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

Abstract

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-V s 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.

Emergency Response to Vehicle Collisions: Feedback from Emergency Medical Service Providers

“…A more detailed observation into issues related to information about the vehicle suggested several important areas. There was an expectation that information about the vehicle kinematics and presence of a rollover would be mentioned, as it aligned with the necessary inputs for previous predictive injury studies [11][12][13][14]. Less expected, however, was the expressed interest in quantifying and conveying the degree of deformation experienced by the vehicle, particularly within the occupant compartment.…”

“…A second area of technological advancement that can improve EMS response effectiveness involves the development of injury prediction metrics based on MVC kinematics. Weaver et al [11] produced predictive injury risk curves per affected body regions for specified frontal, single-impact crashes. Theoretically, their injury detection algorithm could be combined with electronic data recorder (EDR) outputs on a quasi-real time basis to provide EMS with an initial assessment of potential injuries during an MVC, as suggested by some AACN future development plans.…”

“…Theoretically, their injury detection algorithm could be combined with electronic data recorder (EDR) outputs on a quasi-real time basis to provide EMS with an initial assessment of potential injuries during an MVC, as suggested by some AACN future development plans. Stigson et al [12] and Andricevic et al [13] similarly produced injury risk curves for frontal and oblique frontal impacts, respectively, but their models tended to be less specific than those in Weaver et al [11], as they were based on a maximum abbreviated injury scale (MAIS) score of 2+ that was not differentiated by body region. These prediction algorithms can also be influenced by occupant details such as age, height, and weight, (as also noted in [10]) which may not be easy to obtain prior to EMS arrival at an MVC scene.…”

The analysis of the impact on the safety of traffic participants and causing the accidents of working machines – tractors

“…Since this study analyses traffic accidents in which one of the participants was a tractor, the focus is on the injuries in the field of transport and agriculture. Literary review notes that there are not so many studies that deal specifically with the analysis of traffic accidents in which one of the participants was a tractor (for more see [25][26][27][28][29]). In some studies there have been applied descriptive statistics and ANOVA, while more rigorous statistical tests such as post hoc tests (Scheffe) have not been applied.…”

Rib Cortical Bone Fracture Risk as a Function of Age and Rib Strain: Updated Injury Prediction Using Finite Element Human Body Models

“…Among patients admitted to emergency care for blunt chest trauma, MVCs are the major cause of injury; moreover, having three (or more) fractured ribs is a risk factor for mortality ( Sirmali et al, 2003 ; Veysi et al, 2009 ; Battle et al, 2012 ). Epidemiological studies reveal that risk of thoracic injury, including rib fractures, in MVCs increases with impact speed and age—and is greater for females than for males ( Bose et al, 2011 ; Carter et al, 2014 ; Weaver et al, 2015 ; Brumbelow, 2019 ; Forman et al, 2019 ). Increased impact speed increases the energy (and concomitant mechanical load) transferred to the occupant’s thorax from vehicle safety systems, e.g., seatbelts.…”