On-Premise Alcohol Establishments and Ambulance Calls for Trauma, Assault, and Intoxication
Bibliographic record
Abstract
Alcohol contributes to intentional and unintentional injury. We explored on-premise licensed alcohol establishments (LAEs) and emergency medical service (EMS) ambulance calls.We completed a retrospective population-based study in the Region of Peel, Ontario, 2005 to 2014, where alcohol sales are tightly regulated and healthcare is universally available. We included participants age ≥ 19 years. Longitude-latitude coordinates of all 696 LAEs and all 267,477 EMS ambulance calls were ascertained, and then assigned to 1 of 1568 dissemination areas (DA) in Peel. Relative risks (RRs) described the association between density of on-premise LAEs (by DA deciles) and the rate of EMS calls, adjusted for material deprivation, and density of beer/liquor stores in each DA.There was a curvilinear relation between LAE density and EMS calls for trauma, rising from 45.3 per 1000 in DAs with no LAEs to 381.0 per 1000 in decile-10 (adjusted RR 7.83, 95% confidence interval [CI] 6.15-9.97). This relation was more pronounced for alcohol-focused LAEs, and highest among younger males. Calls for assault (RR 2.67, 95% CI 1.26-5.65) and intoxication (RR 4.00, 95% CI 1.41-11.38) were more likely on the last day of the month and the day thereafter, compared to 1 week prior. At 02:00 hours, when LAEs must stop selling alcohol, there was a considerable rise in assault-related calls in DAs with LAE but not in DAs without LAEs.On-premise LAEs contribute to EMS calls for trauma and assault, especially among young males, around last call, and when monthly pay cheques are cashed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".