Ecological study of road traffic injuries in the eastern Mediterranean region: country economic level, road user category and gender perspectives
Bibliographic record
Abstract
BACKGROUND: The Eastern Mediterranean region has the second highest number of road traffic injury mortality rates after the African region based on 2013 data, with road traffic injuries accounting for 27% of the total injury mortality in the region. Globally the number of road traffic deaths has plateaued despite an increase in motorization, but it is uncertain whether this applies to the Region. This study investigated the regional trends in both road traffic injury mortality and morbidity and examined country-based differences considering on income level, categories of road users, and gender distribution. METHODS: Register-based ecological study linking data from Global Burden of Disease Study with the United Nations Statistics Division for population and World Bank definition for country income level. Road traffic injury mortality rates and disability-adjusted life years were compiled for all ages at country level in 1995, 2005, 2015 and combined for a regional average (n = 22) and a global average (n = 122). The data were stratified by country economic level, road user category and gender. RESULTS: Road traffic injury mortality rates in the Region were higher than the global average for all three reference years but suggest a downward trend. In 2015 mortality rates were more than twice as high in low and high income countries compared to global income averages and motor vehicle occupants had a 3-fold greater mortality than the global average. Severe injuries decreased by more than half for high/middle income countries but remained high for low income countries; three times higher for males than females. CONCLUSION: Despite a potential downward trend, inequalities in road traffic injury mortality and morbidity burden remain high in the Eastern Mediterranean region. Action needs to be intensified and targeted to implement and enforce safety measures that prevent and mitigate severe motor vehicle crashes in high income countries especially and invest in efforts to promote public, active transport for vulnerable road users in the resource poor countries of the Region.
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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 | 0.000 |
| 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".