Impact of the WHO "best buys" for alcohol policy on consumption and health in the Baltic countries and Poland 2000–2020
Bibliographic record
Abstract
Alcohol use is a major risk factor for burden of disease. This narrative review aims to document the effects of major alcohol control policies, in particular taxation increases and availability restrictions in the three Baltic countries (Estonia, Latvia, and Lithuania) between 2000 and 2020. These measures have been successful in curbing alcohol sales, in general without increasing consumption of alcoholic beverages from unrecorded sources; although for more recent changes this may have been partly due to the COVID-19 pandemic. Moreover, findings from time-series analyses suggest improved health, measured as reductions in all-cause and alcohol-attributable mortality, as well as narrowing absolute mortality inequalities between lower and higher educated groups. For most outcomes, there were sex differences observed, with alcohol control policies more strongly affecting males. In contrast to this successful path, alcohol control policies were mostly dismantled in the neighbouring country of Poland, resulting in a rising death toll due to liver cirrhosis and other alcohol-attributable deaths. The natural experiment in this region of high-income European countries with high consumption levels highlights the importance of effective alcohol control policies for improving population health.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".