Erika Garcia1, Sally Picciotto1, Sadie Costello1, Patrick T Bradshaw2, Ellen A Eisen1. 1. Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, California, USA. 2. Epidemiology Division, School of Public Health, University of California, Berkeley, California, USA.
Abstract
OBJECTIVE: The healthy worker survivor effect (HWSE) can affect the validity of occupational studies when data are analysed incorrectly. HWSE depends on three underlying conditions: (1) leaving work predicts future exposure, (2) leaving work is associated with disease outcome and (3) prior exposure increases probability of leaving work. If all these conditions are satisfied, then employment status is a time-varying confounder affected by prior exposure, and standard regression will produce bias. We assessed these conditions for cancer outcomes in a cohort of autoworkers exposed to metalworking fluids (MWF). METHODS: The cohort includes 31 485 workers followed for cancer incidence from 1985 to 1994. As occupational exposures to straight, soluble and synthetic MWFs are necessarily zero after leaving work, condition (1) is satisfied. Cox models for cancer incidence and for employment termination were used to assess conditions (2) and (3), respectively. Employment termination by select ages was examined to better gauge the presence of condition (2). RESULTS: The HR for leaving work as a predictor of all cancers combined and prostate cancer was null, but elevated for lung and colorectal cancers among men. Condition (2) was more clearly satisfied for all cancer outcomes when leaving work occurred by age 50. Higher exposures to all three MWF types were associated with increased rates of leaving work (condition (3)), with the exception of straight MWF among women. CONCLUSIONS: We found evidence for the structural conditions underlying HWSE in a cohort of autoworkers. G-methods should be applied to reduce HWSE bias in studies of all cancers presently examined. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
OBJECTIVE: The healthy worker survivor effect (HWSE) can affect the validity of occupational studies when data are analysed incorrectly. HWSE depends on three underlying conditions: (1) leaving work predicts future exposure, (2) leaving work is associated with disease outcome and (3) prior exposure increases probability of leaving work. If all these conditions are satisfied, then employment status is a time-varying confounder affected by prior exposure, and standard regression will produce bias. We assessed these conditions for cancer outcomes in a cohort of autoworkers exposed to metalworking fluids (MWF). METHODS: The cohort includes 31 485 workers followed for cancer incidence from 1985 to 1994. As occupational exposures to straight, soluble and synthetic MWFs are necessarily zero after leaving work, condition (1) is satisfied. Cox models for cancer incidence and for employment termination were used to assess conditions (2) and (3), respectively. Employment termination by select ages was examined to better gauge the presence of condition (2). RESULTS: The HR for leaving work as a predictor of all cancers combined and prostate cancer was null, but elevated for lung and colorectal cancers among men. Condition (2) was more clearly satisfied for all cancer outcomes when leaving work occurred by age 50. Higher exposures to all three MWF types were associated with increased rates of leaving work (condition (3)), with the exception of straight MWF among women. CONCLUSIONS: We found evidence for the structural conditions underlying HWSE in a cohort of autoworkers. G-methods should be applied to reduce HWSE bias in studies of all cancers presently examined. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
Authors: Stella Koutros; Jay H Lubin; Barry I Graubard; Aaron Blair; Patricia A Stewart; Laura E Beane Freeman; Debra T Silverman Journal: Am J Epidemiol Date: 2019-08-01 Impact factor: 4.897
Authors: Orianne Dumas; Krislyn M Boggs; Catherine Quinot; Raphaëlle Varraso; Jan-Paul Zock; Paul K Henneberger; Frank E Speizer; Nicole Le Moual; Carlos A Camargo Journal: Am J Ind Med Date: 2019-11-06 Impact factor: 2.214
Authors: Peter Stephen; Thulasingam Mahalakshmy; Rajaram Manju; Karthik B Laksham; Sudalai Subramani; Kanhu Panda; Sonali Sarkar Journal: Indian J Occup Environ Med Date: 2018 Jan-Apr