A data fusion approach of police-hospital linked data to examine injury severity of motor vehicle crashes. (January 2023)

• Record Type: Journal Article
• Authors: Alzaffin, Khalid; Kaye, Sherrie-Anne; Watson, Angela; Haque, Md Mazharul
• Is Part Of: Accident analysis and prevention. Volume 179(2023)
• Publication Date: 2023-01
• Subjects: Police-hospital linked data -- Data fusion approach -- Injury severity -- Unobserved heterogeneity -- Random thresholds random parameters -- Probit model; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2022.106897
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Police-reported and police-hospital linked data are compared in injury severity models. A random thresholds random parameters hierarchical ordered probit model is superior. Head-on crashes were misestimated in the model with police-reported data. Motorcycle and overturned crashes were only found significant in the model with police-hospital linked data. Findings highlight the accuracy of police-hospital linked data for understanding traffic injuries. Abstract: Injury severity studies typically rely on police-reported crash data to examine risk factors associated with traffic injuries. The police crash database includes essential information on roadways, crashes and driver-vehicle characteristics but may not contain accurate and sufficient information on traffic injuries. Despite sizable efforts on injury severity modelling, very few studies have employed hospital records to classify injury severities accurately. As such, the inferences drawn from the police-recorded injury severity classifications may be questionable. This study investigates factors affecting road traffic injuries of motor vehicle crashes in two approaches (1) police-reported injury severity data and (2) a data fusion approach linking police and hospital records. Data from 2015 to 2019 were collected from the Abu Dhabi Traffic Police Department and linked with hospital records by the Department of Health, Abu Dhabi. A total of 6, 333 casualty crashes were categorised into non-severe, severe, and fatal crashes following police-reported data and non-hospitalised, hospitalised and fatal crashes based on the police-hospital linked data. The state-of-the-art random thresholds random parameters hierarchical ordered Probit models were then employed to examine the differences in factors affecting crash-injury severities between police-reported and police-hospital linked data. While there are similarities between these two approaches, there are numerous notable differences in injury severity factors. For instance, head-on collisions are associated with high crash-injury severities in the model with police-hospital linked data, but they tend to show low injury severities in the model with police-reported data. In addition, the police-reported approach identifies that crashes occurred in remote areas and angle collisions are associated with low injury severities, which is not intuitive. These findings highlight that modelling the misclassified injury severity in police crash data may lead to wrong estimations and misleading inferences. Instead, the data fusion approach of police-hospital linked data provides critical and accurate insights into road traffic injuries and is a valuable approach for understanding traffic injuries.