Colder Weather and Fewer Sunlight Hours Increase Alcohol Consumption and Alcoholic Cirrhosis Worldwide
Bibliographic record
Abstract
Risk of alcoholic cirrhosis is determined by genetic and environmental factors. We aimed to investigate if climate has a causal effect on alcohol consumption and its weight on alcoholic cirrhosis. We collected extensive data from 193 sovereign countries as well as 50 states and 3,144 counties in the United States. Data sources included World Health Organization, World Meteorological Organization, and the Institute on Health Metrics and Evaluation. Climate parameters comprised Koppen-Geiger classification, average annual sunshine hours, and average annual temperature. Alcohol consumption data, pattern of drinking, health indicators, and alcohol-attributable fraction (AAF) of cirrhosis were obtained. The global cohort revealed an inverse correlation between mean average temperature and average annual sunshine hours with liters of annual alcohol consumption per capita (Spearman's rho -0.5 and -0.57, respectively). Moreover, the percentage of heavy episodic drinking and total drinkers among population inversely correlated with temperature -0.45 and -0.49 (P < 0.001) and sunshine hours -0.39 and -0.57 (P < 0.001). Importantly, AAF was inversely correlated with temperature -0.45 (P < 0.001) and sunshine hours -0.6 (P < 0.001). At a global level, all included parameters in the univariable and multivariable analysis showed an association with liters of alcohol consumption and drinkers among population once adjusted by potential confounders. In the multivariate analysis, liters of alcohol consumption associated with AAF. In the United States, colder climates showed a positive correlation with the age-standardized prevalence of heavy and binge drinkers. Conclusion: These results suggest that colder climates may play a causal role on AAF mediated by alcohol consumption.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".