Assessing global risk factors for non-fatal injuries from road traffic accidents and falls in adults aged 35–70 years in 17 countries: a cross-sectional analysis of the Prospective Urban Rural Epidemiological (PURE) study
Bibliographic record
Abstract
OBJECTIVES: To assess risk factors associated with non-fatal injuries (NFIs) from road traffic accidents (RTAs) or falls. METHODS: Our study included 151 609 participants from the Prospective Urban Rural Epidemiological study. Participants reported whether they experienced injuries within the past 12 months that limited normal activities. Additional questions elicited data on risk factors. We employed multivariable logistic regression to analyse data. RESULTS: Overall, 5979 participants (3.9% of 151 609) reported at least one NFI. Total number of NFIs was 6300: 1428 were caused by RTAs (22.7%), 1948 by falls (30.9%) and 2924 by other causes (46.4%). Married/common law status was associated with fewer falls, but not with RTA. Age 65-70 years was associated with fewer RTAs, but more falls; age 55-64 years was associated with more falls. Male versus female was associated with more RTAs and fewer falls. In lower-middle-income countries, rural residence was associated with more RTAs and falls; in low-income countries, rural residence was associated with fewer RTAs. Previous alcohol use was associated with more RTAs and falls; current alcohol use was associated with more falls. Education was not associated with either NFI type. CONCLUSIONS: This study of persons aged 35-70 years found that some risk factors for NFI differ according to whether the injury is related to RTA or falls. Policymakers may use these differences to guide the design of prevention policies for RTA-related or fall-related NFI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".