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

时空TR多尺度边界分析的地统计框架

基本信息

批准号：
8444188
负责人：
PIERRE E GOOVAERTS
金额：
$ 22.55万
依托单位：
BIOMEDWARE
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-12-01 至 2014-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8444188
关键词：
Accounting Age Air Pollution Algorithms Birth Breast Cancer Control Censuses Clinic Code Congresses County Data Data Analyses Data Set Detection Development Diagnosis Diagnostic Neoplasm Staging Diagnostic tests Diffusion Diffusion of Innovation Disease Economic Conditions Educational workshop Enrollment Environment Environmental Exposure Epidemiology Ethnic Origin Frequencies Geographic Locations Goals Health Health Insurance Health Services Accessibility Health Services Needs Health Status Healthy People 2020 Imagery Incidence Income Individual Infant Mortality International Intervention Investigation Knowledge Lead Link Low Birth Weight Infant Malignant Neoplasms Mammography Maps Medicaid Medicare/Medicaid Methodology Methods Metric Michigan Modeling Morbidity - disease rate Neighborhoods Outcome Paper Pathway interactions Pattern Policies Publications Race Research Resource Allocation Risk Factors Scientist Screening for cancer Side Simulate Smoking and Health Research Socioeconomic Status Spatial Distribution Stage at Diagnosis Staging Testing Time Uncertainty Variant base breast cancer diagnosis cancer care cancer type disability disorder risk health care delivery health disparity innovation malignant breast neoplasm member mortality neoplasm registry screening socioeconomics theories trend tumor registry

项目摘要

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

描述（由申请人提供）：尽管健康状况总体改善，但健康结果变化的时间和程度（例如癌症晚期诊断的百分比、不良出生结果）显示出强烈的种族和地理差异。量化不同尺度上这些差异的严重程度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为这些重要目标做出贡献：1）开发地统计方法来识别和绘制与个人 -> 邻里 -> 区域相对应的嵌套变化规模，探索它们与协变量的关系（例如其他健康结果或推定因素） ) 通过边界重叠分析并量化这些空间模式的时间稳定性，以及 2) 对乳腺癌晚期诊断和不良出生结果的发病率的地理和社会经济差异进行深入的边界和多层次分析（死亡率和低出生体重）及其时间变化，在密歇根州。具体来说，该项目将实现三个目标： 1. 开发新的方法，将边界分析应用于栅格数据（例如图像或疾病风险地图），并在多尺度框架内解释不同空间级别边界的存在（例如，县、邮政编码和人口普查区），并在时间框架中实施边界分析，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的假定因素的关系。 3. 应用该方法论来证明该方法及其独特的优势，用于调查几种健康结果（晚期乳腺癌、婴儿死亡率和低出生体重）的时间趋势中的地理和种族差异，并探索它们与潜在因素，例如靠近筛查设施（例如乳房X光检查诊所）、社会经济地位、空气污染和个人因素（例如吸烟、健康保险、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跨时间上可视化和解释癌症发病率变化的能力，这将有助于为因果或影响生存或发病风险因素的深入个体研究产生假设，并建立有针对性的癌症控制干预措施的基本原理，包括考虑卫生服务筛查和诊断测试的需求和资源分配。它还将有助于对当前战略和政策的效益进行长期量化，以减少在癌症诊断阶段和婴儿死亡率发生率方面观察到的地理和种族差异