Healthy worker survivor bias: implications of truncating follow-up at employment termination. Issue 10 (19th July 2013)

• Record Type: Journal Article
• Authors: Picciotto, Sally; Brown, Daniel M; Chevrier, Jonathan; Eisen, Ellen A
• Is Part Of: Occupational and environmental medicine. Volume 70:Issue 10(2013)
• Page Start: 736
• Page End: 742
• Publication Date: 2013-07-19
• Subjects: Healthy Worker Survivor Effect; Medicine, Industrial -- Periodicals; Environmental health -- Periodicals; 616.980305
• DOI: 10.1136/oemed-2012-101332
• Languages: English
• ISSNs: 1351-0711

Abstract

Abstract : Objectives: The healthy worker survivor effect is a bias that occurs in occupational studies when less healthy workers are more likely to reduce their workplace exposures. When variables on the pathway from health status to exposure are measured, g-methods can avoid this bias. However, studies in which follow-up ends at employment termination have additional potential for selection bias. This paper examines the structure of the healthy worker survivor effect, compares results with and without censoring at employment termination, and addresses how to prevent bias when such censoring occurs. Methods: G-estimation of structural accelerated failure time models was applied in the United Autoworkers—General Motors cohort study to examine relationships between metalworking fluid exposure and cause-specific mortality. Subjects were followed from hire through 1994, regardless of employment status. To answer the central question, g-estimation analysis was repeated after truncating at employment termination and censoring outcomes that occurred thereafter, with adjustment for censoring by inverse probability weighting. Results: Using full follow-up time, HRs were estimated for all-cause mortality (1.09), ischaemic heart disease death (1.19), and death from any cancer (1.09), comparing 5 years of metalworking fluid exposure to no exposure. For all three outcomes, the HR estimates based on data censored at termination of employment were below 1 (respectively, 0.92, 0.97, 0.79). Conclusions: In this application, g-estimation together with weighting did not prevent selection bias due to employment termination. However, the bias might be avoided in studies with measured health-related variables on the pathway from health status to employment termination.