Trends in alcohol‐attributable hospitalisations and emergency department visits by age, sex, drinking group and health condition in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Alcohol-attributable harms are increasing in Canada. We described trends in alcohol-attributable hospitalisations and emergency department (ED) visits by age, sex, drinking group, attribution and health condition. METHODS: Hospitalisation and ED visits for partially or wholly alcohol-attributable health conditions by age and sex were obtained from population-based health administrative data for individuals aged 15+ in Ontario, Canada. Population-level alcohol exposure was estimated using per capita alcohol sales and alcohol use data. We estimated the number and rate of alcohol-attributable hospitalisations (2008-2018) and ED visits (2008-2019) using the International Model of Alcohol Harms and Policies (InterMAHP). RESULTS: Over the study period, the modelled rates of alcohol-attributable health-care encounters were higher in males, but increased faster in females. Specifically, rates of alcohol-attributable hospitalisations and ED visits increased by 300% (19-76 per 100,000) and 37% (774-1,064 per 100,000) in females, compared to 20% (322-386 per 100,000) and 2% (2563-2626 per 100,000) in males, respectively. Alcohol-attributable ED visit rates were highest among individuals aged 15-34, however, increased faster among individuals aged 65+ (females: 266%; males: 44%) than 15-34 years (females:+17%; males: -16%). High-volume drinkers had the highest rates of alcohol-attributable health-care encounters; yet, low-/medium-volume drinkers contributed substantial hospitalisations (11%) and ED visits (36%), with increasing rates of ED visits in females drinking low/medium volumes. DISCUSSION AND CONCLUSIONS: Alcohol-attributable health-care encounters increased overall, and faster among females, adults aged 65+ and low-/medium-volume drinkers. Monitoring trends across subpopulations is imperative to inform equitable interventions to mitigate alcohol-attributable harms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".