Epidemiology, patterns, and outcomes of road traffic accidents in Saudi Arabia: a systematic review and meta-analysis
Bibliographic record
Abstract
Road traffic accidents (RTAs) remain a significant public health concern worldwide, contributing to high morbidity and mortality rates. In Saudi Arabia, RTAs are a leading cause of injuries and fatalities. This study aimed to analyze the epidemiological patterns, demographics, crash characteristics, and injury outcomes of RTAs in Saudi Arabia. This systematic review and meta-analysis followed PRISMA 2020 guidelines, with a comprehensive search in PubMed, Scopus, and Web of Science using relevant keywords and MeSH terms. Observational studies reporting data on RTAs in Saudi Arabia were included. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). Pooled prevalence estimates were calculated using a random-effects model, and heterogeneity was assessed using the I² statistic and Cochran's Q test. A total of 10 studies were included, representing diverse regions of Saudi Arabia and involving 154,165 participants. Male predominance was consistent across studies, with males comprising 59.1% to 91% of participants. Younger males (20–35 years) were disproportionately affected, often engaging in high-risk behaviors such as speeding and mobile phone use. Head and neck injuries were the most common (50%–63%), followed by chest and abdominal injuries. Urban areas accounted for the majority of accidents (63%–68.6%). Pooled prevalence estimates revealed substantial heterogeneity across studies (I² > 90%). RTAs in Saudi Arabia predominantly affect young males and are frequently associated with high-risk behaviors and urbanization. While safety measures have mitigated some outcomes, targeted interventions addressing risky behaviors, traffic enforcement, and emergency care are essential to reduce the burden of RTAs further.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.002 | 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".