Using sickness absence records to predict future depression in a working population: prospective findings from the GAZEL cohort

Am J Public Health. 2009 Aug;99(8):1417-22. doi: 10.2105/AJPH.2008.142273. Epub 2009 Jun 18.

Abstract

Objectives: We tested the hypothesis that sickness absence from work predicts workers' risk of later depression.

Methods: Study participants (n = 7391) belonged to the French GAZEL cohort of employees of the national gas and electricity company. Sickness absence data (1996-1999) were obtained from company records. Participants' depression in 1996 and 1999 was assessed with the Center for Epidemiologic Studies-Depression (CES-D) scale. The analyses were controlled for baseline age, gender, marital status, occupational grade, tobacco smoking status, alcohol consumption, subthreshold depressive symptoms, and work stress.

Results: Among workers who were free of depression in 1996, 13% had depression in 1999. Compared with workers with no sickness absence during the study period, those with sickness absence were more likely to be depressed at follow-up (for 1 period of sickness absence, fully adjusted odds ratio [OR] = 1.53, 95% confidence interval [CI] = 1.28, 1.82; for 2 or more periods, fully adjusted OR = 1.95, 95% CI = 1.61, 2.36). Future depression was predicted both by psychiatric and nonpsychiatric sickness absence (fully adjusted OR = 3.79 [95% CI = 2.81, 5.10] and 1.41 [95% CI = 1.21, 1.65], respectively).

Conclusions: Sickness absence records may help identify workers vulnerable to future depression.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Catchment Area, Health
  • Cohort Studies
  • Depression / epidemiology*
  • Depression / psychology*
  • Employment / psychology*
  • Employment / statistics & numerical data*
  • Female
  • Follow-Up Studies
  • Forecasting*
  • France / epidemiology
  • Health Status
  • Humans
  • Male
  • Medical Records*
  • Middle Aged
  • Prospective Studies
  • Sick Leave / statistics & numerical data*
  • Substance-Related Disorders / epidemiology