The Burden of Road Traffic Injuries in Iran and 15 Surrounding Countries: 1990-2016.
Bibliographic record
Abstract
BACKGROUND: The Global Burden of Disease (GBD) Study provides estimates of deaths, years of life lost (YLL), years of life lived with disability (YLD), and disability-adjusted life years (DALYs) due to 249 causes of death, 315 diseases and injuries, and 79 behavioral, environmental, occupational, and metabolic risk factors in 195 countries, territories, and regions by sex and 20 age categories in 195 countries and regions since 1990. In this study, we aimed to present the burden of road traffic injuries (RTIs) in Iran and 15 surrounding countries in 1990-2016. METHODS: The standard Cause of Death Ensemble modeling (CODEm) is used to estimate deaths due to all causes of injury by age, sex, country and year. A range of 27 causes is used for estimating non-fatal health outcomes based on inpatient and outpatient datasets using DisMod-MR 2.0. Disability-adjusted life years (DALYs) estimate quantify the total burden of years lost due to premature death or disability and was computed by summing the fatal burden and non-fatal burden associated with a cause (i.e., YLL+YLD). RESULTS: In 2016, age-standardized transport injuries in Iran accounted for 35.6 (UI: 29.64-43.44) deaths per 100000 compared to 60.8 (UI: 51.04-72.49) in 1990. Transport injury became the fourth leading cause of death in Iran in 2016, up from the 5th leading cause of death in 1990. The burden of RTIs was mainly caused by motor vehicles and motorcycles and mostly affected the economically productive age groups (15-49), males and children, especially those at school age. Afghanistan with 59.14 deaths (52.09-66.8) and UAE with 53.71 deaths (36.59-72.77) had the largest transport injury death rates per 100000. From 1990 to 2016, Iran had -2.06 annual percent change in transport death rates. The lowest annual percent change is reported for Turkmenistan at -3.43. While Pakistan, UAE and Qatar had the highest annual percent change in transport injury. Across all countries, the observed-to-expected ratios for transport injury death rates varied considerably in 2016.The UAE had the largest age-standardized ratios of observed-to-expected rate (2.93), followed by Oman (2.39), Saudi Arabia (2.23), Afghanistan (2.04) and Iran (1.95). CONCLUSIONS: RTIs continue to be a public health burden in Iran and its neighboring countries, even though, there is evidence for decline in RTIs across all countries except Pakistan. The most frequent sub-causes of death and injury are the motor vehicle, motorcycle, and pedestrian injuries. The most vulnerable road users are children and young adults.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".