More knowledge on the cost-effectiveness of various depression treatment programmes can promote efficient treatment allocation and improve the quality of depression care.
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.
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.
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.
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.
This study provides valuable information to decision-makers for optimising treatment allocation and enhancing quality of care cost-effectively.