The epidemiology of benzodiazepine-related toxicity in Ontario, Canada: a population-based descriptive study
Bibliographic record
Abstract
OBJECTIVES: Despite the widespread use of prescription benzodiazepines, there are few studies examining trends and patterns of benzodiazepine-related toxicity. We describe the epidemiology of benzodiazepine-related toxicity in Ontario, Canada. METHODS: We conducted a population-based, cross-sectional study of Ontario residents who had an emergency department visit or hospitalization for benzodiazepine-related toxicity between January 1, 2013 and December 31, 2020. We reported annual crude and age-standardized rates of benzodiazepine-related toxicity overall, by age, and by sex. In each year, we characterized the history of benzodiazepine and opioid prescribing among people who experienced benzodiazepine-related toxicity, and reported the percentage of encounters with opioid, alcohol, or stimulant co-involvement. RESULTS: Between 2013 and 2020, there were 32,674 benzodiazepine-related toxicity encounters among 25,979 Ontarians. During this period, the crude rate of benzodiazepine-related toxicity declined overall, from 28.0 to 26.1 per 100,000 population (age-standardized rate: 27.8 to 26.4 per 100,000), but increased among young adults aged 19 to 24 (39.9 to 66.6 per 100,000 population). Moreover, by 2020, the percentage of encounters associated with active benzodiazepine prescriptions had declined to 48.9%, while the percentage of encounters that had opioid, stimulant, or alcohol co-involvement rose to 28.8%. CONCLUSION: Benzodiazepine-related toxicity has declined in Ontario overall, but has increased among youth and young adults. Furthermore, there is growing co-involvement of opioids, stimulants, and alcohol, which may reflect the recent emergence of benzodiazepines in the unregulated drug supply. Multifaceted public health initiatives comprising harm reduction, mental health supports, and promotion of appropriate prescribing are needed to reduce benzodiazepine-related harm.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| 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".