Traffic injury prevention
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Vehicle change in velocity (delta-v) is a widely used crash severity metric used to estimate occupant injury risk. Despite its widespread use, delta-v has several limitations. Of most concern, delta-v is a vehicle-based metric which does not consider the crash pulse or the performance of occupant restraints, e.g. seatbelts and airbags. Such criticisms have prompted the search for alternative impact severity metrics based upon vehicle kinematics. The purpose of this study was to assess the ability of the occupant impact velocity (OIV), acceleration severity index (ASI), vehicle pulse index (VPI), and maximum delta-v (delta-v) to predict serious injury in real world crashes. ⋯ The broad findings of this study suggest it is feasible to improve injury prediction if we consider adding restraint performance to classic measures, e.g. delta-v. Applications, such as advanced automatic crash notification, should consider the use of different metrics for belted versus unbelted occupants.
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Traffic injury prevention · Jan 2015
New Methodology for an Expert-Designed Map From International Classification of Diseases (ICD) to Abbreviated Injury Scale (AIS) 3+ Severity Injury.
There has been a longstanding desire for a map to convert International Classification of Diseases (ICD) injury codes to Abbreviated Injury Scale (AIS) codes to reflect the severity of those diagnoses. The Association for the Advancement of Automotive Medicine (AAAM) was tasked by European Union representatives to create a categorical map classifying diagnoses codes as serious injury (Abbreviated Injury Scale [AIS] 3+), minor/moderate injury (AIS 1/2), or indeterminate. This study's objective was to map injury-related ICD-9-CM (clinical modification) and ICD-10-CM codes to these severity categories. ⋯ Robust maps of ICD-9-CM and ICD-10-CM injury codes to AIS severity categories (3+ versus <3) were successfully created from an in-person panel discussion and electronic survey. These maps provide a link between the common ICD diagnostic lexicons and the AIS severity coding system and are of value to injury researchers, public health scientists, and epidemiologists using large databases without available AIS coding.
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Traffic injury prevention · Jan 2015
On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants.
The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures. ⋯ The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.
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Traffic injury prevention · Jan 2015
Alcohol-related road traffic injuries in Al-Ain City, United Arab Emirates.
We aimed to prospectively study the demography, severity of injury and outcome of alcohol-related road traffic collision (RTC) injuries in the United Arab Emirates. ⋯ Self reported alcohol-related car collisions in Al-Ain City had a low incidence. It affected older Emirati male nationals and was associated with lower revised trauma score, mainly due to head injury. There is a need for a national registry with data on alcohol abuse so as to assess its effects and strategies for its prevention.
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Traffic injury prevention · Jan 2015
The risk of a safety-critical event associated with mobile device use in specific driving contexts.
We explored drivers' mobile device use and its associated risk of a safety-critical event (SCE) in specific driving contexts. Our premise was that the SCE risk associated with mobile device use increases when the driving task becomes demanding. ⋯ Drivers' engagement in mobile device subtasks varies by driving context. The SCE risk associated with mobile device use is dependent on the types of subtasks performed and the driving context. The findings of this exploratory study can be applied to the design of driver-vehicle interfaces that mitigate distraction by preventing visual-manual subtasks while driving.