Alcohol Sales and Adverse Events during the Covid-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Alcohol sales increased at the onset of the coronavirus disease 2019 (Covid-19) pandemic, while alcohol-related emergency department (ED) visits decreased. It is unknown whether these patterns of alcohol use persisted or led to delayed effects on health. METHODS: We conducted a cross-sectional time series analysis of alcohol sales and alcohol-related adverse events in Ontario, Canada. We obtained 6 years of alcohol sales data from the largest regional alcohol distributor. We obtained monthly counts of alcohol-related ED visits, hospital admissions, and toxicity deaths. We defined our exposure as the start of the Covid-19 pandemic (March 1, 2020). We used linear mixed models to compare mean monthly alcohol sales and adverse events during prepandemic and pandemic periods. We used univariate Poisson regression models to generate incident rate ratios for alcohol-related adverse events comparing the prepandemic (February 28, 2016, to February 29, 2020) and pandemic (March 1, 2020, to February 26, 2022) periods. RESULTS: Alcohol sales increased, on average, by CA$43.5 million per month (95% confidence interval [CI], CA$26.1 million to CA$60.9 million; P<0.01) during the pandemic years compared with the prepandemic period. We observed a 7% increase (95% CI, 5 to 8) in the proportion of alcohol-related ED visits during the pandemic years, due to a modest decrease in alcohol-related ED visits and a larger decrease in all-cause ED visits. Overall, an average increase of 191 alcohol-related admissions occurred per month (95% CI, 101 to 282). We also observed an average increase of eight toxicity deaths per month (95% CI, 4 to 12). CONCLUSIONS: Alcohol sales and alcohol-related adverse events increased during the Covid-19 pandemic.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".