Injury Severity of Motorcycle Riders Involved in Traffic Crashes in Hunan, China: A Mixed Ordered Logit Approach

BackgroundPaediatric injuries have significant physical, psychological and socioeconomic consequences. This study aimed to determine the epidemiology and pattern of paediatric injuries presenting at a teaching hospital in Enugu South-East Nigeria. MethodsA retrospective study of injured patients less than 18 years who presented to the Accident and Emergency department of University of Nigeria Teaching Hospital Ituku/Ozalla, in Enugu Nigeria, was conducted from 1st January to 31st December 2015. Analysis of data was done using Statistical Package for the Social Sciences version 22.0. ResultsA total of 1191 children presented to the hospital within the study period and 120 injured paediatric patients (10.1%) with 170 injuries were attended to in the A & E. Adolescents (12 -17 years) accounted for 34.2% of injuries and males (70%) were more affected than the females. Road traffic crashes and falls were the most common causes of paediatric injuries. There was a significant correlation between age of the children and the aetiology of the injuries (X2 = 28.08, p = 0.001). Bruises/lacerations/abrasions (27.6%) and fractures (27.1%) were the most common types of injuries sustained. The injury severity score (ISS) of the patients ranged from 1-25 with a mean of 6.4 + 2.8. Roadside/street (35%), school (23.3%) and home (21.7%) were the main locations where injuries occurred. Mean injury-arrival interval was 1.2 + 0.7 days. Majority (67.5%) of children had full recovery and the mortality rate was 5.8%.ConclusionsRoad traffic crashes and falls were the most common cause of paediatric injuries. Soft tissue trauma and fractures were the predominant types of injuries. Roadside, school and home were the leading locations of childhood injuries. Prevention of road traffic crashes and falls, prompt treatment of injuries, and rehabilitation of injured children will reduce the negative impact of paediatric injuries.

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Injury severity on traffic crashes: A text mining with an interpretable machine-learning approach

Safety Science ◽

10.1016/j.ssci.2020.104988 ◽

2020 ◽

Vol 132 ◽

pp. 104988 ◽

Cited By ~ 1

Author(s):

Cristian Arteaga ◽

Alexander Paz ◽

JeeWoong Park

Keyword(s):

Machine Learning ◽

Text Mining ◽

Injury Severity ◽

Learning Approach ◽

Traffic Crashes ◽

Interpretable Machine Learning ◽

Machine Learning Approach

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Factors Associated with Higher Levels of Injury Severity in Occupants of Motor Vehicles That Were Severely Damaged in Traffic Crashes in Kentucky, 2000-2001

Traffic Injury Prevention ◽

10.1080/15389580490435169 ◽

2004 ◽

Vol 5 (2) ◽

pp. 144-150 ◽

Cited By ~ 40

Author(s):

MICHAEL SINGLETON ◽

HUIFANG QIN ◽

JINGYU LUAN

Keyword(s):

Injury Severity ◽

Motor Vehicles ◽

Traffic Crashes ◽

Factors Associated

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Pedestrian Injury Severities in Pedestrian-Vehicle Crashes and the Partial Proportional Odds Logit Model: Accounting for Age Difference

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119842828 ◽

2019 ◽

Vol 2673 (5) ◽

pp. 731-746 ◽

Cited By ~ 7

Author(s):

Yang Li ◽

Wei (David) Fan

Keyword(s):

Logit Model ◽

Injury Severity ◽

Parameter Estimates ◽

Age Difference ◽

Age Group ◽

Vehicle Crashes ◽

Ordered Logit ◽

Proportional Odds ◽

Pedestrian Injury ◽

Injury Severities

This study investigates factors that significantly contribute to the severity of pedestrian injuries resulting from pedestrian-vehicle crashes. Multinomial logit (MNL) models, mixed logit (ML) models, and ordered logit/probit models have been widely used in modeling crash injury severity, including pedestrian injury severity in pedestrian-vehicle crashes. However, both MNL and ML models treat injury severity levels as non-ordered, ignoring the inherent hierarchical nature of crash injury severities, and the data used in ordered logit models need to be strictly subjected to the proportional odds (PO) assumption. In this study, a partial proportional odds (PPO) logit model approach is employed to explore the issues of pedestrian safety associated with each age group: young (aged under 24), middle-aged (aged 25–55), and older pedestrians (aged over 55). Data used in this study are police-reported pedestrian crash data collected from 2007 to 2014 in North Carolina. A variety of motorist, pedestrian, environmental, and roadway characteristics are inspected. Results from likelihood ratio tests statistically show the better performance of developing separate injury severity models for each age group compared with estimating a single model utilizing all data. Relevant parameter estimates and associated marginal effects are used to interpret the results, followed by recommendations made in the concluding section.

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