Cost-Effectiveness of Semaglutide in Patients With Obesity and Cardiovascular Disease
Bibliographic record
Abstract
BACKGROUND: Randomized clinical trials have shown that semaglutide is associated with a clinically relevant reduction in body weight and a lower risk of adverse cardiovascular events in those who are overweight or obese with a history of cardiovascular disease but no diabetes. The objective of this study was to assess the cost-effectiveness of semaglutide for this indication. METHODS: A decision analytic Markov model was used to compare the lifetime benefits and costs of semaglutide 2.4-mg subcutaneous weekly vs standard care in a hypothetical cohort of patients who were overweight or obese with preexisting cardiovascular disease (and no diabetes) from the health care payer perspective. Our model included ischemic stroke, heart failure hospitalization and/or urgent visit or myocardial infarction, and death over monthly transition cycles. Model outcomes included costs (2023 CAD$), quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios. RESULTS: Base case analysis showed that the incremental cost-effectiveness ratio for semaglutide compared with standard care was $72,962 per QALY gained with a 14% likelihood of cost-effectiveness adopting a $50,000 per QALY gained willingness to pay threshold. Factors with the greatest influence on cost-effectiveness were medication efficacy on mortality and medication cost. When the price of semaglutide was reduced by 50%, it was economically attractive at $37,190 per QALY gained with an 80% likelihood of cost-effectiveness at a $50,000 per QALY threshold. CONCLUSIONS: Semaglutide might be a cost-effective option for the publicly funded health care system contingent on initial pricing. Considering the candidate population-patients who are overweight or obese with preexisting cardiovascular disease-policymakers should consider the budget effect of funding semaglutide and weigh it against other ways scarce health care dollars might be used.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".