Circumstances surrounding alcohol toxicity deaths and prior pharmacotherapy for alcohol use disorder in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Despite a high prevalence of alcohol use disorder (AUD) in Canada, access to medication-based treatment remains poor. Therefore, our aim was to explore patterns of alcohol toxicity deaths in Ontario, Canada, circumstances surrounding death, prior healthcare interactions, and pharmacotherapy for AUD. METHODS: We conducted a population-based repeated cross-sectional study of alcohol toxicity deaths occurring between 1 January 2018 and 30 June 2022 in Ontario, Canada. We reported trends in deaths over time and determined demographic characteristics of decedents, circumstances surrounding death, and prior healthcare interactions. Among a subset of the cohort with an AUD diagnosis eligible for public drug benefits, we reported receipt of medications used to treat AUD before death. RESULTS: We identified 1346 alcohol toxicity deaths over the study period, at a median age of 42 years, with 73.8% occurring among men. The majority of alcohol toxicity deaths involved other substances, including opioids (75.2%), benzodiazepines (10.8%), and/or stimulants (45.2%). Half had an AUD (50.4%) and 62.7% had an opioid, benzodiazepine or stimulant use disorder. Among decedents who were public drug beneficiaries with an AUD (N = 361), only 3.6% were actively prescribed first-line AUD pharmacotherapies (naltrexone and/or acamprosate) at time of death. CONCLUSIONS: We found that the majority of alcohol toxicity deaths in Ontario involved other non-alcohol substances. We also detected a high prevalence of prior healthcare encounters for substance use disorders (SUDs) and low prevalence of evidence-based AUD pharmacotherapy. This suggests a need for integrated treatment across concurrent SUDs and improved access to pharmacotherapies for AUD across Ontario.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".