environmental exposures should include exposure-response analyses accounting for uncertainties in exposure estimates.

Investigation of the Relationship Between Radiation Dose and Gene Mutations and Fusions in Post-Chernobyl Thyroid Cancer

Background

Exposure to ionizing radiation during childhood is a well-established risk factor for thyroid cancer. However, the genetic mechanisms of radiation-associated carcinogenesis remain not fully understood.

Methods

In this study, we used targeted next-generation sequencing and RNA-Seq to study 65 papillary thyroid cancers (PTCs) from patients in the Ukrainian-American cohort with measurement-based iodine-131 (I-131) thyroid doses received as a result of the Chernobyl accident. We fitted linear regression models to evaluate differences in distribution of risk factors for PTC according to type of genetic alteration and logistic regression models to evaluate the I-131 dose response. All statistical tests were two-sided.

Results

Driver mutations were identified in 96.9% of these thyroid cancers, including point mutations in 26.2% and gene fusions in 70.8% of cases. Novel driver fusions such as POR-BRAF, as well as STRN-ALK fusions that have not been implicated in radiation-associated cancer before, were found. The mean I-131 dose in cases with point mutations was 0.2 Gy (range = 0.013-1.05 Gy), statistically significantly lower than 1.4 Gy (range = 0.009-6.15 Gy) for cases with fusions (P < .001). No driver point mutations were found in tumors from individuals who received more than 1.1 Gy of radiation. Relative to tumors with point mutations, the proportion of tumors with gene fusions increased with radiation dose, reaching 87.8% among individuals exposed to 0.3 Gy or higher. With a limited study sample size, the estimated odds ratio at 1 Gy was 20.01 (95% confidence interval = 2.57 to 653.02, P < .001). In addition, after controlling for I-131 dose, we found higher odds ratios for gene fusion-positive PTCs associated with several specific demographic and geographic features.

Conclusions

Our data provide support for a link between I-131 thyroid dose and generation of carcinogenic gene fusions, the predominant mechanism of thyroid cancer associated with radiation exposure from the Chernobyl accident.

Thyroid Cancer and Benign Nodules After Exposure In Utero to Fallout From Chernobyl

Background

Children and adolescents exposed to radioactive iodine-131 (I-131) in fallout from the 1986 Chernobyl nuclear accident appear to be at increased risk of thyroid cancer and benign thyroid nodules. The prenatal period is also considered radiosensitive, and the fetal thyroid can absorb I-131 from the maternal circulation.

Objectives

We aimed to estimate the risk of malignant and benign thyroid nodules in individuals exposed prenatally.

Methods

We studied a cohort of 2582 subjects in Ukraine with estimates of I-131 prenatal thyroid dose (mean = 72.6 mGy), who underwent two standardized thyroid screening examinations. To evaluate the dose-response relationship, we estimated the excess OR (EOR) using logistic regression.

Results

Based on a combined total of eight cases diagnosed at screenings from 2003 to 2006 and 2012 to 2015, we found a markedly elevated, albeit not statistically significant, dose-related risk of thyroid cancer (EOR/Gy = 3.91, 95% CI: -1.49, 65.66). At cycle 2 (n = 1,786), there was a strong and significant association between I-131 thyroid dose and screen-detected large benign nodules (≥10 mm) (EOR/Gy = 4.19, 95% CI: 0.68, 11.62; P = 0.009), but no significant increase in risk for small nodules (<10 mm) (EOR/Gy = 0.34, 95% CI: -0.67, 2.24; P = 0.604).

Conclusions

The dose effect by nodule size, with I-131 risk for large but not small nodules, is similar to that among exposed children and adolescents in Belarus. Based on a small number of cases, there is also a suggestive effect of I-131 dose on thyroid cancer risk.

Cellular adaptive response and regulation of HIF after low dose gamma-radiation exposure

PURPOSE

Cellular damage due to low dose of γ-radiation (≤0.1 Gy) is generally extrapolated from observing the effects at higher doses. These estimations are not accurate. This has led to uncertainties while assessing the radiation risk factors at low doses. Although there are reports on the radiation induced adaptive response, the mechanism of action is not fully elucidated, leading to the uncertainties. One of the outcomes of low dose radiation exposure is believed to be an adaptive response. The mechanism of adaptive response is not fully understood. Therefore, the study was undertaken to understand the role of hypoxia inducible factor (HIF) on radiation induced adaptive response.

MATERIALS AND METHODS

Human breast cancer cell line MCF-7 cells pre-exposed to low dose γ-radiation (0.1 Gy; priming dose) were exposed to 2 Gy (challenging dose) 8 h after the priming dose and studied for the adaptive response. Cell death was measured by 3-(4,5-dimethylthiazol-2-Yl)-2,5-diphenyltetrazolium bromide (MTT) assay and apoptosis was measured by fluorescence-activated cell sorting analysis. DNA damage was measured by alkaline comet assay. HIF transcription activity was assayed using transiently transfected plasmid having HIF consensus sequence and luciferase as the reporter gene.

RESULTS

Cells when exposed to 0.1 Gy priming dose 8 h prior to the higher dose (2 Gy; challenging dose) results in lower amount of radiation induced damages compared to the cells exposed to 2 Gy alone. Cobalt chloride treatment in place of priming dose also results in the protection to cells when exposed to challenging dose. There was up-regulation of HIF activity when cells were exposed to priming dose, indicating the role of HIF in radiation induced response.

CONCLUSION

Results indicate the γ-radiation induced adaptive response. One of the mechanism proposed is up-regulation of HIF after low dose exposure, which protects the cells from damages when they are exposed to challenging dose of 2 Gy radiation.

Assessment of thyroid cancer risk associated with radiation dose from personal diagnostic examinations in a cohort study of US radiologic technologists, followed 1983-2014

OBJECTIVE

To assess whether personal medical diagnostic procedures over life, but particularly those associated with exposure in adulthood, were associated with increased thyroid cancer risk.

DESIGN

Participants from the US Radiologic Technologists Study, a large, prospective cohort, were followed from the date of first mailed questionnaire survey completed during 1983-1989 to the earliest date of self-reported diagnosis of thyroid cancer or of any other cancer than non-melanoma skin cancer (NMSC) in any of three subsequent questionnaires up to the last in 2012-2014.

SETTING

US nationwide, occupational cohort.

PARTICIPANTS

US radiologic technologists with exclusion of: those who reported a previous cancer apart from NMSC on the first questionnaire; those who reported a cancer with an unknown date of diagnosis on any of the questionnaires; and those who did not respond to both the first questionnaire and at least one subsequent questionnaire.

PRIMARY OUTCOME MEASURE

We used Cox proportional hazards models with age as timescale to compute HRs and 95% CI for thyroid cancer in relation to cumulative 5-year lagged diagnostic thyroid dose.

RESULTS

There were 414 self-reported thyroid cancers (n=275 papillary) in a cohort of 76 415 persons. Cumulative thyroid dose was non-significantly positively associated with total (excess relative risk/Gy=2.29 (95% CI -0.91 to 7.01, p=0.19)) and papillary thyroid cancer (excess relative risk/Gy=4.15 (95% CI -0.39, 11.27, p=0.08)) risk. These associations were not modified by age at, or time since, exposure and were independent of occupational exposure.

CONCLUSION

Our study provides weak evidence that thyroid dose from diagnostic radiation procedures over the whole of life, in particular associated with exposure in adulthood, influences adult thyroid cancer risk.

Shared and unshared exposure measurement error in occupational cohort studies and their effects on statistical inference in proportional hazards models

Exposure measurement error represents one of the most important sources of uncertainty in epidemiology. When exposure uncertainty is not or only poorly accounted for, it can lead to biased risk estimates and a distortion of the shape of the exposure-response relationship. In occupational cohort studies, the time-dependent nature of exposure and changes in the method of exposure assessment may create complex error structures. When a method of group-level exposure assessment is used, individual worker practices and the imprecision of the instrument used to measure the average exposure for a group of workers may give rise to errors that are shared between workers, within workers or both. In contrast to unshared measurement error, the effects of shared errors remain largely unknown. Moreover, exposure uncertainty and magnitude of exposure are typically highest for the earliest years of exposure. We conduct a simulation study based on exposure data of the French cohort of uranium miners to compare the effects of shared and unshared exposure uncertainty on risk estimation and on the shape of the exposure-response curve in proportional hazards models. Our results indicate that uncertainty components shared within workers cause more bias in risk estimation and a more severe attenuation of the exposure-response relationship than unshared exposure uncertainty or exposure uncertainty shared between individuals. These findings underline the importance of careful characterisation and modeling of exposure uncertainty in observational studies.

Gilbert W. Beebe Symposium on 30 Years after the Chernobyl Accident: Current and Future Studies on Radiation Health Effects

This commentary summarizes the presentations and discussions from the 2016 Gilbert W. Beebe symposium "30 years after the Chernobyl accident: Current and future studies on radiation health effects." The symposium was hosted by the National Academies of Sciences, Engineering, and Medicine (the National Academies). The symposium focused on the health consequences of the Chernobyl accident, looking retrospectively at what has been learned and prospectively at potential future discoveries using emerging 21st Century research methodologies.

Somatic health effects of Chernobyl: 30 years on

2016 marked the 30th anniversary of the Chernobyl Nuclear Power Plant accident. We and others wrote reviews for the 25th anniversary. Since then, additional papers have appeared and it seems timely to highlight lessons learned. To present, not a systematic review, but a commentary drawing attention to notable findings. We include not only recent reports and updates on previous results, but key findings from prior Chernobyl studies. The dose-dependent increase in Papillary Thyroid Cancer (PTC) following childhood I-131 exposure in Ukraine and Belarus has now been shown to persist for decades. Studies of post-Chernobyl PTCs have produced novel information on chromosomal rearrangements and gene fusions, critical to understanding molecular mechanisms. Studies of clean-up workers/liquidators suggest dose-related increases of thyroid cancer and hematological malignancies in adults. They also report increases in cardiovascular and cerebrovascular disease. If confirmed, these would have significant public health and radiation protection implications. The lens opacities following low to moderate doses found earlier are also a concern, particularly among interventional radiologists who may receive substantial lens doses. Finally, there is some, inconsistent, evidence for genetic effects among offspring of exposed persons. Further efforts, including improved dosimetry, collection of information on other risk factors, and continued follow-up/monitoring of established cohorts, could contribute importantly to further understand effects of low doses and dose-rates of radiation, particularly in young people, and ensure that appropriate public health and radiation protection systems are in place. This will require multinational collaborations and long-term funding.

Thyroid neoplasia risk is increased nearly 30 years after the Chernobyl accident

To evaluate risk of thyroid neoplasia nearly 30 years following exposure to radioactive iodine (I-131) from the 1986 Chernobyl nuclear accident, we conducted a fifth cycle of thyroid screening of the Ukrainian-American cohort during 2012-2015, following four previous screening cycles started in 1998. We identified 47 thyroid cancers (TC) and 33 follicular adenomas (FA) among 10,073 individuals who were <18 years at the time of the accident and had a mean I-131 dose of 0.62 Gy. We found a significant I-131 dose response for both TC and FA, with an excess odd ratio per Gy of 1.36 (95% CI: 0.39-4.15) and 2.03 (95% CI: 0.55-6.69), respectively. The excess risk of malignant and benign thyroid neoplasia persists nearly three decades after exposure and underscores the importance of continued follow-up of this cohort to characterize long-term pattern of I-131 risk.

Assessing the reliability of dose coefficients for exposure to radioiodine by members of the public, accounting for dosimetric and risk model uncertainties

Assessments of risk to a specific population group resulting from internal exposure to a particular radionuclide can be used to assess the reliability of the appropriate International Commission on Radiological Protection (ICRP) dose coefficients used as a radiation protection device for the specified exposure pathway. An estimate of the uncertainty on the associated risk is important for informing judgments on reliability; a derived uncertainty factor, UF, is an estimate of the 95% probable geometric difference between the best risk estimate and the nominal risk and is a useful tool for making this assessment. This paper describes the application of parameter uncertainty analysis to quantify uncertainties resulting from internal exposures to radioiodine by members of the public, specifically 1, 10 and 20-year old females from the population of England and Wales. Best estimates of thyroid cancer incidence risk (lifetime attributable risk) are calculated for ingestion or inhalation of ¹²⁹I and ¹³¹I, accounting for uncertainties in biokinetic model and cancer risk model parameter values. These estimates are compared with the equivalent ICRP derived nominal age-, sex- and population-averaged estimates of excess thyroid cancer incidence to obtain UFs. Derived UF values for ingestion or inhalation of ¹³¹I for 1 year, 10-year and 20-year olds are around 28, 12 and 6, respectively, when compared with ICRP Publication 103 nominal values, and 9, 7 and 14, respectively, when compared with ICRP Publication 60 values. Broadly similar results were obtained for ¹²⁹I. The uncertainties on risk estimates are largely determined by uncertainties on risk model parameters rather than uncertainties on biokinetic model parameters. An examination of the sensitivity of the results to the risk models and populations used in the calculations show variations in the central estimates of risk of a factor of around 2-3. It is assumed that the direct proportionality of excess thyroid cancer risk and dose observed at low to moderate acute doses and incorporated in the risk models also applies to very small doses received at very low dose rates; the uncertainty in this assumption is considerable, but largely unquantifiable. The UF values illustrate the need for an informed approach to the use of ICRP dose and risk coefficients.

A geographical study of thyroid cancer incidence in north-west England following the Windscale nuclear reactor fire of 1957

The Windscale nuclear reactor fire at Sellafield, United Kingdom, in October 1957 led to an uncontrolled release of iodine-131 (radioactive half-life, 8 d) into the atmosphere. Contamination from the accident was most pronounced in the counties of Cumbria and Lancashire, north-west England. Radioiodine concentrates in the thyroid gland producing an excess risk of thyroid cancer, notably among those exposed as children, which persists into later life. For an initial investigation of thyroid cancer incidence in north-west England, data were obtained on cases of thyroid cancer among people born during 1929-1973 and diagnosed during 1974-2012 while resident in England, together with corresponding populations. Incidence rate ratios (IRRs), with Poisson 95% confidence intervals (CIs), compared thyroid cancer incidence rates in Cumbria and in Lancashire with those in the rest of England. For those aged  <20 years in 1958, a statistically significantly increased IRR was found for those diagnosed during 1974-2012 while living in Cumbria (IRR  =  1.29; 95% CI 1.09-1.52), but the equivalent IRR for Lancashire was marginally non-significantly decreased (IRR  =  0.91; 95% CI 0.80-1.04). This pattern of IRRs was also apparent for earlier births, and the significantly increased IRR in Cumbria extended to individuals born in 1959-1963, who would not have been exposed to iodine-131 from the Windscale accident. Moreover, significant overdispersion was present in the temporal distributions of the IRRs, so that Poisson CIs substantially underestimate statistical uncertainties. Consequently, although further investigations are required to properly understand the unusual patterns of thyroid cancer IRRs in Cumbria and Lancashire, the results of this preliminary study are not consistent with an effect of exposure to iodine-131 from the Windscale accident.

Integration of a radiation biomarker into modeling of thyroid carcinogenesis and post-Chernobyl risk assessment

Strong evidence for the statistical association between radiation exposure and disease has been produced for thyroid cancer by epidemiological studies after the Chernobyl accident. However, limitations of the epidemiological approach in order to explore health risks especially at low doses of radiation appear obvious. Statistical fluctuations due to small case numbers dominate the uncertainty of risk estimates. Molecular radiation markers have been searched extensively to separate radiation-induced cancer cases from sporadic cases. The overexpression of the CLIP2 gene is the most promising of these markers. It was found in the majority of papillary thyroid cancers (PTCs) from young patients included in the Chernobyl tissue bank. Motivated by the CLIP2 findings we propose a mechanistic model which describes PTC development as a sequence of rate-limiting events in two distinct paths of CLIP2-associated and multistage carcinogenesis. It integrates molecular measurements of the dichotomous CLIP2 marker from 141 patients into the epidemiological risk analysis for about 13 000 subjects from the Ukrainian-American cohort which were exposed below age 19 years and were put under enhanced medical surveillance since 1998. For the first time, a radiation risk has been estimated solely from marker measurements. Cross checking with epidemiological estimates and model validation suggests that CLIP2 is a marker of high precision. CLIP2 leaves an imprint in the epidemiological incidence data which is typical for a driver gene. With the mechanistic model, we explore the impact of radiation on the molecular landscape of PTC. The model constitutes a unique interface between molecular biology and radiation epidemiology.

Low dose radiation risks for women surviving the a-bombs in Japan: generalized additive model

BACKGROUND

Analyses of cancer mortality and incidence in Japanese A-bomb survivors have been used to estimate radiation risks, which are generally higher for women. Relative Risk (RR) is usually modelled as a linear function of dose. Extrapolation from data including high doses predicts small risks at low doses. Generalized Additive Models (GAMs) are flexible methods for modelling non-linear behaviour.

METHODS

GAMs are applied to cancer incidence in female low dose subcohorts, using anonymous public data for the 1958 - 1998 Life Span Study, to test for linearity, explore interactions, adjust for the skewed dose distribution, examine significance below 100 mGy, and estimate risks at 10 mGy.

RESULTS

For all solid cancer incidence, RR estimated from 0 - 100 mGy and 0 - 20 mGy subcohorts is significantly raised. The response tapers above 150 mGy. At low doses, RR increases with age-at-exposure and decreases with time-since-exposure, the preferred covariate. Using the empirical cumulative distribution of dose improves model fit, and capacity to detect non-linear responses. RR is elevated over wide ranges of covariate values. Results are stable under simulation, or when removing exceptional data cells, or adjusting neutron RBE. Estimates of Excess RR at 10 mGy using the cumulative dose distribution are 10 - 45 times higher than extrapolations from a linear model fitted to the full cohort. Below 100 mGy, quasipoisson models find significant effects for all solid, squamous, uterus, corpus, and thyroid cancers, and for respiratory cancers when age-at-exposure > 35 yrs. Results for the thyroid are compatible with studies of children treated for tinea capitis, and Chernobyl survivors. Results for the uterus are compatible with studies of UK nuclear workers and the Techa River cohort.

CONCLUSION

Non-linear models find large, significant cancer risks for Japanese women exposed to low dose radiation from the atomic bombings. The risks should be reflected in protection standards.

Doses for post-Chernobyl epidemiological studies: are they reliable?

On 26 April 2016, thirty years will have elapsed since the occurrence of the Chernobyl accident, which has so far been the most severe in the history of the nuclear reactor industry. Numerous epidemiological studies were conducted to evaluate the possible health consequences of the accident. Since the credibility of the association between the radiation exposure and health outcome is highly dependent on the adequacy of the dosimetric quantities used in these studies, this paper makes an effort to overview the methods used to estimate individual doses and the associated uncertainties in the main analytical epidemiological studies (i.e. cohort or case-control) related to the Chernobyl accident. Based on the thorough analysis and comparison with other radiation studies, the authors conclude that individual doses for the Chernobyl analytical epidemiological studies have been calculated with a relatively high degree of reliability and well-characterized uncertainties, and that they compare favorably with many other non-Chernobyl studies. The major strengths of the Chernobyl studies are: (1) they are grounded on a large number of measurements, either performed on humans or made in the environment; and (2) extensive effort has been invested to evaluate the uncertainties associated with the dose estimates. Nevertheless, gaps in the methodology are identified and suggestions for the possible improvement of the current dose estimates are made.

30 years After the Chernobyl Nuclear Accident: Time for Reflection and Re-evaluation of Current Disaster Preparedness Plans

It has been 30 years since the worst accident in the history of the nuclear era occurred at the Chernobyl power plant in Ukraine close to densely populated urban areas. To date, epidemiological studies reported increased long-term risks of leukemia, cardiovascular diseases, and cataracts among cleanup workers and of thyroid cancer and non-malignant diseases in those exposed as children and adolescents. Mental health effects were the most significant public health consequence of the accident in the three most contaminated countries of Ukraine, Belarus, and the Russian Federation. Timely and clear communication with affected populations emerged as one of the main lessons in the aftermath of the Chernobyl nuclear accident.

Levels of naturally occurring gamma radiation measured in British homes and their prediction in particular residences

Gamma radiation from natural sources (including directly ionising cosmic rays) is an important component of background radiation. In the present paper, indoor measurements of naturally occurring gamma rays that were undertaken as part of the UK Childhood Cancer Study are summarised, and it is shown that these are broadly compatible with an earlier UK National Survey. The distribution of indoor gamma-ray dose rates in Great Britain is approximately normal with mean 96 nGy/h and standard deviation 23 nGy/h. Directly ionising cosmic rays contribute about one-third of the total. The expanded dataset allows a more detailed description than previously of indoor gamma-ray exposures and in particular their geographical variation. Various strategies for predicting indoor natural background gamma-ray dose rates were explored. In the first of these, a geostatistical model was fitted, which assumes an underlying geologically determined spatial variation, superimposed on which is a Gaussian stochastic process with Matérn correlation structure that models the observed tendency of dose rates in neighbouring houses to correlate. In the second approach, a number of dose-rate interpolation measures were first derived, based on averages over geologically or administratively defined areas or using distance-weighted averages of measurements at nearest-neighbour points. Linear regression was then used to derive an optimal linear combination of these interpolation measures. The predictive performances of the two models were compared via cross-validation, using a randomly selected 70 % of the data to fit the models and the remaining 30 % to test them. The mean square error (MSE) of the linear-regression model was lower than that of the Gaussian-Matérn model (MSE 378 and 411, respectively). The predictive performance of the two candidate models was also evaluated via simulation; the OLS model performs significantly better than the Gaussian-Matérn model.

Risk of thyroid follicular adenoma among children and adolescents in Belarus exposed to iodine-131 after the Chornobyl accident

Several studies reported an increased risk of thyroid cancer in children and adolescents exposed to radioactive iodines, chiefly iodine-131 ((131)I), after the 1986 Chornobyl (Ukrainian spelling) nuclear power plant accident. The risk of benign thyroid tumors following such radiation exposure is much less well known. We have previously reported a novel finding of significantly increased risk of thyroid follicular adenoma in a screening study of children and adolescents exposed to the Chornobyl fallout in Ukraine. To verify this finding, we analyzed baseline screening data from a cohort of 11,613 individuals aged ≤18 years at the time of the accident in Belarus (mean age at screening = 21 years). All participants had individual (131)I doses estimated from thyroid radioactivity measurements and were screened according to a standardized protocol. We found a significant linear dose response for 38 pathologically confirmed follicular adenoma cases. The excess odds ratio per gray of 2.22 (95% confidence interval: 0.41, 13.1) was similar in males and females but decreased significantly with increasing age at exposure (P < 0.01), with the highest radiation risks estimated for those exposed at <2 years of age. Follicular adenoma radiation risks were not significantly modified by most indicators of past and current iodine deficiency. The present study confirms the (131)I-associated increases in risk of follicular adenoma in the Ukrainian population and adds new evidence on the risk increasing with decreasing age at exposure.

Impact of Uncertainties in Exposure Assessment on Thyroid Cancer Risk among Persons in Belarus Exposed as Children or Adolescents Due to the Chernobyl Accident

Background

The excess incidence of thyroid cancer in Ukraine and Belarus observed a few years after the Chernobyl accident is considered to be largely the result of ¹³¹I released from the reactor. Although the Belarus thyroid cancer prevalence data has been previously analyzed, no account was taken of dose measurement error.

Methods

We examined dose-response patterns in a thyroid screening prevalence cohort of 11,732 persons aged under 18 at the time of the accident, diagnosed during 1996–2004, who had direct thyroid ¹³¹I activity measurement, and were resident in the most radio-actively contaminated regions of Belarus. Three methods of dose-error correction (regression calibration, Monte Carlo maximum likelihood, Bayesian Markov Chain Monte Carlo) were applied.

Results

There was a statistically significant (p<0.001) increasing dose-response for prevalent thyroid cancer, irrespective of regression-adjustment method used. Without adjustment for dose errors the excess odds ratio was 1.51 Gy⁻ (95% CI 0.53, 3.86), which was reduced by 13% when regression-calibration adjustment was used, 1.31 Gy⁻ (95% CI 0.47, 3.31). A Monte Carlo maximum likelihood method yielded an excess odds ratio of 1.48 Gy⁻ (95% CI 0.53, 3.87), about 2% lower than the unadjusted analysis. The Bayesian method yielded a maximum posterior excess odds ratio of 1.16 Gy⁻ (95% BCI 0.20, 4.32), 23% lower than the unadjusted analysis. There were borderline significant (p = 0.053–0.078) indications of downward curvature in the dose response, depending on the adjustment methods used. There were also borderline significant (p = 0.102) modifying effects of gender on the radiation dose trend, but no significant modifying effects of age at time of accident, or age at screening as modifiers of dose response (p>0.2).

Conclusions

In summary, the relatively small contribution of unshared classical dose error in the current study results in comparatively modest effects on the regression parameters.

Major Factors Affecting Incidence of Childhood Thyroid Cancer in Belarus after the Chernobyl Accident: Do Nitrates in Drinking Water Play a Role?

One of the major health consequences of the Chernobyl Nuclear Power Plant accident in 1986 was a dramatic increase in incidence of thyroid cancer among those who were aged less than 18 years at the time of the accident. This increase has been directly linked in several analytic epidemiological studies to iodine-131 (131I) thyroid doses received from the accident. However, there remains limited understanding of factors that modify the 131I-related risk. Focusing on post-Chernobyl pediatric thyroid cancer in Belarus, we reviewed evidence of the effects of radiation, thyroid screening, and iodine deficiency on regional differences in incidence rates of thyroid cancer. We also reviewed current evidence on content of nitrate in groundwater and thyroid cancer risk drawing attention to high levels of nitrates in open well water in several contaminated regions of Belarus, i.e. Gomel and Brest, related to the usage of nitrogen fertilizers. In this hypothesis generating study, based on ecological data and biological plausibility, we suggest that nitrate pollution may modify the radiation-related risk of thyroid cancer contributing to regional differences in rates of pediatric thyroid cancer in Belarus. Analytic epidemiological studies designed to evaluate joint effect of nitrate content in groundwater and radiation present a promising avenue of research and may provide useful insights into etiology of thyroid cancer.

Thyroid Dose Estimates for a Cohort of Belarusian Children Exposed to (131)I from the Chernobyl Accident: Assessment of Uncertainties

Deterministic thyroid radiation doses due to iodine-131 ((131)I) intake were reconstructed in a previous article for 11,732 participants of the Belarusian-American cohort study of thyroid cancer and other thyroid diseases in individuals exposed during childhood or adolescence to fallout from the Chernobyl accident. The current article describes an assessment of uncertainties in reconstructed thyroid doses that accounts for the shared and unshared errors. Using a Monte Carlo simulation procedure, 1,000 sets of cohort thyroid doses due to (131)I intake were calculated. The arithmetic mean of the stochastic thyroid doses for the entire cohort was 0.68 Gy. For two-thirds of the cohort the arithmetic mean of individual stochastic thyroid doses was less than 0.5 Gy. The geometric standard deviation of stochastic doses varied among cohort members from 1.33 to 5.12 with an arithmetic mean of 1.76 and a geometric mean of 1.73. The uncertainties in thyroid dose were driven by the unshared errors associated with the estimates of values of thyroid mass and of the (131)I activity in the thyroid of the subject; the contribution of shared errors to the overall uncertainty was small. These multiple sets of cohort thyroid doses will be used to evaluate the radiation risks of thyroid cancer and noncancer thyroid diseases, taking into account the structure of the errors in the dose estimates.

Shared dosimetry error in epidemiological dose-response analyses

Radiation dose reconstruction systems for large-scale epidemiological studies are sophisticated both in providing estimates of dose and in representing dosimetry uncertainty. For example, a computer program was used by the Hanford Thyroid Disease Study to provide 100 realizations of possible dose to study participants. The variation in realizations reflected the range of possible dose for each cohort member consistent with the data on dose determinates in the cohort. Another example is the Mayak Worker Dosimetry System 2013 which estimates both external and internal exposures and provides multiple realizations of "possible" dose history to workers given dose determinants. This paper takes up the problem of dealing with complex dosimetry systems that provide multiple realizations of dose in an epidemiologic analysis. In this paper we derive expected scores and the information matrix for a model used widely in radiation epidemiology, namely the linear excess relative risk (ERR) model that allows for a linear dose response (risk in relation to radiation) and distinguishes between modifiers of background rates and of the excess risk due to exposure. We show that treating the mean dose for each individual (calculated by averaging over the realizations) as if it was true dose (ignoring both shared and unshared dosimetry errors) gives asymptotically unbiased estimates (i.e. the score has expectation zero) and valid tests of the null hypothesis that the ERR slope β is zero. Although the score is unbiased the information matrix (and hence the standard errors of the estimate of β) is biased for β≠0 when ignoring errors in dose estimates, and we show how to adjust the information matrix to remove this bias, using the multiple realizations of dose. The use of these methods in the context of several studies including, the Mayak Worker Cohort, and the U.S. Atomic Veterans Study, is discussed.