Dose Estimation from Daily and Weekly Dosimetry Data

Abstract

Statistical analyses of data from epidemiologic studies of workers exposed to radiation have been based on recorded annual radiation doses (yearly dose of record). It is usually assumed that the dose values are known exactly, although it is generally recognized that the data contain uncertainty due to measurement error and bias. In our previous work with weekly data, a probability distribution was used to describe an individual's dose during a specific period of time and statistical methods were developed for estimating it from weekly film dosimetry data. This study showed that the yearly dose of record systematically underestimates doses for Oak Ridge National Laboratory (ORNL) workers. This could result in biased estimates of doseresponse coefficients and their standard errors. The results of this evaluation raise serious questions about the suitability of the yearly dose of record for direct use in low-dose studies of nuclear industry workers. Here, we extend our previous work to use full information in Pocket meter data and develop the Data Synthesis for Individual Dose Estimation (DSIDE) methodology. Although the DSIDE methodology in this study is developed in the context of daily and weekly data to produce a cumulative yearly dose estimate, in principle it is completely general and can be extended to other time period and measurement combinations. The new methodology takes into account the "measurement error" that is produced by the film and pocketmeter dosimetry systems, the biases introduced by policies that lead to recording left-censored doses as zeros, and other measurement and recording practices. The DSIDE method is applied to a sample of dose histories obtained from hard copy dosimetry records at ORNL for the years 1945 to 1955. First, the rigorous addition of daily pocket-meter information shows that the negative bias is generally more severe than was reported in our work based on weekly film data only, however, the amount of bias also varies greatly between person-years. Second, the addition of pocket-meter information reduces uncertainty for some person-years, while increasing it for others. Together, these results suggest that detailed pocket-meter and film dosimetry information is required to obtain unbiased and reliable dosimetry data for use in epidemiologic studies of workers at ORNL.

Semiparametric Regression Modeling with Mixtures of Berkson and Classical Error, with Application to Fallout from the Nevada Test Site

“…Stevens et al (1992) describe a study of thyroid disease in relation to fallout from the Nevada test site (NTS). Similar statistical issues arise in the Hanford Thyroid Disease Study (Davis et al, 1998) and the Oak Ridge Radiation Study (Ostrouchov, Frome, and Kerr, 1998). In the Nevada study, 2473 individuals who were exposed to radiation as children were examined for thyroid disease.…”

Moment Reconstruction and Moment-Adjusted Imputation When Exposure Is Generated by a Complex, Nonlinear Random Effects Modeling Process

“…However, it is of much broader applicability to problems in biology, physics, or social science in which individual true exposures are modeled as a possibly complex function of mismeasured variables, covariates, and random effects. For example, as detailed in Supplementary Material Appendix S.1, in radiation research (Reeves et al, 1998;Ostrouchov et al, 2000;Davis et al, 2002;Mallick et al, 2002), it is thought that calculated doses contain a mixture of Berkson errors (from physical transport systems modeling) and classical error (from measuring factors such a milk). If one knows the mixture percentage, the model in Reeves et al (1998) and Mallick et al (2002) is a special case of (1).…”