2005Unpublished venueRequires access

An evaluation of the risk of brain injury for real-world crashes involving occupants of passenger vehicles in ANCIS

David Logan, James Edmond Scully, Brian Fildes

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Abstract

Brain injuries are a major cause of death and permanent disability for victims of motor vehicle crashes. The present study isolated characteristics of crashes involving passenger vehicles that were likely to result in brain injury for their occupants. The study used data from 268 seriously injured or killed occupants collected as part of the Australian National Crash In-depth Study (ANCIS). Univariate analyses were completed on a range of potential confounders in order build a multivariate logistic regression model of the risk of brain injury. Potential confounders included a wide range of variables related to the configuration of the crash, the severity of the impact, the size of the vehicles involved, the deployment of airbags, the occupant's age and sex and the level of damage to the case occupant's vehicle. The resulting model showed that the risk of brain injury was significantly dependent on the geometry of the collision partner, the speed zone in which the crash occurred, the level of intrusion into the passenger cabin and the area of the occupant's vehicle that received most of the damage. The study discusses possible countermeasures by reviewing the contact sources that resulted in brain injuries for occupants in the sample.

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What this paper is about

Brain injuries are a major cause of death and permanent disability for victims of motor vehicle crashes. The present study isolated characteristics of crashes involving passenger vehicles that were likely to result in brain injury for their occupants. The study used data from 268 seriously injured or killed occupants collected as part of the Australian National Crash In-depth Study (ANCIS). Univariate analyses were completed on a range of potential confounders in order build a multivariate logistic regression model of the risk of brain injury. Potential confounders included a wide range of variables related to the configuration of the crash, the severity of the impact, the size of the vehicles involved, the deployment of airbags, the occupant's age and sex and the level of damage to the case occupant's vehicle. The resulting model showed that the risk of brain injury was significantly dependent on the geometry of the collision partner, the speed zone in which the crash occurred, the level of intrusion into the passenger cabin and the area of the occupant's vehicle that received most of the damage. The study discusses possible countermeasures by reviewing the contact sources that resulted in brain injuries for occupants in the sample.

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Available abstract

Brain injuries are a major cause of death and permanent disability for victims of motor vehicle crashes. The present study isolated characteristics of crashes involving passenger vehicles that were likely to result in brain injury for their occupants. The study used data from 268 seriously injured or killed occupants collected as part of the Australian National Crash In-depth Study (ANCIS). Univariate analyses were completed on a range of potential confounders in order build a multivariate logistic regression model of the risk of brain injury. Potential confounders included a wide range of variables related to the configuration of the crash, the severity of the impact, the size of the vehicles involved, the deployment of airbags, the occupant's age and sex and the level of damage to the case occupant's vehicle. The resulting model showed that the risk of brain injury was significantly dependent on the geometry of the collision partner, the speed zone in which the crash occurred, the level of intrusion into the passenger cabin and the area of the occupant's vehicle that received most of the damage. The study discusses possible countermeasures by reviewing the contact sources that resulted in brain injuries for occupants in the sample.

Key concepts: Crash, Logistic regression, Confounding, Poison control, Injury prevention, Environmental health, Univariate, Multivariate statistics

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