Geostatistical software for detection of cancer

描述（由申请人提供）：该项目的总体目标是开发第一个基于GIS的软件，以提供专门为时空分析和癌症差异检测而设计的工具，提供：描述癌症发病率和死亡率的空间格局，并确定变异的规模，空间平滑和过滤，以纠正由于少数民族人口规模较小而造成的统计不稳定性，检测健康差异明显高或低的集群和热点，以及随时间变化的视差的可视化。该产品将允许调查和可视化健康差异数据与潜在因素之间的关系，例如环境或职业暴露、社会经济条件，导致：（1）更好地了解在癌症发病率、死亡率和发病率方面观察到的种族差异的根本原因（即癌症流行病学），和（2）长期量化的好处，目前的战略，以减少癌症发病率和死亡率不成比例的发病率在少数民族和医疗服务不足的美国。将编制教学材料，以促进卫生科学家使用这种相对较新的方法。 项目第一阶段将： （a）在已完成的模拟研究的坚实基础上，进一步展示和评价地质统计学过滤空间变化噪声的能力，特别是对于在地理单位之间通常表现出巨大差异的少数群体而言。 B）发展统计测试，以检测各亚群体之间癌症发病率的显著差异，并使用空间自相关局部指标（丽莎）来确定健康差异的集群和局部异常。 （c）开发和测试软件原型，以进行少量纠正和发现健康差距。

DESCRIPTION (provided by applicant): The overall objective of this project is to develop the first GIS-based software to offer tools that are specifically designed for the space-time analysis and detection of cancer disparities, providing: description of spatial patterns of cancer incidence and mortality rates and identification of scales of variability, spatial smoothing and filtering to correct for statistical instability caused by the smaller size of minority populations, detection of clusters and hotspots of significantly high or low health disparities, and visualization of changes in disparity through time. This product will allow the investigation and visualization of relationships between health disparity data and potential factors, such as environmental or occupational exposures, socio-economic conditions, leading to: (1) a better understanding of the causes underlying observed racial disparities in cancer incidence, mortality and morbidity (i.e. cancer epidemiology), and (2) long-term quantification of the benefits of current strategies for reducing the disproportionate incidence of cancer morbidity and mortality among minorities and the medically underserved in the United States. Instructional materials will be developed to promote the use of this relatively new methodology among health scientists. Phase I of the project will: a) Build on a firm foundation of completed simulation studies to further demonstrate and evaluate the ability of geostatistics to filter spatially varying noise, in particular for minority populations that commonly display drastic variations among geographical units. b) Develop statistical tests to detect significant differences in cancer rates among sub-populations, and use Local Indicators of Spatial Autocorrelation (LISA) to identify clusters and local anomalies of health disparities. c) Develop and test software prototype to perform the small number correction and detection of health disparities.