Estimated Injury Risk for Specific Injuries and Body Regions in Frontal Motor Vehicle Crashes

Objective: Injury risk curves estimate motor vehicle crash (MVC) occupant injury risk from vehicle, crash, and/or occupant factors. Many vehicles are equipped with event data recorders (EDRs) that collect data including the crash speed and restraint status during a MVC. This study's goal was to use regulation-required data elements for EDRs to compute occupant injury risk for (1) specific injuries and (2) specific body regions in frontal MVCs from weighted NASS-CDS data.

Methods: Logistic regression analysis of NASS-CDS single-impact frontal MVCs involving front seat occupants with frontal airbag deployment was used to produce 23 risk curves for specific injuries and 17 risk curves for Abbreviated Injury Scale (AIS) 2+ to 5+ body region injuries. Risk curves were produced for the following body regions: head and thorax (AIS 2+, 3+, 4+, 5+), face (AIS 2+), abdomen, spine, upper extremity, and lower extremity (AIS 2+, 3+). Injury risk with 95% confidence intervals was estimated for 15–105 km/h longitudinal delta-Vs and belt status was adjusted for as a covariate.

Results: Overall, belted occupants had lower estimated risks compared to unbelted occupants and the risk of injury increased as longitudinal delta-V increased. Belt status was a significant predictor for 13 specific injuries and all body region injuries with the exception of AIS 2+ and 3+ spine injuries. Specific injuries and body region injuries that occurred more frequently in NASS-CDS also tended to carry higher risks when evaluated at a 56 km/h longitudinal delta-V. In the belted population, injury risks that ranked in the top 33% included 4 upper extremity fractures (ulna, radius, clavicle, carpus/metacarpus), 2 lower extremity fractures (fibula, metatarsal/tarsal), and a knee sprain (2.4–4.6% risk). Unbelted injury risks ranked in the top 33% included 4 lower extremity fractures (femur, fibula, metatarsal/tarsal, patella), 2 head injuries with less than one hour or unspecified prior unconsciousness, and a lung contusion (4.6–9.9% risk). The 6 body region curves with the highest risks were for AIS 2+ lower extremity, upper extremity, thorax, and head injury and AIS 3+ lower extremity and thorax injury (15.9–43.8% risk).

Conclusions: These injury risk curves can be implemented into advanced automatic crash notification (AACN) algorithms that utilize vehicle EDR measurements to predict occupant injury immediately following a MVC. Through integration with AACN, these injury risk curves can provide emergency medical services (EMS) and other patient care providers with information on suspected occupant injuries to improve injury detection and patient triage.     

Analysis of risk factors affecting driver injury and crash injury with drivers under the influence of alcohol (DUI) and non-DUI

Objective: The objective of this research was to study risk factors that significantly influence the severity of crashes for drivers both under and not under the influence of alcohol.

Methods: Ordinal logistic regression was applied to analyze a crash data set involving drivers under and not under the influence of alcohol in China from January 2011 to December 2014.

Results: Four risk factors were found to be significantly associated with the severity of driver injury, including crash partner and intersection type. Age group was found to be significantly associated with the severity of crashes involving drivers under the influence of alcohol. Crash partner, intersection type, lighting conditions, gender, and time of day were found to be significantly associated with severe driver injuries, the last of which was also significantly associated with severe crashes involving drivers not under the influence of alcohol.

Conclusions: This study found that pedestrian involvement decreases the odds of severe driver injury when a driver is under the influence of alcohol, with a relative risk of 0.05 compared to the vehicle-to-vehicle group. The odds of severe driver injury at T-intersections were higher than those for traveling along straight roads. Age was shown to be an important factor, with drivers 50–60 years of age having higher odds of being involved in severe crashes compared to 20- to 30-year-olds when the driver was under the influence of alcohol.

When the driver was not under the influence of alcohol, drivers suffered more severe injuries between midnight and early morning compared to early nighttime. The vehicle-to-motorcycle and vehicle-to-pedestrian groups experienced less severe driver injuries, and vehicle collisions with fixed objects exhibited higher odds of severe driver injury than did vehicle-to-vehicle impacts. The odds of severe driver injury at cross intersections were 0.29 compared to travel along straight roads. The odds of severe driver injury when street lighting was not available at night were 3.20 compared to daylight. The study indicated that female drivers are more likely to experience severe injury than male drivers when not under the influence of alcohol. Crashes between midnight and early morning exhibited higher odds of severe injury compared to those occurring at other times of day.

The identification of risk factors and a discussion on the odds ratio between levels of the impact of the driver injury and crash severity may benefit road safety stakeholders when developing initiatives to reduce the severity of crashes.     

New Methodology for an Expert-Designed Map From International Classification of Diseases (ICD) to Abbreviated Injury Scale (AIS) 3+ Severity Injury

Objective: 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.

Methods: Approximately 19,000 ICD codes were mapped, including injuries from the following categories: amputations, blood vessel injury, burns, crushing injury, dislocations/sprains/strains, foreign body, fractures, internal organ, nerve/spinal cord injury, intracranial, laceration, open wounds, and superficial injury/contusion. Two parallel activities were completed to create the maps: (1) An in-person expert panel and (2) an electronic survey. The panel consisted of expert users of AIS and ICD from North America, the United Kingdom, and Australia. The panel met in person for 5 days, with follow-up virtual meetings to create and revise the maps. Additional qualitative data were documented to resolve potential discrepancies in mapping. The electronic survey was completed by 95 injury coding professionals from North America, Spain, Australia, and New Zealand over 12 weeks. ICD-to-AIS maps were created for: ICD-9-CM and ICD-10-CM. Both maps indicated whether the corresponding AIS 2005/Update 2008 severity score for each ICD code was AIS 3+, 1/2, or indeterminable. Though some ICD codes could be mapped to multiple AIS codes, the maximum severity of all potentially mapped injuries determined the final severity categorization.

Results: The in-person panel consisted of 13 experts, with 11 Certified AIS specialists (CAISS) with a median of 8 years and an average of 15 years of coding experience. Consensus was reached for AIS severity categorization for all injury-related ICD codes. There were 95 survey respondents, with a median of 8 years of injury coding experience. Approximately 15 survey responses were collected per ICD code. Results from the 2 activities were compared, and any discrepancies were resolved using additional qualitative and quantitative data from the in-person panel and survey results, respectively.

Conclusions: 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.     

An evaluation scheme for assessing the effectiveness of intersection movement assist (IMA) on improving traffic safety

Objective: Intersection movement assist (IMA) has been recognized as one of the prominent countermeasures to reduce angle crashes at intersections, which constitute 22% of total crashes in the United States. Utilizing vehicle-based sensors, vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communications, IMA offers extended vision to provide early warning for an imminent crash. However, most of IMA-related research implements their methods and strategies only in simulations, test tracks, or driving simulator studies that have quite a few assumptions and limitations and hence the effectiveness evaluations reported may not be transferable or comparable.

Methods: This study seeks to develop a generalized evaluation scheme that can be used not only to assess the effectiveness of IMA on improving traffic safety at intersections but to facilitate comparisons across similar studies. The proposed evaluation scheme utilizes the concepts of traffic conflict in terms of time-to-collision (TTC) as a crash surrogate. This approach avoids the issue of having insufficient crash frequency data for system evaluation. To measure the effectiveness of IMA on reducing traffic conflicts, a relative risk is calculated for comparing the risk of with/without using the IMA. As a proof-of-concept study, this study applied the proposed evaluation scheme and reported the effectiveness of IMA on improving traffic safety in a field operation test (FOT). Seven test scenarios were conducted at 4 intersections, and a total of 40 participants were recruited to use the IMA for 6 months.

Results: It was estimated that IMA users have 26% fewer conflicts with TTC less than 5 s and have 15% fewer conflicts with TTC less than 4 s. However, the results vary across different sites and different definitions of conflicts in terms of TTC.

Conclusions: Overall, IMA is promising to effectively reduce angle crashes related to sight obstruction and has potential to reduce not only crash frequency but crash severity.