Joran Lokkerbol1, Ben Wijnen1,2, Henricus G Ruhe3,4,5, Jan Spijker6,7, Arshia Morad8, Robert Schoevers3,9, Marrit K de Boer3, Pim Cuijpers10,11, Filip Smit1,10,11. 1. Centre for Economic Evaluation and Machine Learning, Department of Public Mental Health, Trimbos Institute (Netherlands Institute of Mental Health and Addiction), Utrecht, The Netherlands. 2. Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, The Netherlands. 3. Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 4. Radboudumc, Department of Psychiatry, Radboud University of Nijmegen, Nijmegen, The Netherlands. 5. Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands. 6. Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands. 7. Depression Expertise Centre, Pro Persona Mental Health Care, Nijmegen, The Netherlands. 8. School of Psychology, The University of Sydney, New South Wales, Australia. 9. Research School of Behavioural and Cognitive Neurosciences (BCN), Interdisciplinary Center for Psychopathology and Emotion Regulation (ICPE), Groningen, The Netherlands. 10. Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, The Netherlands. 11. Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Academic Medical Centers Amsterdam, Location VUmc, Amsterdam, The Netherlands.
Abstract
Background/objective: To describe the design of 'DepMod,' a health-economic Markov model for assessing cost-effectiveness and budget impact of user-defined preventive interventions and treatments in depressive disorders. Methods: DepMod has an epidemiological layer describing how a cohort of people can transition between health states (sub-threshold depression, first episode of mild, moderate or severe depression (partial) remission, recurrence, death). Superimposed on the epidemiological layer, DepMod has an intervention layer consisting of a reference scenario and alternative scenario comparing the effectiveness and cost-effectiveness of a user-defined package of preventive interventions and psychological and pharmacological treatments of depression. Results are presented in terms of quality-adjusted life years (QALYs) gained and healthcare expenditure. Costs and effects can be modeled over 5 years and are subjected to probabilistic sensitivity analysis. Results: DepMod was used to assess the cost-effectiveness of scaling up preventive interventions for treating people with subclinical depression, which showed that there is an 82% probability that scaling up prevention is cost-effective given a willingness-to-pay threshold of €20,000 per QALY. Conclusion: DepMod is a Markov model that assesses the cost-utility and budget impact of different healthcare packages aimed at preventing and treating depression and is freely available for academic purposes upon request at the authors.
Background/objective: To describe the design of 'DepMod,' a health-economic Markov model for assessing cost-effectiveness and budget impact of user-defined preventive interventions and treatments in depressive disorders. Methods: DepMod has an epidemiological layer describing how a cohort of people can transition between health states (sub-threshold depression, first episode of mild, moderate or severe depression (partial) remission, recurrence, death). Superimposed on the epidemiological layer, DepMod has an intervention layer consisting of a reference scenario and alternative scenario comparing the effectiveness and cost-effectiveness of a user-defined package of preventive interventions and psychological and pharmacological treatments of depression. Results are presented in terms of quality-adjusted life years (QALYs) gained and healthcare expenditure. Costs and effects can be modeled over 5 years and are subjected to probabilistic sensitivity analysis. Results: DepMod was used to assess the cost-effectiveness of scaling up preventive interventions for treating people with subclinical depression, which showed that there is an 82% probability that scaling up prevention is cost-effective given a willingness-to-pay threshold of €20,000 per QALY. Conclusion: DepMod is a Markov model that assesses the cost-utility and budget impact of different healthcare packages aimed at preventing and treating depression and is freely available for academic purposes upon request at the authors.
Entities:
Keywords:
Cost-effectiveness; budget impact; depression; health-economic modeling; major depressive disorder