Community-level infrastructure risk factors for motor vehicle injuries of car occupants and pedestrians: results from the PURE study
Bibliographic record
Abstract
Disproportionately more of the world's fatalities and injuries on the roads occur in low- and middle-income countries, despite these countries having approximately only 60% of the world's vehicles. Injury rates due to motor-vehicles are related to a complex multidimensional array of risk factors, embedded in the social and economic infrastructure of a country or region. Whether environmental infrastructure factors differ in determining the risk of an injury for motor vehicle occupants compared to pedestrians and other vulnerable road users has not been extensively studied. We explored the role of environmental infrastructure factors on motor-vehicle-related non-fatal injury using the Prospective Urban and Rural Epidemiology (PURE) cohort study of 162,793 adults from 23 high-, middle- and low-income countries. As expected, low-income countries had slightly higher motor vehicle injury rates, with pedestrians tending to have higher injury rates in these countries. There was considerable variation in motor vehicle injury rates within country-income-categories, while there were similarities in motor vehicle injury rates despite large differences in motorization of countries. There was a meaningful community effect on motor vehicle injury rates. We found that community-level infrastructure risk factors for motor vehicle injuries differed for car occupants and for pedestrians, with road quality and alcohol use being the main factors associated with an injury for car occupants, while poor roadside infrastructure (streetlights, sidewalks) and alcohol use were the main risk factors for an injury as a pedestrian. Active transport, such as walking and bicycling, are being promoted as leading to healthy lifestyle habits and reduced pollution. These require improved walkability for pedestrians, but also separation from motorized vehicles, which leads to recommending that low-and middle-income countries devote more funds for roadway quality and streetlight infrastructure. Policies to reduce motor vehicle injuries should be supported at the national level, but should be specific at the community level, since they must be focused on the specific local infrastructure. Countermeasures for reducing road transport injuries for pedestrians have different risk factors than for reducing injuries for car occupants.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".