UC Berkeley

Abstract

Respiratory exposures to solvents and metalworking fluids in relation to chronic health outcomes: strategies for reducing bias within conditional modeling approaches

by

Stella Fay Beckman

Doctor of Philosophy in Epidemiology

University of California, Berkeley

Professor Ellen A. Eisen, Chair

Background

Occupational injury and illness are significant contributors to the global burden of disease, with more than 2 million deaths caused by work-related hazards estimated to occur worldwide each year. The goal of occupational epidemiology is to identify and characterize the causes of work-related injury and illness in order to inform workplace health and safety interventions. While occupational groups provide opportunities to study exposures at higher levels than those seen in the general population and with well-characterized exposures, studies of chronic diseases among workers are subject to a unique form of bias: the healthy worker effect. This results in downward bias, which can even make a truly hazardous exposure appear protective, under two circumstances: 1) when workers, who are overall healthier, are compared to the general population and 2) when workers with more robust health (survivors) remain in highly exposed jobs and accrue more exposure than those who leave work. While causal inference techniques are required to fully address this effect, there are approaches that may mitigate the bias within a standard conditional model.

Methods

In this dissertation, I explore the associations between occupational exposures and health outcomes with particular attention to bias caused by the healthy worker effect. In the second chapter, I estimate the association between acquired color vision defects and past exposure to n-hexane in a cross-sectional study of active and retired automobile repair workers in the San Francisco Bay Area, California. Color vision defects are hypothesized to be an early indicator of neurotoxicity caused by n-hexane. In the third and fourth chapters, I consider exposure to metalworking fluid (MWF) in a large cohort of machinists. I investigate the association between MWF and chronic obstructive pulmonary disease mortality in chapter three, using indirect adjustment for confounding by cigarette smoking status as well adjustment for cardiovascular disease mortality as a potential competing risk. In chapter four, I estimate the association between mineral oil-based MWF and mortality due to natural causes and cardiopulmonary disease in weighted Cox proportional hazards models, using inverse probability weights to address informative censoring and selection bias in a cross-sectional sample of the cohort. I contextualize all findings in within subject matter literature and discuss limitations of the methods chosen to address bias due to the healthy worker effect.

Conclusion

The chapters of this dissertation explore the associations between common industrial exposures (solvents and metalworking fluid) and health outcomes occurring many years after exposure. I find an elevated, though not statistically significant, association between n-hexane exposure and acquired color vision defects among automobile repair mechanics exposed to levels of n-hexane below current regulatory limits. For exposure to MWF, I find elevated risk of chronic obstructive pulmonary disease with 95% confidence intervals excluding 1 when adjusted for cardiovascular disease as a competing risk in Cox proportional hazards models. I report modestly but inconsistently elevated risks of natural cause and cardiopulmonary disease mortality with exposure to mineral oil-based MWF in models weighted to adjust for informative censoring and sample selection. Given the evidence of residual downward bias due to elements of the healthy worker survivor effect that can only be addressed with causal inference methods, these results taken as a whole suggest that current occupational safety and health regulations are not sufficiently protective of worker health, and should be reconsidered in light of this evidence.