Material deprivation and rates of all-terrain vehicle- and snowmobile-related injuries in Ontario from 2003 to 2018: a population-based study
Bibliographic record
Abstract
OBJECTIVES: Socio-economic status (SES) is a well-established predictor of health outcomes; however, there is a dearth of evidence on the relationship between SES and off-road vehicle (ORV) injuries. In Ontario, all-terrain vehicles (ATVs) and snowmobiles present a serious risk for preventable injury. This study assessed the association between area-level material deprivation and the risk of ATV- and snowmobile-related injuries in Ontario, as well as the impact of sex and age. METHODS: A population-based, repeat cross-sectional study was conducted using administrative data of ATV- and snowmobile-related emergency room visits from 2003 to 2018. Material deprivation was measured using the Ontario Marginalization Index, which assigned a score and quintile of deprivation to each dissemination area in Ontario. Age-standardized incidence rates and relative index of inequality values were calculated, stratified by quintile of deprivation, sex, age group, vehicle type, and health region. RESULTS: We found a significant, positive relationship between ORV-related injuries and quintile of material deprivation (RII = 1.28, 95% CI: 1.01-1.63). Rates of ATV- and snowmobile-related injuries remained stable over time. Across all age groups, sex, and rural categories, we found an inverse u-shaped relationship between rates of injuries and quintile material deprivation. Males, individuals living in rural areas, and adolescents and young adults experienced the highest rates of injuries. CONCLUSION: Despite the positive relationship between ORV-related injuries and quintiles of deprivation, the inverse u-shaped relationship suggests that this increased risk of injury is likely related to exposure to ORVs. These results contribute to an understanding of the prevalence of the injury problem at a local level in Ontario. Stable rates of injury over time suggest that current public health programs are not sufficient in reducing these injuries, and further research should determine which factors amenable to intervention are contributing to increased risk of injury.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".