Application of environmental dose-response models to epidemiology and animal data for the effects of ionizing radiation

Abstract

Previous extrapolations of risk from exposure to radiation at low levels (such as environmental exposures) have focused on various empirical models that have some axiomatic base, sometimes called state-vector models and usually involving linear and quadratic functions. Such models are based on representations of the physical processes occurring in irradiated cells, but do not include consideration of biological factors that could cause variability in biological response. Some mathematical models employed in environmental risk assessments (such statistically based models as the multistage, logit, probit, and Weibull) reverse this problem, showing little axiomatic support but incorporating variability, although this is not biological variability at doses of interest here. This paper presents the results of the predictions of these latter models as they apply to environmental levels of exposure to ionizing radiation compared to the estimates made by the more commonly used models. The study involves analysis of data from hard rock miner exposure to radon, exposure of rats to radon, the ingestion of radium by watch dial painters and beagle dogs, and the exposure of Japanese citizens to atomic bomb explosions. These results then are compared with inferences obtained using the more conventional axiomatic models, such as the linear/quadratic models. It is demonstrated that the results are similar, providing partial evidence that current ranges placed on risk estimates are not altered much by the selection of a particular class of models for use in quantitative risk assessment and uncertainty analysis. (Ranges are dependent on how far the data is to be extrapolated.)