Exploring disparities: a regional analysis of harm reduction supply distribution and opioid-related deaths across Ontario’s Public Health Units
Bibliographic record
Abstract
BACKGROUND: It is critical that a range of harm reduction supplies are available through Ontario's Public Health Units (PHU) to meet the varying needs of people who use drugs. Therefore, we assessed geographic variation in opioid-related deaths and the distribution of these harm reduction supplies among 34 PHUs in Ontario, Canada. METHODS: We conducted a population-based repeated cross-sectional study using publicly available administrative datasets between January 1, 2019, and December 31, 2022. Rates of opioid-related deaths and the distribution of harm reduction supplies (inhalation supplies, naloxone, and needles provided) were calculated per PHU. Small area rate variation statistics including the extremal quotient (EQ) were used to assess variation across PHUs in 2022. RESULTS: Over the study period, the quarterly number of opioid-related deaths increased by 40.6% (3.2 to 4.5 per 100,000) in Ontario. The distribution rate of all harm reduction supplies increased, although there was considerable variation by type of supply. For example, the EQ ranged from 34.7 for naloxone to 1,610.6 for foil. In 2022, there were three PHUs with significantly higher rates of opioid-related deaths compared to the provincial average. In general, these PHUs also had significantly higher distribution rates of naloxone, needles, and inhalation supplies. CONCLUSIONS: Across Ontario, there is high variability in harm reduction supply distribution and opioid-related mortality. Regions with elevated opioid-related death rates also had high supply distribution rates, suggesting that efforts are concentrated in regions with particular need. To minimize harms related to substance use, ongoing efforts are needed to ensure a clear understanding of community-based needs for 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".