Estimated crash injury risk and crash characteristics for motorsport drivers.

OBJECTIVE Motorsport crash events are complex and driver restraint systems are unique to the motorsport environment. The National Association for Stock Car Auto Racing, Incorporated (NASCAR®) crash and medical datasets provide an opportunity to assess crash statistics and the relationship between crash characteristics and driver injury. Injury risk curves can estimate driver injury risk and can be developed using vehicle incident data recorder information as inputs. These relationships may provide guidance and insight for at-track emergency response, driver triage and treatment protocols. METHOD Eight race seasons of crash and medical record data (including Association for the Advancement of Automotive Medicine Abbreviated Injury Scale (AIS) scores) from the Monster Energy NASCAR Cup Series & NASCAR Xfinity Series were processed and analyzed. Multiple logistic regression modeling was used to produce injury risk curves from longitudinal and lateral resultant change in velocity, resultant peak acceleration, principal direction of force and the number of impacts per incident. RESULTS 2065 Unique IDR data files were matched with 246 cases of driver injury or sub-injury (severity below AIS 1) and 1819 no-injury cases. Multiple logistic regression modeling showed increasing resultant change in velocity, resultant peak acceleration and the number of impacts during a crash event all increase estimated driver injury risk. After accounting for the other predictors in the model, right lateral impacts were found to have a lower estimated injury risk. The model produced an Area Under the Receiver Operating Characteristics curve of 0.80. Across the eight race seasons in this study the overall average resultant change in velocity was 34.4 kph (21.4 mph) and the average resultant peak acceleration was 19.0 G for an average of 258 crashes per season. For 2011 through 2015, full time drivers experienced 134 times more crashes per mile traveled than passenger vehicles, but experienced 9.3 times fewer injuries per crash. CONCLUSION Multiple logistic regression was used to estimate AIS 1+ injury only and AIS 1+ with sub-injury risk for motorsport drivers using motorsport-specific crash and medical record databases. The injury risk estimate models can provide future guidance and insight for at-track emergency medical response dispatch immediately following an on-track crash. These models may also inform future driver triage protocols and influence future expenditures on motorsports safety research.

[1]  Thomas Gideon,et al.  Testing, Development & Implementation of an Incident Data Recorder System for Stock Car Racing , 2011 .

[2]  John W. Melvin,et al.  Examination of a Properly Restrained Motorsport Occupant , 2013 .

[3]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[4]  J. Concato,et al.  A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.

[5]  John W. Melvin,et al.  Stock Car Racing Driver Restraint – Development and Implementation of Seat Performance Specification , 2008 .

[6]  Joel D Stitzel,et al.  Estimated Injury Risk for Specific Injuries and Body Regions in Frontal Motor Vehicle Crashes , 2015, Traffic injury prevention.

[7]  Joel D Stitzel,et al.  Evaluation of the effectiveness of toe board energy-absorbing material for foot, ankle, and lower leg injury reduction , 2018, Traffic injury prevention.

[8]  John W. Melvin,et al.  Improved Seat Belt Restraint Geometry for Frontal, Frontal Oblique and Rollover Incidents , 2015 .

[9]  Carol A C Flannagan,et al.  Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. , 2011, Accident; analysis and prevention.

[10]  Melonie P. Heron,et al.  Deaths: Leading Causes for 2016. , 2018, National vital statistics reports : from the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System.

[11]  Dean L Sicking,et al.  Crash protection of stock car racing drivers--application of biomechanical analysis of Indy car crash research. , 2006, Stapp car crash journal.

[12]  George Bahouth,et al.  Development of URGENCY 2.1 for the prediction of crash injury severity , 2004 .

[13]  Michael Gernhardt,et al.  Development of head injury assessment reference values based on NASA injury modeling. , 2011, Stapp car crash journal.

[14]  Dean L Sicking,et al.  Initial In-Service Performance Evaluation of the SAFER Racetrack Barrier , 2004 .

[15]  Tsuyoshi Yasuki,et al.  A Study of Driver Injury Mechanism in High Speed Lateral Impacts of Stock Car Auto Racing Using a Human Body FE Model , 2011 .

[16]  Joel D. Stitzel,et al.  Influence of Driver Position and Seat Design on Thoracolumbar Loading During Frontal Impacts , 2018 .