Algorithm-guided treatment of depression reduces treatment costs – Results from the German Algorithm Project

Algorithm-guided treatment of depression reduces treatment costs – Results from the German Algorithm Project Background: Major depressive disorder (MDD) belongs to the major health burdens 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. Treatment algorithms are designed to improve outcomes by enhancing the quality of care. Phase 2 of the German Algorithm Project (GAP2) compared a standardized stepwise drug treatment regimen (SSTR) to treatment as usual (TAU) of inpatients with depression in a randomized controlled study. Aim of this study was a health economic evaluation of these data. Methods: A cost-effectiveness analysis and a calculation of costs of hospitalisation and medication was applied to investigate the financial benefit of SSTR compared to TAU. Results: Completer analysis reveals a financial superiority of SSTR compared to TAU (SSTR: 10 862.14 €, TAU: 15 247.97 €; p=0,014). The cost-effectiveness analysis in an intention-to-treat approach shows that the costs per remission achieved in TAU is approximately twice as high as in SSTR (TAU: 39 097.36 €, SSTR: 20 115.07 €). Conclusion: We demonstrated that algorithm-guided treatment of inpatients with depression results in a significant decrease of health care costs. Our study can represent an approach to estimate the benefits of algorithm-guided treatment from a health economic point of view.