Spatio-Temporal Epidemiology: Methods and Applications

描述(由申请人提供)：本申请寻求支持开发分析时空区域数据的方法。这些方法的目的是：查明空间和/或时间上的趋势；发现风险的“热”或“冷”点；评估干预措施的实施情况；发现健康差距较大的人群；并就可能的风险因素提出假设。聚集的时空疾病数据提出了独特的统计挑战，特别是必须考虑到：空间和时间相关性；罕见事件的内在不稳定性；分子和分母的错误；以及聚集引起的问题(如果不加以处理，可能导致“生态偏差”)。该应用程序解决了其中的每一个问题，并具有特定的目标： 1.开发和应用时空模型，特别是开展监测。 2.开发一个框架，用于分析综合健康结果与在点位置测量的环境暴露(在空气、水或土壤中)之间的关联。 3.开发有效的设计，将生态和个体水平的数据结合起来，为生态学研究提供更有力的分析基础。 开发的方法将在基因上适用于非传染性疾病，并将使用一系列数据进行说明，包括公开可获得的美国癌症发病率和死亡率数据，以及华盛顿州小区域癌症发病率数据。开发的方法将通过在免费软件中的实施向研究人员提供。

DESCRIPTION (provided by applicant): This application seeks support to develop methods for the analysis of spatio-temporal area data. These methods aim to: identify trends in space and/or time; detect "hot" or "cold" spots of risk; assess delivery of interventions; detect populations who are subject to health disparities; and generate hypotheses concerning possible risk factors. Aggregated spatio-temporal disease data present unique statistical challenges, in particular one must account for: spatial and temporal dependence; the inherent instability of rare events; errors in numerators and denominators; and problems due to aggregation (which if not addressed may lead to "ecological bias"). This application addresses each of these issues, and has specific aims: 1. To develop and apply spatio-temporal models, in particular to carry out surveillance. 2. To develop a framework for analysis of the association between aggregated health outcomes and environmental exposures (in air, water, or soil) measured at point locations. 3. To develop efficient designs for combining ecological- and individual-level data to provide a stronger analytic basis for ecological studies. The methods developed will be genetically applicable to non-infectious diseases and will be illustrated using a range of data including publicly-available U.S. cancer incidence and mortality data, and Washington State small-area cancer incidence data. Developed methods will be made available to researchers via implementation within freely-available software.