Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention

• Published in: PloS one Vol. 16; no. 1; p. e0245636
• Main Authors: Giummarra, Melita J, Beck, Ben, Gabbe, Belinda J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27-01-2021; Public Library of Science (PLoS)
• Subjects: Accidents, Traffic - classification; Accidents, Traffic - mortality; Accidents, Traffic - prevention & control; Adolescent; Adult; Causes of; Collision avoidance; Compensation; Craniocerebral Trauma - classification; Craniocerebral Trauma - mortality; Craniocerebral Trauma - prevention & control; Crash injuries; Data Mining; Demographic aspects; Demographics; Earth Sciences; Editing; Education; Engineering and Technology; Epidemiology; Fatalities; Female; Head injuries; Health aspects; Health care; Hospitals; Humans; Identification and classification; Injury analysis; Injury prevention; Male; Medical research; Medicine; Medicine and Health Sciences; Methodology; Middle Aged; Mortality; Motor vehicles; Motorcycles; Pain; Pedestrians; People and Places; Physical Sciences; Prevention; Preventive medicine; Public health; Registries; Research facilities; Reviews; Roads; Roads & highways; Social aspects; Social Sciences; Socioeconomic factors; Traffic accidents; Traffic accidents & safety; Traffic safety; Trauma; Vehicles; Victoria - epidemiology; Victoria; Australia; United Kingdom > UK; Victoria Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0245636

Road traffic injuries are a leading cause of morbidity and mortality globally. Understanding circumstances leading to road traffic injury is crucial to improve road safety, and implement countermeasures to reduce the incidence and severity of road trauma. We aimed to characterise crash characteristics of road traffic collisions in Victoria, Australia, and to examine the relationship between crash characteristics and fault attribution. Data were extracted from the Victorian State Trauma Registry for motor vehicle drivers, motorcyclists, pedal cyclists and pedestrians with a no-fault compensation claim, aged > = 16 years and injured 2010-2016. People with intentional injury, serious head injury, no compensation claim/missing injury event description or who died < = 12-months post-injury were excluded, resulting in a sample of 2,486. Text mining of the injury event using QDA Miner and Wordstat was used to classify crash circumstances for each road user group. Crashes in which no other was at fault included circumstances involving lost control or avoiding a hazard, mechanical failure or medical conditions. Collisions in which another was predominantly at fault occurred at intersections with another vehicle entering from an adjacent direction, and head-on collisions. Crashes with higher prevalence of unknown fault included multi-vehicle collisions, pedal cyclists injured in rear-end collisions, and pedestrians hit while crossing the road or navigating slow traffic areas. We discuss several methods to promote road safety and to reduce the incidence and severity of road traffic injuries. Our recommendations take into consideration the incidence and impact of road trauma for different types of road users, and include engineering and infrastructure controls through to interventions targeting or accommodating human behaviour.