Indicators of alcohol consumption and attributable harm for monitoring and surveillance in European Union countries
Bibliographic record
Abstract
AIMS: Alcohol is a major risk factor for burden of disease and injury in Europe, and contributes markedly to between region differences in life expectancy. Monitoring and surveillance systems have shown to be a key factor in implementing effective policies. The aim of this paper is to propose a system of indicators for alcohol consumption and attributable harm which can be used as an over-time monitoring tool at the country level as well as for comparisons between countries. DESIGN: A systematic research in electronic data bases was conducted but most of the information was derived from ongoing international efforts to establish alcohol monitoring and surveillance systems. SETTING: European Union. PARTICIPANTS: Countries. MEASUREMENTS: Exposure to alcohol, mortality, burden of disease. FINDINGS: Adult per capita alcohol consumption, prevalence of abstention, and frequency of drinking more than 60 g pure alcohol in one occasion are proposed as a minimal set of alcohol exposure indicators, which can quickly be implemented in all EU countries. With respect to health harm indicators, the best minimal choice which can be implemented quickly in all countries of the EU would be alcohol-attributable years of life lost due to premature death. In addition, country specific indicators could be added, when alcohol places specific burden on specific diseases. CONCLUSIONS: National and European Union-wide monitoring systems for alcohol exposure and attributable harm to inform public health-related policy decisions could be implemented easily. The establishement of such monitoring systems would follow the recent World Assembly resolution for a global strategy to reduce alcohol-related harm.
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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 | 0.045 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".