The burden of alcohol on health care during <scp>COVID</scp>‐19
Bibliographic record
Abstract
Alcohol's impact on global health is substantial and of a similar order of magnitude to that from COVID-19. Alcohol now also poses specific concerns, such as increased risk of severe lung infections, domestic violence, child abuse, depression and suicide. Its use is unlikely to aid physical distancing or other preventative behavioural measures. Globally, alcohol contributes to 20% of injury and 11.5% of non-injury emergency room presentations. We provide some broad comparisons between alcohol-attributable and COVID-19-related hospitalisations and deaths in North America using most recent data. For example, for Canada in 2017 it was recently estimated there were 105 065 alcohol-attributable hospitalisations which represent a substantially higher rate over time than the 10 521 COVID-19 hospitalisations reported during the first 5 months of the pandemic. Despite the current importance of protecting health-care services, most governments have deemed alcohol sales to be as essential as food, fuel and pharmaceuticals. In many countries, alcohol is now more readily available and affordable than ever before, a situation global alcohol producers benefit from and have helped engineer. We argue that to protect frontline health-care services and public health more generally, it is essential that modest, evidence-based restrictions on alcohol prices, availability and marketing are introduced. In particular, we recommend increases in excise taxation coupled with minimum unit pricing to both reduce impacts on health-care services and provide much-needed revenues for governments at this critical time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".