Cost-Effectiveness of Community Pharmacist-Led Smoking Cessation Programs (2015–2025): A Meta-Analysis Review
Bibliographic record
Abstract
To systematically review the types of economic evaluations used for community pharmacist-delivered smoking cessation programs and to conduct a meta-analysis of their clinical effectiveness. Methods A systematic search was conducted in PubMed, Scopus, Science Direct, EBSCO, and ProQuest for studies published between 2015 and 2025. The participant criteria are active smokers aged over 18 years, not limited by gender, and the program focuses on community settings. Full economic evaluations of pharmacist-led smoking cessation programs for adults were included. Study quality was assessed using the CHEERS 2022 checklist. Clinical effectiveness data were pooled using a random-effects meta-analysis. The protocol was registered with PROSPERO (CRD420251117242). Results out of 681 identified records, four studies met the inclusion criteria, encompassing more than 1,300 participants. Interventions were conducted in Spain, Canada, Malaysia, and the USA. The majority used cost-effectiveness analyses and reported significant improvements in smoking cessation rates. Meta-analysis showed that pharmacist-led programs significantly improved quit rates (pooled OR = 2.74; 95% CI: 1.83–4.11; p < 0.001) with low heterogeneity (I² = 8.8%). ICERs were consistently below accepted cost-effectiveness thresholds. Based on a threshold analysis using a WTP of CAD$20,000 per QALY, the intervention by Phillips et al. remains cost-effective. The intervention would remain below the threshold unless total costs increased by 23.6% or QALY gains reduced by more than 18%. Conclusion community pharmacist-delivered smoking cessation programs are a clinically effective and economically favorable public health strategy. Policymakers should consider establishing reimbursement models to facilitate their broader implementation.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".