Cost–Utility Analysis of Electroconvulsive Therapy and Repetitive Transcranial Magnetic Stimulation for Treatment-Resistant Depression in Ontario
Bibliographic record
Abstract
OBJECTIVES: To evaluate the cost-effectiveness of repetitive transcranial magnetic stimulation (rTMS) and electroconvulsive therapy (ECT), and combining both treatments in a stepped care pathway for patients with treatment-resistant depression (TRD) in Ontario. METHODS: A cost-utility analysis evaluated the lifetime costs and benefits to society of rTMS and ECT as first-line treatments for TRD using a Markov model, which simulates the costs and health benefits of patients over their lifetime. Health states included acute treatment, maintenance treatment, remission, and severe depression. Treatment efficacy and health utility data were extracted and synthesized from randomized controlled trials and meta-analyses evaluating these techniques. Direct costing data were obtained from national and provincial costing databases. Indirect costs were derived from government records. Scenario, threshold, and probabilistic sensitivity analyses were performed to test robustness of the results. RESULTS: rTMS dominated ECT, as it was less costly and produced better health outcomes, measured in quality-adjusted life years (QALYs), in the base case scenario. rTMS patients gained an average of 0.96 additional QALYs (equivalent to approximately 1 year in perfect health) over their lifetime with costs that were $46,094 less than ECT. rTMS remained dominant in the majority of scenario and threshold analyses. However, results from scenarios in which the model's maximum lifetime allowance of rTMS treatment courses was substantially limited, the dominance of rTMS over ECT was attenuated. The scenario that showed the highest QALY gain (1.19) and the greatest cost-savings ($46,614) was when rTMS nonresponders switched to ECT. CONCLUSION: From a societal perspective utilizing a lifetime horizon, rTMS is a cost-effective first-line treatment option for TRD relative to ECT, as it is less expensive and produces better health outcomes. The reduced side effect profile and greater patient acceptability of rTMS that allow it to be administered more times than ECT in a patient's lifetime may contribute to its cost-effectiveness.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".