Is algorithm-guided treatment of depression cost-effective?

Abstract

Major depressive disorder (MDD) belongs to the major health burden in the developed countries. Because of the financial burden arising from MDD an economic perspective in the evaluation of health care is of high importance. In Germany more than 50 percent of the costs are caused by in-patient-care and 80% of the direct treatment cost of depression are caused by 10% of the patients. Treatment algorithms are designed to improve outcomes by enhancing the quality of care and avoid treatment resistance and can thereby be assumed as an instrument to reduce treatment costs. The multi-phasic German Algorithm Project (GAP) has evaluated algorithm-guided treatment of inpatients with MDD. GAP 2 compared a standardized stepwise treatment regime (SSTR) with treatment as usual (TAU) in 148 inpatients with depressive disorders in an RCT. Its final phase GAP 3 compared SSTR and a computerized documentation and expert system (CDES) with TAU in a five-arm multicenter randomized controlled trial. 429 inpatients treated for MDD were included. The health economic evaluation of GAP2 performing a cost effectiveness-analysis revealed that the costs per remission achieved in TAU are approximately twice as high as in SSTR. We demonstrated that algorithm-guided treatment of inpatients with depression results in a significant decrease of health care costs. To further validate the results of GAP2, we will now present health economic data and cost effectiveness analysis of GAP3.