Clarifying rural‐urban disparities in alcohol‐related emergency department visits and hospitalizations in Ontario, Canada: A spatial analysis
Bibliographic record
Abstract
PURPOSE: Rates of alcohol-related harm are higher in rural versus urban Canada. This study characterized the spatial distribution and regional determinants of alcohol-related emergency department (ED) visits and hospitalizations in Ontario to better understand this rural-urban disparity. METHODS: This was a cross-sectional spatial analysis of rates of alcohol-related ED visits and hospitalizations by Ministry of Health subregion (n = 76) in Ontario, Canada between 2016 and 2019. Regional hot- and cold-spots of alcohol-related harm were identified using spatial autocorrelation methods. Rurality was measured as the population weighted geographic remoteness of a subregion. The associations between rurality and rates of alcohol-related ED visits and hospitalizations were evaluated using hierarchical Bayesian spatial regression models. FINDINGS: Rates of alcohol-related ED visits and hospitalizations varied substantially between subregions, with high rates clustering in Northern Ontario. Overall, increasing rurality was associated with higher subregion-level rates of alcohol-related ED visits (males adjusted relative rate [aRR]: 1.67, 95% credible interval [CI]: 1.49-1.87; females aRR: 1.78, 95% CI: 1.60-1.98) and hospitalizations (males aRR: 1.34, 95% CI: 1.24-1.45; females aRR: 1.59, 95% CI: 1.45-1.74). However, after the province was separated into Northern and Southern strata, this association only held in Northern subregions. In contrast, increasing rurality was associated with lower rates of alcohol-related ED visits in Southern subregions (males aRR: 0.87, 95% CI: 0.79-0.96; females aRR: 0.88, 95% CI: 0.81-0.97). CONCLUSIONS: There are regional differences in the association between rurality and alcohol-related health service use. This regional variation should be considered when developing health policies to minimize geographic disparities in alcohol-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 | 0.001 | 0.005 |
| 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".