Rates of emergency department visits attributable to alcohol use in Ontario from 2003 to 2016: a retrospective population-level study
Bibliographic record
Abstract
BACKGROUND: Alcohol use causes a large burden on the health of Canadians, and alcohol-related harms appear to be increasing in many high-income countries. We sought to analyze changes in emergency department visits attributable to alcohol use, by sex, age and neighbourhood income over time. METHODS: All individuals aged 10 to 105 years living in Ontario, Canada, between 2003 and 2016 were included in this study. The primary outcome was age-standardized rates of emergency department visits attributable to alcohol use, defined using diagnostic codes from the Canadian Institute for Health Information Health Indicator "hospitalizations entirely caused by alcohol." We compared rates of these visits using a retrospective population-level design. RESULTS: Among 15 121 639 individuals, there were 765 346 emergency department visits attributable to alcohol use over the study period. Between 2003 and 2016, the age-standardized rates of these visits increased more in women (86.5%) than in men (53.2%), and the increase in rates of emergency department visits attributable to alcohol use was 4.4 times greater than the increases in the rates of overall emergency department visits. Individuals aged 25-29 years experienced the largest change in the rate of emergency department visits attributable to alcohol use (175%). We found evidence of age-cohort effects, whereby the rate of emergency department visits attributable to alcohol use at age 19 years increased on average by 4.07% (95% confidence interval [CI] 3.71%-4.44%) per year for each cohort born between 1986 and 1999. Individuals in the lowest neighbourhood income quintile had 2.37 (95% CI 2.27-2.49) times the rate of emergency department visits attributable to alcohol use than those in the highest income quintile. This disparity increased slightly over the study period. INTERPRETATION: Although men and lower-income individuals have the highest burden of emergency department visits attributable to alcohol use, the largest increases in visits have been in women and younger adults. Further research should focus on potential causes of these trends to provide guidance on how to reduce alcohol-related harms.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".