Risk factors for malignant mesothelioma in people with no known exposure to asbestos.

OBJECTIVES: Malignant mesothelioma (MM) is a rare and generally fatal cancer, usually caused by asbestos, although about 5-10% of cases report no asbestos exposure. This study aimed to identify sources whereby people in Western Australia (WA) may be unknowingly exposed to asbestos or to other exposures which may cause MM. METHODS: Cases with no known asbestos exposure were selected from the WA Mesothelioma Register (WAMR). Matched controls were selected from hospital patients admitted for conditions unrelated to asbestos. Occupational histories were coded by an industrial hygienist. Data were analyzed using conditional logistic regression. RESULTS: Thirty-eight MM participants and 134 controls were recruited. Risk of MM was increased (OR = 3.1, 95%CI 1.0-9.6) after no known, but likely, exposure to asbestos at work. CONCLUSIONS: Because of its extensive use, few people in WA have never been exposed to asbestos. Unrecognized exposure may cause most MM cases initially regarded as "no exposure." Am. J. Ind. Med. 60:432-436, 2017. © 2017 Wiley Periodicals, Inc.

Development of a Job-Exposure Matrix (AsbJEM) to Estimate Occupational Exposure to Asbestos in Australia.

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(-1). 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.

Validation of an Asbestos Job-Exposure Matrix (AsbJEM) in Australia: Exposure-Response Relationships for Malignant Mesothelioma.

OBJECTIVES: An asbestos job-exposure matrix (AsbJEM) has been developed to systematically and cost-effectively evaluate occupational exposures in population-based studies. The primary aim of this study was to examine the accuracy of the AsbJEM in determining exposure-response relationships between asbestos exposure estimates and malignant mesothelioma (MM) incidence (indirect validation). The secondary aim was to investigate whether the assumptions used in the development of the original AsbJEM provided accurate asbestos exposure estimates. METHODS: The study population consisted of participants in an annual health surveillance program, who had at least 3-month occupational asbestos exposure. Calculated asbestos exposure indices included cumulative asbestos exposure and the average exposure intensity, estimated using the AsbJEM and duration of employment. Asbestos and MM exposure-response relationships were compared between the original AsbJEM and its variations based on manipulations of the intensity, duration and frequency of exposure. Twenty-four exposure estimates were calculated for both cumulative asbestos exposure and the average exposure intensity using three exposure intensities (50th, 75th and 90th percentile of the range of mode exposure), four peak durations (15, 30, 60 and 120 min) and two patterns of peak frequency (original and doubled). Cox proportional hazards models were used to describe the associations between MM incidence and each of the cumulative and average intensity estimates. RESULTS: Data were collected from 1602 male participants. Of these, 40 developed MM during the study period. There were significant associations between MM incidence and both cumulative and average exposure intensity for all estimates. The strongest association, based on the regression-coefficient from the models, was found for the 50th percentile of mode exposure, 15-min peak duration and the doubled frequency of peak exposure. Using these assumptions, the hazard ratios for mesothelioma were 1 (reference), 1.91, 3.24 and 5.37 for the quartiles of cumulative asbestos exposure and 1 (reference), 1.84, 2.31 and 4.40 for the quartiles of the average exposure intensity, respectively. CONCLUSION: The well-known positive exposure-response relationship between MM incidence and both estimated cumulative asbestos exposure and average exposure intensity was confirmed. The strongest relationship was found when the frequency of peak exposure in the AsbJEM was doubled from the originally published estimates.