Hospital discharges for substance-related injuries before and during the COVID-19 pandemic: a descriptive surveillance study using administrative data
Bibliographic record
Abstract
Background: The COVID-19 pandemic and associated behavioural changes have contributed to an increase in substance-related hospital discharges, and has altered the injury epidemiology landscape in Canada. We sought to evaluate hospital discharges for substance-related injuries during the pandemic compared with prepandemic and to identify subpopulations that have been greatly affected by substance-related injuries during the first year of the pandemic. Methods: We compared data on hospital discharges in Canada from before the pandemic (March 2019–February 2020) with discharges during the first year of the pandemic (March 2020–February 2021) using the Discharge Abstract Database. We identified discharges for substance-related injuries using codes from the International Statistical Classification of Diseases and Related Health Problems, 10th Revision. We calculated percent changes, age-standardized rates and age-specific rates of discharges for substance-related injuries. Results: Hospital discharges for substance-related injuries increased by 7.1% during the first year of the pandemic. Discharges for intentional injuries decreased by 6.3%, whereas unintentional substance-related injuries increased by 15.1% during this period. Male patients accounted for 95.6% of the increase in hospital discharges for substance-related injuries during the first year of the pandemic. We observed a percent increase among discharges for injuries related to alcohol, opioid, cannabinoid, hallucinogen, tobacco, volatile solvents, other psychoactive substances and polysubstance use. Interpretation: We observed an increase in hospital discharges for substance-related injuries during the first year of the COVID-19 pandemic, compared with the same time period before the pandemic. This work will provide useful insight into the ongoing management of the COVID-19 pandemic, as well as future policy and health care planning related to substance use in Canada.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".