Geostatistical software for the space-time analysis of health disparities

描述（由申请人提供）：SBIR项目正在开发第一个地理统计软件，提供专门用于分析健康差异的工具，提供：描述癌症死亡率的空间模式，确定可变性的尺度，进行空间过滤，以纠正少数民族人口规模较小造成的统计不稳定性，统计检验，以检测各亚群之间癌症风险的显著差异，检测健康差异显著高或低的集群和离群值，探索与协变量的局部关系（即人口统计学、行为或社会经济变量），并使用地理加权回归和多层次分析，以及随时间推移的差异变化的可视化。该项目将 实现6个目标：1.进行需求分析，以确定要纳入软件的空间方法和功能。2.开发创新的地理统计技术，对癌症发病率进行空间过滤，并进行统计检验，以发现各亚群体之间癌症发病率的显著差异。3.开发新的方法，用于在小的地理区域进行速率过滤，将邻里之间的空间相互作用纳入多级分析，通过统计分析评估和传播过滤速率所附带的不确定性。4.基于研究结果和仿真研究构建和测试一套完整的功能，并将其纳入Biomedware的时空可视化和分析技术中。5.应用软件和方法来展示方法及其在测量、绘制、检测和解释健康差异方面的独特优势。6.创建教学材料，包括短期课程，以促进在健康科学中采用这种方法。该项目的可行性已在第一阶段得到论证。该项目第二阶段将实现目标三至六。这些技术、科学和商业创新将彻底改变我们的能力，使我们能够了解癌症差异的地理差异，检测空间变化（如集群或异常）或时间变化（如采取改善癌症预防和早期发现的战略后健康差异的变化），并更好地了解观察到的癌症发病率、死亡率和发病率种族差异的原因。

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.