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

该研究项目将开发和测试纳入居住史数据的健康和疾病地图绘制的新空间统计方法。这些统计方法将使研究人员能够评估慢性病(如癌症)和其他健康后果(如早产)的风险，作为与居住史相关的特定地理暴露的函数。新的统计方法将为研究人员提供一个强大而强大的工具，在他们测试有关地理暴露在时间和空间上的假设及其对健康和疾病的影响时，使用居住历史。该项目将增加对忽略居住历史时引入的大量信息偏差的基本理解。项目成果将为地理学家、政府官员和其他科学家提供经验范例，说明为什么居住历史数据应该用于健康和疾病监测系统，如何将这些数据纳入其中，以及为什么在进行健康地理分析时应该包括这些信息。项目方法和结果将帮助那些解决更广泛的与健康有关的问题的人。使用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得居住史以进行卫生地理分析的见解。新方法将适用于研究人员以及公共卫生从业者和医务人员在解决长潜伏期疾病以外的问题时使用。例如，通过分析每日尺度的运动，将有可能绘制出沙门氏菌等急性疾病或哮喘发作等健康事件的地图。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。根据特定地理暴露或健康和疾病事件聚集来评估风险因素的常用方法通常依赖于限于单个时间点和空间点的静态数据，如诊断时的位置。忽略居住史是此类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。调查人员将提供一个框架，将多点纵向居住史数据与通常基于单一时间点的健康和疾病数据结合起来，以解决这一缺点。在健康地理学、地理信息科学和数据挖掘之前研究的基础上，他们将开发分层贝叶斯模型，该模型评估疾病风险，同时考虑潜伏期和时间变化的社会和环境暴露、地理不确定性和丢失的数据。他们将使用包括居住史在内的癌症和出生结果的合成数据和经验二级数据集来测试和演示这些新的统计模型。

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.