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Healthy worker survivor bias: implications of truncating follow-up at employment termination
  1. Sally Picciotto1,
  2. Daniel M Brown1,2,
  3. Jonathan Chevrier1,3,
  4. Ellen A Eisen1,3
  1. 1Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California, USA
  2. 2Division of Biostatistics, School of Public Health, University of California, Berkeley, California, USA
  3. 3Division of Epidemiology, School of Public Health, University of California, Berkeley, California, USA
  1. Correspondence to Dr Sally Picciotto, Division of Environmental Health Sciences, UC Berkeley School of Public Health, 789 University Hall, Berkeley, CA 94720, USA; sallypicciotto{at}


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.

  • Healthy Worker Survivor Effect

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