Comparative analysis of algorithm-guided treatment and predefined duration treatment programmes for depression: exploring cost-effectiveness using routine care data.

Background: More knowledge on the cost-effectiveness of various depression treatment programmes can promote efficient treatment allocation and improve the quality of depression care.
Objective: This study aims to compare the real-world cost-effectiveness of an algorithm-guided programme focused on remission to a predefined duration, patient preference-centred treatment programme focused on response using routine care data.
Methods: A naturalistic study (n=6295 in the raw dataset) was used to compare the costs and outcomes of two programmes in terms of quality-adjusted life years (QALY) and depression-free days (DFD). Analyses were performed from a healthcare system perspective over a 2-year time horizon. Incremental cost-effectiveness ratios were calculated, and the uncertainty of results was assessed using bootstrapping and sensitivity analysis.
Findings: The algorithm-guided treatment programme per client yielded more DFDs (12) and more QALYs (0.013) at a higher cost (€3070) than the predefined duration treatment programme. The incremental cost-effectiveness ratios (ICERs) were around €256/DFD and €236 154/QALY for the algorithm guided compared with the predefined duration treatment programme. At a threshold value of €50 000/QALY gained, the programme had a probability of <10% of being considered cost-effective. Sensitivity analyses confirmed the robustness of these findings.
Conclusions: The algorithm-guided programme led to larger health gains than the predefined duration treatment programme, but it was considerably more expensive, and hence not cost-effective at current Dutch thresholds. Depending on the preferences and budgets available, each programme has its own benefits.
Clinical Implication: This study provides valuable information to decision-makers for optimising treatment allocation and enhancing quality of care cost-effectively.
Competing Interests: Competing interests: SOdV was involved in the design of the algorithm-guided treatment programme investigated in this study. BG was involved in the running of the predefined duration treatment programme investigated in this study. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. Published by BMJ.)