Opioid-related overdose deaths among people experiencing homelessness, 2017 to 2021: A population-based analysis using coroner and health administrative data from Ontario, Canada
Bibliographic record
Abstract
ObjectiveWe aimed to measure the change in proportion of opioid-related overdose deaths attributed to people experiencing homelessness and to compare the opioid-related fatalities between individuals experiencing homelessness and not experiencing homelessness at time of death. ApproachWe conducted a population-based, time-trend analysis using coroner and health administrative databases from Ontario, Canada (2017-2021). Quarterly proportion of opioid-related overdose deaths attributed to people experiencing homelessness were reported. We also obtained socio-demographic and health characteristics, healthcare encounters preceding death, substances directly contributing to death and circumstances surrounding deaths. ResultsOverall, 13.3% individuals who died of an opioid-related overdose between 2017 and 2021 were identified as experiencing homelessness at the time of death. The quarterly proportion of opioid-related overdose deaths attributed to people experiencing homelessness increased from 7.2% in 2017 to 16.8% by 2021. Compared with housed decedents, those experiencing homelessness were younger, had higher prevalence of mental health or substance use disorders and more often visited hospitals and emergency departments in the year prior to death. Fentanyl and stimulants more often directly contributed to death, whereas methadone was less often present. Individuals experiencing homelessness were more often in the presence of a bystander during the event that led to death and more often had a resuscitation attempted or naloxone administered. Conclusions and implicationsPeople experiencing homelessness account for an increasing proportion of fatal opioid-related overdoses in Ontario, Canada, reaching nearly one in six such deaths in 2021. Effective interventions are needed to combat homelessness and opioid related toxicity in homeless populations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".