The Global Distribution of Average Volume of Alcohol Consumption and Patterns of Drinking
Bibliographic record
Abstract
AIMS: To make quantitative estimates on a global basis of exposure of disease-relevant dimensions of alcohol consumption, i.e. average volume of alcohol consumption and patterns of drinking. DESIGN: Secondary data analysis. MEASUREMENTS: Level of average volume of drinking was estimated by a triangulation of data on per capita consumption and from general population surveys. Patterns of drinking were measured by an index composed of several indicators for heavy drinking occasions, an indicator of drinking with meals and an indicator of public drinking. Average volume of consumption was assessed by sex and age within each country, and patterns of drinking only by country; estimates for the global subregions were derived from the population-weighted average of the countries. For more than 90% of the world population, per capita consumption was known, and for more than 80% of the world population, survey data were available. FINDINGS: On the country level, average volume of alcohol consumption and patterns of drinking were independent. There was marked variation between WHO subregions on both dimensions. Average volume of drinking was highest in established market economies in Western Europe and the former Socialist economies in the Eastern part of Europe and in North America, and lowest in the Eastern Mediterranean region and parts of Southeast Asia including India. Patterns were most detrimental in the former Socialist economies in the Eastern part of Europe, in Middle and South America and parts of Africa. Patterns were least detrimental in Western Europe and in developed countries in the Western Pacific region (e.g., Japan). CONCLUSIONS: Although exposure to alcohol varies considerably between regions, the overall exposure by volume is quite high and patterns are relatively detrimental. The predictions for the future are not favorable, both with respect to average volume and to patterns of drinking.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".