Cost–Utility Analysis of Mindfulness-Based Cognitive Therapy Versus Antidepressant Pharmacotherapy for Prevention of Depressive Relapse in a Canadian Context: Analyse coût-utilité de la thérapie cognitive basée sur la pleine conscience contre la pharmacothérapie antidépressive pour prévenir la rechute de la dépression en contexte canadien
Bibliographic record
Abstract
OBJECTIVE: Patients suffering from major depressive disorder (MDD) experience impaired functioning and reduced quality of life, including an elevated risk of episode return. MDD is associated with high societal burden due to increased healthcare utilization, productivity losses, and suicide-related costs, making the long-term management of this illness a priority. The purpose of this study is to evaluate the cost-effectiveness of mindfulness-based cognitive therapy (MBCT), a first-line preventative psychological treatment, compared to maintenance antidepressant medication (ADM), the current standard of care. METHOD: A cost-utility analysis was conducted over a 24-month time horizon to model differences between MBCT and ADM in cost and quality-adjusted life years (QALY). The analysis was conducted using a decision tree analytic model. Intervention efficacy, utility, and costing data estimates were derived from published sources and expert consultation. RESULTS: MBCT was found to be cost-effective compared to maintenance ADM over a 24-month time horizon. Antidepressant pharmacotherapy resulted in 1.10 QALY and $17,255.37 per patient on average, whereas MBCT resulted in 1.18 QALY and $15,030.70 per patient on average. This resulted in a cost difference of $2,224.67 and a QALY difference of 0.08, in favor of MBCT. Multiple sensitivity analyses supported these findings. CONCLUSIONS: From both a societal and health system perspective, utilizing MBCT as a first-line relapse prevention treatment is potentially cost-effective in a Canadian setting. Future economic evaluations should consider combined treatment (e.g., ADM and psychotherapy) as a comparator and longer time horizons as the literature advances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".