Predicting the duration of sickness absence for patients with common mental disorders in occupational health care

Scand J Work Environ Health. 2006 Feb;32(1):67-74. doi: 10.5271/sjweh.978.


Objectives: This study attempted to determine the factors that best predict the duration of absence from work among employees with common mental disorders.

Methods: A cohort of 188 employees, of whom 102 were teachers, on sick leave with common mental disorders was followed for 1 year. Only information potentially available to the occupational physician during a first consultation was included in the predictive model. The predictive power of the variables was tested using Cox's regression analysis with a stepwise backward selection procedure. The hazard ratios (HR) from the final model were used to deduce a simple prediction rule. The resulting prognostic scores were then used to predict the probability of not returning to work after 3, 6, and 12 months. Calculating the area under the curve from the ROC (receiver operating characteristic) curve tested the discriminative ability of the prediction rule.

Results: The final Cox's regression model produced the following four predictors of a longer time until return to work: age older than 50 years [HR 0.5, 95% confidence interval (95% CI) 0.3-0.8], expectation of duration absence longer than 3 months (HR 0.5, 95% CI 0.3-0.8), higher educational level (HR 0.5, 95% CI 0.3-0.8), and diagnosis depression or anxiety disorder (HR 0.7, 95% CI 0.4-0.9). The resulting prognostic score yielded areas under the curves ranging from 0.68 to 0.73, which represent acceptable discrimination of the rule.

Conclusions: A prediction rule based on four simple variables can be used by occupational physicians to identify unfavorable cases and to predict the duration of sickness absence.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adjustment Disorders
  • Anxiety Disorders
  • Depressive Disorder
  • Female
  • Humans
  • Male
  • Mental Disorders*
  • Middle Aged
  • Models, Biological*
  • Occupational Health*
  • Sick Leave*
  • Socioeconomic Factors
  • Workplace