Pattern of Severity of Road Traffic Injuries Among Pedestrians in Low- and Middle-Income Countries: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Background Low-and middle-income countries (LMICs) contribute about 93 per cent of road traffic injuries (RTIs) and deaths worldwide with a significant proportion of pedestrians (22 per cent). Various scales are used to assess the pattern of injury severity, which are useful in predicting the outcomes of RTIs. We conducted this systematic review to determine the pattern of RTI severity among pedestrians in LMICs. Methods We searched the electronic databases PubMed, CINHAL, CENTRAL, Web of Science, Scopus, EMBASE, ProQuest and SciELO, and examined the references of the selected studies. Original research articles published on the RTI severity among pedestrians in LMICs during 1997-2016 were eligible for this review. Quality of publications was assessed using an adapted Newcastle-Ottawa Scale of observational studies. Findings of this study were presented as a meta-summary. Results Five articles from 3 LMICs were eligible for the systematic review. Abbreviated Injury Score, Glasgow Coma Scale and Maxillofacial Injury Severity Score were used to assess the injury severity in the selected studies. In a multicentric study from China (2013), 21, 38 and 19 per cent pedestrians with head injuries had AIS scores 1-2, 3-4 and 5-6, respectively. In another study from China (2010), the proportion of AIS score 1-2 and AIS score 3 and above (serious to un-survivable) injuries occurred due to crash with sedan cars were 65 and 35 per cent, respectively. Such injuries due to minivan crashes were 49.5 per cent and 50.5 per cent, respectively. Two studies Ikeja, Nigeria (2014) and Elazig, Turkey (2009) presented, 24.5 and 32.5 per cent injured had a severe head injury (GCS < 8), respectively. In another study from Ibadan, Nigeria (2014), the severe maxillofacial injuries were seen in the victims of car/minibus pedestrian crashes 46 per cent, and 17 per cent had a fatal outcome. Conclusion A varied percent of pedestrians (24.5 to 57 percent) had road traffic injuries of serious to fatal nature, depending on type of collision and injury severity scale. This study pressed the need to conduct studies with a robust methodology on the pattern of RTI severity among pedestrians to guide the programme managers, researchers and policymakers in LMICs to formulate the policies and programmes to save the pedestrian lives. African relevance Prior RTI research reveals that pedestrians and cyclists were at the highest risk of fatality of in Sub-Saharan Africa, whereas motorcyclists had significantly higher fatality rates in Asian countries such as Malaysia and Thailand (1–3). Fifty-seven type of injury severity scoring systems have been developed to assess the injury severity for triage and timely decision making for patient treatment need, outcome prediction, quality of trauma care, and epidemiological research and evaluation (4,5). We found two studies from sub-Saharan Africa in this review which showed that severe pedestrian injuries ranged from 24.5 to 46 per cent of total pedestrian RTIs. Despite the findings of review affected by limited and variegated sample, it could be useful to guide for future research.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".