UC Berkeley

Work has long been recognized as a fundamental determinant of health, and stressful work is linked with symptoms of psychiatric illness such as distress, sadness, hopelessness, and depressive disorder. Blue-collar work emerges in the literature as an important example of this connection. Not only do repetitive, monotonous, and physically demanding tasks predispose workers to disability and injury, the erratic schedules and long hours oftentimes associated with blue-collar jobs may uniquely predispose workers to depression. Blue-collar jobs, in contrast with clerical and white-collar jobs, are also frequently characterized by exposure to a range of physical hazards, including heat, noise, chemicals, and particulate matter.

Women now represent over one-fourth of the 12 million workers in manufacturing jobs in the U.S. Female blue-collar workers may have less opportunity to adjust work demands or modify their duties following injury and take longer work-related sick leave. However, our current understanding of the effects of blue-collar work on mental health outcomes, such as depression are based largely on the experiences of men. Because depression is more common and more persistent in women, the lack of evidence regarding risk for depression among female blue-collar workers constitutes an important gap in the literature.

In response, this dissertation is focused on the implications of gender and aspects of the work environment for the mental health of workers in the U.S. aluminum industry. The American Manufacturing Cohort (AMC) study database is comprised of multiple, linked administrative datasets that collectively provide detailed, longitudinal follow-up for understanding work-life exposures, health and economic outcomes in a cohort of U.S. light and specialty metals workers. The study data are the product of an academic-corporate partnership forged in 1997 between Alcoa, Inc. and the Occupational and Environmental Medicine Program at the Yale University School of Medicine with the goal of incorporating research findings into company policy in an effort to improve worker safety. Study data consist of several distinct administrative datasets, and records for individual workers can be deterministically linked across datasets with a unique, encrypted identifier. Complete medical claims data are available for workers enrolled in their local preferred provider organization (PPO) health insurance plan (approximately 97% of the workforce). The analyses described herein are based on records from 30,035 men and 7,148 women employed in blue- and white-collar jobs by Alcoa Inc. between January 2003 and December 2013. They are the first to explicitly focus on the experiences of blue-collar women in the AMC Study.

For Chapter 1, I compare trends in depression by gender and occupational class in the study population of 37,183 workers. Chapters 2 and 3 are focused only on blue-collar workers. For Chapter 2, I characterize the association between the workplace gender composition and treatment for depression among male and female blue-collar workers. More specifically, I use g-computation to estimate risk differences that contrast the predicted risk of treatment for depression under hypothetical interventions on gender composition as compared with the observed data. Finally, in Chapter 3 I examine the effects of layoffs on remaining blue-collar workers’ use of mental healthcare services and injury risk with a difference-in-differences approach. Collectively, these three chapters provide a novel examination of the association between specific aspects of blue-collar work and risk for depression, with an emphasis on the unique experiences of women, in an unusually rich and unique administrative database.