Impact of the WHO "best buys" for alcohol policy on consumption and health in the Baltic countries and Poland 2000–2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it