Calibrating a population-based job-exposure matrix using inspection measurements to estimate historical occupational exposure to lead for a population-based cohort in Shanghai, China
Koh, Dong Hee; Bhatti, Parveen; Coble, Joseph B.; Stewart, Patricia A.; Lu, Wei; Shu, Xiao Ou; Ji, Bu Tian; Xue, Shouzheng; Locke, Sarah J.; Portengen, Lutzen; Yang, Gong; Chow, Wong Ho; Gao, Yu Tang; Rothman, Nathaniel; Vermeulen, Roel; Friesen, Melissa C.
(2014) Journal of Exposure Science and Environmental Epidemiology, volume 24, issue 1, pp. 9 - 16
(Article)
Abstract
The epidemiologic evidence for the carcinogenicity of lead is inconsistent and requires improved exposure assessment to estimate risk. We evaluated historical occupational lead exposure for a population-based cohort of women (n=74,942) by calibrating a job-exposure matrix (JEM) with lead fume (n=20,084) and lead dust (n=5383) measurements collected over four decades
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in Shanghai, China. Using mixed-effect models, we calibrated intensity JEM ratings to the measurements using fixed-effects terms for year and JEM rating. We developed job/industry-specific estimates from the random-effects terms for job and industry. The model estimates were applied to subjects' jobs when the JEM probability rating was high for either job or industry; remaining jobs were considered unexposed. The models predicted that exposure increased monotonically with JEM intensity rating and decreased 20-50-fold over time. The cumulative calibrated JEM estimates and job/industry-specific estimates were highly correlated (Pearson correlation=0.79-0.84). Overall, 5% of the person-years and 8% of the women were exposed to lead fume; 2% of the person-years and 4% of the women were exposed to lead dust. The most common lead-exposed jobs were manufacturing electronic equipment. These historical lead estimates should enhance our ability to detect associations between lead exposure and cancer risk in the future epidemiologic analyses.
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Keywords: Cancer, Exposure assessment, Job-exposure matrix, Lead, Mixed-effects model, Occupational exposure, Pollution, Public Health, Environmental and Occupational Health, Toxicology, Epidemiology
ISSN: 1559-0631
Publisher: Nature Publishing Group
(Peer reviewed)
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