|Grant Number:||2R44CA117171-02 Interpret this number|
|Primary Investigator:||Jacquez, Geoffrey|
|Project Title:||Cancer Clustering for Residential Histories|
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.