Trends, characteristics, and circumstances surrounding stimulant toxicity deaths in Ontario, Canada from 2018 to 2021
Bibliographic record
Abstract
INTRODUCTION: As the drug toxicity crisis continues to evolve globally, harms related to non-opioid substances, including stimulants, have risen in parallel. Our study aims were to describe trends in accidental stimulant toxicity deaths and to characterize demographic characteristics of decedents and the circumstances surrounding death. METHODS: We conducted a population-based repeated cross-sectional study, of all accidental stimulant toxicity deaths between January 1, 2018, and December 31, 2021, in Ontario, Canada. We reported monthly rates of stimulant toxicity deaths per 100,000 people residing in Ontario and the circumstances surrounding death. All analyses were stratified by the type of stimulant(s) involved in death. RESULTS: Between 2018 and 2021, we identified 5210 stimulant toxicity deaths with the monthly rate rising from 0.4 to 1.0 per 100,000. Both cocaine and methamphetamine were involved in 16.2 % of deaths, and 56.2 % and 27.7 % involved cocaine or methamphetamine (without other stimulants), respectively. Over 80 % of deaths also involved an opioid. Among all deaths, 75.2 % of decedents were male, 53.1 % were aged 25-44, and over half of all deaths occurred in private residences (64.7 %). CONCLUSIONS: The rate of stimulant toxicity deaths has continued to grow, more than doubling over a three-year period. As stimulant-related deaths continue to rise, comprehensive social supports and mental health services, including harm reduction and treatment programs adapted to the unique needs of people who use stimulants alone or in combination with other substances, are urgently required to meet the changing needs of people who use drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".