Small Area Estimation for State and Local Health Departments

州和地方卫生部门的小面积估计

基本信息

批准号：
10275680
负责人：
Harrison Quick
金额：
$ 23.64万
依托单位：
DREXEL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-20 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10275680
关键词：
Address Age Age Factors American Heart Association Area Bayesian Analysis Bayesian Method Case Study Cause of Death Censuses Centers for Disease Control and Prevention (U.S.)Cessation of life Cities Collaborations Collection Complex Computer software Country Data Data Set Death Rate Dependence Development Disease Surveillance Equilibrium Ethnic Origin Event Face Fright Funding Future Gender Geographic Locations Geography Goals Health Health Surveys Heart Diseases Household Individual Intervention Investigation Joints Literature Methodology Methods Modeling Multivariate Analysis Neighborhoods Obesity Outcome Pattern Peer Review Pennsylvania Philadelphia Policies Population Prevalence Production Public Health Publications Race Reporting Research Research Personnel Research Project Grants Resolution Risk Factors Sample Size Small-Area Analysis Software Tools Specific qualifier value Standardization Statistical Methods Statistical Models Subgroup Surveys Training Weights and Measures Work base dashboard data space disease disparity experience health data health disparity improved information model insight large datasets men mortality prevent racial disparity sex spatiotemporal statistics tool trend

项目摘要

PROJECT SUMMARY Researchers at state and local health departments producing small area estimates often face a lose-lose situation. On one hand, there is a wealth of evidence of racial disparities in many health outcomes and their risk factors, but stratifying data by space and race (in addition to factors such as age and sex) only exacerbates the issues associated with small area estimation by dividing a dataset with small sample sizes into a larger dataset with smaller sample sizes. On the other hand, while the use of complex statistical models can be used to produce more precise estimates from limited data, estimates produced by state and local health departments may be treated as “ofﬁcial statistics” and thus these agencies may be reluctant to rely too heavily on statistical models for fear of the bias they may introduce. The objective of the proposed work is three-fold. Our ﬁrst task will be to develop statistical models for the analysis of multivariate spatial data that allow users to pre-specify an upper bound on the model's informativeness — i.e., a measure of the weight given to the model as compared to the data when producing model-based estimates. This work will build on the rich spatial statistics literature and recent research that provides insight into how to quantify the informativeness of spatial models. We will extend this approach to the setting of multivariate spatial data for the purposes of calculating demographic group-speciﬁc estimates and age-adjusted estimates. Because we envision these methods being useful for researchers at state and local health departments, we believe a thorough case study of our methods should be conducted to assess their suitability. To this end, our second task will be to partner with the Philadelphia Department of Public Health and use the methods we've developed to conduct a rigorous analysis of heart disease mortality and its risk factors in Philadelphia. This analysis will produce yearly census tract-level estimates for rates of death due to several forms of heart disease and estimates of the prevalence of key risk factors by age, gender, and race/ethnicity. The product of this research will include a collection of reports — one focused on city-level trends and one focused on neighborhood-level trends — an interactive online dashboard, and peer-reviewed publications that add context to our ﬁndings. Finally, we recognize that few state and local health departments have staff who are trained in advanced spatial Bayesian statistical methods, a fact that could serve as an impediment to the use of the methods we develop. To remedy this, our third task will be to partner with the CDC-funded GIS Capacity Building Project, which provides training in geospatial analyses to state and local health departments. This training begins by introducing users to the ArcGIS software package and concludes with an overview of a tool created by the GIS Capacity Building Project — the Rate Stabilizing Tool (RST). For this project, we will partner with the GIS Capacity Building Project to incorporate the methods we developed into the RST in a “black-box” framework and provide additional training on the use of spatial Bayesian methods in disease surveillance.

项目概要州和地方卫生部门的研究人员进行小面积估计时常常面临双输的局面。一方面，有大量证据表明许多健康结果及其风险因素存在种族差异，但按空间和种族（除了年龄和性别等因素）对数据进行分层只会加剧问题通过将小样本量的数据集划分为更大的数据集来与小区域估计相关联另一方面，可以使用复杂的统计模型来产生较小的样本量。根据有限的数据进行更精确的估计，州和地方卫生部门做出的估计可能是被视为“官方统计数据”，因此这些机构可能不愿意过于依赖统计模型担心他们可能引入偏见。拟议工作的目标有三个：我们的首要任务是开发统计模型。多元空间数据分析，允许用户预先指定模型信息量的上限 — 即，在生成基于模型时与数据相比给予模型的权重的度量这项工作将建立在丰富的空间文献统计数据和最新研究的基础上，提供深入的见解。如何量化空间模型的信息量我们将把这种方法扩展到多元的设置。用于计算特定人口群体估计值和年龄调整估计值的空间数据。因为我们预计这些方法对州和地方卫生部门的研究人员有用，所以我们相信应该对我们的方法进行彻底的案例研究，以评估它们的适用性。第二项任务是与费城公共卫生部合作并使用我们已经采取的方法旨在对费城的心脏病死亡率及其危险因素进行严格分析。分析将产生年度人口普查区层面的几种心脏病死亡率估计值以及按年龄、性别和种族/民族划分的关键风险因素的患病率估计。将包括一系列报告 - 一份侧重于城市层面的趋势，一份侧重于社区层面趋势——交互式在线仪表板和同行评审的出版物，为我们的发现增添了背景。最后，我们认识到，很少有州和地方卫生部门拥有接受过高级培训的工作人员。空间贝叶斯统计方法，这一事实可能成为我们使用这些方法的障碍为了解决这个问题，我们的第三项任务是与 CDC 资助的 GIS 能力建设项目合作，向州和地方卫生部门提供地理空间分析培训。向用户介绍 ArcGIS 软件包，最后概述 GIS 创建的工具能力建设项目 — 速率稳定工具 (RST) 对于该项目，我们将与 GIS Capacity 合作。构建项目，将我们开发的方法纳入“黑盒”框架中的 RST 中，并提供关于在疾病监测中使用空间贝叶斯方法的额外培训。