Alcohol-attributable mortality and alcohol control policy in the Baltic Countries and Poland in 2001–2020: an interrupted time-series analysis
Bibliographic record
Abstract
BACKGROUND: The Baltic countries-Lithuania, Latvia and Estonia-are characterized by a high rate of fully alcohol-attributable mortality, compared with Poland. Alcohol control policy measures implemented since 2001 in the Baltic countries included a restriction on availability and an increase in excise taxation, among others. The aim of the current study was to evaluate the relationship between alcohol control policy implementation and alcohol-attributable mortality in the Baltic countries and Poland. METHODS: Alcohol-attributable mortality data for 2001-2020 was defined by codes 100% alcohol-attributable for persons aged 15 years and older in the Baltic countries and Poland. Alcohol control policies implemented between 2001 and 2020 were identified, and their impact on alcohol-attributable mortality was evaluated using an interrupted time-series methodology by employing a generalized additive model. RESULTS: Alcohol-attributable mortality was significantly higher in the Baltic countries, compared with Poland, for both males and females. In the final reduced model, alcohol control policy significantly reduced male alcohol-attributable mortality by 7.60% in the 12 months post-policy implementation. For females, the alcohol control policy mean-shift effect was higher, resulting in a significant reduction of alcohol-attributable mortality by 10.77% in the 12 months post-policy implementation. The interaction effects of countries and policy tested in the full model were not statistically significant, which indicated that the impact of alcohol control policy on alcohol-attributable mortality did not differ across countries for both males and females. CONCLUSIONS: Based on the findings of the current study, alcohol control policy in the form of reduced availability and increased taxation was associated with a reduction in alcohol-attributable mortality among both males and females.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".