title: A method for estimating occupational radiation dose to individuals, using weekly dosimetry data.
abstract: Statistical analyses of data from epidemiological studies of workers exposed to radiation have been based on recorded annual radiation doses. It is usually assumed that the annual doses are known exactly, although it is generally recognized that the data contain uncertainty due to measurement error and bias. We propose the use of a probability distribution to describe an individual's dose during a specific period and develop statistical methods for estimating this distribution. The methods take into account the "measurement error" that is produced by the dosimetry system and the bias that was introduced by policies of recording doses below a threshold as zero. The method is applied to a sample of dose histories over the period 1945 to 1955 obtained from hard-copy dosimetry records at Oak Ridge National Laboratory (ORNL). The result of this evaluation raises serious questions about the validity of the historical personnel dosimetry data that are currently being used in studies of the effects of low doses in nuclear industry workers. In particular, it appears that there was a systematic underestimation of doses for ORNL workers. This may result in biased estimates of dose-response coefficients and their standard errors.
authors.affiliation: Computer Science and Mathematics Division, Oak Ridge National Laboratory, Tennessee 37831-6367, USA.
authors.forename: T J
authors.initials: TJ
authors.lastname: Mitchell
authors.forename: G
authors.initials: G
authors.lastname: Ostrouchov
authors.forename: E L
authors.initials: EL
authors.lastname: Frome
authors.forename: G D
authors.initials: GD
authors.lastname: Kerr
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D015331:Cohort Studies; D004868:Equipment Failure; D006801:Humans; D016013:Likelihood Functions; D016273:Occupational Exposure; D011829:Radiation Dosage; D011834:Radiation Monitoring / Q000295:instrumentation / Q000379:methods*; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric