A cost-effectiveness analysis of a community pharmacist-based smoking cessation programme in Thailand
Bibliographic record
Abstract
OBJECTIVE: To estimate the incremental cost-effectiveness ratio of a structured community pharmacist-based smoking cessation programme compared with usual care. DESIGN: A cost-effectiveness study using a healthcare system perspective Population: Two simulated cohorts of smokers: male and female aged 40, 50 and 60 years who regularly smoke 10-20 cigarettes per day. Intervention and comparator: A structured community pharmacist-based smoking cessation (CPSC) programme compared to usual care. MAIN OUTCOME MEASURE: Cost per life year gained (LYG) attributable to the smoking cessation programme. RESULTS: The CPSC programme results in cost savings of 17,503.53 baht ( pound250; euro325; $500) to the health system and life year gains of 0.18 years for men and; costs savings of 21,499.75 baht ( pound307; euro399; $614) and life year gains of 0.24 years for women. A series of sensitivity analyses demonstrate that both cost savings and life year gains are sensitive to variations in the discount rate and the long-term smoking quit rate associated with the intervention. CONCLUSION: From the perspective of the health system, the CPSC programme yields cost savings and life year gains. This finding provides important information for health policy decision-makers when determining the magnitude of resources to be allocated to smoking cessation service in community pharmacy.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 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".