Changes in depression domains as predictors of return to work in common mental disorders

Depression highly impairs function and reduces quality of life. Therefore, both symptomatic and functional recovery are important treatment goals. Depression consists of several cognitive, somatic, and affective symptom factors that differently affect function. However, it is unclear whether changes in these domains predict return to work (RTW) after treatment. Data were collected during treatment from patients on full or partial sick leave reporting depression symptoms (N = 300) at an out-patient clinic. Information on work status was assessed pre- and post-treatment and at 6 months follow-up. Multiple logistic regression was used to investigate if residualized changes in symptom factors predicted full RTW, controlling for gender, education level, and age. Changes (as symptom improvement) in the cognitive, somatic, and affective factor scores each significantly predicted full RTW post-treatment and at follow-up for patients on full and partial sick leave, even after controlling for gender, education level, and age. The change in the somatic factor explained the largest proportion of variance for full work post-treatment in patients on full sick-leave, while change in the cognitive factor explained most unique variance for patients on graded sick leave. The sample consisted of a majority of women with a relatively high level of education. This study should be replicated in more heterogeneous samples. Changes in depression symptom domains are significant predictors for RTW work post-treatment. The change in the somatic factor explained the largest proportion of variance in patients on full sick leave and thus may particularly influence RTW after treatment. •Depression highly impairs function and quality of life.•Symptomatic and functional recovery are important treatment goals.•Change in cognitive, somatic and affective domains predict return to work.•Improvement in somatic domains may be uniquely associated with return to work.•This knowledge can help clinicians tailor treatment for patients on sick leave.