Data Integration and Advanced Statistical Modeling to Describe and Control Pediatric Pedestrian Injuries in The United States

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 DESCRIPTION (provided by applicant): Pediatric pedestrian injury kills 1,000 children every year in the United States, and results in 51,000 annual injuries and 5,300 hospitalizations. Our goal in this project is to apply, translate and disseminate large-data analytic methods for epidemiology and population health research by quantifying and characterizing the small- area spatiotemporal risk of pediatric pedestrian injury in the United States. Our specific aims to achieve this goal are: (1) Create a comprehensive national database of pediatric pedestrian injuries to describe and analyze pediatric pedestrian injury in the United States using time-series and regression methods. (2) Quantify pediatric pedestrian injury risk at the county and census-tract level to identify high-risk areas, and evaluate the preventative effect of the National Safe Routes to School intervention program. And, (3) use online street imagery to identify road, sidewalk and intersection characteristics associated with intersections where pedestrians are commonly injured despite Safe Routes to Schools Interventions having been implemented in the neighborhood. These aims are designed to test the hypotheses that 1) Large informative pediatric injury health data sets can be efficiently created, manipulated and queried using desktop systems, (2) Integrated nested Laplace approximations are a practical, reliable and accessible approach to identifying high-risk areas for pediatric injury in large spatiotemporal datasets, and (3) Street imagery audits are a feasible alternative to site visits to identify risk factors in high-risk areas. At the end of the project period, we will post online materials for heath researchers to replicate the methods for health-related data sets, and create a simple user-friendly interface and data query system for the results of our analyses that can be used by researchers, policy makers and other interested parties to inform local injury prevention and control efforts. By applying, demonstrating and translating advances in computer science for large national child health data the application is responsive to the NIH Big Data to Knowledge (BD2K) research priories to "address the challenges facing all biomedical researchers in releasing, accessing, managing, analyzing, and integrating datasets of diverse data types" and the NICHD priority to support "research on pediatric trauma, including prevention, treatment, and rehabilitation", and will evaluate, demonstrate and disseminate cutting edge computer science and statistical tools to address a pressing child health issue using approaches that can be applied to other epidemiological and public health research.