Design of a health-economic Markov model to assess cost-effectiveness and budget impact of the prevention and treatment of depressive disorder

Joran Lokkerbol, Ben Wijnen*, Henricus G. Ruhe, Jan Spijker, Arshia Morad, Robert Schoevers, Marrit K. de Boer, Pim Cuijpers, Filip Smit

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
JournalExpert review of pharmacoeconomics & outcomes research
Early online date2020
DOIs
Publication statusE-pub ahead of print - 2020

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