Can alcohol control policies reduce cirrhosis mortality? An interrupted time‐series analysis in Lithuania
Bibliographic record
Abstract
BACKGROUND AND AIMS: The relationship between alcohol consumption and cirrhosis is well established. Policies that can influence population-level use of alcohol should, in turn, impact cirrhosis. We examined the effect of population-level alcohol control policies on cirrhosis mortality rates in Lithuania - a high-income European Union country with high levels of alcohol consumption. METHODS: Age-standardized, monthly liver mortality data (deaths per 100,000 adults, aged 15+) from Lithuania were analysed from 2001 to 2018 (n = 216 months) while controlling for economic confounders (gross domestic product and inflation). An interrupted time-series analysis was conducted to estimate the effect of three alcohol control policies implemented in 2008, 2017 and 2018 and the number of cirrhosis deaths averted. RESULTS: There was a significant effect of the 2008 (P < .0001) and 2017 (P = .0003) alcohol control policies but a null effect of the 2018 policy (P = .40). Following the 2008 policy, the cirrhosis mortality rate dropped from 4.93 to 3.41 (95% CI: 3.02-3.80) deaths per 100,000 adults, which equated to 493 deaths averted. Further, we found that following the 2017 policy, the mortality rate dropped from 2.85 to 2.01 (95% CI: 1.50-2.52) deaths per 100,000 adults, corresponding to 245 deaths averted. CONCLUSIONS: Our findings support the hypothesis that alcohol control policies can have a significant, immediate effect on cirrhosis mortality. These policy measures are cost-effective and aid in reducing the burden of liver disease.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".