The distribution of alcohol-attributable healthcare encounters across the population of alcohol users in Ontario, Canada
Bibliographic record
Abstract
Recent evidence suggests there may be no safe level of alcohol use as even low levels are associated with increased risk for harm. However, the magnitude of the population-level health burden from lower levels of alcohol use is poorly understood. The objective was to estimate the distribution of alcohol-attributable healthcare encounters (emergency department (ED) visits and hospitalizations) across the population of alcohol users aged 15+ in Ontario, Canada. Using the International Model of Alcohol Harms and Policies (InterMAHP) tool, wholly and partially alcohol-attributable healthcare encounters were estimated across alcohol users: (1) former (no past-year use); (2) low volume (≤67.3 g ethanol/week); (3) medium volume (>67.3-134.5 g ethanol/week for women and >67.3-201.8 g ethanol/week for men); and (4) high volume (>134.5 g ethanol/week for women and >201.8 g ethanol/week for men). The alcohol-attributable healthcare burden was distributed across the population of alcohol users. A small population of high volume users (23% of men, 13% of women) were estimated to have contributed to the greatest proportion of alcohol-attributable healthcare encounters, particularly among men (men: 65% of ED visits and 71% of hospitalizations, women: 49% of ED visits and 50% of hospitalizations). The 71% of women low and medium volumes users were estimated to have contributed to a substantial proportion of alcohol-attributable healthcare encounters (47% of ED visits and 34% of hospitalizations). Findings provide support for universal alcohol policies (i.e., delivered to the entire population) for reducing population-level alcohol-attributable harm in addition to targeted policies for high-risk users.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 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".