Background: The management of depression in primary care is a significant issue for health services worldwide. "Collaborative care" interventions are effective, but little is known about which aspects of these complex interventions are essential.
Aims: To use meta-regression to identify "active ingredients" in collaborative care models for depression in primary care.
Method: Studies were identified using systematic searches of electronic databases. The content of collaborative care interventions was coded, together with outcome data on antidepressant use and depressive symptoms. Meta-regression was used to examine relationships between intervention content and outcomes.
Results: There was no significant predictor of the effect of collaborative care on antidepressant use. Key predictors of depressive symptom outcomes included systematic identification of patients, professional background of staff and specialist supervision.
Conclusions: Meta-regression may be useful in examining "active ingredients" in complex interventions in mental health.