Community Distribution of Naloxone: A Systematic Review of Economic Evaluations
Bibliographic record
Abstract
BACKGROUND: As a core component of harm-reduction strategies to address the opioid crisis, several countries have instituted publicly funded programs to distribute naloxone for lay administration in the community. The effectiveness in reducing mortality from opioid overdose has been demonstrated in multiple systematic reviews. However, the economic impact of community naloxone distribution programs is not fully understood. OBJECTIVES: Our objective was to conduct a review of economic evaluations of community distribution of naloxone, assessing for quality and applicability to diverse contexts and settings. DATA SOURCES: The search strategy was performed on MEDLINE, Embase, and EconLit databases. STUDY ELIGIBILITY CRITERIA AND INTERVENTIONS: Search criteria were developed based on two themes: (1) papers involving naloxone or narcan and (2) any form of economic evaluation. A focused search of the grey literature was also conducted. Studies exploring the intervention of community distribution of naloxone were selected. STUDY APPRAISAL AND SYNTHESIS METHODS: Data extraction was done using the British Medical Journal guidelines for economic submissions, assigning quality levels based on the impact of the missing or unclear components on the strength of the conclusions. RESULTS: A total of nine articles matched our inclusion criteria: one cost-effectiveness analysis, eight cost-utility analyses, and one cost-benefit analysis. Overall, the quality of the studies was good (six of high quality, two of moderate quality, and one of low quality). All studies concluded that community distribution of naloxone was cost effective, with an incremental cost-utility ratio range of $US111-58,738 (year 2020 values) per quality-adjusted life-year gained. LIMITATIONS: Our search strategy was developed iteratively, rather than following an a priori design. Additionally, our search was limited to English terms. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Based on this review, community distribution of naloxone is a worthwhile investment and should be considered by other countries dealing with the opioid epidemic.
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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 | 0.050 | 0.191 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".