DETECTING DISEASE CLUSTERS IN STRUCTURED ENVIRONMENTS

对疾病模式的研究经常源于以下指控： 忧心忡忡的公民，他们识别出小结构中的模式 家庭、家庭、兄弟姐妹、病房、牢房、 教室、工作类型和建筑物内的位置。病毒的检测 疾病聚集性是社区医学中的一个严重问题。最重要的 卫生专业人员面临的困难之一是，数据集 通常都很小，推论是基于相对较少的 观察。对于卫生专业人员来说，了解什么是至关重要的 测试在这些小环境问题中是最好的，并且拥有这些 可在用户友好的计算机包中使用的方法。的目标是 这个项目是实现新的，快速的，精确的组合 用于疾病模式分析的表达式。第一阶段的结果 对这些和之前发表的其他文章的力量进行了调查 将使用结构化小型环境中的方法来推荐 针对不同类型的问题和不同数量的问题进行不同的测试 数据，并提供用户友好的、交互的程序EPIC(准确 事件聚类的概率)，包括直观界面 以及一套完整的教程和指南，以帮助专业人员 为特定问题选择最好的测试。 建议的商业应用：公共卫生专业人员经常分析 所谓的疾病聚集性发生在样本稀少的小环境中。 根据第一阶段的调查结果，我们将在第二阶段开发EPIC，a 用于最佳且通常精确的统计分析的微型计算机程序 小型结构化环境中的群集。许多技术 实施之前没有在研究和监管中提供 社区。

Studies of disease patterns frequently result from allegations made by concerned citizens who identify patterns within small structured environments such as households, families, sibships, wards, cellblocks, classrooms, job types, and locations within a building. The detection of disease clusters is a serious problem in community medicine. Foremost among the difficulties faced by health professionals is that the data sets often are small and inferences are based on a relative handful of observations. It is crucial for the health professional to know what tests are best in these small-environment problems, and to have these methods available in a user-friendly computer package. The objective of this project is to implement new, rapid, and exact combinatorial expressions for analysis of patterns of disease. The results of Phase I investigations into the power of both these and other previously published methods in structured small environments will be used to recommend different tests for different kinds of problems and different amounts Of data, and to provide a user-friendly, interactive program EPIC (Exact Probabilities for Incidence Clusters) that includes an intuitive interface and a thorough set of tutorials and guidelines to help the professional choose the best test for a particular problem. PROPOSED COMMERCIAL APPLICATION: Public health professionals often analyze alleged disease clusters in small environments where samples are rare. Based on Phase I findings, we will in Phase II develop EPIC, a microcomputer program for optimal and often exact statistical analysis of clusters in small, structured environments. Many of the techniques implemented have not before been available in the research and regulatory communities.