Changes in Alcohol-Specific Mortality During the COVID-19 Pandemic in 14 European Countries
Bibliographic record
Abstract
Aim: Exploring trends in 1) alcohol-specific mortality and 2) alcohol sales in European countries in the years before and during the COVID-19 pandemic. Method: Complete data on alcohol-specific mortality and alcohol sales were obtained for 14 European countries (13 EU countries and UK) for the years 2010 to 2020, with six countries having mortality data available up to 2021. Age-standardised mortality rates were calculated and descriptive statistics used. Results: When compared to 2019, alcohol-specific mortality rates in 2020 increased by 7.7 % and 8.2 % for women and men, respectively. Increases in alcohol-specific mortality were seen in the majority of countries and continued in 2021. In contrast, alcohol sales declined by an average of 5.0 %. Conclusion: Despite a drop in alcohol consumption, more people died due to alcohol-specific causes during the COVID-19 pandemic in Europe.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".