Epidemiology and alcohol policy in Europe
Bibliographic record
Abstract
AIMS: To describe three aspects of the epidemiology of alcohol-attributable deaths in Europe, dose, demography and place, and to illustrate how such knowledge can better be used to inform alcohol policy formulation and implementation. DESIGN: epidemiological and population health modeling. SETTING: Europe. PARTICIPANTS: Based on country-specific aggregate statistics. EXPOSURE: country-specific adult per capita consumption triangulated with survey data; outcomes: mortality statistics. FINDINGS: The absolute risk of dying from an alcohol-attributable disease and injury (accounting for a protective effect for ischaemic diseases) increases with increasing daily alcohol consumption beyond 10 g alcohol per day, the first data point. Over 2/3 of all alcohol-attributable deaths occurring amongst the 20-64 year old population of the European Union (minus Cyprus and Malta) occur in the 45-64 year olds. About 25% of the difference in life expectancy between western and eastern Europe for men aged 20-64 years in 2002 can be attributed to alcohol, largely, but not exclusively, as a result of differences in heavy episodic drinking patterns. CONCLUSIONS: Any reduction in the dose of alcohol consumed, at least down to 10 g/day, will reduce the annual and lifetime risk of an alcohol-related death. There is a need for alcohol policy to focus on measures in reducing alcohol consumption, throughout middle age, with immediacy of impact. Policy should strive to reduce alcohol-related health inequalities, with the specific recommendations for policy depending on the cost-effectiveness of interventions related to the epidemiological profile of the country or region under consideration. Fortunately, there are evidence-based policy options that reduce the amount of alcohol consumed and many alcohol-related harms with immediate effect, that reduce the risk of an alcohol-related death in middle age, and that would help to close the health gap between eastern and western 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 | 0.000 | 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".