Detecting and analyzing patterns in spatial data

空间分布的感知模式是环境病因最具挑衅性的线索之一。根据重大事件数据制作的地图很容易获得，这提高了公众对疾病模式中这些差异的兴趣和认识。另一方面，流行病学家对这类数据的方法学局限性感到不安，许多人认为随访是徒劳的。以前对集群和生态(集团层面)分析的方法论批评大多是理论上的，并不知道在实践中实际出现问题的程度或原因。回答这些问题需要详细的个人层面的空间流行病学数据(“黄金标准”)进行比较，但这些数据很少获得。CAPE Code关于癌症的地理编码数据(来自当前的SBRP中心)和生殖/发育影响(项目1，本申请)提供了这样的资源。两种新的方法，一种用于识别疾病“热点”(由我们小组开发)，另一种用于生态分析(King的“EI方法”)，显示出在环境流行病学中使用的前景。在这个项目中，我们将继续开发用于显示局部疾病过度的自适应比率平滑方法，增加对协变量进行调整的能力；并开发方法学工具来分析和可视化来自生态数据的潜在问题。我们将把这些方法应用于CAPE数据，检查空间混杂造成的集聚程度，并使用传统和新的生态学方法将生态推断与CAPE数据的个体水平推断进行比较。

Perceived patterns in spatial distributions are among the most provocative clues to environmental etiologies. The easy availability of maps made from vital events data have heightened public interest and awareness of these differences in disease patterns. Epidemiologists, on the other hand, are uneasy about the methodological limitations of these kinds of data and many believe follow-up is unproductive. Previous methodological critiques of cluster and ecological (group-level) analyzes are largely theoretical, and it not known to what degree problems actually occur in practice or why. Answering these questions requires detailed individual- level spatial epidemiological data ("the gold standard") for comparison, but these data are rarely available. Geographically coded data from Cape Code on cancer (from the current SBRP center) and reproductive/developmental effects (Project 1, this application) provide such a resource. Two new methods, one for identifying disease "hotspots" (developed by our group), and one for ecological analysis (King's "EI method") show promise for use in environmental epidemiology. In this project we will continue development of the adaptive rate smooth method for showing local disease excesses, adding the ability to adjust for covariates; and develop methodological tools to analyze and visualize the potential for problems form ecologic data. We will apply these methods to the Cape data, examining the degree of clustering caused by spatial confounding and compare ecological inference with individual-level inference of the Cape data using both conventional and novel ecological methods.