Meeting the Global NCD Target of at Least 10% Relative Reduction in the Harmful Use of Alcohol: Is the WHO European Region on Track?
Bibliographic record
Abstract
BACKGROUND: The Global Action Plan for the Prevention and Control of Noncommunicable Diseases set the target of an "at least 10% relative reduction in the harmful use of alcohol, as appropriate, within the national context". This study investigated progress in the World Health Organization (WHO) European Region towards this target based on two indicators: (a) alcohol per capita consumption (APC) and (b) the age-standardized prevalence of heavy episodic drinking (HED). METHODS: Alcohol exposure data for the years 2010-2017 were based on country-validated data and statistical models. RESULTS: Between 2010 and 2017, the reduction target for APC has been met with a decline by -12.4% (95% confidence interval (CI) -17.2, -7.0%) in the region. This progress differed greatly across the region with no decline for the EU-28 grouping (-2.4%; 95% CI -12.0, 7.8%) but large declines for the Eastern WHO EUR grouping (-26.2%; 95% CI -42.2, -8.1%). Little to no progress was made concerning HED, with an overall change of -1.7% (-13.7% to 10.2%) in the WHO European Region. CONCLUSIONS: The findings indicate a divergence in alcohol consumption reduction in Europe, with substantial progress in the Eastern part of the region and very modest or no progress in EU countries.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".