The cost-effectiveness of improved brief interventions for tobacco cessation in Thailand
Bibliographic record
Abstract
Background: This study estimated the cost-effectiveness of four strategies enhancing the quality and accessibility of Brief Intervention (BI) service for smoking cessation in Thailand during 2022-2030: (1) current-BI (status quo), (2) the effective-training standard-BI, (3) the current-BI plus the village health volunteers (VHV) mobilization, and (4) the effective-training BI plus VHV mobilization. Methods: By interviewing five public health officers, nine healthcare professionals aiding these services, and fifteen BI service experts, we explored the status quo situation of the Thai smoking cessation service system, including main activities, their quantity assumptions, and activities' unit prices needed to operate the current cessation service system. Then, we modeled additional activities needed to implement the other three simulated scenarios. We estimated the costs and impacts of implementing these strategies over a nine-year operating horizon (2022-2030), covering 3 years of service system preparation and 6 years of full implementation. The modeled costs of these four strategies included intervention and program costs. The study focused on current smokers age 15 years or older. The assessed impact parameters encompassed smoking prevalence, deaths averted, and healthy life-years gained. An Incremental Cost-Effectiveness Analysis compared the four simulated strategies was employed. Data analysis was performed using the One Health Tool software, which the World Health Organization developed. Results: The findings of this investigation reveal that all three intervention strategies exhibited cost-effectiveness compared to the prevailing status quo. Among these strategies, Strategy 2, enhancing BI service quality, emerged as the most efficient and efficacious option. Therefore, the expansion of quality services should be synergistically aligned with augmented training, service delivery optimization, and managerial enhancements. Conclusion: This approach is particularly poised to enhance accessibility to and the efficacy of smoking cessation interventions across 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 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".