Cancer Clustering for Residential Histories

描述（由申请人提供）：癌症集群住宅历史摘要：该项目将开发一种新的，全面的方法来评估集群的病例对照数据，占住宅历史，风险因素，协变量和癌症的诱导和潜伏期。到目前为止，癌症地理研究的两个主要缺陷是，他们缺乏一个适当的方法来调查集群的生命过程和风险因素/协变量不包括在分析中。这个项目克服了这些限制。本地，全球和重点测试的住宅历史将开发的基础上，矩阵的最近邻关系，反映了不断变化的时空几何的情况下，控制的住宅地址。将定义暴露轨迹，其说明暴露与疾病表现之间的潜伏期，并使用不同持续时间的暴露窗口。几个这样得出的方法将被应用于评估集群的居住史在一个正在进行的病例对照研究膀胱癌在密歇根州东南部，并确定当地过度的乳腺癌在马林县，加州。由于人类是移动的，这些新方法是一个重大的进步，而不是忽视居住历史的方法，而是只依赖于诊断或死亡时的居住地。主要的创新是创建了用于分析和建模病例和对照的居住历史的方法，以识别研究人群本身以及与致癌物的点源释放等推定危害有关的地理过度癌症风险。居住历史相关性的癌症聚类：本项目中开发的技术和软件将提供对癌症病例聚类的更简洁和准确的描述，该描述考虑了风险因素、协变量、居住历史、癌症潜伏期、诊断时间以及假设发生致病暴露的暴露窗口。据我们所知，该项目的技术和软件将是第一个在一个单一的综合框架内解决所有这些因素的项目。

DESCRIPTION (provided by applicant): Cancer Clustering for Residential Histories Abstract: This project will develop a new, comprehensive approach for evaluating clustering in case-control data that accounts for residential histories, risk factors, covariates and cancer induction and latency periods. To date, two of the major deficiencies of geographic studies of cancer are that they lack an appropriate methodology for investigating clusters over the life course and risk factors/covariates are not included in the analysis. These limitations are overcome by this project. Local, global and focused tests for residential histories will be developed based on sets of matrices of nearest neighbor relationships that reflect the changing space-time geometry of the residential addresses of cases and controls. Exposure traces that account for the latency between exposure and disease manifestation, and that use exposure windows of varying duration will be defined. Several of the methods so derived will be applied to evaluate clustering of residential histories in an ongoing case-control study of bladder cancer in south eastern Michigan, and to identify local excesses of breast cancer in Marin County, California. Because humans are mobile, these new methods are a significant advance over approaches that ignore residential histories and instead rely only on place of residence at time of diagnosis or death. The major innovation is the creation of methods for analyzing and modeling the residential histories of cases and controls to identify geographic excesses of cancer risk, both in the study population itself as well as in relation to putative hazards such as point-source releases of carcinogens. Cancer Clustering for Residential Histories Relevance: The techniques and software to be developed in this project will provide a more concise and accurate description of clustering of cancer cases that accounts for risk factors, covariates, residential history, cancer latency, time of diagnosis, and the exposure windows during which causative exposures are hypothesized to have occurred. To our knowledge the techniques and software from this project will be the first to address all of these factors within a single, comprehensive framework.