Development of a Simple Tool to Enhance Driver Identification in Motorcycle Accidents, Covering both Collision and Non-collision Subtypes
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Abstract
In Lampang province, Thailand, motorcycle-related road traffic collisions are common, and the differentiation between drivers and passengers is crucial for legal proceedings. This study aimed to develop and validate two multivariable predictive models that differentiate between motorcycle drivers and passengers based on demographics and fracture patterns. Retrospective data from patients involved in motorcycle accidents from January 2014 to December 2017 at Lampang Hospital were used to construct models. Model 1 focused on motorcycle collisions with other vehicles, while Model 2 focused on non-collision motorcycle accidents. Predictor selection was done through multivariable logistic regression using a stepwise backward elimination method. A total of 1,816 patients with fractures from motorcycle accidents were included, with 1,583 categorized as drivers and 233 as passengers. The final model identified six significant predictors: age categories, gender, pelvis and lumbar spine fractures, wrist and hand level fractures, femur fractures, and lower leg fractures. Both models demonstrated acceptable discriminative abilities, indicating their potential as userfriendly tools for medical and legal adjudication following motorcycle accidents.
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