The socioeconomic profile of alcohol use in Europe: findings from 33 European countries
Bibliographic record
Abstract
Background: Alcohol’s detrimental health effects do not affect everyone equally but accumulate in people with low socioeconomic status (SES). Using data from the 2021 Standard European Alcohol Survey, we explore gender- and SES-specific consumption patterns, and COVID-19 related changes in consumption across Europe. Methods: Cross-sectional population-based survey data from 54,354 adults from 33 European countries plus Spain-Catalonia were analysed. Five alcohol indicators were of interest: prevalence of past-year alcohol use; and, among past-year alcohol users, prevalence of monthly/more frequent risky-single-occasion-drinking (monthly+ RSOD); prevalence of high-risk alcohol use (40+/60+ grams pure alcohol daily for women/men); mean daily grams of pure alcohol consumed; and self-reported consumption changes during COVID-19. Alcohol indicators were age-standardised and decomposed by gender and SES (education attainment), and analysed using regression models with location-specific random intercepts. Results: Across jurisdictions, past-year alcohol use, monthly+ RSOD, and high-risk drinking were all commonly reported, with distinct gender-specific socioeconomic profiles. While high-SES men and women were generally more likely to report past-year alcohol use, monthly+ RSOD and high-risk drinking were more prevalent among currently drinking low/mid-SES compared to high-SES men. No such SES differences in risky drinking were observed among women, however, female alcohol users with high SES reported higher mean daily drinking levels. High-SES women but not men were more likely to both increase and decrease their drinking during COVID-19 compared to their low/mid-SES counterparts. Conclusion: High consumption levels and distinct socioeconomic profiles among men and women highlight the need for effective alcohol policies to reduce health inequalities in Europe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".