NCD control policies and alcohol consumption trends in WHO Europe countries between 2000 and 2014
Bibliographic record
Abstract
Background One of the nine voluntary global non-communicable disease (NCD) targets is 10% reduction in harmful use of alcohol by 2025. This analysis aimed to evaluate the change in alcohol consumption between 2000 and 2014 in relation to NCD control policies in WHO European countries. Methods Total alcohol consumption per capita (recorded and unrecorded) for each country were retrieved from Global Information System on Alcohol and Health. The NCD policy score (NCDPS) was developed using the 2010 WHO Global NCD Country Capacity Survey (CCS) and the Global Tobacco Epidemic Report 2011 results. Using absolute change in total alcohol per capita between 2000 and 2014, countries were dichotomized as either decrease or increase in alcohol consumption. Man Whitney-U test was used to compare NCDPS between the groups. Logistic regression was used to analyse the association between NCDPS and change in alcohol consumption. R2 values indicated the variability explained in change in alcohol consumption by the NCDPS. Results Total alcohol consumption decreased in 23 countries but increased in 27 countries between 2000 and 2014. The highest decrease was observed in Republic of Moldova (7.2 L) and Ireland (4.0L) and the highest increases were observed in Montenegro (6.6L), Ukraine (5L) and Lithuania (3.8L). Mean NCDPS was higher in countries that alcohol consumption decreased, compared with the countries increased (84.9±12.1 and 70.6±20.6 respectively, p = 0.02). NCD Policy score explained 21% of the change in mean alcohol consumption (Beta=0.05, p = 0.01, R2=0.21) and this significant association was persisted when adjusted to GDP ppp (Beta=0.06 p = 0.01, R2=0.23). Conclusions NCD policies seem to contribute change in alcohol consumption. Implementation of the NCD policies should be intensified in order to observe larger changes especially in the countries that have high alcohol consumption. Key messages: Alcohol consumption decreased in half of the countries. In order to observe larger decreases in alcohol consumption, NCD control policies should be enforced.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".