Statistical Considerations in the Development of Injury Risk Functions

<div><p><b>Objective:</b> We address 4 frequently misunderstood and important statistical ideas in the construction of injury risk functions. These include the similarities of survival analysis and logistic regression, the correct scale on which to construct pointwise confidence intervals for injury risk, the ability to discern which form of injury risk function is optimal, and the handling of repeated tests on the same subject.</p><p><b>Methods:</b> The statistical models are explored through simulation and examination of the underlying mathematics.</p><p><b>Results:</b> We provide recommendations for the statistically valid construction and correct interpretation of single-predictor injury risk functions.</p><p><b>Conclusions:</b> This article aims to provide useful and understandable statistical guidance to improve the practice in constructing injury risk functions.</p></div

Crash safety concerns for out-of-position occupant postures: A look toward safety in highly automated vehicles

<p><b>Objective:</b> Highly automated vehicle occupants will all be passengers and may be free to ride while in postures for which existing occupant safety systems such as seat belts and airbags were not originally designed. These occupants could therefore face increased risk of injury when a crash occurs. Given that current vehicles are capable of supporting a variety of occupant postures outside of the normal design position, such as reclined or turned passengers, an evaluation of current field data was performed to better understand the risks of being out of position.</p> <p><b>Methods:</b> We investigated the frequency, demographics, and injury outcomes for out-of-position occupants using NASS-CDS. A matched analysis was performed to compare injury outcomes for out-of-position passengers with in-position drivers involved in similar crashes. Finally, case studies for out-of-position occupants were examined in the Crash Injury Research (CIREN) database.</p> <p><b>Results:</b> Only 0.5% of occupants in NASS-CDS with a coded posture were out of position at the time of crash. Of the out-of-position occupants, being turned or seated sideways was almost as likely as being reclined. Out-of-position occupants were younger and less likely to be belted than their in-position counterparts. Analysis of the injury data indicated a trend that being out of position was associated with an elevated risk for serious injury. However, the number of out-of-position occupants was too small to provide a definitive or statistically significant conclusion on injury outcome.</p> <p><b>Conclusion:</b> Though highly automated vehicles may eventually reduce the number of crashes and traffic fatalities in the future, there will be a transition period when these vehicles remain at risk from collisions with human-driven vehicles. These crashes could cause higher than anticipated rates of injury if occupants are less likely to be belted or tend to be in positions for which restraints are not optimized. This study highlights the need for future research on occupant response and countermeasure design for out-of-position occupants.</p

Survival Model for Foot and Leg High Rate Axial Impact Injury Data

<div><p><b>Objectives:</b> Understanding how lower extremity injuries from automotive intrusion and underbody blast (UBB) differ is of key importance when determining whether automotive injury criteria can be applied to blast rate scenarios. This article provides a review of existing injury risk analyses and outlines an approach to improve injury prediction for an expanded range of loading rates. This analysis will address issues with existing injury risk functions including inaccuracies due to inertial and potential viscous resistance at higher loading rates.</p><p><b>Methods:</b> This survival analysis attempts to minimize these errors by considering injury location statistics and a predictor variable selection process dependent upon failure mechanisms of bone. Distribution of foot/ankle/leg injuries induced by axial impact loading at rates characteristic of UBB as well as automotive intrusion was studied and calcaneus injuries were found to be the most common injury; thus, footplate force was chosen as the main predictor variable because of its proximity to injury location to prevent inaccuracies associated with inertial differences due to loading rate. A survival analysis was then performed with age, sex, dorsiflexion angle, and mass as covariates. This statistical analysis uses data from previous axial postmortem human surrogate (PMHS) component leg tests to provide perspectives on how proximal boundary conditions and loading rate affect injury probability in the foot/ankle/leg (<i>n</i> = 82).</p><p><b>Results:</b> Tibia force-at-fracture proved to be up to 20% inaccurate in previous analyses because of viscous resistance and inertial effects within the data set used, suggesting that previous injury criteria are accurate only for specific rates of loading and boundary conditions. The statistical model presented in this article predicts 50% probability of injury for a plantar force of 10.2 kN for a 50th percentile male with a neutral ankle position. Force rate was found to be an insignificant covariate because of the limited range of loading rate differences within the data set; however, compensation for inertial effects caused by measuring the force-at-fracture in a location closer to expected injury location improved the model's predictive capabilities for the entire data set.</p><p><b>Conclusions:</b> This study provides better injury prediction capabilities for both automotive and blast rates because of reduced sensitivity to inertial effects and tibia–fibula load sharing. Further, a framework is provided for future injury criteria generation for high rate loading scenarios. This analysis also suggests key improvements to be made to existing anthropomorphic test device (ATD) lower extremities to provide accurate injury prediction for high rate applications such as UBB.</p></div