Changes in alcohol use during the<scp>COVID</scp>‐19 pandemic in Europe: A meta‐analysis of observational studies
Bibliographic record
Abstract
ISSUES: Numerous studies have examined the impact of the COVID-19 pandemic on alcohol use changes in Europe, with concerns raised regarding increased use and related harms. APPROACH: We synthesised observational studies published between 1 January 2020 and 31 September 2021 on self-reported changes in alcohol use associated with COVID-19. Electronic databases were searched for studies evaluating individual data from European general and clinical populations. We identified 646 reports, of which 56 general population studies were suitable for random-effects meta-analyses of proportional differences in alcohol use changes. Variations by time, sub-region and study quality were assessed in subsequent meta-regressions. Additional 16 reports identified were summarised narratively. KEY FINDINGS: Compiling reports measuring changes in overall alcohol use, slightly more individuals indicated a decrease than an increase in their alcohol use during the pandemic [3.8%, 95% confidence interval (CI) 0.00-7.6%]. Decreases were also reported more often than increases in drinking frequency (8.0%, 95% CI 2.7-13.2%), quantity consumed (12.2%, 95% CI 8.3-16.2%) and heavy episodic drinking (17.7%, 95% CI 13.6-21.8%). Among people with pre-existing high drinking levels/alcohol use disorder, high-level drinking patterns appear to have solidified or intensified. IMPLICATIONS: Pandemic-related changes in alcohol use may be associated with pre-pandemic drinking levels. Increases among high-risk alcohol users are concerning, suggesting a need for ongoing monitoring and support from relevant health-care services. CONCLUSION: Our findings suggest that more people reduced their alcohol use in Europe than increased it since the onset of the pandemic. However high-quality studies examining specific change mechanisms at the population level are lacking.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".