Alcohol consumption during the COVID-19 pandemic in Europe: a large-scale cross-sectional study in 21 countries (preprint)
Bibliographic record
Abstract
<title>Abstract</title> <bold>Aims: </bold>The aim of this project was to investigate changes in alcohol consumption during the first months of the pandemic in Europe as well as its associations with income and experiences of distress related to the COVID-19 pandemic. <bold>Design: </bold>Cross-sectional online survey conducted between April 24 and July 22 of 2020.<bold> </bold><bold>Setting: </bold>21 European countries.<bold> </bold><bold>Participants: </bold>31,964 adults reporting past-year drinking. <bold>Measurements: </bold>Changes in alcohol consumption were measured by asking respondents about changes during the previous month in their drinking frequency, the quantity they consumed, and incidence of heavy episodic drinking events. Individual indicators were combined into an aggregated consumption-change score and scaled to a possible range: -1 to +1. Using this score as outcome, multilevel linear regressions tested changes in overall drinking, taking into account sampling weights and baseline alcohol consumption (AUDIT-C) and country of residence serving as random intercept. Similar models were conducted for each single consumption-change indicator. <bold>Findings: </bold>In almost all countries, the consumption-change score indicated alcohol use to decrease on average; except in Ireland and the UK, where alcohol consumption on average remained unchanged or increased, respectively. Decreases in drinking were mostly driven by a reduced frequency of heavy episodic drinking. Declines in consumption were less marked among those with low- or average incomes, and those experiencing distress. <bold>Conclusions: </bold>Our research suggests alcohol consumption to decline on average during the first months of the pandemic in Europe. The findings suggest both reduced availability of alcohol and increased distress may have affected alcohol consumption, although the former seemed to have a greater impact, at least in terms of immediate effects. Monitoring of mid- and long-term consequences will be crucial in understanding how this public health crisis impacts alcohol consumption.
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How this classification was reachedexpand
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".