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A review of job-exposure matrix methodology for application to workers exposed to radiation from internally deposited plutonium or other radioactive materials

Research output: Contribution to journalReview article

Original languageEnglish
Article numberR1
Pages (from-to)R1-R22
Number of pages23
JournalJournal of Radiological Protection
Volume36
Issue number1
Early online date10 Feb 2016
DOIs
DateAccepted/In press - 19 Nov 2015
DateE-pub ahead of print - 10 Feb 2016
DatePublished (current) - Mar 2016

Abstract

Any potential health effects of radiation emitted from radionuclides deposited in the body in workers exposed to radioactive materials can be directly investigated through epidemiological research studies. However, estimates of radionuclide exposure and consequent tissue-specific doses, particularly for early workers for whom monitoring was relatively crude but exposures tended to be highest, can be uncertain, limiting the accuracy of risk estimates. We review the use of job-exposure matrices (JEMs) in peer-reviewed epidemiological and exposure assessment studies of the nuclear industry workers exposed to radioactive materials as a method for addressing gaps in exposure data, and discuss methodology and comparability between studies. We identified nine studies of nuclear worker cohorts in France, Russia, the USA and the UK that had incorporated JEMs in their exposure assessments. All these JEMs were study or cohort-specific, and although broadly comparable methodologies were used in their construction, this is insufficient to enable the transfer of any one JEM to another study. Moreover there was often inadequate detail on whether, or how, JEMs were validated. JEMs have become more detailed and more quantitative, and this trend may eventually enable better comparison across, and the pooling of, studies. We conclude that JEMs have been shown to be a valuable exposure assessment methodology for imputation of missing exposure data for nuclear worker cohorts with data not missing at random. The next step forward for direct comparison or pooled analysis of complete cohorts would be the use of transparent and transferable methods.

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