Evaluating regional variation in neighbourhood socioeconomic inequalities in motor vehicle injury collisions
Bibliographic record
Abstract
Disadvantaged communities have higher rates of traffic injury. Our goal was to measure regional variation in the association between small-area socioeconomic deprivation and motor vehicle crashes resulting in injury within British Columbia, Canada. We analyzed road traffic injury crashes from universal auto insurance claims (2019-2023) across 16 urban regions of British Columbia, aggregated by census dissemination areas (DAs). We measured socioeconomic deprivation using the Vancouver Area Neighbourhood Deprivation Index, a normalized score combining seven health-related sociodemographic variables applied to the 2021 Canadian census. We examined associations between deprivation and three injury crash types (motor vehicle, bicycle-motor vehicle, and pedestrian-motor vehicle) using Bayesian spatial regression models. Increased socioeconomic deprivation was consistently associated with higher injury crash incidence across all crash types, with regional variation in the strength of the relationship. Among the most populous regions, a one standard deviation increase in deprivation was associated with injury crash increases from 17 % (95 % Credible Interval [CI]: 12 %-21 %) in Vancouver-Fraser Valley to 51 % (95 % CI: 36 %-68 %) in the Okanagan region. Similar patterns were observed for cyclist injuries, from 10 % (95 % CI: 4 %-16 %) in Vancouver-Fraser Valley to 64 % (95 % CI: 38 %-93 %) in Okanagan, and for pedestrian injuries, from 17 % (95 % CI: 11 %-24 %) in Vancouver-Fraser Valley to 77 % (95 % CI: 48 %-110 %) in Okanagan. Smaller regions had wide credible intervals and more uncertainty in associations. To address spatial inequalities, prioritization of the placement of road safety interventions should incorporate equity considerations, as well as area-level interventions that address fundamental risk factors of traffic volume and speed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".