A cost-effectiveness analysis of national smoking cessation services among chronic obstructive pulmonary disease patients in Thailand
Bibliographic record
Abstract
AIMS: Thailand's national smoking cessation services (FAH-SAI clinics) were founded in 2010. A cost-effectiveness analysis (CEA) is needed to inform policymakers of the allocation and prioritization of the limited budget to maximize the value for money of reimbursing these services. Chronic obstructive pulmonary disease (COPD) patients would benefit from smoking cessation services. Therefore, this study aimed to assess the cost-effectiveness of these multidisciplinary services compared to the usual care among COPD patients in Thailand from a societal perspective. METHODS: We conducted a CEA from a societal perspective using a Markov model to simulate lifetime costs and quality-adjusted life years (QALYs) gained by each smoking cessation intervention over the patient's lifetime. We derived the effectiveness of the smoking cessation services from a multicenter, longitudinal study of smoking cessation services in Thailand and estimated the natural quit rate, transition probabilities, health utility, and cost data from the published literature. Costs and outcomes were discounted at 3%. Sensitivity analyses were performed. RESULTS: Compared to the usual care, FAH-SAI clinics were associated with higher costs (4,207 THB (US$133)) and improved QALYs (0.11), with an incremental cost-effectiveness ratio of 37,675 THB/QALY (US$1,187/QALY). The effectiveness of FAH-SAI clinics was a key driver of the cost-effectiveness results. At the willingness-to-pay (WTP) threshold of 160,000 THB (US$5,042) per QALY gained, the probability of being cost-effective was 96.5%. CONCLUSIONS: FAH-SAI clinics were cost-effective under Thailand's WTP threshold. Our results could inform policymakers in allocating resources to support smoking cessation services for COPD patients in Thailand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".