Evaluation of exposure-response relationships is a crucial component in assessment of causes of cancer. Quantitative exposure assessment is therefore an essential element in high-quality epidemiologic investigations. Occupational and Environmental Epidemiology Branch (OEEB) investigators devote considerable effort to improving exposure assessment techniques and evaluating the reliability and validity of procedures used in cohort and case-control studies of occupational and environmental exposures. Below are a few examples.
OEEB investigators have developed state-of-the-art quantitative exposure assessment methods that maximized the available measurements and exposure determinant information to predict historical exposure levels in several studies including diesel exhaust in the Diesel Exhaust in Miners Study, benzene in a cohort study Benzene-Exposed Workers in China, and specific pesticides within the Agricultural Health Study.
Job Exposure Matrices (JEMs) are an efficient way to assign exposure estimates in population-based studies and may be the only exposure assessment option when only job and industry are available for study participants. DCEG investigators develop methods to improve JEMs by incorporating databases of exposure measurements to calibrate JEMs across time and across jobs and industries. For example, in the Shanghai Women's Health Study, investigators developed a novel framework to systematically combine a JEM with historical measurements to calibrate JEM exposure ratings used to estimate historical benzene and lead exposure.
Occupation- and industry-specific modules ask detailed questions about work activities and exposures within its population-based case-control studies to better capture within-occupation differences in exposure. Usually these module responses are reviewed job-by-job by an exposure assessor to assign exposure estimates. OEEB investigators develop methods to more efficiently and transparently assign exposure in studies that use modules and examines the validity and reliability of these methods. For example, programmable decision rules based on questionnaire response patterns were developed to estimate occupational diesel exhaust exposure for the New England Bladder Cancer Study that had moderately-high agreement with estimates obtained from expert reviews of each job (Pronk et al. 2012; Friesen et al. 2012). We have also developed a method to extract patterns in the questionnaire responses that predict an expert’s exposure assignments using classification and regression tree (CART) models. The extracted decision rules can be used to improve the transparency and efficiency of applying the exposure decisions to other study subjects.
For more information, contact Melissa Friesen.