A scoping review of road traffic data systems in the Eastern Mediterranean Region
Bibliographic record
Abstract
Background: Road traffic injury is a major global health risk, however, under-reporting of road traffic crashes data and the use of different reporting systems have made it difficult to compare data across countries. Aim: To examine published and grey literature for better understanding of available health and non-health road traffic data systems in the Eastern Mediterranean Region (EMR) countries. Methods: We conducted a systematic search of databases to identify studies reporting road traffic data systems in the EMR countries between 2011 and January 2022. We also searched grey literature on the websites of government, WHO, World Bank, UNICEF, regional economic commissions and other relevant institutions. We assembled the data in Microsoft Excel and presented the counts of data sources, data types and data quality. Results: We included 84 of 2238 studies accessed in this review. One-third of the publications was from the Islamic Republic of Iran while 10% was from Pakistan. Police databases were the primary sources of data in most of the studies (79%) while hospital and death registration systems together accounted for one-quarter of the databases. The most common indicators reported in the publications were deaths (61%), crashes (48%) and injuries (35%). Only 40% of the studies disaggregated their analyses by gender and 44% by age. No papers identified permanently linked data sources, although more than a quarter of the papers reviewed used some form of modelling or data mining. Conclusion: This scoping review highlights an over-reliance on police data, poor quality traffic data systems, and multiple stakeholders collecting similar data, leading to redundancy. EMR countries need to establish robust road safety data systems to provide data for relevant policies and interventions.
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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 | 0.029 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".