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Geostatistical software for spatial and multi-dimensional joinpoint regression analysis of time series of health outcomes

用于健康结果时间序列的空间和多维连接点回归分析的地统计软件

基本信息

批准号：
9047005
负责人：
PIERRE E GOOVAERTS
金额：
$ 20.46万
依托单位：
BIOMEDWARE
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-01-01 至 2016-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9047005
关键词：
American Cancer Burden Cancer Control Cancer Surveillance Research Program Caring Cessation of life Cluster Analysis Code Computer software Congresses County Crime Data Aggregation Data Analyses Development Diagnosis Economic Burden Evaluation Fishes Florida Future Growth Health Health Sciences Health Services Research Imagery Incidence Individual Information Systems International Intervention Knowledge Malignant Neoplasms Malignant neoplasm of prostate Maps Marketing Methodology Methods Modeling Noise Online Systems Outcome Paper Pattern Peer Review Phase Policy Maker Population Prevention Process Protocols documentation Public Health Publications Race Regression Analysis Research Scanning Series Small Business Innovation Research Grant Staging Techniques Technology Testing Time Time Series Analysis United States National Institutes of Health Variant Weight aging population base burden of illness cancer diagnosis cancer epidemiology climate change cost design dimensional analysis disparity reduction health data improved innovation insight mortality neoplasm registry novel prototype public health relevance simulation statistics symposium theories tool trend trend analysis usability user-friendly working group

项目摘要

DESCRIPTION (provided by applicant): Analyzing temporal trends in cancer incidence and mortality rates can provide a more comprehensive picture of the burden of the disease and generate new insights about the impact of various interventions. Join point regression developed by NCI Surveillance Research Program is increasingly used to identify the timing and extent of changes in time series of health outcomes and to project future cancer burden through the prediction of the future number of new cancer cases or deaths. The analysis of temporal trends outside a spatial framework is however unsatisfactory, since it has long been recognized that there is significant variation among U.S. counties and states with regard to the incidence of cancer. It is thus critical to implement join point regression within Geographical Information Systems (GIS), and develop interfaces offering user-friendly tools for pre-processing, modeling, visualizing and summarizing large ensembles of time series of health outcomes. This SBIR project is developing the first commercial software to offer tools for the geostatistical modeling and join point regression analysis of time series of health outcomes. The research product will be a stand-alone module into the desktop space-time visualization core developed by BioMedware, an Esri partner. This software package will provide a comprehensive suite for: 1) the computation and geostatistical noise-filtering (kriging) of time series of health outcomes at various spatial scales (e.g. ZIP codes, counties), 2) the visualization of how the parameters of the regression model (e.g. join point years, Average Annual Percent Change) change in space and across spatial scales, and 3) the analysis of similarities among time series and their aggregation through multi-dimensional scaling and clustering analysis. These tools will be suited for the analysis of data outside health sciences, such as in crime mapping, fish stock assessment or climate change, broadening significantly the commercial market for the end product. This project will accomplish three aims:  Conduct simulation-based studies to assess the benefits of: 1) the application of join point regression to smoothed time series (kriging-based and Bayesian filters) for identifying temporal trends from unstable rates recorded in small geographical units, 2) multi-dimensional scaling to visualize differences among ensemble of time series, and 3) clustering analysis to group geographical units with similar temporal trend. Develop and test a prototype module that will guide users through the creation, join point regression modeling, visualization and multi-dimensional analysis of time series of health outcomes, based on BioMedware's space-time visualization and analysis technology.  Conduct a usability study and identify additional methods and tools to consider in Phase II. These technologic, scientific and commercial innovations will revolutionize our ability to detect changes in cancer incidence and mortality across space and through time, bringing important information and knowledge that will benefit substantially cancer epidemiology, control and surveillance and help reducing these disparities.

描述（由申请人提供）：分析癌症发病率和死亡率的时间趋势可以更全面地了解疾病负担，并对各种干预措施的影响产生新的见解。NCI监测研究计划开发的连接点回归越来越多地用于确定健康结果时间序列变化的时间和程度，并通过预测未来新发癌症病例或死亡人数来预测未来癌症负担。然而，在空间框架之外对时间趋势的分析并不令人满意，因为人们早就认识到，美国各县和各州之间在癌症发病率方面存在显着差异。因此，必须在地理信息系统中实施连接点回归，并开发界面，提供用户友好的工具，用于预处理、建模、可视化和总结大量健康结果时间序列。该SBIR项目正在开发第一个商业软件，为健康结果时间序列的地统计建模和连接点回归分析提供工具。该研究产品将是由Esri合作伙伴BioMedware开发的桌面时空可视化核心的独立模块。该软件包将提供一个全面的套件，用于：1）在不同空间尺度上对健康结果的时间序列进行计算和地质统计噪声过滤（克里金法）（例如邮政编码，县），2）如何回归模型的参数的可视化（例如，连接点年、平均年百分比变化）空间和跨空间尺度的变化，（3）通过多维标度和聚类分析，分析时间序列之间的相似性及其聚合性。这些工具将适用于分析健康科学以外的数据，如犯罪绘图、鱼类资源评估或气候变化，从而大大拓宽最终产品的商业市场。该项目将实现三个目标：1）进行基于模拟的研究，以评估以下方面的好处：1）将连接点回归应用于平滑时间序列（基于克里金法）和贝叶斯滤波器），用于从小地理单元中记录的不稳定速率中识别时间趋势，2）多维缩放以可视化时间序列集合之间的差异，以及3）聚类分析以将具有相似时间趋势的地理单元分组。基于BioMedware的时空可视化和分析技术，开发并测试一个原型模块，该模块将指导用户完成健康结果时间序列的创建、连接点回归建模、可视化和多维分析。进行可用性研究，并确定在第二阶段考虑的其他方法和工具。这些技术、科学和商业创新将彻底改变我们检测癌症发病率和死亡率在空间和时间上的变化的能力，带来重要的信息和知识，将大大有利于癌症流行病学、控制和监测，并有助于缩小这些差距。