A Temporal-Spatial Analysis of Breast Cancer in Upper C*

描述（由申请人提供）：尽管进行了流行病学研究以调查可能的环境风险因素，但马萨诸塞州科德角乳腺癌发病率升高的原因仍然未知。两项现有的基于人群的上科德角乳腺癌病例对照研究（1983-1993）提供了所有受试者的地理编码居住史和混杂因素信息。结合这两项研究，创建了一个宝贵的数据集，从1943年到1993年的主题的住所的时空分析。 地理信息系统（GIS）允许在公共卫生研究中使用空间分析技术，以前是不可行的。广义加性模型是绘制数据空间分布图的一种有效方法，它结合了一些可取的特点：地理位置和日历年的平滑、纳入风险因素、选择最佳平滑程度（带宽）、假设检验、处理各种类型结果数据的能力以及使用标准软件。映射和统计建模的结合为可视化疾病风险提供了强大的工具。 对乳腺癌流行病学数据的时空分析有助于研究者了解环境危险因素在乳腺癌病因学中的作用。将GAM和GIS应用于病例对照数据将创建在时间和空间上平滑的乳腺癌风险图。拟议的研究将跟踪50年住宅历史中风险升高的程度、地理大小和位置。该研究还将调查时间变量的空间效应：居住时间，启动期间，和非上科德角居住。地理信息系统将被用来覆盖乳腺癌地图和环境数据，为未来的研究产生新的假设。 已知的风险因素不能完全解释上科德角乳腺癌的风险升高。通过了解乳腺癌的“热点”在哪里，以及它们是否会随着时间的推移而增长、加剧或移动，研究人员将更好地了解环境风险因素是如何参与的。

DESCRIPTION (provided by applicant): The reason for elevated breast cancer rates on Cape Cod, Massachusetts remains unknown despite epidemiological studies to investigate possible environmental risk factors. Two existing population-based breast cancer case-control studies of upper Cape Cod (1983-1993) provide a geocoded residential history and information on confounders for all subjects. Combining the two studies creates an invaluable dataset for temporal-spatial analysis of subjects' residences from 1943 to 1993. Geographic Information Systems (GIS) have allowed the use of spatial analytic techniques in public health studies previously not feasible. Generalized Additive Models (GAMs) are an effective approach to mapping spatial distributions of data, combining a number of desirable features: smoothing of geographical location and calendar years, inclusion of risk factors, selection of optimum degree of smoothing (bandwidth), hypothesis testing, ability to handle various types of outcome data, and use of standard software. The combination of mapping and statistical modeling provides a powerful tool for visualizing disease risk. Temporal-spatial analysis of the breast cancer epidemiological data will help researchers understand the role of environmental risk factors in the etiology of breast cancer. The application of GAMs and GIS to the casecontrol data will create breast cancer risk maps that are smoothed over time and space. The proposed research will track the magnitude, geographic size and location of elevated risk for the fifty years of residential history. The study will also investigate the spatial effects of temporal variables: residency duration, promoter periods, and non-upper Cape Cod residency. GIS will be used to overlay breast cancer maps and environmental data to generate new hypotheses for future research. The elevated risk of breast cancer on upper Cape Cod cannot be entirely explained by known risk factors. By learning where breast cancer "hot spots" are and if they grow, intensify, or move over time, researchers will have a better understanding of how environmental risk factors may be involved.