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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".