Mapping the overdose crisis in Ontario: geographic disparities in opioid-related harms and services
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».