Traffic injury prevention
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Traffic injury prevention · Jan 2015
Lumbar Bone Mineral Density Phantomless Computed Tomography Measurements and Correlation with Age and Fracture Incidence.
Low bone quality is a contributing factor to motor vehicle crash (MVC) injury. Quantification of occupant bone mineral density (BMD) is important from an injury causation standpoint. The first aim of this study was to validate a technique for measuring lumbar volumetric BMD (vBMD) from phantomless computed tomography (CT) scans. The second aim was to apply the validated phantomless technique to quantify lumbar vBMD in Crash Injury Research and Engineering Network (CIREN) occupants for correlation with age, fracture incidence, and osteopenia/osteoporosis diagnoses. ⋯ Because lumbar vBMD was estimated from phantomless CT scans with accuracy similar to qCT, the phantomless technique can be broadly applied to both prospectively and retrospectively assess patient bone quality for research and clinical studies related to MVCs, falls, and aging.
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Traffic injury prevention · Jan 2015
Observational StudyThe prevalence of distraction among passenger vehicle drivers: a roadside observational approach.
Distracted driving contributes to a large proportion of motor vehicle crashes, yet little is known about the prevalence of distracted driving and the specific types of distracting behaviors. The objective of this study was to estimate the prevalence of driver distraction using a roadside observational study design. ⋯ When using similar methodology, roadside observational studies generate comparable prevalence estimates of driver distraction as naturalistic driving studies. Driver distraction is a common problem among passenger vehicle drivers. Despite the increased awareness on the dangers of texting and cell phone use while driving, these specific activities were 2 of the most frequently observed distractions. There is a continued need for road safety education about the dangers of distracted driving, especially for younger drivers.
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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.