Small Area Estimation for State and Local Health Departments

项目总结 州和地方卫生部门的研究人员在编制小面积估计时经常面临双输局面。 一方面，有大量证据表明，在许多健康结果及其风险因素方面存在种族差异， 但是，根据空间和种族(除了年龄和性别等因素)对数据进行分层只会加剧问题 通过将样本大小较小的数据集划分为较大的数据集，与小区域估计相关联 样本量较小。另一方面，虽然使用复杂的统计模型可以用来产生 更准确的估计来自有限的数据，由州和地方卫生部门产生的估计可能是 被视为“fi社会统计”，因此这些机构可能不愿过于依赖统计模型 对他们可能带来的偏见的恐惧。 拟议工作的目标有三个方面。我们的fi首要任务将是为 分析允许用户预先指定模型信息量上限的多变量空间数据 -即，在生成基于模型的数据时，相对于数据赋予模型的权重的度量 估计。这项工作将建立在丰富的空间统计文献和最近的研究基础上，这些研究提供了对 如何量化空间模型的信息量。我们将把这种方法扩展到多变量的设置 用于计算人口统计组别的空间数据--fic估计数和年龄调整估计数。 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 我认为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。为此，我们的 第二项任务将是与费城公共卫生部合作，并使用我们已经 开发的目的是在费城对心脏病死亡率及其风险因素进行严格分析。这 分析将产生每年人口普查地区水平的几种形式的心脏病死亡率估计。 以及按年龄、性别和种族/民族对关键风险因素流行率的估计。这项研究的成果 将包括一系列报告-一份专注于城市层面的趋势，另一份专注于社区层面 趋势-一个交互式的在线仪表板，以及为我们的fi规则添加上下文的同行评议出版物。 最后，我们认识到，很少有州和地方卫生部门的工作人员接受过高级培训。 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 发展。为了纠正这一点，我们的第三项任务将是与疾控中心资助的地理信息系统能力建设项目合作，该项目 为州和地方卫生部门提供地理空间分析方面的培训。这项为期一个月的培训计划 首先向用户介绍ArcGIS软件包，最后概述由创建的工具 地理信息系统能力建设项目--速率稳定工具(RST)。在这个项目中，我们将与地理信息系统合作 能力建设项目，将我们开发的方法纳入“黑箱”框架中的区域科学技术小组，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。

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 month-long training program 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 develop into the RST in a “black-box” framework and provide additional training on the use of spatial Bayesian methods in disease surveillance.