Research: 

ABSTRACT – Because small overlap impacts have recently emerged as a crash mode posing great injury risk to occupants, a detailed analysis of US crash data was conducted using the NASS/CDS and CIREN databases. Frontal crashes were subcategorized into small overlap impact (SOI) and large overlap impact (LOI) using crash and crush characteristics from the datasets. Injuries to head, spine, chest, hip and pelvis, and lower extremities were parsed and compared between crash types. MAIS 3+ occupants in NASS/CDS and CIREN demonstrated increased incidence of head, chest, spine, and hip/pelvis injuries in SOI compared to LOI. In NASS/CDS, subgaleal hematoma represented 48.6% of SOI head injury codes but 27.6% in LOI. Cervical spine posterior element fractures also represented greater proportions of SOI spine injuries (e.g., facet fractures: 27.8 vs. 14.0%), and proximal femur fractures represented a greater proportion of hip/pelvis injuries (e.g., intertrochanteric fracture: 32.5 vs. 11.8%). Tarsal/metatarsal fractures were a lesser proportion of lower extremity injuries in SOI compared to LOI. Occupant contact points inducing these injuries were observed in CIREN cases in some instances without compartment intrusion. These injuries suggest the substantial role of occupant kinematics in SOI which may induce suboptimal occupant restraint interaction.

Objective: To describe a new method for analyzing and documenting the causes of injuries in motor vehicle crashes that has been implemented since 2005 in cases investigated by the Crash Injury Research Engineering Network (CIREN). Methods: The new method, called BioTab, documents injury causation using evidence from in-depth crash investigations. BioTab focuses on developing injury causation scenarios (ICSs) that document all factors considered essential for an injury to have occurred as well as factors that contributed to the likelihood and/or severity of an injury. The elements of an injury causation scenario are (1) the source of the energy that caused the injury, (2) involved physical components (IPCs) contacted by the occupant that are considered necessary for the injury to have occurred, (3) the body region or regions contacted by each IPC, (4) the internal paths between body regions contacted by IPCs and the injured body region, (5) critical intrusions of vehicle components, and (6) factors that contributed to the likelihood and/or the severity of injury.
Results: Advantages of the BioTab method are that it

• attempts to identify all factors that cause or contribute to clinically significant injuries,
• allows for coding of scenarios where one injury causes another injury,
• associates injuries with a source of energy and allows injuries to be associated with sources of energy other than the crash, such as air bag deployment energy,
• allows for documenting scenarios where an injury was caused by two different body regions contacting two different IPCs,
• identifies and documents the evidence that supports ICSs and IPCs,
• assigns confidence levels to ICSs and IPCs based on available evidence, and
• documents body region and organ/component-level "injury mechanisms" and distinguishes these mechanisms from ICSs. Conclusion: The BioTab method provides for methodical and thorough evidenced-based analysis and documentation of injury causation in motor vehicle crashes.

Severe-to-fatal head injuries in motor vehicle environments were analyzed using the United States Crash Injury Research and Engineering Network database for the years 1997-2006. Medical evaluations included details and photographs of injury, and on-scene, trauma bay, emergency room, intensive care unit, radiological, operating room, in-patient, and rehabilitation records. Data were synthesized on a case-by-case basis. X-rays, computed tomography scans, and magnetic resonance images were reviewed along with field evaluations of scene and photographs for the analyses of brain injuries and skull fractures. Injuries to the parenchyma, arteries, brainstem, cerebellum, cerebrum, and loss of consciousness were included. In addition to the analyses of severe-to-fatal (AIS4+) injuries, cervical spine, face, and scalp trauma were used to determine the potential for head contact. Fatalities and survivors were compared using nonparametric tests and confidence intervals for medians. Results were categorized based on the mode of impact with a focus on head contact. Out of the 3178 medical cases and 169 occupants sustaining head injuries, 132 adults were in frontal (54), side (75), and rear (3) crashes. Head contact locations are presented for each mode. A majority of cases clustered around the mid-size anthropometry and normal body mass index (BMI). Injuries occurred at change in velocities (_V) representative of US regulations. Statistically significant differences in_V between fatalities and survivors were found for side but not for frontal impacts. Independent of the impact mode and survivorship, contact locations were found to be superior to the center of gravity of the head, suggesting a greater role for angular than translational head kinematics. However, contact locations were biased to the impact mode: anterior aspects of the frontal bone and face were involved in frontal impacts while temporal-parietal regions were involved in side impacts. Because head injuries occur at regulatory _V in modern vehicles and angular accelerations are not directly incorporated in crashworthiness standards, these findings from the largest dataset in literature, offer a field-based rationale for including rotational kinematics in injury assessments. In addition, it may be necessary to develop injury criteria and evaluate dummy biofidelity based on contact locations as this parameter depended on the impact mode. The current field-based analysis has identified the importance of both angular acceleration and contact location in head injury assessment and mitigation.

The Crash Injury Research Engineering Network (CIREN) database contains detailed medical and crash information on a large number of severely injured occupants in motor vehicle crashes. CIREN's major limitation for stand-alone analyses to explore injury risk factors is that control subjects without a given injury type must have another severe injury to be included in the database. This leads to bias toward the null in the estimation of risk associations. One method to cope with this limitation is to obtain information about occupants without a given injury type from the National Automotive Sampling System's Crashworthiness Data System (NASS-CDS), which is a probability sample of tow-away crashes, containing similar crash information, but less medical detail. Combining CIREN and NASS-CDS in this manner takes advantage of the increased sample size when outcomes are available in both datasets; otherwise NASS-CDS can serve as a sample of controls to be combined with CIREN cases, possibly under a sensitivity analysis that includes and excludes NASS-CDS subjects whose status as a control is uncertain. Because CIREN is not a probability sample of crashes that meet its inclusion criteria, we develop a method to estimate the probability of selection for the CIREN cases using data from NASS-CDS. These estimated probabilities are then used to compute "pseudo-weights" for the CIREN cases. These pseudo-weights not only allow for reduced bias in the estimation of risk associations, they allow direct prevalence estimates to be made using medical outcome data available only in CIREN. We illustrate the use of these methods with both simulation studies and application to estimation of prevalence and predictors of AIS 3+ injury risk to head, thorax, and lower extremity regions, as well as prevalence and predictors of acetabular pelvic fractures. Results of these analyses demonstrate combining NASS and CIREN data can yield improvements in mean square error and nominal confidence interval coverage over analyses that use either the NASS-CDS or the CIREN sample alone.