Impact of Public Health Policies on Alcohol‐Associated Liver Disease in Latin America: An Ecological Multinational Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Alcohol-associated liver disease (ALD) is the leading cause of liver-related mortality in Latin America, yet the impact of public health policies (PHP) on liver disease is unknown. We aimed to assess the association between alcohol PHP and deaths due to ALD in Latin American countries. APPROACH AND RESULTS: We performed an ecological multinational study including 20 countries in Latin America (628,466,088 inhabitants). We obtained country-level sociodemographic information from the World Bank Open Data source. Alcohol-related PHP data for countries were obtained from the World Health Organization Global Information System of Alcohol and Health. We constructed generalized linear models to assess the association between the number of PHP (in 2010) and health outcomes (in 2016). In Latin America, the prevalence of obesity was 27% and 26.1% among male and female populations, respectively. The estimated alcohol per capita consumption among the population at 15 years old or older was 6.8 L of pure alcohol (5.6 recorded and 1.2 unrecorded). The overall prevalence of alcohol use disorders (AUD) was 4.9%. ALD was the main cause of cirrhosis in 64.7% of male and 40.0% of female populations. A total of 19 (95%) countries have at least one alcohol-related PHP on alcohol. The most frequent PHP were limiting drinking age (95%), tax regulations (90%), drunk-driving policies and countermeasures (90%), and government monitoring systems and community support (90%). A higher number of PHP was associated with a lower ALD mortality (PR, 0.76; 95% CI, 0.61-0.93; P = 0.009), lower AUD prevalence (PR, 0.80; 95% CI, 0.65-0.99; P = 0.045), and lower alcohol-attributable road traffic deaths (PR, 0.81; 95% CI, 0.65-1.00; P = 0.051). CONCLUSIONS: Our study indicates that in Latin America, countries with higher number of PHP have lower mortality due to ALD, lower prevalence of AUD, and lower alcohol-attributable road traffic mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".