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.     

Development of a Computationally Efficient Full Human Body Finite Element Model

Introduction: A simplified and computationally efficient human body finite element model is presented. The model complements the Global Human Body Models Consortium (GHBMC) detailed 50th percentile occupant (M50-O) by providing kinematic and kinetic data with a significantly reduced run time using the same body habitus.

Methods: The simplified occupant model (M50-OS) was developed using the same source geometry as the M50-O. Though some meshed components were preserved, the total element count was reduced by remeshing, homogenizing, or in some cases omitting structures that are explicitly contained in the M50-O. Bones are included as rigid bodies, with the exception of the ribs, which are deformable but were remeshed to a coarser element density than the M50-O. Material models for all deformable components were drawn from the biomechanics literature. Kinematic joints were implemented at major articulations (shoulder, elbow, wrist, hip, knee, and ankle) with moment vs. angle relationships from the literature included for the knee and ankle. The brain of the detailed model was inserted within the skull of the simplified model, and kinematics and strain patterns are compared.

Results: The M50-OS model has 11 contacts and 354,000 elements; in contrast, the M50-O model has 447 contacts and 2.2 million elements. The model can be repositioned without requiring simulation. Thirteen validation and robustness simulations were completed. This included denuded rib compression at 7 discrete sites, 5 rigid body impacts, and one sled simulation. Denuded tests showed a good match to the experimental data of force vs. deflection slopes. The frontal rigid chest impact simulation produced a peak force and deflection within the corridor of 4.63 kN and 31.2%, respectively. Similar results vs. experimental data (peak forces of 5.19 and 8.71 kN) were found for an abdominal bar impact and lateral sled test, respectively. A lateral plate impact at 12 m/s exhibited a peak of roughly 20 kN (due to stiff foam used around the shoulder) but a more biofidelic response immediately afterward, plateauing at 9 kN at 12 ms. Results from a frontal sled simulation showed that reaction forces and kinematic trends matched experimental results well. The robustness test demonstrated that peak femur loads were nearly identical to the M50-O model. Use of the detailed model brain within the simplified model demonstrated a paradigm for using the M50-OS to leverage aspects of the M50-O. Strain patterns for the 2 models showed consistent patterns but greater strains in the detailed model, with deviations thought to be the result of slightly different kinematics between models. The M50-OS with the deformable skull and brain exhibited a run time 4.75 faster than the M50-O on the same hardware.

Conclusions: The simplified GHBMC model is intended to complement rather than replace the detailed M50-O model. It exhibited, on average, a 35-fold reduction in run time for a set of rigid impacts. The model can be used in a modular fashion with the M50-O and more broadly can be used as a platform for parametric studies or studies focused on specific body regions.     

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 Analysis of the Effects of Land Use and Land Cover on Flood Losses along the Gulf of Mexico Coast from 1999 to 2009

Major coastal flooding events over the last decade have led decision makers in the United States to favor structural engineering solutions as a means to protect vulnerable coastal communities from the adverse impacts of future storms. While a resistance‐based approach to flood mitigation involving large‐scale construction works may be a central component of a regional flood risk reduction strategy, it is equally important to consider the role of land use and land cover (LULC) patterns in protecting communities from floods. To date, little observational research has been conducted to quantify the effects of various LULC configurations on the amount of property damage occurring across coastal regions over time. In response, we statistically examine the impacts of LULC on observed flood damage across 2,692 watersheds bordering the Gulf of Mexico. Specifically, we analyze statistical linear regression models to isolate the influence of multiple LULC categories on over 372,000 insured flood losses claimed under the National Flood Insurance Program per year from 2001 to 2008. Results indicate that percent increase in palustrine wetlands is the equivalent to, on average, a $13,975 reduction in insured flood losses per year, per watershed. These and other results provide important insights to policy makers on how protecting specific types of LULC can help reduce adverse impacts to local communities.     

Modeling Streamflow and Water Quality Sensitivity to Climate Change and Urban Development in 20 U.S. Watersheds

Watershed modeling in 20 large, United States (U.S.) watersheds addresses gaps in our knowledge of streamflow, nutrient (nitrogen and phosphorus), and sediment loading sensitivity to mid‐21st Century climate change and urban/residential development scenarios. Use of a consistent methodology facilitates regional scale comparisons across the study watersheds. Simulations use the Soil and Water Assessment Tool. Climate change scenarios are from the North American Regional Climate Change Assessment Program dynamically downscaled climate model output. Urban and residential development scenarios are from U.S. Environmental Protection Agency's Integrated Climate and Land Use Scenarios project. Simulations provide a plausible set of streamflow and water quality responses to mid‐21st Century climate change across the U.S. Simulated changes show a general pattern of decreasing streamflow volume in the central Rockies and Southwest, and increases on the East Coast and Northern Plains. Changes in pollutant loads follow a similar pattern but with increased variability. Ensemble mean results suggest that by the mid‐21st Century, statistically significant changes in streamflow and total suspended solids loads (relative to baseline conditions) are possible in roughly 30‐40% of study watersheds. These proportions increase to around 60% for total phosphorus and total nitrogen loads. Projected urban/residential development, and watershed responses to development, are small at the large spatial scale of modeling in this study.