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（由适用提供）：分析癌症发病率和死亡率的临时趋势可以为疾病的伯恩提供更全面的了解，并就各种干预措施的影响产生新的见解。 NCI监视研究计划开发的JOIN点回归越来越多地用于确定健康结果的时间序列变化的时机和程度，并通过预测未来的新癌症病例或死亡人数来预测未来的癌症伯宁。但是，对空间框架之外的临时趋势的分析并不令人满意，因为长期以来人们已经认识到，在癌症事件中，美国县和州之间存在很大差异。因此，在地理信息系统（GIS）中实施联接点回归并开发界面至关重要，从而为用户友好的工具提供了用于预处理，建模，可视化和总结时间序列的健康结果的大型合奏的工具。该SBIR项目正在开发第一个为地理建模提供工具的商业软件，并加入时间序列的健康结果分析。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。此软件包将提供一个全面的套件：1）在各种空间量表（例如邮政编码，县）中，健康结果的计算和地列为噪声过滤（KRIGING），2）在空间和跨越时间的分析中，跨度分析的变化以及3）的分析以及3）的可视化，2）在空间变化中的可视化（例如，平均年龄变化）多维缩放和聚类分析。这些工具将适合分析健康科学以外的数据，例如在犯罪制图，鱼类种群评估或气候变化中，大大拓宽了最终产品的商业市场。该项目将实现三个目标：进行基于模拟的研究以评估以下方面的好处：1）连接点回归的应用到平滑时间序列（基于Kriging）。和贝叶斯过滤器），以识别小地理单元记录的不稳定率的临时趋势，2）多维缩放，以可视化时间序列之间的差异，3）聚类分析到具有相似临时趋势的组地理单位。开发和测试一个原型模块，该模块将基于生物部的时空可视化和分析技术，将指导用户进行创建，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第二阶段中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到跨太空和随着时间的流逝的癌症发病率和死亡率变化的能力，从而带来重要的信息和知识，从而使癌症的流行病学，控制和监视有益，并有助于减少这些分布。