Mortality and hospitalizations fully attributable to alcohol use before versus during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic period was associated with increased alcohol consumption. We sought to estimate excess mortality and hospitalizations attributable to alcohol use in Canada between April 2020 and December 2022. METHODS: Using data from the Canadian Vital Statistics Database and hospital Discharge Abstract Database (Jan. 2016 to Dec. 2022), we analyzed monthly mortality and hospitalization rates for conditions fully attributable to alcohol use in people in Canada aged 15 years and older. We estimated excess rates during the study period of April 2020 to December 2022 by comparing observed rates to expected rates, modelled using the autoregressive integrated moving average method, accounting for trends, seasonality, autocorrelation, and pandemic waves. RESULTS: Between April 2020 and December 2022, mortality fully attributable to alcohol in Canada increased by 17.6% (1600 excess deaths), and hospitalizations fully attributable to alcohol rose by 8.1% (7142 excess hospitalizations). Most increases occurred in the first 2 years, with deaths up about 24% and hospitalizations about 14%. Mortality rose by 55.4% in adults aged 25-44 years, 19.1% in those aged 45-64 years, and 2.6% in those aged 65 years and older, with similar increases among males (17.0%) and females (17.8%). Deaths rose by 11.7% in the highest income quintile, as compared with 17.0%-21.5% in the other quintiles. Excess hospitalizations were highest among people aged 15-24 years (20.3%) and 25-44 years (13.1%) and increased more for females (15.6%) than for males (5.7%). Regionally, mortality increased most in the Prairie provinces (Manitoba, Saskatchewan, and Alberta; 28.1%) and British Columbia (24.2%), whereas hospitalizations increased the most in the territories (Northwest Territories, Nunavut, and Yukon; 27.3%) and the Prairie provinces (14.6%). Alcoholic liver disease was the leading cause of excess mortality and hospitalizations, which increased by 22% and 23%, respectively. INTERPRETATION: Mortality and hospitalizations fully attributable to alcohol increased substantially across different demographics and regions in Canada during the April 2020 to December 2022 period of the COVID-19 pandemic. A comprehensive approach to preventing and managing high-risk drinking, alcohol use disorder, and alcoholic liver disease in the aftermath of the pandemic should comprise both public health and clinical management interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".