Geographic variation in the provision of naloxone by pharmacies in Ontario, Canada: A population-based small area variation analysis
Bibliographic record
Abstract
BACKGROUND: Regional variation in pharmacy-dispensed naloxone rates could create access disparities that undermine the effectiveness of this approach. We explored individual and public health unit (PHU)-level determinants of regional variation in naloxone distribution through the Ontario Naloxone Program for Pharmacies. METHODS: We conducted a population-based study between April 1, 2017 and March 31, 2018. We calculated age- and sex-standardized pharmacy-dispensed naloxone rates for the 35 Ontario PHUs, and identified determinants of these rates using generalized estimating equations negative binomial regression. RESULTS: The age- and sex-standardized pharmacy-dispensed naloxone rate in Ontario was 5.5 (range 1.8-11.6) kits per 1000 population. Variables associated with higher naloxone dispensing rates included opioid use disorder history [rate ratio (RR) 2.27; 95% confidence interval (CI) 1.75-2.96], opioid agonist therapy (RR 11.17; 95% CI 7.15-17.44), and PHU opioid overdose rate (RR 1.09 per 10 deaths; 95% CI 1.06-1.13). Pharmacy-dispensed naloxone rates were lower in rural areas (RR 0.83; 95% CI 0.73-0.94) and among individuals dispensed one (RR 0.72; 95% CI 0.65-0.79), two to five (RR 0.67; 95% CI 0.54-0.84) or 6-10 (RR 0.92; 95% CI 0.74-1.14) opioids in the prior year relative to those receiving no opioids. CONCLUSION: Pharmacy-dispensed naloxone programs are important components of a public health response to the opioid overdose crisis. We found considerable variation in pharmacy-dispensed naloxone rates that could limit program effectiveness, particularly in rural settings with limited access to health and harm reduction services..
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".