Optimizing placement of public-access naloxone kits using geospatial analytics: a modelling study
Bibliographic record
Abstract
BACKGROUND: More than 85 000 people die annually across North America from opioid poisoning; naloxone in the hands of the public is an effective intervention and saves lives. We compared the accessibility of different placement strategies for public-access naloxone kits. METHODS: We evaluated all opioid-poisoning incidents recorded by BC Emergency Health Services between December 2014 and August 2020 in Metro Vancouver, Canada. We determined the number of opioid poisonings "covered" (i.e., within a 3-minute walk) by 3 different coverage strategies: (1) existing locations participating in take-home naloxone programs; (2) blanket naloxone kit placement at chain businesses, pharmacies, and registered public-defibrillator locations; and (3) optimization-based strategic kit placement at transit stops based on historical poisonings. RESULTS: We included 14 089 opioid poisonings. Existing locations participating in take-home naloxone programs (647 locations) covered 4988 (35.4%) opioid poisonings. Chain businesses (10-233 locations) covered 6 (0.0%) to 1165 (8.3%) opioid poisonings, and chain business categories (12-810 locations), pharmacies (790 locations), and public-defibrillator locations (980 locations) covered 97 (0.7%) to 3152 (22.4%) opioid poisonings. Optimization-based strategic placement of naloxone kits at transit stops yielded generally higher coverage levels, ranging from 2907 (20.6%) opioid poisonings covered with 10 kit locations, to 7506 (53.3%) with 1000 kit locations. INTERPRETATION: Optimized placement of publicly accessible naloxone kits at transit locations was most effective at improving public accessibility of naloxone, and blanket placement at take-home naloxone program locations covered a substantial proportion of opioid poisonings. Public-access naloxone may improve community access to naloxone in response to opioid poisonings.
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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 | 0.002 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".