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Gender, Depression, and Blue-collar Work

A Retrospective Cohort Study of US Aluminum Manufacturers

Elser, Hollya; Rehkopf, David H.b; Meausoone, Valeriec; Jewell, Nicholas P.d; Eisen, Ellen A.e; Cullen, Mark R.c

doi: 10.1097/EDE.0000000000000993
Occupational Epidemiology

Background: Industrial blue-collar workers face multiple work-related stressors, but evidence regarding the burden of mental illness among today’s blue-collar men and women remains limited.

Methods: In this retrospective cohort study, we examined health and employment records for 37,183 blue- and white-collar workers employed by a single US aluminum manufacturer from 2003 to 2013. Using Cox proportional hazards regression, we modeled time to first episode of treated depression by gender and occupational class. Among cases, we modeled rates of depression-related service utilization with generalized gamma regression.

Results: Compared with their white-collar counterparts, blue-collar men were more likely to be treated for depression (hazard ratio [HR] = 1.3; 95% confidence interval [CI] = 1.1, 1.4) as were blue-collar women (HR = 1.4; 1.2, 1.6). Blue-collar women were most likely to be treated for depression as compared with white-collar men (HR = 3.2; 95% CI = 2.1, 5.0). However, blue-collar workers used depression-related services less frequently than their white-collar counterparts among both men (rate ratio = 0.91; 95% CI = 0.84, 0.98) and women (rate ratio = 0.82; 95% CI = 0.77, 0.88).

Conclusions: Blue-collar women were more likely to be treated for depression than white-collar workers, and blue-collar women were most likely to be treated for depression compared with white-collar men. However, blue-collar men and women used depression-related healthcare services less frequently than white-collar workers. These findings underscore that blue-collar women may be uniquely susceptible to depression, and suggest that blue-collar workers may encounter barriers to care-seeking related mental illness other than their insurance status.

aDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA

bDivision of Primary Care and Population Health, Department of Medicine, School of Medicine, Stanford University, Stanford, CA

cCenter for Population Health Sciences, Stanford University, Stanford, CA

dDivision of Biostatistics, School of Public Health, University of California, Berkeley, CA

eDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA.

Editor’s Note: A related commentary appears on p. 445.

Submitted June 6, 2018; accepted January 30, 2019.

Supported by the National Institute on Mental Health grant F31 MH 112246; the National Institute on Aging grant R01 AG 026291; and by the National Institute on Occupational Safety and Health R01 OH 009939. The conclusions expressed are solely those of the authors.

The authors report no conflicts of interest.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (

As an alternative to providing a de-identified data set to the public domain, we currently allow access for the purpose of reanalyzes or appropriate follow-up analyses by any qualified investigator willing to sign a contract with the host institution limiting use of data without direct PHI/PII identifiers, in accordance to HIPAA regulations, and with a 15-day manuscript review for compliance purposes. For access to the data, interested parties can contact the study PI, Mark Cullen, at

Correspondence: Holly Elser, Division of Epidemiology, UC Berkeley School of Public Health, 2121 Berkeley Way West, Berkeley, CA 94704. E-mail:

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