Cancer Clustering for Residential Histories

描述(由申请人提供)：癌症聚集性的居住史摘要：该项目将开发一种新的、全面的方法来评估病例对照数据中的聚集性，该方法考虑了居住史、风险因素、协变量以及癌症诱导期和潜伏期。到目前为止，癌症地理研究的两个主要缺陷是，它们缺乏适当的方法来调查生命过程中的聚集性，并且风险因素/协变量没有包括在分析中。这个项目克服了这些限制。将根据最近邻关系的矩阵集开发针对住宅历史的本地、全球和重点测试，这些矩阵反映了病例和对照住宅地址不断变化的时空几何形状。将定义说明暴露和疾病表现之间潜伏期的暴露痕迹，以及使用不同持续时间的暴露窗口的暴露痕迹。这样得出的几种方法将被用于在密歇根州东南部正在进行的膀胱癌病例对照研究中评估居住史的聚集性，并在加利福尼亚州马林县识别局部过度乳腺癌。由于人类是流动的，这些新方法相对于忽略居住历史的方法是一个重大进步，相反，这些方法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模拟病例和对照的居住历史的方法，以识别在研究人群中以及与假定的危险相关的癌症风险的地理过度，例如点源致癌物质的释放。癌症聚集性与居住史的相关性：本项目将开发的技术和软件将对癌症病例的聚集性提供更简明和准确的描述，包括风险因素、协变量、居住史、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，这个项目的技术和软件将第一次在一个单一的、全面的框架内解决所有这些因素。

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.