Unrecorded Alcohol Consumption in Seven European Union Countries
Bibliographic record
Abstract
INTRODUCTION: Unrecorded alcohol, that is, alcohol not reflected in official statistics of the country where it is consumed, contributes markedly to overall consumption of alcohol. However, empirical data on unrecorded alcohol consumption are scarce, especially in high-income countries. This study measures the contribution of unrecorded alcohol in 7 member states of the European Union. METHODS: Two categories of unrecorded consumption were assessed in general population surveys (reducing alcohol related harm Standardized European Alcohol Survey; n = 11,224): home-made alcohol and cross-border shopping. Country-specific logistic regressions were used to link respondent characteristics to odds of acquisition of unrecorded alcohol. Total per capita alcohol consumption was estimated under different assumptions of calculating unrecorded alcohol consumption. RESULTS: Individuals with higher drinking levels were more likely to acquire unrecorded alcohol in all 7 countries. In some countries, male sex and more affluent social class were also positively linked to acquisition of unrecorded alcohol. There was a substantial contribution of unrecorded alcohol to overall consumption in 5 out of 7 member states (Croatia, Finland, Greece, Hungary, Portugal), but not in Poland or Spain. In Greece, up to two-thirds of all alcohol consumed was estimated to be unrecorded. CONCLUSION: Unrecorded alcohol contributes to overall consumption even in high-income countries, and thus needs to be monitored. In monitoring, as many categories of unrecorded alcohol as possible should be clearly defined (e.g., surrogate alcohol) and included in future surveys.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".