Impact of substance-related harms on injury hospitalizations in Canada, from 2010 to 2020
Bibliographic record
Abstract
INTRODUCTION: Injuries continue to be a leading cause of death and contribute significantly to hospitalizations each year in Canada. Substance use has been associated with an increase in intentional and unintentional injuries, resulting in hospitalizations. This study examines trends in injury hospitalizations with a co-occurring substance diagnosis, to quantify the burden of injuries and identify at risk populations. METHODS: We analyzed Discharge Abstract Database data between 2010/11 and 2020/21, for clinical and demographic information about hospital discharges across Canada. We used ICD-10 codes to identify injury hospitalizations with co-occurring substance diagnostic codes, by injury intent and substance type. Rates, proportions, age-specific rates and age-standardized rates were calculated, trends quantified using average annual percent change and results stratified by sex and age group. RESULTS: From 2010/11 to 2020/21, unintentional injuries accounted for over half of all substance-related injury hospitalizations. Substance-related injuries accounted for 12% of total injury hospitalizations over this period. Overall, substance-related injury hospitalizations with co-occurring use of stimulants, opioids, cannabinoids and alcohol increased significantly among males and females. Unintentional substance-related, injury hospitalizations were more common later in life, and intentional substancerelated injuries were more common among adolescents and young adults. CONCLUSION: These results highlight key demographic groups with higher rates of substance-related injury hospitalizations that would benefit from targeted prevention efforts.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".