Cycling and Pedestrian Injuries in the Region of Peel, Ontario; An application of linked administrative health data to enhance local road safety and health service delivery, through an Applied Health Research Question (AHRQ)
Bibliographic record
Abstract
ObjectivesAn Applied Health Research Question request from Peel Public Health was investigated, to provide a comprehensive overview of injury patterns and healthcare utilization, among cyclists and pedestrians in the Peel Region of Ontario. ApproachEmergency department visits and hospitalizations across the Peel region were analyzed from 2018 to 2022, and categorized by collision type (pedestrian, cyclist motor vehicle collision (MVC), and cyclist non-MVC). Data was segmented quarterly and annually, and further stratified by residence status, sex, and age, by linking individuals to other administrative health databases. Additionally, 30-day mortality outcomes were evaluated. To contextualize incidence rates with population demographics, Public Health Data Zone and Census Subdivision data were also utilized. ResultsFor ambulatory visits, a substantial majority of Peel residents received treatment within the region, evidenced by 4,483 cyclist visits (55%) and 1,338 pedestrian visits (50%). However, a noteworthy portion of Peel residents — 188 cyclists (45%) and 182 pedestrians (46%) — received treatment outside the region. Hospitalization patterns echoed these visits. Overall, non-MVCs accounted for a large proportion of cycling incidents, particularly among females, stressing the need for targeted safety measures. Still, survival rates post-incident were notable, with a 100% survival rate for cyclists after ambulatory visits. Pedestrian survival rates were similarly high. ConclusionThis study provided valuable insights into patterns of pedestrian and cyclist injuries among Peel residents, revealing considerable cross-regional healthcare utilization. ImplicationsFindings will be utilized by the Region of Peel and partner organizations, to enhance local road safety initiatives and service delivery for vulnerable road users.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".