Small Area Estimation for State and Local Health Departments

州和地方卫生部门的小面积估计

• 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
• 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

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.

项目摘要 州和地方卫生部门的研究人员估计量较小，通常面临失败的情况。 一方面，在许多健康成果及其风险因素中，有很多证据表明种族差异， 但是，按空间和种族对数据进行分层（除了年龄和性别等因素外，还会加剧问题 与小面积估计相关联，通过将小样本量的数据集划分为较大的数据集， 较小的样本量。另一方面，虽然可以使用复杂的统计模型来生产 来自有限数据的更精确的估计，州和地方卫生部门产生的估计可能是 被视为“官方统计”，因此这些机构可能不愿过分依赖统计模型 害怕他们可能引入的偏见。 拟议工作的目的是三倍。我们的第一个任务是为 分析多元空间数据，该数据允许用户预先指定模型信息的上限 - 即，与数据相比，在产生基于模型的数据时，对模型的权重的度量 估计。这项工作将建立在丰富的空间统计文献和最新研究的基础上 如何量化空间模型的信息性。我们将把这种方法扩展到多元的设置 为了计算人口组的特定估计和年龄调整估计的目的，空间数据。 因为我们设想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详尽的案例研究，以评估其适用性。为此，我们的 第二个任务将是与费城公共卫生部合作，并使用我们已有的方法 为了对费城的心脏病死亡率及其危险因素进行严格的分析而开发。这 分析将对由于多种形式的心脏病而导致死亡率的年度人口普查级估计 以及按年龄，性别和种族/种族划分的关键风险因素流行的估计。这项研究的产物 将包括一系列报告 - 专注于城市级别的趋势，专注于邻里级别的趋势 趋势 - 交互式在线仪表板和经过同行评审的出版物，为我们的发现增加了上下文。 最后，我们认识到，很少有州和地方卫生部门接受过高级培训的员工 空间贝叶斯统计方法，这一事实可能会障碍我们使用这些方法 发展。为了解决这个问题，我们的第三个任务是与CDC资助的GIS能力建设项目合作， 为州和地方卫生部门提供地理空间分析的培训。该培训始于 向用户介绍ArcGIS软件包，并以GIS创建的工具的概述结束 能力建设项目 - 稳定工具（RST）。对于这个项目，我们将与GIS能力合作 建筑项目将我们开发到REST中的方法中的建筑项目以“ Black-Box”框架并提供 关于在疾病监测中使用空间贝叶斯方法的其他培训。