Citation

Alzaffin K, Kaye SA, Watson A, Haque MM. Anal. Meth. Accid. Res. 2023; 40: e100291.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.amar.2023.100291

PMID

unavailable

Abstract

Injury severity in police crash reports is usually recorded in three to five classes, including property damage, slight, moderate, serious, and fatal injuries. Among these classifications, serious injuries are commonly classified as cases where a road user is admitted to a hospital. In this classification, the length of hospital stay is not differentiated, whether one day or ten days, as long as the road user has been admitted to the hospital. As such, the inferences drawn from assuming that all serious injuries (1 if a road user is admitted to the hospital; 0 otherwise) are at the same severity level inherently suffer from aggregation bias and may not provide a thorough understanding of this severity category. This study proposes a hazard-based duration modelling approach to examine the severity of serious injury crashes measured in a continuous spectrum. Specifically, using the length of hospital stay as the measure of serious injuries, a random parameters hazard-based duration model with heterogeneity in means was applied to model serious injury crashes obtained by linking crash records in police and hospital databases. To address temporal instability, the injury records sources from Abu Dhabi, United Arab Emirates (UAE), between 2015 and 2019 were modelled separately for each year. The results showed that factors positively associated with more serious injury severity (prolonged length of hospital stay) are rural areas, high posted speed limits of 100-160 km/h, overturned crashes, speeding, impaired driving, involvements of a heavy vehicle, nighttime crashes, lack of restraint usage, and injuries to the head or lower extremities. In particular, speeding violations during nighttime are positively associated with more serious injuries. Furthermore, the means of the random parameters of head injury are positively influenced by speeding, lack of restraint usage, and motorcycle involvement through the heterogeneity-in-mean specification of the hazard-based duration model. The proposed modelling approach to model serious traffic injuries using a hazard-based duration model provides a comprehensive understanding of the factors associated with serious injuries.

Language: en

Keywords

Hazard-based duration model; Heterogeneity; Police-hospital linked data; Random parameters; Serious injury; Temporal instability