The cost-effectiveness of semaglutide in reducing cardiovascular risk among people with overweight and obesity and existing cardiovascular disease, but without diabetes
Bibliographic record
Abstract
BACKGROUND AND AIMS: The Semaglutide Effects on Cardiovascular Outcomes in People with Overweight or Obesity (SELECT) trial demonstrated significant reductions in cardiovascular outcomes in people with cardiovascular disease (CVD) and overweight or obesity (but without diabetes). However, the cost of the medication has raised concerns about its financial viability and accessibility within healthcare systems. This study explored whether the use of semaglutide for the secondary prevention of CVD in overweight or obesity is cost-effective from the Australian healthcare perspective. METHODS AND RESULTS: A Markov model was developed based on the SELECT trial to model the clinical outcomes and costs of a hypothetical population treated with semaglutide vs. placebo, in addition to standard care, and followed up over 20 years. With each annual cycle, subjects were at risk of having non-fatal CVD events or dying. Model inputs were derived from SELECT and published literature. Costs were obtained from Australian sources. All outcomes were discounted by 5% annually. The main outcome of interest was the incremental cost-effectiveness ratio (ICER) in terms of cost per year of life saved (YoLS) and cost per quality-adjusted life year (QALY) gained. With an annual estimated cost of semaglutide of A$4175, the model resulted in ICERs of A$99 853 (US$143 504; £40 873) per YoLS and A$96 055 (US$138 046; £39 318) per QALY gained. CONCLUSION: Assuming a willingness-to-pay threshold of A$50 000, semaglutide is not considered cost-effective at the current price. A price of ≤A$2000 per year or more targeted use in high-risk patients would be needed for it to be considered cost-effective in the Australian setting.
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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 | 0.004 | 0.011 |
| 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".