Trends in emergency department visits related to acute alcohol consumption before and during the COVID-19 pandemic in the United States, 2018–2020
Bibliographic record
Abstract
Background: Excessive drinking accounts for more than 95,000 deaths annually in the United States. Coronavirus disease 2019 (COVID-19) pandemic-related factors (e.g., social, economic, policy) may have affected alcohol consumption. Emergency department (ED) visits involving acute alcohol consumption (referred to as "alcohol-related") are a useful indicator for assessing changes in alcohol-related harms. Methods: The 2018-2020 National Syndromic Surveillance Program data, which include nonfatal ED visits from facilities in 49 states and Washington, DC, were analyzed. Trends in the number of alcohol-related ED visits among people ≥15 years, and weekly alcohol-related ED visit rates (per 10,000 total visits) overall, by demographic characteristics, and quarter (Q) were assessed. Quarterly rates for 2018 and 2019 were averaged to increase baseline data stability. Results: Alcohol-related visits accounted for 1.6% of 60,474,770 total visits (2018), 1.7% of 61,564,380 total visits (2019), and 1.8% of 52,174,507 total visits (2020). The number of alcohol-related ED visits generally increased during the first eight months of 2018 and 2019. However, it sharply declined at the onset of the COVID-19 pandemic in mid-March-mid-April 2020, before resuming pre-pandemic patterns. Alcohol-related ED visits per 10,000 were higher during quarters in 2020 than corresponding quarters in 2018-2019 (Q1: +7.3%, Q2: +23.8%, Q3: +9.7%, Q4: +6.5%). Conclusions: Alcohol-related ED visit rates per 10,000 total visits increased during 2020 versus 2018-2019, with the greatest relative difference in the second quarter. Fewer people sought ED care in 2020 than 2018-2019 but alcohol-related visits declined to a lesser extent than total visits.
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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 | 0.002 | 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) | 0.002 | 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".