A weighted logistic regression analysis for predicting the odds of head/face and neck injuries during rollover crashes.

Abstract

A weighted logistic regression with careful selection of crash, vehicle, occupant and injury data and sequentially adjusting the covariants, was used to investigate the predictors of the odds of head/face and neck (HFN) injuries during rollovers. The results show that unbelted occupants have statistically significant higher HFN injury risks than belted occupants. Age, number of quarter-turns, rollover initiation type, maximum lateral deformation adjacent to the occupant, A-pillar and B-pillar deformation are significant predictors of HFN injury odds for belted occupants. Age, rollover leading side and windshield header deformation are significant predictors of HFN injury odds for unbelted occupants. The results also show that the significant predictors are different between head/face (HF) and neck injury odds, indicating the injury mechanisms of HF and neck injuries are different.