Characteristics of Opioid Toxicity Deaths Among Adolescents and Young Adults in Ontario Prior To and During the COVID-19 Pandemic
Bibliographic record
Abstract
PURPOSE: To characterize opioid toxicity deaths among adolescents and young adults in Ontario, Canada, prior to and during the first year of the COVID-19 pandemic. METHODS: We conducted a descriptive, cross-sectional study of opioid toxicity deaths among individuals aged 15-24 in Ontario in the year prior to (March 17, 2019, to March 16, 2020) and the first year of the pandemic (March 17, 2020, to March 16, 2021) using administrative health databases. We analyzed circumstances surrounding death, substances contributing to death, and health-care encounters prior to death. RESULTS: We identified 284 deaths among Ontarians aged 15-24, including 115 in the year preceding and 169 in the first year of the pandemic. Fentanyl contributed to 84.3% of deaths in the prepandemic year, rising to 93.5% (p = .012) the following year. Stimulants contributed to approximately half of deaths in both periods (41.7% prepandemic and 49.1% during pandemic). In both periods, roughly one in 4 decedents had a health-care encounter in the week prior to death and less than 20% of those with an opioid use disorder received opioid agonist treatment in the 30 days prior to death. DISCUSSION: Among young Ontarians, the number of opioid-related deaths increased by 47% in the first year of the COVID-19 pandemic. Fentanyl contributed to the vast majority of deaths, with non-opioid substances (primarily stimulants) also contributing to approximately half of deaths. Patterns of health-care utilization prior to death suggest opportunities to better connect this population to services that address opioid use disorder needs and promote harm reduction.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".