The changing role of substances: trends, characteristics of individuals and prior healthcare utilization among individuals with accidental substance-related toxicity deaths in Ontario Canada
Bibliographic record
Abstract
OBJECTIVE: To investigate trends and the circumstances surrounding fatal substance-related toxicities directly attributed to alcohol, stimulants, benzodiazepines or opioids and combinations of substances in Ontario, Canada. METHODS: We conducted a population-based cross-sectional study of all accidental substance-related toxicity deaths in Ontario, Canada from January 1, 2018 to June 30, 2022. We reported monthly rates of substance-related toxicity deaths and investigated the combination of substances most commonly involved in deaths. Demographic characteristics, location of incident, and prior healthcare encounters for non-fatal toxicities and substance use disorders were examined. RESULTS: Overall, 10,022 accidental substance-related toxicity deaths occurred, with the annual number of deaths nearly doubling between the first and last 12 months of the study period (N = 1,570-2,702). Opioids were directly involved in the majority of deaths (84.1%; N = 8,431), followed by stimulants (60.9%; N = 6,108), alcohol (13.4%; N = 1,346) and benzodiazepines (7.8%; N = 782). In total, 56.9% (N = 5,698) of deaths involved combinations of substances. Approximately one-fifth of individuals were treated in a hospital setting for a substance-related toxicity in the past year, with the majority being opioid-related (17.4%; N = 1,748). Finally, 60.9% (N = 6,098) of people had a substance use disorder diagnosis at time of death. CONCLUSIONS: Our study shows not only the enormous loss of life from substance-related toxicities but also the growing importance of combinations of substances in these deaths. A large proportion of people had previously interacted within an hospital setting for prior substance-related toxicity events or related to a substance use disorder, representing important missed intervention points in providing appropriate care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".