Building a job-exposure matrix for early plutonium workers at the Sellafield nuclear site, United Kingdom.

The potential for adverse health effects from internal exposure to Plutonium has been recognised since its discovery in the 1940s. However, in the absence of specific information, potential risks from Plutonium exposure have always largely been controlled through knowledge of radiation exposure risks in general, much of which comes from external radiation exposures. To try to obtain more direct estimates of potential internal exposure risks, epidemiological studies of Plutonium workers need to be conducted. Such epidemiological analyses require individual Plutonium exposure estimates that are as accurate and unbiased as possible. The UK Sellafield workforce includes one of the world's largest cohorts of Plutonium workers, which constitutes, by some considerable margin, the group of workers most comprehensively monitored for internal exposure to this alpha-particle-emitter. However, for several hundred workers employed at the start of Plutonium work at the facility, during the period from 1952 through to 1963, the historical urinalysis results available cannot provide sufficiently accurate and unbiased exposure assessments needed for use in epidemiological studies. Consequently, these early workers have had to be excluded from epidemiological analyses and this has significantly reduced the power of these studies. A promising quantitative methodology to overcome the issue of missing or deficient exposure data, is to use exposure data from other sources to estimate the average exposure a 'typical worker' would have received, and to collate this information for specific occupations and years. This approach is called a Job-Exposure Matrix (JEM). Work on a pilot study to construct a population-specific quantitative JEM for the early Plutonium workers at Sellafield during 1952-1963, for whom reliable urinalysis results do not exist, has shown the potential for a JEM approach to produce more reliable and useful exposure estimates for epidemiological research.