O2143: Local Infrastructure and Economy Predicts Traffic Related Fatalities in Children

Background: Previous research on pediatric traffic fatalities has primarily focused on patient demographics and crash specific information (i.e. seatbelt/car seat use, vehicle type, impact location, etc.). This study hypothesizes that local infrastructure, reflected in the National Walk Index (NWI) and density of public schools, and macroeconomic forces, reflected in the Social Vulnerability Index (SVI) and food scarcity, predict which counties are most at risk for traffic fatalities.

Methods: A retrospective cohort study was conducted using the Fatality Analysis Reporting System (FARS) dataset from 2017-2021. Patients less than 18 years-old who died in motor vehicle collisions or as pedestrians/bicyclists struck by motor vehicles were identified in the FARS. Rates of traffic related fatalities per 100,000 children were extrapolated by county and logarithmically transformed to achieve a normal distribution. Average percentile rank was calculated using NWI, SVI, food scarcity, and school density. The dataset was split into training and validation samples. A linear regression model created using the training sample was then tested against the validation sample.

Results: 10,736 patients were identified as dying in a traffic related collision. Rates of traffic fatalities were correlated with county percentile rank of NWI, SVI, food scarcity, and school density (p < 0.001 for all predictors). Multivariate linear regression using these predictors in the training sample was highly predictive of traffic fatality rates in the validation sample (R2=0.81, RMSE=0.117, Figure 1). These correlations were consistent regardless of whether children were motor-vehicle occupants or pedestrians/cyclists struck by a motor-vehicle (R2=0.82 vs 0.75). Patients living in counties within the top quartile of predictive values within the validation sample had a median fatality rate of 17.01 per 100,000 children, compared to 2.42 per 100,000 children in the bottom quartile of counties, representing a seven-fold increased risk of a traffic related fatality.

Conclusion: Proper restraint use and car safety mechanisms remain a vital part of reducing traffic fatalities in the pediatric population. Nevertheless, areas with poor walkability, high social vulnerability, food scarcity, and a low density of public schools, predict the likelihood of pediatric traffic fatality with more fidelity than individual accident factors alone. These findings highlight tangible local and state policy changes that could be implemented in order to decrease the likelihood of traffic related child fatality rates in specific counties.