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

用于描述和控制美国儿童行人伤害的数据集成和高级统计模型

• 批准号: 9235300
• 负责人: Charles DiMaggio
• 金额: $ 35.83万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9235300
• 关键词: Accident and Emergency department; Address; Area; Big Data; Big Data to Knowledge; Censuses; Characteristics; Child; Child health care; Childhood; Childhood Injury; Code; Communities; Complex Analysis; Computer Assisted; County; Data; Data Analytics; Data Set; Database Management Systems; Databases; Economic Factors; Effectiveness of Interventions; Epidemiologic Methods; Epidemiologist; Epidemiology; Exposure to; Goals; Health; Health Promotion; Hospitalization; Hospitals; Imagery; Incidence; Injury; Intervention; Lighting; Link; Maps; Methods; Modeling; National Institute of Child Health and Human Development; Neighborhoods; Outcome; Policy Maker; Prevention; Preventive Intervention; Probability; Public Health; Records; Rehabilitation therapy; Reproducibility; Research; Research Personnel; Research Support; Resistance; Risk; Risk Factors; Route; Safety; Schools; Series; Severities; Site Visit; Software Tools; Statistical Models; System; Testing; Translating; United States; United States National Institutes of Health; Vehicle crash; Virtual Tool; Visual; Width; Writing; base; built environment; computer science; data integration; density; design; disorder prevention; health data; high risk; injured; injury prevention; interest; intervention program; killings; open source; pedestrian injury; pediatric trauma; population health; programs; public health relevance; public health research; social; socioeconomics; spatiotemporal; time use; tool; user-friendly

 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.

 描述（由适用提供）：在美国，小儿行人损伤每年造成1,000名儿童造成1,000名儿童，并导致51,000例年度伤害和5,300例住院治疗。我们在该项目中的目标是通过量化和表征美国小区域时空风险，应用，翻译和传播大型数据分析方法，以进行流行病学和人口健康研究。我们实现此目标的具体目的是：（1）创建一个全面的国家行人伤害数据库，以使用时间表来描述和分析美国的小儿行人伤害 和回归方法。 （2）量化该县和人口普查水平的小儿行人伤害风险以识别高风险地区，并评估国家安全学校干预计划的预防效果。 （3）使用在线街道图像识别与交叉路口相关的道路，人行道和交叉路口特征，在该路口是行人通常受伤的目的地安全途径到附近实施的学校干预措施。这些目的旨在测试1）可以有效地创建，使用桌面系统来有效地创建，操纵和查询大量的小儿损伤健康数据集，（2）（2）集成的嵌套拉普拉斯近似值是实用，可靠和可访问的方法，是一种识别高风险的空间访问的高风险访问范围的范围范围的范围访问的方法（3）街道范围的范围。高风险地区的风险因素。在项目期结束时，我们将在线材料供热研究人员复制与健康相关的数据集的方法，并创建一个简单的用户友好界面和数据查询系统，以供研究人员，政策制定者和其他有趣的党派使用该分析结果，以告知当地伤害预防和控制工作。 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 priorities to "address the challenges facing all biomedical researchers in releasing, accessing, managing, analyzing, and integrating datasets of divers data types" and the NICHD priority to support "research on pediatric trauma, including prevention, treatment, and rehabilitation", and will评估，演示和传播尖端的计算机科学和统计工具，以使用可以应用于其他流行病学和公共卫生研究的方法来解决紧迫的儿童健康问题。