A cost‐effectiveness analysis of behavioural, pharmacological, and surgical obesity treatments in Canada
Bibliographic record
Abstract
AIMS: Effective weight management pharmacotherapies are a new alternative to bariatric surgery or health behaviour intervention (HBI) alone. Comparative cost-effectiveness evaluations can guide decision-making. We aimed to evaluate the cost-effectiveness of sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), semaglutide, tirzepatide, and HBI compared to no treatment in preventing cardiometabolic complications among Canadian adults with class III obesity. MATERIALS AND METHODS: ) without type 2 diabetes or cardiovascular disease at baseline. We compared SG, RYGB, semaglutide 2.4 mg, tirzepatide 15 mg, HBI, and no treatment. We obtained data on treatment effects, probabilities, utilities, and costs from published literature. We expressed effectiveness in quality-adjusted life years (QALYs) and estimated costs from a Canadian public payer perspective. Outcomes included incremental cost-effectiveness ratios (ICERs) evaluated at a CAD $50 000/QALY willingness-to-pay threshold. RESULTS: RYGB and HBI were cost-effective strategies. HBI was cost-effective versus no treatment (ICER $14 279/QALY). RYGB demonstrated the highest QALYs (20.20) and was the most cost-effective strategy versus tirzepatide (ICER $44 667/QALY). Semaglutide and SG were strongly dominated due to higher costs and lower effectiveness. Tirzepatide was extendedly dominated by RYGB. Sensitivity analyses confirmed these findings and showed that lower drug prices could improve pharmacotherapy cost-effectiveness. CONCLUSIONS: RYGB and HBI are cost-effective for managing class III obesity. While RYGB provided the greatest health gains, access remains limited. Neither pharmacotherapy was cost-effective at current prices. Lower drug prices could significantly improve pharmacotherapy 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".