DETECTING DISEASE CLUSTERS IN STRUCTURED ENVIRONMENTS

检测结构化环境中的疾病群

• 批准号: 2430476
• 负责人: SCOTT D FERSON
• 金额: $ 22.41万
• 依托单位: APPLIED BIOMATHEMATICS, INC.
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-08-01 至 1998-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2430476
• 关键词: computer program /software; disease reservoirs; epidemiology; mathematical model; model design /development; statistics /biometry

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.

对疾病模式的研究经常来自以下方面的指控： 关心的公民在小型结构中识别模式 诸如家庭、家庭、同胞、病房、牢房区等环境， 教室、工作类型和建筑物内的位置。 检测 疾病群是社区医学中的一个严重问题。 最重要的是 卫生专业人员面临的困难之一是数据集 通常很小，并且推论基于相对少数的 观察。 对于健康专业人士来说，了解什么是至关重要的 测试最适合解决这些小环境问题，并且拥有这些 方法可在用户友好的计算机包中使用。 的目标 该项目旨在实施新的、快速的、精确的组合 用于分析疾病模式的表达式。 第一阶段的结果 对这些和其他先前发布的内容的权力进行调查 将使用结构化小环境中的方法来推荐 针对不同类型的问题和不同数量的不同测试 数据，并提供用户友好的交互式程序EPIC（Exact 发生率集群的概率），包括直观的界面 以及一套全面的教程和指南来帮助专业人士 选择针对特定问题的最佳测试。 拟议的商业应用：公共卫生专业人员经常分析 所谓的疾病聚集在样本稀少的小环境中。 基于第一阶段的研究结果，我们将在第二阶段开发 EPIC， 微计算机程序，用于优化且通常准确的统计分析 小型结构化环境中的集群。 很多技巧 实施以前在研究和监管中从未有过的 社区。