F. Owen Hoffman

Thyroid disease associated with exposure to the Nevada nuclear weapons test site radiation: A reevaluation based on corrected dosimetry and examination data

Background: A study was begun in 1965 to 1966 to determine whether children exposed to radioactive iodine from nuclear weapons testing at the Nevada Test Site from 1951 through 1962 were at higher risk of thyroid disease. In 1993, we reported that among those examined in 1985 to 1986 (Phase II) there was an association between radiation from the Nevada Test Site and thyroid neoplasms. Methods: We reevaluated the relationship between exposure to Nevada Test Site fallout and thyroid disease using newly corrected dose estimates and disease outcomes from the Phase II study. A prospective cohort of school children 12 to 18 years old living in Utah, Nevada, and Arizona was first examined for thyroid disease in 1965 to 1966 and reexamined in 1985 to 1986. In the Phase II report, 2497 subjects formed the basis for this analysis. Thyroid disease, including thyroid neoplasms and thyroiditis, was expressed as cumulative incidence and risk ratios (RRs) with a dose–response expressed as excess risk ratio (ERR/Gy). Results: The RR between thyroid radiation dose in the highest dose group and thyroid neoplasms increased from 3.4 (in the earlier analysis) to 7.5. The RR for thyroiditis increased from 1.1 to 2.7 with an ERR/Gy of 4.9 (95% confidence interval = 2.0 to 10.0). There were too few malignant thyroid neoplasms to estimate risk. Conclusions: Persons exposed to radioactive iodine as children have an increased risk of thyroid neoplasms and autoimmune thyroiditis up to 30 years after exposure.

RadRAT: A Radiation Risk Assessment Tool for Lifetime Cancer Risk Projection

Risk projection methods allow for timely assessment of the potential magnitude of radiation-related cancer risks following low-dose radiation exposures. The estimation of such risks directly through observational studies would generally require infeasibly large studies and long-term follow-up to achieve reasonable statistical power. We developed an online radiation risk assessment tool (RadRAT) which can be used to estimate the lifetime risk of radiation-related cancer with uncertainty intervals following a user-specified exposure history (https://irep.nci.nih.gov/radrat). The uncertainty intervals constitute a key component of the program because of the various assumptions that are involved in such calculations. The risk models used in RadRAT are broadly based on those developed by the BEIR VII committee for estimating lifetime risk following low-dose radiation exposure of the US population for eleven site-specific cancers. We developed new risk models for seven additional cancer sites, oral, oesophagus, gallbladder, pancreas, rectum, kidney and brain/central nervous system (CNS) cancers, using data from Japanese atomic bomb survivors. The lifetime risk estimates are slightly higher for RadRAT than for BEIR VII across all exposure ages mostly because the weighting of the excess relative risk and excess absolute risk models was conducted on an arithmetic rather than a logarithmic scale. The calculator can be used to estimate lifetime cancer risk from both uniform and non-uniform doses that are acute or chronic. It is most appropriate for low-LET radiation doses < 1 Gy, and for individuals with life-expectancy and cancer rates similar to the general population in the US.

INTERACTIVE RADIOEPIDEMIOLOGICAL PROGRAM (IREP): A WEB-BASED TOOL FOR ESTIMATING PROBABILITY OF CAUSATION/ASSIGNED SHARE OF RADIOGENIC CANCERS

The Interactive RadioEpidemiological Program (IREP) is a Web-based, interactive computer code that is used to estimate the probability that a given cancer in an individual was induced by given exposures to ionizing radiation. IREP was developed by a Working Group of the National Cancer Institute and Centers for Disease Control and Prevention, and was adopted and modified by the National Institute for Occupational Safety and Health (NIOSH) for use in adjudicating claims for compensation for cancer under the Energy Employees Occupational Illness Compensation Program Act of 2000. In this paper, the quantity calculated in IREP is referred to as “probability of causation/assigned share” (PC/AS). PC/AS for a given cancer in an individual is calculated on the basis of an estimate of the excess relative risk (ERR) associated with given radiation exposures and the relationship PC/AS = ERR/ERR+1. IREP accounts for uncertainties in calculating probability distributions of ERR and PC/AS. An accounting of uncertainty is necessary when decisions about granting claims for compensation for cancer are made on the basis of an estimate of the upper 99% credibility limit of PC/AS to give claimants the “benefit of the doubt.” This paper discusses models and methods incorporated in IREP to estimate ERR and PC/AS. Approaches to accounting for uncertainty are emphasized, and limitations of IREP are discussed. Although IREP is intended to provide unbiased estimates of ERR and PC/AS and their uncertainties to represent the current state of knowledge, there are situations described in this paper in which NIOSH, as a matter of policy, makes assumptions that give a higher estimate of the upper 99% credibility limit of PC/AS than other plausible alternatives and, thus, are more favorable to claimants.

Comparison of interception and initial retention of wet-deposited contaminants on leaves of different vegetation types☆

Abstract Simulated rain containing both soluble radionuclides and insoluble particles labeled with a radionuclide was manually applied to several kinds of vegetation, including a conifer, a broad-leafed tree, and several herbaceous species. The fraction of each radioactive material intercepted and initially retained by the vegetation was determined for each plant type. This fraction was determined both as the mass interception factor, r Y , and the leaf area interception fraction, LAIF. Mean values of r Y ranged from 0.16 to 2.9 m2 kg−1 and of the LAIF, from 0.011 to 0.16. There was a greater range in mean retention values among radionuclide types than among plant species; the range among plant types tended to be less with the LAIF than the r Y . Significantly less interception and initial retention was measured for anions than for cations or the insoluble particles.

Quantification of the interception and initial retention of radioactive contaminants deposited on pasture grass by simulated rain

Abstract Simulated rain containing both soluble radionuclides and insoluble particles labeled with radionuclides was applied to pasture-type vegetation under conditions similar to those found during convective storms. The fraction of material in rain intercepted by vegetation and initially retained was determined for three sizes of insoluble polystyrene microspheres (3, 9 and 25 μm), soluble 7Be2+ and soluble 131I as periodate or iodide, over a range of rainfall amounts of both moderate- and high-intensity precipitation. Values for the interception and initial retention by vegetation (interception fractions) for soluble forms of 131I in simulated rain are much less than those for insoluble particles and the reactive cation 7Be2+. The interception fraction for soluble 131I is an inverse function of rain amount, varying from about 0.3 at 1 mm rain to 0.006 at 30 mm. The mass interception factor (the interception fraction normalized for biomass) of 131I is almost solely dependent on the amount of rain, with values from about 2.5 m2 kg−1 at 1 mm to less than 0.1 m2 kg−1 at 30 mm; the 131I vegetation-to-rain concentration ratio is relatively constant at approximately 2.6 l kg−1. For 7Be2+ and the insoluble particles, the interception fractions range from 0.1 to 0.6 with geometric means of approximately 0.3. For these materials there is a greater dependence on biomass than on rain amount; the geometric means of the mass interception factors for these substances range from 0.99 to 2.4 m2 kg−1, with no single variable being a major controlling factor. These results indicate that anionic 131I is essentially removed with the water once the vegetation surface becomes saturated and that the 7Be cation and the in s oluble particles are adsorbed to or settle out on the plant surface. The discrepancy between the behavior of the anionic and the cationic species is consistent with a negative charge on the plant surface.

2004 Update of Dosimetry for the Utah Thyroid Cohort Study

Abstract Simon, S. L., Anspaugh, L. R., Hoffman, F. O., Scholl, A. E., Stone, M. B., Thomas, B. A. and Lyon, J. L. 2004 Update of Dosimetry for the Utah Thyroid Cohort Study. Radiat. Res. 165, 208–222 (2006). In the 1980s, individual thyroid doses and uncertainties were estimated for members of a cohort of children identified in 1965 in Utah and Nevada who had potentially been exposed to fallout from the Nevada Test Site. That reconstruction represented the first comprehensive assessment of doses received by the cohort and was the first large effort to assess the uncertainty of dose on an individual person basis. The data on dose and thyroid disease prevalence during different periods were subsequently used in an analysis to determine risks of radiogenic thyroid disease. This cohort has received periodic medical follow-up to observe changes in disease frequency and to reassess the previously reported radiation-related risks, most recently after a Congressional mandate in 1998. In a recent effort to restore the databases and computer codes used to estimate doses in the 1980s, various deficiencies were found in the estimated doses due to improperly operating computer codes, corruption of secondary data files, and lack of quality control procedures. From 2001 through 2004, the dosimetry system was restored and corrected and all doses were recalculated. In addition, two parameter values were updated. While the mean of all doses has not changed significantly, many individual doses have changed by more than an order of magnitude.

Uncertainty of the long-term resuspension factor

Abstract Resuspension of contaminated soil into the atmosphere is one of the key processes that must be considered in the estimation of inhalation doses to humans. Data for air and soil contamination collected in Ukraine over several years since the Chernobyl accident have permitted analysis of resuspension in terms of the underlying mechanisms. Various empirical models for the resuspension factor as a function of time (e.g. Linsley, Garland, Anspaugh, etc.) are compared to the observed resuspension factors over time (9 yr) at two sites; in general, these models give overestimates for the resuspension factor as a function of time. The observed values of the resuspension factor range from greater than 10−5 m−1 at early time points to around 10−10 m−1 at later points. The uncertainty in the resuspension factor is decreased to within 1 order of magnitude if annual averaging of the experimental data is used and if the resuspension factor is determined as a function of time and of the predominant regional conditions of vegetative cover and climate.

Uncertainty Is Part of Making Decisions

Advances in computer technology and applied statistics have provided the opportunity for the non-statistician to investigate uncertainty in a quantitative manner. The following discussion argues, notwithstanding the possible misuse of uncertainty analysis, that uncertainty is always present and that decisions based on human or ecological risk assessment would benefit from disclosure of uncertainty in the estimated risks.

Accounting for Shared and Unshared Dosimetric Uncertainties in the Dose Response for Ultrasound-Detected Thyroid Nodules after Exposure to Radioactive Fallout

Dosimetic uncertainties, particularly those that are shared among subgroups of a study population, can bias, distort or reduce the slope or significance of a dose response. Exposure estimates in studies of health risks from environmental radiation exposures are generally highly uncertain and thus, susceptible to these methodological limitations. An analysis was published in 2008 concerning radiation-related thyroid nodule prevalence in a study population of 2,994 villagers under the age of 21 years old between August 1949 and September 1962 and who lived downwind from the Semipalatinsk Nuclear Test Site in Kazakhstan. This dose-response analysis identified a statistically significant association between thyroid nodule prevalence and reconstructed doses of fallout-related internal and external radiation to the thyroid gland; however, the effects of dosimetric uncertainty were not evaluated since the doses were simple point “best estimates”. In this work, we revised the 2008 study by a comprehensive treatment of dosimetric uncertainties. Our present analysis improves upon the previous study, specifically by accounting for shared and unshared uncertainties in dose estimation and risk analysis, and differs from the 2008 analysis in the following ways: 1. The study population size was reduced from 2,994 to 2,376 subjects, removing 618 persons with uncertain residence histories; 2. Simulation of multiple population dose sets (vectors) was performed using a two-dimensional Monte Carlo dose estimation method; and 3. A Bayesian model averaging approach was employed for evaluating the dose response, explicitly accounting for large and complex uncertainty in dose estimation. The results were compared against conventional regression techniques. The Bayesian approach utilizes 5,000 independent realizations of population dose vectors, each of which corresponds to a set of conditional individual median internal and external doses for the 2,376 subjects. These 5,000 population dose vectors reflect uncertainties in dosimetric parameters, partly shared and partly independent, among individual members of the study population. Risk estimates for thyroid nodules from internal irradiation were higher than those published in 2008, which results, to the best of our knowledge, from explicitly accounting for dose uncertainty. In contrast to earlier findings, the use of Bayesian methods led to the conclusion that the biological effectiveness for internal and external dose was similar. Estimates of excess relative risk per unit dose (ERR/Gy) for males (177 thyroid nodule cases) were almost 30 times those for females (571 cases) and were similar to those reported for thyroid cancers related to childhood exposures to external and internal sources in other studies. For confirmed cases of papillary thyroid cancers (3 in males, 18 in females), the ERR/Gy was also comparable to risk estimates from other studies, but not significantly different from zero. These findings represent the first reported dose response for a radiation epidemiologic study considering all known sources of shared and unshared errors in dose estimation and using a Bayesian model averaging (BMA) method for analysis of the dose response.

The Two-Dimensional Monte Carlo: A New Methodologic Paradigm for Dose Reconstruction for Epidemiological Studies

Retrospective dose estimation, particularly dose reconstruction that supports epidemiological investigations of health risk, relies on various strategies that include models of physical processes and exposure conditions with detail ranging from simple to complex. Quantification of dose uncertainty is an essential component of assessments for health risk studies since, as is well understood, it is impossible to retrospectively determine the true dose for each person. To address uncertainty in dose estimation, numerical simulation tools have become commonplace and there is now an increased understanding about the needs and what is required for models used to estimate cohort doses (in the absence of direct measurement) to evaluate dose response. It now appears that for dose-response algorithms to derive the best, unbiased estimate of health risk, we need to understand the type, magnitude and interrelationships of the uncertainties of model assumptions, parameters and input data used in the associated dose estimation models. Heretofore, uncertainty analysis of dose estimates did not always properly distinguish between categories of errors, e.g., uncertainty that is specific to each subject (i.e., unshared error), and uncertainty of doses from a lack of understanding and knowledge about parameter values that are shared to varying degrees by numbers of subsets of the cohort. While mathematical propagation of errors by Monte Carlo simulation methods has been used for years to estimate the uncertainty of an individual subjects dose, it was almost always conducted without consideration of dependencies between subjects. In retrospect, these types of simple analyses are not suitable for studies with complex dose models, particularly when important input data are missing or otherwise not available. The dose estimation strategy presented here is a simulation method that corrects the previous deficiencies of analytical or simple Monte Carlo error propagation methods and is termed, due to its capability to maintain separation between shared and unshared errors, the two-dimensional Monte Carlo (2DMC) procedure. Simply put, the 2DMC method simulates alternative, possibly true, sets (or vectors) of doses for an entire cohort rather than a single set that emerges when each individuals dose is estimated independently from other subjects. Moreover, estimated doses within each simulated vector maintain proper inter-relationships such that the estimated doses for members of a cohort subgroup that share common lifestyle attributes and sources of uncertainty are properly correlated. The 2DMC procedure simulates inter-individual variability of possibly true doses within each dose vector and captures the influence of uncertainty in the values of dosimetric parameters across multiple realizations of possibly true vectors of cohort doses. The primary characteristic of the 2DMC approach, as well as its strength, are defined by the proper separation between uncertainties shared by members of the entire cohort or members of defined cohort subsets, and uncertainties that are individual-specific and therefore unshared.