Cost-effectiveness analysis of the SMART quit clinic programin smokers with cardiovascular disease in Thailand
Bibliographic record
Abstract
INTRODUCTION: The SMART Quit Clinic Program (FAHSAI Clinic) has been implemented in Thailand since 2010; however, it remains unclear whether the benefits gained from this program justify its costs. We assessed its cost-effectiveness compared to usual care in a population of Thai smokers with cardiovascular disease (CVD) from a societal perspective. METHODS: We conducted a cost-utility analysis using a Markov model to simulate lifetime costs and quality-adjusted life years (QALYs) of Thai smokers aged ≥35 years receiving smoking cessation services offered from FAHSAI Clinic or usual care over a horizon of 50 years. The model used a 6-month continuous abstinence rate from a multicenter prospective study of 24 FAHSAI Clinics. A series of sensitivity analyses including probabilistic sensitivity analysis were conducted to assess robustness of study findings. Cost data are presented in US$ for 2020. RESULTS: The FAHSAI Clinic was dominant as it was less costly ($9537.92 vs $10964.19) and more effective (6.06 vs 5.96 QALYs) compared with usual care over the 50-year time horizon. Changes in risks of stroke and coronary heart disease among males had the largest impact on the cost-effectiveness findings. The probability that FAHSAI Clinic was cost-effective was 99.8% at a willingness-to-pay threshold of $5120. CONCLUSIONS: The FAHSAI Clinic smoking cessation program was clinically superior and cost-saving compared to usual care for Thai patients with CVD in all scenarios. A budget impact analysis is needed to estimate the financial impact of adopting this program within the Thai healthcare system.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".