Burden of road traffic injuries in Iran: a national and subnational perspective, 1990−2019
Bibliographic record
Abstract
OBJECTIVES: Reliable and valid information on burden of road traffic injuries (RTIs) is essential for short-term and long-term planning. We designed the present study to describe the levels and trends of burden of RTIs in Iran from 1990 to 2019. METHODS: This is an observational epidemiological study. We used the Global Burden of Disease (GBD) 2019 estimates to report RTIs incidence, prevalence, mortality and disability-adjusted life-years (DALYs) by sex, age group and road user category in Iran and each of the 31 provinces from 1990 to 2019. RESULTS: Age-standardised incidence, prevalence, death and DALY rates of RTIs decreased by 31.7% (95% uncertainty interval (UI): 29.4 to 33.9), 34.9% (33.8 to 36.0), 57.7% (48.1 to 62.3) and 60.1% (51.7 to 65.2), respectively between 1990 and 2019. The 2019 age-standardised DALY rates varied from smallest value in Tehran 303.8 (216.9 to 667.2) per 100 000 to largest value in Sistan-Baluchistan 2286.8 (1978.1 to 2627.9) per 100 000. The burden of RTIs was mainly related to injuries sustained by drivers or passengers of motorised vehicles with three or more wheels and pedestrians' injuries, mostly affected males aged 15-29 years and individuals aged ≥70 years. CONCLUSION: The reducing trend in the burden of RTIs in Iran possibly reflects the effectiveness of the intervention programmes. However, with regard to the Sustainable Development Goals the burden is still at an alarming level. Further reductions are necessary for specific road user groups such as adolescent and adult male drivers or passengers of motorised vehicles, also pedestrians aged ≥70 years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".