Risky Alcohol Use: The Impact on Health Service Use
Bibliographic record
Abstract
OBJECTIVE: To examine health services use on the basis of alcohol consumption. MATERIAL AND METHODS: A cross-sectional study was carried out on patients visiting the Primary Health Care (PHC) settings in Catalonia during 2011 and 2012; these patients had a history of alcohol consumption. Information about outpatient visits in the PHC setting, hospitalizations, specialists' visits and emergency room visits for the year 2013 was obtained from 2 databases (the Information System for the Development of Research in PHC and the Catalan Health Surveillance System). Risky drinkers were defined as those who consumed more than 280 g per week for men or more than 170 g per week for women, or any amount of alcohol while being involved in a high risk work activity, or taking medication that significantly interferes with alcohol or when being pregnant. Binge drinkers (> 60 g in men or > 50 g in women in a short amount of time more than once a month) were also considered risky drinkers. RESULTS: A total of 606,948 patients reported consuming alcohol (of which 10.5% were risky drinkers). Risky drinkers were more likely to be admitted to hospitals or emergency departments (range of ORs 1.08-1.18) compared to light drinkers. Male risky drinkers used fewer PHC services than male light drinkers (OR 0.89, 95% CI 0.87-0.92). In general, risky alcohol users used services more and had longer hospital stays. When stratifying by socioeconomic level of the residential area, we found that risky drinking failed significance, while current or past cigarette smoking was associated with higher healthcare use. CONCLUSIONS: Risky drinkers use more expensive services, such as hospitals and emergency rooms, but not PHC services, which may suggest that prevention strategies and alcohol interventions should also be implemented in those settings.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".