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

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 statist...

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Bibliographic Details
Published in:Annual proceedings - Association for the Advancement of Automotive Medicine 2007, Vol.51, p.363-379
Main Authors: Hu, Jingwen, Chou, Clifford C, Yang, King H, King, Albert I
Format: Article
Language:English
Subjects:
Accidents, Traffic
Automobiles
Craniocerebral Trauma - etiology
Databases as Topic
Facial Injuries - etiology
Female
Field Data Analysis
Head Protective Devices
Humans
Logistic Models
Male
Neck Injuries - etiology
Odds Ratio
Predictive Value of Tests
Risk Assessment
Safety
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Summary: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.
ISSN:1540-0360