Geostatistical software for detection of cancer

项目描述（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，提供专门用于癌症差异时空分析和检测的工具，提供：描述癌症发病率和死亡率的空间格局并确定变异性尺度，空间平滑和过滤以纠正少数民族人口规模较小造成的统计不稳定性，检测健康差异显著高或低的集群和热点，并可视化差异随时间的变化。该产品将允许调查和可视化健康差距数据与环境或职业暴露、社会经济条件等潜在因素之间的关系，从而导致：(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原因（即癌症流行病学），以及(2)长期量化当前战略的效益，以减少美国少数民族和医疗服务不足人群中不成比例的癌症发病率和死亡率。将编写教学材料，促进卫生科学家使用这种相对较新的方法。

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.