An economic evaluation of community pharmacy–dispensed naloxone in Canada
Bibliographic record
Abstract
Aims: To determine the cost-effectiveness of pharmacy-based intranasal (IN) and intramuscular (IM) naloxone distribution in Canada. Methods: We developed a state-transition model for pharmacy-based naloxone distribution, every 3 years, to illicit, prescription, opioid-agonist therapy and nonopioid use populations compared to no naloxone distribution. We used a monthly cycle length, lifetime horizon and a Canadian provincial Ministry of Health perspective. Transition probabilities, cost and utility data were retrieved from the literature. Costs (2020) and quality-adjusted life years (QALY) were discounted 1.5% annually. Microsimulation, 1-way and probabilistic sensitivity analyses were conducted. Results: Distribution of naloxone to all Canadians compared to no distribution prevented 151 additional overdose deaths per 10,000 persons, with an incremental cost-effectiveness ratio (ICER) of $50,984 per QALY for IM naloxone and an ICER of $126,060 per QALY for IN naloxone. Distribution of any naloxone to only illicit opioid users was the most cost-effective. One-way sensitivity analysis showed that survival rates for illicit opioid users were most influenced by the availability of either emergency medical services or naloxone. Conclusion: Distribution of IM and IN naloxone to all Canadians every 3 years is likely cost-effective at a willingness-to-pay threshold of $140,000 Canadian dollars/QALY (~3 × gross domestic product from the World Health Organization). Distribution to people who use illicit opioids was most cost-effective and prevented the most deaths. This is important, as more overdose deaths could be prevented through nationwide public funding of IN naloxone kits through pharmacies, since individuals report a preference for IN naloxone and these formulations are easier to use, save lives and are cost-effective. Can Pharm J (Ott) 2024;157:xx-xx.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".