Changes in alcohol use during the COVID‐19 pandemic and previous pandemics: A systematic review
Bibliographic record
Abstract
The objective of this study is to summarize the research on the relationships between exposure to the COVID-19 pandemic or previous pandemics and changes in alcohol use. A systematic search of Medline and Embase was performed to identify cohort and cross-sectional population studies that examined changes in alcohol use during or following a pandemic compared to before a pandemic occurred. Outcomes examined included differences in the volume and frequency of alcohol consumption and the frequencies of heavy episodic drinking (HED) and alcohol-related problems during a pandemic compared to before a pandemic. Quality assessment was performed using the Cochrane Risk of Bias Tool for Nonrandomized Studies. This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search yielded 672 articles; 27 were included in the narrative review, of which 6 were cohort studies (all from high-income countries). A total of 259,188 participants were included. All cohort studies examined the impact of COVID-19 and associated pandemic-related policies, including social distancing and alcohol-specific policies, on alcohol use. Cohort studies demonstrated a consistent significant decrease in total alcohol consumption (Australia) and a significant increase in the frequency of alcohol use (United States). A significant decrease in the frequency of HED was observed in Australia and Spain but not in the United States. A significant increase in the proportion of people with problematic alcohol use was observed in the United Kingdom. Initial insights into changes in alcohol use indicate substantial heterogeneity. Alcohol use may have decreased in some countries, while HED and the proportion of people with problematic alcohol use may have increased. The lack of high-quality studies from low- and middle-income countries reflects a dearth of information from countries inhabited by most of the world's population.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".