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Biological dosimetry of chernobyl cleanup workers: inclusion of data on age and smoking provides improved radiation dose estimates
Authors:Moore II D H  Tucker J D
Affiliation:Research Institute, California Pacific Medical Center, Department of Epidemiology, University of California, San Francisco, California 94143-0808, USA.
Abstract:We report the results of a study of chromosome translocations in 126 Russian subjects who participated in the cleanup activities at Chernobyl and another 53 subjects, from other places in Russia, who were not exposed at Chernobyl. In agreement with our earlier study, we find increased translocation frequencies among the exposed compared to Russian controls. We describe statistical methods for estimating the dose of ionizing radiation determined by scoring chromosome translocations found in circulating lymphocytes sampled several years after exposure. Two statistical models were fitted to the data. One model assumed that translocation frequencies followed an overdispersed Poisson distribution. The second model assumed that translocation frequencies followed a negative binomial distribution. In addition, the effects of radiation exposure were modeled as additive or as multiplicative to the effects of age and smoking history. We found that the negative binomial model fit the data better than the overdispersed Poisson model. We could not distinguish between the additive and the multiplicative model with our data. Individual dose estimates ranged from 0 (for 43 subjects) to 0.56 Gy (mean 0.14 Gy) under the multiplicative model and from 0 to 0.95 Gy (mean 0.15 Gy) under the additive model. Dose estimates were similar under the two models when the number of translocations was less than 4 per 100 cells. The additive model tended to estimate larger doses when the number of translocations was greater than 4 per 100 cells. We also describe a method for estimating upper 95% tolerance bounds for numbers of translocations in unexposed individuals. We found that inclusion of data on age and smoking history was important for dose estimation. Ignoring these factors could result in gross overestimation of exposures, particularly in older subjects who smoke.
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