Epidemiology and association of neighbourhood marginalisation on violent knife assaults in Ontario: a population-based case-control study
Bibliographic record
Abstract
BACKGROUND: Violent knife assaults ('stabbings') are underappreciated as a source of morbidity and mortality. The two objectives of this study were to describe the epidemiology of stabbing injuries in the population of Ontario, Canada and to assess the associations between two measures of neighbourhood marginalisation-material deprivation and housing instability, and the risk of stabbing injury. METHODS: We conducted a population-based case-control study over 2004-18 using linked administrative data. Cases suffered a stabbing injury resulting in an emergency department visit, hospitalisation or death. Four age and sex-matched controls were matched to each case. Multivariate logistic regression was used to assess the associations between neighbourhood material deprivation as well as housing instability and the risk of injury. Mean annualised injury incidences were estimated using the number of cases identified divided by the total population of Ontario for that year. RESULTS: We identified 26 657 individuals with a stabbing injury, of which 724 (2.7%) were fatal. The mean annualised incidence was 13.4 per 100 000 (95% CI: 12.7 to 15.9). Victims were disproportionately young (median age 25 years; IQR: 20-37 years) males (84.1%), from large urban centres (77.5%), and in the lowest income quintile (39.3%). In multivariate models, neighbourhood material deprivation (OR 1.45, 95% CI: 1.43 to 1.47) and housing instability (OR 1.4, 95% CI: 1.22 to 1.26) were associated with risk of injury. CONCLUSIONS: Stabbing injuries are a substantial public health problem that affects individuals of all ages and demographics but disproportionately affects younger men in urban settings. There is a weak association between residence in marginalised neighbourhoods and the risk of stabbing injury. Future studies should aim to better understand the nature of this association and consider opportunities for public health interventions to reduce the burden of violent knife injuries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".