Burden of Transport-Related Injuries in the Eastern Mediterranean Region: A Systematic Analysis for the Global Burden of Disease Study 2017
Bibliographic record
Abstract
BACKGROUND: Transport-related injuries (TIs) are a substantial public health concern for all regions of the world. The present study quantified the burden of TIs and deaths in the Eastern Mediterranean region (EMR) in 2017 by sex and age. METHODS: TIs and deaths were estimated by age, sex, country, and year using Cause of Death Ensemble modelling (CODEm) and DisMod-MR 2.1. Disability-adjusted life years (DALYs), which quantify the total burden of years lost due to premature death or disability, were also estimated per 100000 population. All estimates were reported along with their corresponding 95% uncertainty intervals (UIs). RESULTS: In 2017, there were 5.5 million (UI 4.9-6.2) transport-related incident cases in the EMR - a substantial increase from 1990 (2.8 million; UI 2.5-3.1). The age-standardized incidence rate for the EMR in 2017 was 787 (UI 705.5-876.2) per 100000, which has not changed significantly since 1990 (-0.9%; UI -4.7 to 3). These rates differed remarkably between countries, such that Oman (1303.9; UI 1167.3-1441.5) and Palestine (486.5; UI 434.5-545.9) had the highest and lowest age-standardized incidence rates per 100000, respectively. In 2017, there were 185.3 thousand (UI 170.8-200.6) transport-related fatalities in the EMR - a substantial increase since 1990 (140.4 thousand; UI 118.7-156.9). The age-standardized death rate for the EMR in 2017 was 29.5 (UI 27.1-31.9) per 100000, which was 30.5% lower than that found in 1990 (42.5; UI 36.8-47.3). In 2017, Somalia (54; UI 30-77.4) and Lebanon (7.1; UI 4.8-8.6) had the highest and lowest age-standardized death rates per 100,000, respectively. The age-standardised DALY rate for the EMR in 2017 was 1,528.8 (UI 1412.5-1651.3) per 100000, which was 34.4% lower than that found in 1990 (2,331.3; UI 1,993.1-2,589.9). In 2017, the highest DALY rate was found in Pakistan (3454121; UI 2297890- 4342908) and the lowest was found in Bahrain (8616; UI 7670-9751). CONCLUSION: The present study shows that while road traffic has become relatively safer (measured by deaths and DALYs per 100000 population), the number of transport-related fatalities in the EMR is growing and needs to be addressed urgently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.006 |
| 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".