A geostatistical framework for the multi-scale boundary analysis of space-time tr

描述（由申请人提供）：尽管健康状况总体上有所改善，但健康结果变化的时间和幅度（例如癌症晚期诊断百分比、不良分娩结果）显示出强烈的种族和地域差异。量化这些差异在不同尺度上的大小以及它们如何随时间变化是了解其起源和跟踪消除其进展的关键指标。拟议的研究将通过以下方式有助于实现这些重要目标：1)发展地质统计学方法，以确定和绘制与个人->邻域->区域对应的嵌套变化尺度，通过边界重叠分析探索其与协变量（例如其他健康结果或假定因素）的关系，并量化这些空间格局的时间稳定性；2)对密歇根州乳腺癌晚期诊断和不良出生结局（死亡率和低出生体重）发生率的地理和社会经济差异及其时间变化进行了深入的边界和多层次分析。具体而言，该项目将实现三个目标：1.；制定新的方法，将边界分析应用于栅格数据（如图像或疾病风险图），并在多尺度框架内考虑到不同空间层次（如县、邮政编码和人口普查区）存在的边界，并在时间框架内实施边界分析，以便研究地理边界的强度和位置的时间趋势。2. 探索使用创新扩散理论和基于模拟退火的空间聚合算法来表示和探索性数据分析健康结果的时间趋势及其与空间和时间上假定因素的关系。3. 应用该方法来展示该方法及其独特的好处，以调查几种健康结果（晚期乳腺癌、婴儿死亡率和低出生体重）在时间趋势上的地理和种族差异，并探索它们与潜在因素的关系，如靠近筛查设施（如乳房x光检查诊所）、社会经济地位、空气污染和个人层面的因素（如吸烟、医疗保险、年龄）。这些技术和科学创新将彻底改变我们在多个空间尺度和时间范围内可视化和解释癌症发病率变化的能力，这将有助于为深入研究因果关系或影响生存或发病率的风险因素提供假设，并为有针对性的癌症控制干预措施（包括考虑卫生服务）建立基本原理

DESCRIPTION (provided by applicant): Despite the overall improvement in health status, the timing and magnitude of changes in health outcomes (e.g. percentage of cancer late-stage diagnosis, adverse birth outcomes) display strong racial and geographical disparities. Quantifying the magnitude of these disparities at different scales and how they change with time are crucial metrics for understanding their origins and tracking progress towards their elimination. The proposed research will contribute to these important goals through: 1) the development of a geostatistical approach to identify and map nested scales of changes corresponding to individual -> neighborhood -> region, explore their relationships with covariates (e.g. other health outcomes or putative factors) through boundary overlap analysis and quantify the temporal stability of these spatial patterns, and 2) an in-depth boundary and multi-level analysis of the geographic and socioeconomic disparities in the incidence of breast cancer late-stage diagnosis and adverse birth outcomes (mortality and low birth weight), as well as their temporal changes, in Michigan. Specifically, this project will accomplish three aims: 1. Develop new methodologies for applying boundary analysis to raster data (e.g. imagery or disease risk maps) and within a multi-scale framework to account for the existence of boundaries at different spatial levels (e.g., counties, ZIP codes and census tracts), and implementing boundary analysis in a temporal framework to allow the study of temporal trends in the strength and location of geographic boundaries. 2. Explore the use of diffusion of innovation theory and simulated annealing-based spatial aggregation algorithms for the representation and exploratory data analysis of temporal trends in health outcomes and their relationship to putative factors in both space and time. 3. Apply the methodology to demonstrate the approach and its unique benefits for the investigation of geographical and racial disparities in temporal trends of several health outcomes (late-stage breast cancer, infant mortality and low birth weight), and the exploration of their relationships with potential factors, such as proximityto screening facilities (e.g. mammography clinics), socio-economic status, air pollution, and individual-level factors (e.g. smoking, health insurance, age). These technologic and scientific innovations will revolutionize our ability to visualize and interpret variation in cancer incidenceat multiple spatial scales and across time, which will help generating hypotheses for in depth individual studies of risk factors that are causal, or impact survival or morbidity, and establishig the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. It will also facilitate the long-term quantification of the benefits of current strategies and policies for reducing the observed geographic and racial disparities in cancer stage at diagnosis and incidence of infant mortality