Pattern of Severity of Road Traffic Injuries Among Pedestrians in Low- and Middle-Income Countries: A Systematic Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,008 |
| Bibliométrie | 0,014 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».