NIOSHTIC-2 Publications Search
The application of dose reconstruction results to NIOSH-IREP (NIOSH's version of the interactive radio-epidemiological program) in estimating the probability of causation of radiogenic cancer.
Health Phys 2003 Jun; 84(Suppl):S210
NIOSH-IREP is an online, interactive software program created specifically for use in adjudicating cancer claims under the Energy Employees Occupational Illness Compensation Program Act (EEOICP A), as promulgated by 42 CFR, Part 81, "Guidelines for Determining Probability of Causation." To qualify for compensation, EEOICP A stipulates that an individual's cancer has to have been "at least as likely as not" caused by exposure to ionizing radiation while in the performance of covered duties. This presentation provides an overview of NIOSH-IREP and its application of reconstructed radiation doses and other variables to calculate the statistical probability, according to the provisions of EEOICP A, that a worker's cancer was induced by occupational exposure to ionizing radiation. Examples of the types of radiation exposures that are likely to be compensated are presented, as well as those not likely to result in compensation. To calculate the unique probability of causation (PC) for each claim, NIOSH-IREP incorporates probability distributions derived from uncertainties associated with cancer risk models, radiation exposure, dose-response assumptions, and personal attributes, employing Monte Carlo simulations to propagate these uncertainties. Individual dose reconstruction results are factored into the calculations via inputs for radiation type and exposure rate, organ dose in cSv, and year of exposure. In order to provide the benefit of doubt to each claimant, a claim is considered compensable if the PC result is 50% or greater at the upper 99th percentile credibility limit.
Regulations; Nuclear-energy; Ionizing-radiation; Employee-exposure; Job-analysis; Cancer; Radiation-exposure; Radiation-injury; Radiation-measurement; Computer-models; Computer-software; Dose-response; Mathematical-models; Statistical-analysis; Environmental-factors; Risk-factors; Decision-making; Data-processing
Abstract; Conference/Symposia Proceedings
Page last reviewed: September 2, 2020
Content source: National Institute for Occupational Safety and Health Education and Information Division