Socioeconomic and Geographic Disparities in Emergency Department Visits due to Alcohol in Ontario: A Retrospective Population-level Study from 2003 to 2017
Bibliographic record
Abstract
OBJECTIVE: While the overall health system burden of alcohol is large and increasing in Canada, little is known about how this burden differs by sociodemographic factors. The objectives of this study were to assess sociodemographic patterns and temporal trends in emergency department (ED) visits due to alcohol to identify emerging and at-risk subgroups. METHODS: We conducted a retrospective population-level cohort study of all individuals aged 10 to 105 living in Ontario, Canada. We identified ED visits due to alcohol between 2003 and 2017 using defined International Classification of Diseases, 10th edition, codes from a pre-existing indicator. We calculated annual age- and sex-standardized, and age- and sex-specific rates of ED visits and compared overall patterns and changes over time between urban and rural settings and income quintiles. RESULTS: There were 829,662 ED visits due to alcohol over 15 years. Rates of ED visits due to alcohol were greater for individual living in the lowest- compared to the highest-income quintile neighbourhoods, and disparities (rate ratio lowest to highest quintile) increased with age from 1.22 (95% CI, 1.19 to 1.25) in 15- to 18-year-olds to 4.17 (95% CI, 4.07 to 4.28) in 55- to 59-year-olds. Rates of ED visits due to alcohol were significantly greater in rural settings (56.0 per 10,000 individuals, 95% CI, 55.7 to 56.4) compared to urban settings (44.8 per 10,000 individuals, 95% CI, 44.7 to 44.9), particularly for young adults. Increases in rates of visits between 2003 and 2017 were greater in rural versus urban settings (82 vs. 68% increase in age- and sex-standardized rates) and varied across sociodemographic subgroups with the largest annual increases in rates of visits in young (15 to 29) low-income women (6.9%, 95%CI, 6.7 to 7.3) and the smallest increase in older (45 to 59) high-income men (2.7, 95%CI, 2.4 to 3.0). CONCLUSION: Alcohol harms display unique patterns with the highest burden in rural and lower-income populations. Rural-urban and income-based disparities differ by age and sex and have increased over time, which offers an imperative and opportunity for further interventions by clinicians and policy makers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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".