269539 Causal Methods to Control for the Healthy Worker Survivor Effect

Tuesday, October 30, 2012 : 1:15 PM - 1:30 PM

Jonathan Chevrier, PhD , Berkeley Research Group in Methods for Occupational Epidemiology, School of Public Health, UC Berkeley, Berkeley, CA
Sally Picciotto, PhD , Berkeley Research Group in Methods for Occupational Epidemiology, School of Public Health, UC Berkeley, Berkeley, CA
Ellen A. Eisen, ScD , Berkeley Research Group in Methods for Occupational Epidemiology, School of Public Health, UC Berkeley, Berkeley, CA
Background: Studies of autoworkers exposed to straight metalworking fluids report excess risks of several cancers. These studies, however, have not addressed the healthy-worker survivor effect. Most methods proposed to address this bias do not consider that it may be caused by time-varying confounders affected by prior exposure. G-estimation of accelerated failure-time models was developed to handle this issue but has never been applied to account for the healthy-worker survivor effect. Methods: We compare results from Cox models and g-estimation in 38,747 autoworkers exposed to straight metalworking fluids. Exposure was defined based on job records and air samples. We examine relationships between duration of exposure and mortality from all causes, cancers, ischemic heart disease, and chronic obstructive pulmonary disease (COPD). Results: In standard models, hazard ratios were elevated for cancers of the larynx, prostate, and rectum, but below or approximately equal to 1.0 for all other outcomes considered. Adjustment for the healthy-worker survivor effect using time off work, employment status, time since hire, and restriction to inactive workers after 15 years of follow-up did not substantially change the hazard ratios. However, g-estimation yielded higher hazard ratios than standard Cox models for most outcomes. Exposure was related to increased risks of mortality from all causes combined, heart disease, COPD, and all cancers, as well as lung and prostate cancers. Conclusions: G-estimation may provide a better control for the healthy-worker survivor effect than standard methods.

Learning Areas:
Biostatistics, economics
Environmental health sciences
Epidemiology
Occupational health and safety

Learning Objectives:
Describe the causes of the healthy worker survivor effect; Identify the limitations of previously proposed methods to address this bias in studies of occupational exposures and the advantages of g-estimation.

Keywords: Occupational Exposure, Methodology

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have been studying the effects of environmental exposure for the last 14 years with a particular interest in epidemiological methods.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

Back to: 4210.0: Occupational Epidemiology