Designing and Testing of a Health-Economic Markov Model for Prevention and Treatment of Early Psychosis
Background: This study aims to report on the design of a model to determine the cost-effectiveness of prevention and treatment of early psychosis (PsyMod) and to estimate ten-year cost-effectiveness and budget impact of interventions targeting individuals with ultra-high risk (UHR) of developing psychosis or with first episode psychosis (FEP). Methods: PsyMod was built in parallel with the development of a new standard of care for treatment of early psychosis in the Netherlands. PsyMod is a state-transition cohort simulation model and considers six health states, namely ultra-high risk of psychosis (UHR), FEP, post-FEP, no-UHR, recovery/remission, and death. Results are expressed as total healthcare costs, QALYs, incremental cost-effectiveness ratio (ICER), and budget impact. Results: PsyMod was used to extrapolate budget impact and cost-effectiveness of cognitive behavioural therapy for preventing FEP for individuals at UHR of psychosis (CBTuhr) compared to care as usual. CBTuhr resulted in a per-patient increase of 0.06 QALYs and a per patient cost reduction of €654 (dominant ICER) with a reduction in 5-year healthcare costs of €1,002,166. Conclusions: PsyMod can be used to examine cost-effectiveness and budget impact of interventions targeting prevention and treatment of FEP and is freely available for academic purposes upon request by the authors.
|Keywords||budget impact, cost-effectiveness, economic evaluation, health economic modelling, psychosis, Schizophrenia|
|Persistent URL||dx.doi.org/10.1080/14737167.2019.1632194, hdl.handle.net/1765/117632|
|Journal||Expert Review of Pharmacoeconomics & Outcomes Research|
Wijnen, B.F.M, Thielen, F.W, Konings, S. (Steef), Feenstra, T.L, van der Gaag, M, Veling, W.A, … Lokkerbol, J. (2019). Designing and Testing of a Health-Economic Markov Model for Prevention and Treatment of Early Psychosis. Expert Review of Pharmacoeconomics & Outcomes Research. doi:10.1080/14737167.2019.1632194