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

 描述（由申请人提供）：在美国，每年有1,000名儿童因儿科行人受伤而死亡，每年有51,000人受伤，5,300人住院。我们在该项目中的目标是通过量化和描述美国儿科行人受伤的小区域时空风险，应用、翻译和传播流行病学和人口健康研究的大数据分析方法。我们的具体目标是实现这一目标：（1）建立一个全面的国家数据库的儿童行人伤害描述和分析儿童行人伤害在美国使用的时间序列 回归方法。(2)在县和人口普查区一级量化儿童行人受伤风险，以确定高风险地区，并评估国家安全路线到学校干预计划的预防效果。和，（3）使用在线街道图像，以确定道路，人行道和交叉口的特点与交叉口，行人通常受伤，尽管安全路线到学校的干预措施已在附近实施。这些目标旨在测试以下假设：（1）使用桌面系统可以有效地创建、操作和查询大量信息丰富的儿科损伤健康数据集，（2）集成嵌套拉普拉斯近似是一种实用、可靠和可访问的方法，可以在大型时空数据集中识别儿科损伤的高风险区域，及（3）街道图像审核是一个可行的替代方法，以确定高风险地区的风险因素。在项目结束时，我们将为健康研究人员发布在线材料，以复制与健康相关的数据集的方法，并为我们的分析结果创建一个简单的用户友好界面和数据查询系统，供研究人员，政策制定者和其他有关各方使用，以告知当地的伤害预防和控制工作。通过应用，展示和翻译大型国家儿童健康数据的计算机科学进步，该应用程序响应NIH大数据到知识（BD 2K）研究优先事项，以“解决所有生物医学研究人员在发布，访问，管理，分析和整合不同数据类型的数据集方面面临的挑战”，以及NICHD优先事项，以支持“儿科创伤研究，包括预防，治疗，儿童基金会还将评估、展示和传播最先进的计算机科学和统计工具，利用可应用于其他流行病学和公共卫生研究的方法，解决紧迫的儿童健康问题。

 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.