Incorporating Residential Histories into Space-Time Models for Health Geographic Analysis

将居住历史纳入健康地理分析的时空模型

• 批准号: 1560888
• 负责人: Kevin Henry
• 金额: $ 30万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1560888&HistoricalAwards=false
• 关键词: Incorporating; Residential; Histories; into; Space

This research project will develop and test new spatial statistical methods for health and disease mapping that incorporate residential history data. These statistical methods will enable researchers to assess risk of chronic diseases, such as cancer, and other health outcomes, such as pre-term births, as a function of the geographic-specific exposures associated with residential history. The new statistical methods will provide researchers with a robust and powerful tool for using residential histories when they test hypotheses about geographic exposures over time and space and their impacts on health and disease. The project will increase basic understanding of the amount of information bias introduced when residential histories are ignored. Project results will provide empirical examples for geographers, public officials, and other scientists that demonstrate why residential history data should be used in health and disease surveillance systems, how these data can be incorporated, and why this information should be included when conducting health geographic analysis. Project methods and findings will assist those addressing a broader set of health-related issues. The use of the data from a state cancer registry and a birth registry will provide insights regarding the use of administrative databases to obtain residential histories for health geographic analysis. The new methods will be adaptable for use by researchers and by public health practitioners and medical personnel in addressing problems besides long-latency diseases. By analyzing daily-scale movement, for example, it will be possible to map acute diseases like salmonella or health events like asthma attacks. The project also will provide education and training opportunities for undergraduate and graduate students in health and medical geography, computer science, and epidemiology.Common methods for assessing risk factors based on geographic-specific exposures or the clustering of health and disease events generally have relied on static data limited to a single point in time and space, such as a person's location at the time of diagnosis. Ignoring residential history is a significant shortcoming in such analyses because of the latency period between causative exposures and resulting health and disease events. The investigators will address this shortcoming by providing a framework for combining multipoint, longitudinal residential history data with health and disease data that is normally based on a single time point. Building upon previous research in health geography, geographical information sciences, and data mining, they will develop hierarchical Bayes models that assess risk of disease while accounting for latency and temporally changing social and environmental exposures, geographic uncertainty, and missing data. They will test and demonstrate these new statistical models using both synthetic data and empirical secondary datasets of cancer and birth outcomes that include residential histories.

该研究项目将开发和测试新的空间统计方法，用于健康和疾病制图，其中包括居住历史数据。 这些统计方法将使研究人员能够评估慢性疾病（如癌症）和其他健康结果（如早产）的风险，作为与居住史相关的地理特定暴露的函数。 新的统计方法将为研究人员提供一个强大而强大的工具，当他们测试关于时间和空间的地理暴露及其对健康和疾病的影响的假设时，可以使用居住历史。 该项目将增加对忽视居住历史时引入的信息偏差量的基本理解。 项目结果将为地理学家、政府官员和其他科学家提供实证范例，说明为什么应在健康和疾病监测系统中使用居住历史数据，如何将这些数据纳入，以及为什么在进行健康地理分析时应包括这些信息。 项目方法和调查结果将有助于解决更广泛的健康相关问题。 使用来自州癌症登记处和出生登记处的数据将提供有关使用行政数据库获取健康地理分析的居住历史的见解。 这些新方法将适用于研究人员、公共卫生从业人员和医务人员，以解决长期潜伏疾病以外的问题。 例如，通过分析日常规模的运动，将有可能绘制出像盐中毒这样的急性疾病或像哮喘发作这样的健康事件。 该项目还将为本科生和研究生提供健康和医学地理学、计算机科学和流行病学方面的教育和培训机会。基于特定地理暴露或健康和疾病事件聚类的风险因素评估的常用方法通常依赖于限于时间和空间上单个点的静态数据，例如诊断时一个人的位置。 忽略居住历史是这种分析的一个重大缺陷，因为致病暴露与由此产生的健康和疾病事件之间存在潜伏期。 研究人员将通过提供一个框架来解决这一缺陷，该框架将多点纵向居住史数据与通常基于单个时间点的健康和疾病数据相结合。 基于以前在健康地理学，地理信息科学和数据挖掘方面的研究，他们将开发分层贝叶斯模型，评估疾病风险，同时考虑延迟和时间变化的社会和环境暴露，地理不确定性和缺失数据。 他们将测试和展示这些新的统计模型，使用合成数据和经验性的癌症和出生结果的二次数据集，包括居住历史。