Varying circumstances surrounding opioid toxicity deaths across ethno-racial groups in Ontario, Canada: a population-based descriptive cross-sectional study
Bibliographic record
Abstract
Introduction: The North American toxic drug crisis has been framed as an epidemic primarily affecting white people. However, evidence suggests that deaths are rising among racialised people. Accordingly, we sought to describe and compare characteristics and circumstances of opioid toxicity deaths across ethno-racial groups. Methods: We conducted a population-based, descriptive cross-sectional study of all individuals who died of accidental opioid toxicity in Ontario, Canada between 1 July 2017 and 30 June 2021. Decedents were categorised as Asian, black, Latin American or white. We summarised decedents' sociodemographic characteristics, circumstances surrounding death and patterns of healthcare utilisation preceding death by ethno-racial group, and used standardised differences (SDs) to draw comparisons. Results: Overall, 6687 Ontarians died of opioid toxicity, of whom 275 were Asian (4.1%), 238 were black (3.6%), 53 were Latin American (0.8%), 5222 were white (78.1%) and 899 (13.4%) had an unknown ethno-racial identity. Black people (median age: 35 years; SD: 0.40) and Asian people (median age: 37 years; SD: 0.30) generally died younger than white people (median age: 40 years), and there was greater male predominance in deaths among Asian people (86.2%; SD: 0.30), Latin American people (83.0%; SD: 0.21) and black people (80.3%; SD: 0.14) relative to white people (74.6%). Cocaine contributed to more deaths among black people (55.9%; SD: 0.37) and Asian people (45.1%; SD: 0.15) compared with white people (37.6%). Racialised people had a lower prevalence of opioid agonist treatment in the 5 years preceding death (black people: 27.9%, SD: 0.73; Asian people: 51.1%, SD: 0.22; white people: 61.9%). Conclusions: There are marked differences in the risk factors, context and patterns of drug involvement in opioid toxicity deaths across ethno-racial groups, and substantial disparities exist in access to harm reduction and treatment services. Prevention and response strategies must be tailored and targeted to racialised people.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".