Mapping the overdose crisis in Ontario: geographic disparities in opioid-related harms and services
Bibliographic record
Abstract
BACKGROUND: Opioid-related harms and deaths remain a persistent public health crisis across Ontario, Canada, with non-urban regions facing a disproportionate burden. However, discussions of opioid-related harms across Ontario's geographic regions have provided an oversimplified assessment, contrasting rural and urban regions which mask the unique challenges and true disparities faced by sparsely populated communities, which are commonly located in the Northern regions. Our study aims to provide a more in depth understanding of the opioid crisis in Ontario across different geographic classifications in accordance to population size, such as rural, urban, and sparsely populated regions, presenting data in both absolute numbers and crude rates with contextual grounding of regional characteristics. A number of different opioid-related indicators such as hospitalizations, overdose rates, opioid service provision and harm reduction supply distribution were analyzed across all 34 of Ontario's public health units (PHUs) to understand the differences in these indicators based on region across the province. The findings can inform the development of targeted interventions and improve service accessibility for those most affected by the overdose crisis in Ontario. METHODS: Publicly-available secondary data for each PHU was collected from several provincial and national data sources and analyzed between November 2024 and January 2025. Annual data from 2022 to 2023 on opioid-related harms, opioid agonist treatment (OAT) prescribers and engagement, and the distribution of harm reduction supplies, as well as annual data from 2024 on opioid-inclusive service provision, were collected. Using Statistics Canada's 2023 Health Region Peer Group Classification, the PHUS were grouped into four geographic classifications: sparsely populated, rural, urban/rural mix, and urban. Crude average rates were calculated for all indicators. Statistical analysis was performed to assess significance of indicators between regions. RESULTS: Sparsely populated PHUs were primarily located in Northern Ontario, while rural, urban/rural mix, and urban PHUs were mainly concentrated in Southern Ontario. Urban PHUs have the highest number and lowest rate of opioid-related harms (e.g. 947 opioid-related deaths, representing a rate of 12.5 per 100,000 population), while sparsely populated PHUs reflect the opposite trend (e.g. 158 opioid-related deaths, representing a rate of 44.2 per 100,000 population). A similar pattern emerges for harm reduction services and naloxone distribution. The number of treatment services is highest in rural PHUs (n = 237) and lowest in sparsely populated PHUs (n = 83), despite having the highest rate. OAT prescribers, OAT engagement, and needle distribution follow a similar trend. Statistical significance was found between geographic regions for most indicators, except opioid-inclusive support services, harm reduction services, and naloxone distribution. CONCLUSION: Sparsely populated and rural PHUs experience the highest burden of opioid-related harms, coupled with limitations in service accessibility, demonstrating a clear need for additional harm reduction services. Decision-makers may be misled into underestimating the crisis in non-urban areas as a result of oversimplified reporting, resulting in inadequate support for these regions. Addressing these disparities is key to reducing opioid-related mortality and ensuring equitable access to life-saving services across Ontario.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".