Leading Causes of Unintentional Injury and Suicide Mortality in Canadian Adults across the Urban-Rural Continuum
Bibliographic record
Abstract
OBJECTIVE: We examined the leading causes of unintentional injury and suicide mortality in adults across the urban-rural continuum. METHODS: Injury mortality data were drawn from a representative cohort of 2,735,152 Canadians aged ≥ 25 years at baseline, who were followed for mortality from 1991 to 2001. We estimated hazard ratios and 95% confidence intervals for urban-rural continuum and cause-specific unintentional injury (i.e., motor vehicle, falls, poisoning, drowning, suffocation, and fire/burn) and suicide (i.e., hanging, poisoning, firearm, and jumping) mortality, adjusting for socioeconomic and demographic characteristics. RESULTS: Rates of unintentional injury mortality were elevated in less urbanized areas for both males and females. We found an urban-rural gradient for motor vehicle, drowning, and fire/burn deaths, but not for fall, poisoning, or suffocation deaths. Urban-rural differences in suicide risk were observed for males but not females. Declining urbanization was associated with higher risks of firearm suicides and lower risks of jumping suicides, but there was no apparent trend in hanging and poisoning suicides. CONCLUSION: Urban-rural gradients in adults were more pronounced for unintentional motor vehicle, drowning, and fire/burn deaths, as well as for firearm and jumping suicide deaths than for other causes of injury mortality. These results suggest that the degree of urbanization may be an important consideration in guiding prevention efforts for many causes of injury fatality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".