BACKGROUND: The healthy worker survivor bias is well-recognized in occupational epidemiology. Three component associations are necessary for this bias to occur: i) prior exposure and employment status; ii) employment status and subsequent exposure; and iii) employment status and mortality. Together, these associations result in time-varying confounding affected by prior exposure. We illustrate how these associations can be assessed using standard regression methods. METHODS: We use data from 2975 asbestos textile factory workers hired between January 1940 and December 1965 and followed for lung cancer mortality through December 2001. RESULTS: At entry, median age was 24 years, with 42% female and 19% non-Caucasian. Over follow-up, 21% and 17% of person-years were classified as at work and exposed to any asbestos, respectively. For a 100 fiber-year/mL increase in cumulative asbestos, the covariate-adjusted hazard of leaving work decreased by 52% (95% confidence interval [CI], 46-58). The association between employment status and subsequent asbestos exposure was strong due to nonpositivity: 88.3% of person-years at work (95% CI, 87.0-89.5) were classified as exposed to any asbestos; no person-years were classified as exposed to asbestos after leaving work. Finally, leaving active employment was associated with a 48% (95% CI, 9-71) decrease in the covariate-adjusted hazard of lung cancer mortality. CONCLUSIONS: We found strong associations for the components of the healthy worker survivor bias in these data. Standard methods, which fail to properly account for time-varying confounding affected by prior exposure, may provide biased estimates of the effect of asbestos on lung cancer mortality under these conditions.
BACKGROUND: The healthy worker survivor bias is well-recognized in occupational epidemiology. Three component associations are necessary for this bias to occur: i) prior exposure and employment status; ii) employment status and subsequent exposure; and iii) employment status and mortality. Together, these associations result in time-varying confounding affected by prior exposure. We illustrate how these associations can be assessed using standard regression methods. METHODS: We use data from 2975 asbestos textile factory workers hired between January 1940 and December 1965 and followed for lung cancer mortality through December 2001. RESULTS: At entry, median age was 24 years, with 42% female and 19% non-Caucasian. Over follow-up, 21% and 17% of person-years were classified as at work and exposed to any asbestos, respectively. For a 100 fiber-year/mL increase in cumulative asbestos, the covariate-adjusted hazard of leaving work decreased by 52% (95% confidence interval [CI], 46-58). The association between employment status and subsequent asbestos exposure was strong due to nonpositivity: 88.3% of person-years at work (95% CI, 87.0-89.5) were classified as exposed to any asbestos; no person-years were classified as exposed to asbestos after leaving work. Finally, leaving active employment was associated with a 48% (95% CI, 9-71) decrease in the covariate-adjusted hazard of lung cancer mortality. CONCLUSIONS: We found strong associations for the components of the healthy worker survivor bias in these data. Standard methods, which fail to properly account for time-varying confounding affected by prior exposure, may provide biased estimates of the effect of asbestos on lung cancer mortality under these conditions.
Authors: Chanelle J Howe; Stephen R Cole; Daniel J Westreich; Sander Greenland; Sonia Napravnik; Joseph J Eron Journal: Epidemiology Date: 2011-11 Impact factor: 4.822
Authors: Maria Melchior; Marcel Goldberg; Nancy Krieger; Ichiro Kawachi; Gwenn Menvielle; Marie Zins; Lisa F Berkman Journal: Cancer Causes Control Date: 2005-06 Impact factor: 2.506
Authors: L Stayner; R Smith; J Bailer; S Gilbert; K Steenland; J Dement; D Brown; R Lemen Journal: Occup Environ Med Date: 1997-09 Impact factor: 4.402
Authors: Mary K Schubauer-Berigan; Amy Berrington de Gonzalez; Elisabeth Cardis; Dominique Laurier; Jay H Lubin; Michael Hauptmann; David B Richardson Journal: J Natl Cancer Inst Monogr Date: 2020-07-01
Authors: Katelyn J Siegrist; Steven H Reynolds; Michael L Kashon; David T Lowry; Chenbo Dong; Ann F Hubbs; Shih-Houng Young; Jeffrey L Salisbury; Dale W Porter; Stanley A Benkovic; Michael McCawley; Michael J Keane; John T Mastovich; Kristin L Bunker; Lorenzo G Cena; Mark C Sparrow; Jacqueline L Sturgeon; Cerasela Zoica Dinu; Linda M Sargent Journal: Part Fibre Toxicol Date: 2014-01-30 Impact factor: 9.400