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.

Comparing Methods to Denote Treatment Outcome in Clinical Research and Benchmarking Mental Health Care.

Approaches based on continuous indicators (the size of the pre-to-post-test change; effect size or ΔT) and on categorical indicators (Percentage Improvement and the Jacobson-Truax approach to Clinical Significance) are evaluated to determine which has the best methodological and statistical characteristics, and optimal performance, in comparing outcomes of treatment providers. Performance is compared in two datasets from providers using the Brief Symptom Inventory or the Outcome Questionnaire. Concordance of methods and their suitability to rank providers is assessed. Outcome indicators tend to converge and lead to a similar ranking of institutes within each dataset. Statistically and conceptually, continuous outcome indicators are superior to categorical outcomes as change scores have more statistical power and allow for a ranking of providers at first glance. However, the Jacobson-Truax approach can complement the change score approach as it presents outcome information in a clinically meaningful manner. Copyright © 2015 John Wiley & Sons, Ltd. KEY PRACTITIONERS MESSAGES: When comparing various indicators or treatment outcome, statistical considerations designate continuous outcomes, such as the effect size of the pre-post change (effect size or ΔT) as the optimal choice. Expressing outcome in proportions of recovered, changed, unchanged or deteriorated patients has supplementary value, as it is more easily interpreted and appreciated by clinicians, managerial staff and, last but not the least, by patients. If categorical outcomes are used with small datasets, true differences in institutional performance may get obscured due to diminished power to detect differences. With sufficient data, outcome according to continuous and categorical indicators converge and lead to similar rankings of institutes' performance.