Spatio-Temporal Epidemiology: Methods and Applications

时空流行病学：方法与应用

• 批准号: 7125963
• 负责人: JONATHAN C WAKEFIELD
• 金额: $ 19.17万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-30 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7125963
• 关键词: clinical research; computer program /software; computer system design /evaluation; data collection methodology /evaluation; human data; mathematical model; neoplasm /cancer epidemiology

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.

说明（由申请人提供）：本申请寻求支持，以开发时空区域数据分析方法。这些方法旨在：确定空间和/或时间趋势;发现风险的“热点”或“冷点”;评估干预措施的提供情况;发现健康状况存在差异的人群;并提出关于可能风险因素的假设。汇总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：空间和时间依赖性;罕见事件固有的不稳定性;分子和参数的错误;以及汇总造成的问题（如果不加以解决，可能导致“生态偏见”）。该应用程序解决了这些问题中的每一个，并具有特定的目标： 1.开发和应用时空模型，特别是进行监测。 2.制定一个框架，分析在点位置测量的综合健康结果与环境暴露（空气、水或土壤）之间的关联。 3.开发有效的设计，结合生态和个人层面的数据，为生态研究提供更强大的分析基础。 开发的方法将在遗传学上适用于非传染性疾病，并将使用一系列数据进行说明，包括公开的美国癌症发病率和死亡率数据，以及华盛顿州小地区癌症发病率数据。开发的方法将通过在免费提供的软件中实施提供给研究人员。