Alcohol consumption and burden of disease in the Americas: implications for alcohol policy
Bibliographic record
Abstract
OBJECTIVE: To describe the volume and patterns of alcohol consumption up to and including 2012, and to estimate the burden of disease attributable to alcohol consumption as measured in deaths and disability-adjusted life years (DALYs) lost in the Americas in 2012. METHODS: Measures of alcohol consumption were obtained from the World Health Organization (WHO) Global Information System on Alcohol and Health (GISAH). The burden of alcohol consumption was estimated in both deaths and DALYs lost based on mortality data obtained from WHO, using alcohol-attributable fractions. Regional groupings for the Americas were based on the WHO classifications for 2004 (according to child and adult mortality). RESULTS: Regional variations were observed in the overall volume of alcohol consumed, the proportion of the alcohol market attributable to unrecorded alcohol consumption, drinking patterns, prevalence of drinking, and prevalence of heavy episodic drinking, with inhabitants of the Americas consuming more alcohol (8.4 L of pure alcohol per adult in 2012) compared to the world average. The Americas also experienced a high burden of disease attributable to alcohol consumption (4.7% of all deaths and 6.7% of all DALYs lost), especially in terms of injuries attributable to alcohol consumption. CONCLUSIONS: Alcohol is consumed in a harmful manner in the Americas, leading to a high burden of disease, especially in terms of injuries. New cost-effective alcohol policies, such as increasing alcohol taxation, increasing the minimum legal age to purchase alcohol, and decreasing the maximum legal blood alcohol content while driving, should be implemented to decrease the harmful consumption of alcohol and the resulting burden of disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".