Uncertainty in modeling the risk of thyroid cancer after Chernobyl

*Mark Peter Little, National Cancer Institute, Radiation Epidemiology Branch 

Keywords: thyroid cancer, dose error, regression calibration, Chernobyl

Uncertainty in modeling the risk of thyroid cancer after Chernobyl

M.P. Little Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20852-7238, USA

Abstract Background The 1986 accident at the Chernobyl nuclear power plant remains the most serious nuclear accident in history. Within five years of the event, rates of thyroid cancers were elevated among the exposed population in Ukraine and neighbouring Belarus, with the excess particularly marked among those exposed in childhood. The thyroid cancer excess is considered to be largely the result of release of radioactive iodine-131 from the Chernobyl reactor. It is well known that failure to take dose measurement error into account in regression can lead to bias in assessments of dose-response slope and curvature, particularly when classical measurement errors are preset.

Methods Dose-response patterns have been examined in a thyroid screening cohort of 12,508 persons aged under 18 at the time of the accident who had direct 131I thyroid activity measurement and were resident in the most radioactively contaminated regions of Ukraine. We have extended earlier analyses of thyroid cancer risk in this cohort by adjusting for error in doses, using two regression calibration methods, taking account of errors in thyroid activity and thyroid mass.

Findings The re-analyses show a highly statistically significant increasing dose response (p<0.01) for incident thyroid cancer, but there are no significant modifications of excess relative risk (ERR) per Gy by gender, age at the time of the accident or time since the accident (p>0.5). There are borderline statistically significant modifications of the dose response by oblast of residence at the time of the accident (p=0.08-0.10) and downward curvature in the dose response (p=0.06-0.14).

Introduction The accident at the Chernobyl nuclear power plant remains the most serious nuclear accident in history. Thyroid cancer was the first cancer to be elevated among the exposed population in Ukraine and Belarus, within 5 years of the accident, and the excess is particularly marked among those exposed in childhood (1-3). The thyroid cancer excess is thought to be largely the result of release of radioactive 131I from the Chernobyl reactor. In collaboration with the Institute of Endocrinology and Metabolism, Kyiv, Ukraine and Columbia University, the U.S. National Cancer Institute initiated a cohort screening study of children and adolescents exposed to Chernobyl fallout in Ukraine to better understand the long-term health effects of exposure to radioactive iodines. There have been a number of analyses of this cohort (2;4), which document the significant increased risk of thyroid cancer. A major source of uncertainty in estimation of low dose risk concerns the extrapolation of risks at high dose and high dose rates to those at low doses and low dose rates. Crucial to the resolution of this area of uncertainty is consideration of both systematic and random dosimetric errors in analyses of the Chernobyl-exposed and other exposed groups. It is well recognized that measurement error can alter substantially the shape of this relationship and hence the derived study risk estimates (5). Typically errors are assumed to be of one of two types, classical or Berkson. Classical errors, in which the measured doses are assumed to be distributed with (independent) error around the true dose, generally result in a downward bias of the dose-response parameter (5). Berkson errors, in which the true dose is randomly distributed around a measured dose estimate, do not result in biased estimates of the dose-response parameter for linear models, although for non-linear models that is not the case (5). Kukush et al. (6) developed a novel methodology for assessing dose error in a Chernobyl-exposed cohort, incorporating both Berkson and classical errors. A commonly used method of dealing with dose error is to replace the dose estimate in any regression with the expected true dose given the measured dose estimate, a process termed regression calibration (5). Bayesian methods have also been used (7), as has Monte Carlo likelihood integration (8). The Ukrainian-US screening cohort dosimetry has recently (2011) been updated (9). In this paper we compare regressions of dose on thyroid cancer incidence using the newly released (2011) thyroid dose estimates with those used in the study of Brenner et al. based on the older (2002) dose estimates (4). We assess the impact on thyroid cancer risk of two types of regression calibration, the first of these based on models of Kukush et al. (6).

Results Comparison of doses We found generally good agreement between the 2002 thyroid-mass-corrected doses used by Brenner et al. (4) and the new (2011) dose estimates, although there was considerable scatter.

Model fitting The re-analyses show a highly statistically significant increasing dose response (p<0.01) for incident thyroid cancer, but there are no significant modifications of excess relative risk (ERR) per Gy by gender, age at the time of the accident or time since the accident (p>0.5). There are borderline statistically significant modifications of the dose response by oblast of residence at the time of the accident (p=0.08-0.10) and downward curvature in the dose response (p=0.06-0.14).

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