Geostatistical software for the space-time analysis of health disparities

描述(由申请人提供)：该SBIR项目正在开发第一个提供专门用于分析健康差距的工具的地统计学软件，提供：癌症死亡率空间模式的描述和变异性尺度的确定，空间过滤以校正少数族裔人口较少造成的统计不稳定，统计测试以检测亚群中癌症风险的显著差异，检测显著高或低健康差距的集群和离群值，利用地理加权回归和多水平分析探索地方与协变量(即人口、行为或社会经济变量)的关系，以及可视化差异随时间的变化。这个项目将 完成6个目标：1.进行需求分析，以确定要整合到软件中的空间方法和功能。2.开发创新的地统计学技术，用于癌症发病率的空间过滤和统计测试，以检测不同亚群之间癌症发病率的显著差异。3.开发在小地理区域进行速率过滤的新方法，将社区之间的空间相互作用纳入多层次分析，通过统计分析评估和传播过滤后的速率的不确定性。4.在研究成果和仿真研究的基础上，构建和测试一整套功能，并将其融入到Biomedware的时空可视化和分析技术中。5.应用软件和方法演示该方法及其在测量、绘制地图、检测和解释健康差距方面的独特好处。6.编写教学材料，包括一个短期课程，以促进在卫生科学中采用这一方法。该项目的可行性在一期工程中得到论证。二期工程将实现目标三至六。这些技术、科学和商业创新将彻底改变我们解释癌症差异的地理差异的能力，检测空间(例如集群或异常)或时间的变化(例如，在改进癌症预防和早期发现的战略之后健康差异的变化)，并更好地理解癌症发病率、死亡率和发病率方面观察到的种族差异的潜在原因。

DESCRIPTION (provided by applicant): This SBIR project is developing the first geostatistical software to offer tools that are specifically designed for the analysis of health disparities, providing: descriptions of spatial patterns of cancer mortality rates and identification of scales of variability, spatial filtering to correct for statistical instability caused by the smaller size of minority populations, statistical tests to detect significant differences in cancer risks among sub-populations, detection of clusters and outliers of significantly high or low health disparities, exploration of local relationships with covariates (i.e. demography, behavioral or socio-economic variables) using geographically-weighted regression and multi-level analysis, and visualization of changes in disparities through time. This project will accomplish 6 aims: 1. Conduct a requirements analysis to identify the spatial methods and functionality to incorporate into the software. 2. Develop innovative geostatistical techniques for spatial filtering of cancer rates and statistical tests to detect significant differences in cancer rates among sub-populations. 3. Develop new methodologies for rate filtering over small geographies, incorporation of spatial interactions among neighborhoods into multi-level analysis, assessment and propagation of the uncertainty attached to filtered rates through the statistical analysis. 4. Build and test a complete set of functionalities based on the research results and simulation studies, and incorporate them into Biomedware's space-time visualization and analysis technology. 5. Apply the software and methods to demonstrate the approach and its unique benefits for the measurement, mapping, detection and explanation of health disparities. 6. Create instructional materials, including a short course, to foster the adoption of this approach in health science. Feasibility of this project was demonstrated in Phase I. This Phase II project will accomplish aims three through six. These technologic, scientific and commercial innovations will revolutionize our ability to interprete geographic variation in cancer disparities, detect changes in space (e.g. cluster or anomalies) or through time (e.g. change in health disparities following strategies to improve cancer prevention and early detection), and to better understand the causes underlying observed racial disparities in cancer incidence, mortality and morbidity.