Development of a job-exposure matrix (AsbJEM) to estimate occupational exposure to asbestos in Australia

Abstract

Introduction: Occupational exposure data on asbestos are limited and poorly integrated in Australia so that estimates of disease risk and attribution of disease causation are usually calculated from data that are not specific for local conditions. Objective: To develop a job-exposure matrix (AsbJEM) to estimate occupational asbestos exposure levels in Australia, making optimal use of the available exposure data. Methods: A dossier of all available exposure data in Australia and information on industry practices and controls was provided to an expert panel consisting of three local industrial hygienists with thorough knowledge of local and international work practices. The expert panel estimated asbestos exposures for combinations of occupation, industry, and time period. Intensity and frequency grades were estimated to enable the calculation of annual exposure levels for each occupation-industry combination for each time period. Two indicators of asbestos exposure intensity (mode and peak) were used to account for different patterns of exposure between occupations. Additionally, the probable type of asbestos fibre was determined for each situation. Results: Asbestos exposures were estimated for 537 combinations of 224 occupations and 60 industries for four time periods (1943-1966; 1967-1986; 1987-2003; =2004). Workers in the asbestos manufacturing, shipyard, and insulation industries were estimated to have had the highest average exposures. Up until 1986, 46 occupation-industry combinations were estimated to have had exposures exceeding the current Australian exposure standard of 0.1 f ml<sup>-1</sup>. Over 90% of exposed occupations were considered to have had exposure to a mixture of asbestos varieties including crocidolite. Conclusion: The AsbJEM provides empirically based quantified estimates of asbestos exposure levels for Australian jobs since 1943. This exposure assessment application will contribute to improved understanding and prediction of asbestos-related diseases and attribution of disease causation.