Abstract
BackgroundMajor depressive disorder (MDD) represents a sizable economic burden in Spain. Pharmacogenetic (PGx) screening to guide the choice of antidepressant medication (ADM) in MDD patients yields higher response and remission rates, which could reduce both healthcare and indirect costs. MethodsWe built a cost-effectiveness probabilistic Markov model with microsimulation using Tree Age Pro 2022, simulating a patient cohort from the SNHS starting ADM for MDD, and comparing PGx screening before starting ADM versus no screening (No PGx). We carried out a probabilistic sensitivity analysis using the Monte Carlo simulation with microsimulation, set for 1000 iterations and 1000 microsimulation trials, both from societal and healthcare provider perspectives, for a time horizon of 3 years. ResultsFrom a societal perspective, the model estimated a mean cost of 3172.85€ and effectiveness of 2.64 quality-adjusted life years (QALYs) for the No PGx strategy, and a mean cost of 1687.02€ and effectiveness of 2.84 QALYs for the PGx strategy. The mean ICER was −7820.56 €/QALY. From a healthcare provider perspective (no indirect costs considered), the mean cost was 662.62€ for the No PGx strategy, and 446.60€ for the PGx strategy. The mean ICER was −1130.16 €/QALY. LimitationsThe heterogeneity of input data from the literature, the need for assumptions of homogeneous distribution of variables and events across population and time, and the inherent limitations of cost-effectiveness analysis should be considered. The model omits combined therapies (ADMs with mood stabilizers, antipsychotics, cognitive behavioral therapy…). ConclusionsPGx screening in MDD prior to ADM start is a dominant strategy in the SNHS.
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