Cost-Effectiveness Analysis of Adaptive Monitoring Strategies for Depression Treatment

Thesis (Master's)--University of Washington, 2016-06

Depression is a significant challenge for the American medical care system and affects as high as 10% of the adult population in the U.S. There are many challenges in treating depression. People are reluctant to reveal their symptoms and seek care because many see mental health problem as a personal weakness. Meanwhile, a lack of effective monitoring and treatment interventions may slow down the recovery progress. This study performs a cost-effectiveness analysis (CEA) of adaptive depression monitoring and care strategies. A Markov decision-analytic model is developed to compare the projected cost and benefit of the adaptive monitoring strategies and the status quo in depression care. Quality-adjusted life-years (QALY) is used as a measurement of patients’ long-term health benefit. We run the baseline analysis applying three different monitoring schedules and sensitivity analysis on major parameters including treatment effect and Markov transition matrices. Results of our CEA study suggest adopting adaptive monitoring strategy can be potentially cost-effective for major depression care and is therefore worthy of further observational research.