Impact of a publicly funded pharmacy-dispensed naloxone program on fatal opioid overdose rates: A population-based study
Bibliographic record
Abstract
BACKGROUND: Studies examining the impact of pharmacy-dispensed naloxone programs on fatal opioid overdose rates are lacking. We examined the impact of the publicly funded Ontario Naloxone Program for Pharmacies (ONPP), implemented in June 2016, on provincial rates of opioid overdose deaths. METHODS: We conducted a population-based interrupted time-series study between July 1, 2012 and December 31, 2018. We considered a parsimonious model with terms for time, ONPP implementation, and time following the ONPP implementation. Models were adjusted for population characteristics, number of pharmacies and rate of naloxone distributed through non-pharmacy sites within provincial public health units. RESULTS: In the parsimonious model, the ONPP was associated with a non-significant 9% reduction in the level of fatal opioid overdoses (rate ratio [RR] 0.91; 95% confidence interval [CI] 0.79-1.06), a finding that was most pronounced in regions in the lowest tertile of implementation (RR 0.75; 95% CI 0.62-0.91). Following multivariable adjustment, there was an increase in the level (RR 1.06; 95% CI 0.94-1.19) and slope change (RR 1.06; 95% CI 1.02-1.10) of fatal overdose rates. CONCLUSION: The ONPP is insufficient as a single intervention to meaningfully reduce rates of fatal opioid overdoses during a period in which the cause of these deaths shifted from prescription opioids to highly potent fentanyl analogs. Access to additional harm reduction, treatment, and other interventions is necessary to prevent deaths and optimize the health of people who use drugs.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".