Global and Country Specific Adult per capita Consumption of Alcohol, 2008
Bibliographic record
Abstract
Aims: Alcohol is a substantial risk factor for mortality and the burden of disease globally. In accordance with the World Health Organization’s (WHO) global strategy to reduce the harmful use of alcohol, we estimated recorded, unrecorded, tourist, and total adult per capita consumption by country and WHO sub-region for 2008, and characterized the association between per capita consumption of alcohol and gross domestic product (GDP-PPP) per capita. Methods: Using data from the Global Information System on Alcohol and Health database ( World Health Organization, 2010 a) and the 2005 Global Burden of Disease study ( Institute for Health Metrics and Evaluation, 2010 ) on adult per capita consumption of alcohol, we estimated recorded adult per capita consumption for 2008 through time series analyses for 189 countries within WHO sub-regions, and then from these estimates calculated recorded adult per capita consumption estimates for each of the WHO sub-regions. Estimates for populations were obtained for 2008 from the United Nations Populations Division. 2008 GDP-PPP data by country (N = 178) were obtained from the International Monetary Fund. Results: Adult per capita consumption of alcohol in 2008 is estimated to have been 6.04 litres (95 % CI: 4.43 to 7.65). This can be broken down into 4.39 l (95 % CI: 3.72 to 4.86) of recorded per capita consumption of alcohol, 1.75 l (95 %CI: 0.25 to 3.25) of unrecorded per capita consumption of alcohol, and 0.00 l (95 %CI: 0.00 to 0.129) per capita consumption of alcohol consumed by tourists. Adult per capita consumption was highest for the European regions and lowest for the Eastern Mediterranean region. Total adult per capita consumption of alcohol showed an increase as GDP-PPP increased until approximately 15,000 international dollars of GDP-PPP per capita. Recorded consumption showed a general increase with GDP-PPP. Unrecorded consumption showed a U-shaped association with GDP-PPP per capita, with countries with the lowest and highest GDP-PPPs per capita having the lowest unrecorded adult per capita consumption of alcohol. Conclusions: In accordance with the WHO’s global strategy to reduce the harmful use of alcohol, we present estimates of the recorded, unrecorded, tourist, and total adult per capita alcohol consumption for 189 countries and the 14 WHO sub-regions. Accurate and up-to-date estimates of alcohol consumption are imperative for monitoring and developing effective strategies to control the large and increasing global alcohol-attributable burden of disease and injury.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".