How does taxation affect liver cirrhosis across age groups? An analysis of alcohol control policies on liver cirrhosis outcomes in Lithuania between 2001 and 2022
Bibliographic record
Abstract
BACKGROUND: Lithuania, a European country, has a history of high alcohol consumption per capita. To reduce harm, Lithuania has implemented the World Health Organization 'best buys' for alcohol control policies, notably two taxation policies in 2008 and 2017. Taxation may affect segments of the population differently; to explore this question, we investigated the effects on liver cirrhosis. AIMS: To analyze the effect of taxation on liver cirrhosis hospitalizations and mortality across four age groups in Lithuania. METHODS: Using a general additive mixed model, we tested taxation on monthly hospitalization and mortality rates between 2001 and 2022 (n = 264 months) across four age groups (young adults: 15-34, middle-aged adults: 35-54, older adults: 55-74, and seniors: 75+ years of age, respectively). We computed standardized hospitalizations and mortality rates (admissions and deaths per 100 000 people) based on summed counts of alcoholic liver disease and fibrosis and cirrhosis of the liver according to the International Classification of Diseases 10th Revision. FINDINGS: Taxation was associated with the largest downward trend in liver cirrhosis mortality among middle-aged and older adults, equivalent to two fewer deaths per 100 000 individuals. In older adults and seniors, taxation was associated with downward trends in hospitalizations, but effects were less robust. CONCLUSION: Taxation may lead to decreases in liver cirrhosis mortality across all age groups but appears to be less consistently impactful for hospitalizations. Younger and middle-aged individuals may experience increased hospitalizations. Taxation appears to impact subsections of the population differently.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".