Trends of fully alcohol-attributable mortality rates before and during COVID-19 in the Baltic and other European countries
Bibliographic record
Abstract
AIMS: We tested the polarization hypothesis, which postulates that during times of crises, such as the COVID-19 pandemic, alcohol consumption increases among the heaviest drinkers but decreases among most other drinkers, resulting in an overall decrease in consumption among the population. We posited the increase in heavy drinking would lead to increases in 100% alcohol-attributable (AA) mortality. Furthermore, based on the high level of alcohol consumption in the Baltic countries compared to other European countries, we predicted that the increases in AA mortality would be more pronounced in these countries. METHODS: Data for 100% AA deaths were obtained from the World Health Organization for the period 2010 to 2022, and standardized to the regional age distribution for 2010. Parametric and non-parametric tests were used to test the study hypotheses. RESULTS: = 0.021). This increase was higher in the Baltic countries (mean difference = 13.41 deaths per 100,000 population; standard deviation (SD) = 7.44; 46% increase) than for other European countries (mean difference = 1.19; SD = 1.55; 8% increase). The increases in 100% AA mortality were associated with decreases in the level of alcohol consumption in the majority of countries. CONCLUSIONS: As predicted, 100% AA mortality increased in 19 European countries during the COVID-19 pandemic, with the Baltic countries seeing a higher increase. Renewed alcohol control policy efforts should be considered.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".