Research Project 2: Analyzing Patterns in Epidemiologic and Toxicologic Data

地理信息系统现在允许在空间流行病学中使用分析技术 以前不可行。因此，对例行收集的健康数据进行绘图现在很常见， 当疾病发生率的模式出现“热点”时， 流行病学家认为，由于未能收集和控制许多已知的风险， 在地图区域上不均匀分布的因子;本项目将改进方法 在上一个资助期开发的，用于绘制病例对照和队列研究的数据， 这些空间混淆。一个意想不到的应用空间方法的分析， 将与毒理学项目合作探讨化学混合物中的相互作用。作为第二 为了了解重要的流行病学模式，该项目将开发方法， 评估组水平（生态学）研究中发生的偏倚的方向和数量。因为他们可以 使用常规收集的组数据，这样的研究在逻辑上更容易进行，但理论上受到 严重错误，尽管真实的数据中的错误幅度尚不清楚。评估这些错误的方法 是重要的，因为在使用组水平的部分生态学研究中存在潜在的生态偏倚。 暴露的测量（例如，大多数关于空气和水污染的研究），并且由于假设， 某些群体层面的社会经济（“背景”）变量需要包括在其他个人层面的 问题研究在开发出评估这些问题的方法之后，我们将把它们应用于来自以下数据的真实的数据： Cape Cod，MA附近酒店最后，该项目将应用离散数学的新方法来显示 数据集的内部结构，特别是危险废物特有的“小”数据集 调查事务所

Geographic Information Systems now allow the use of analytic techniques in spatial epidemiology previously not feasible. As a result the mapping of routinely collected health data is now common and often provokes concern when patterns of disease rates appear to have "hot spots," although it is well understood by epidemiologists that the results may be biased by failure to collect and control for many known risk factors that are unevenly distributed over the area of the map; This project will refine the methods developed in the previous funding period for mapping data from case-control and cohort studies that adjust for these spatial confounders. An unexpected application of the spatial methods to the analysis of interactions in chemical mixtures will be explored in collaboration with the toxicology projects. As a second specific aim for understanding important epidemiologic patterns, this project will develop methods for assessing the direction and amount of bias occurring in group-level (ecologic) studies. Because they can use routinely collected group data, such studies are logistically easier to conduct but theoretically subject to serious error, although the magnitude of error in real data is not known. Methods for assessing these errors are important because there is potential for ecologic bias in partially ecologic studies that use a group-level measure of exposure (e.g., most studies of air and water pollution) and because of the hypothesis that certain group-level socioeconomic ("contextual") variables need to be included in otherwise individual-level studies. After developing methods for assessing these problems, we will apply them to real data from studies of Cape Cod, MA. Finally, the project will apply new methods from discrete mathematics to display the internal structure of data sets, especially the "small" data sets characteristic of hazardous waste investigations.