Bayesian Mortality Estimation from Disparate Data Sources

来自不同数据源的贝叶斯死亡率估计

• 批准号: 10717177
• 负责人: JONATHAN C WAKEFIELD
• 金额: $ 32.31万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-06 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10717177
• 关键词: Accounting; Address; Adopted; Age; Area; Bayesian Modeling; Benchmarking; Birth; Birth History; COVID-19 pandemic; Caring; Censuses; Cessation of life; Child; Child Mortality; Childhood; Collaborations; Complex; Computer software; Country; Data; Data Collection; Data Reporting; Data Sources; Decision Making; Dedications; Development; Disease; Disparate; Disparity; Elements; Event; Excess Mortality; Exercise; Geography; Goals; Guidelines; Health; Household; Individual; Intervention; Link; Manuscripts; Measures; Methodology; Methods; Modeling; Mothers; Neonatal Mortality; Paper; Peer Review; Population; Procedures; Process; Production; Public Health; Publishing; Reporting; Reproducibility; Reproducibility of Results; Research Personnel; Software Validation; Statistical Methods; Stratification; Surveys; Sustainable Development; System; Time; Training; Translations; Twitter; Uncertainty; United Nations; Update; Walking; Work; World Health Organization; computing resources; data modeling; data streams; design; discrete data; flexibility; global health; interest; low and middle-income countries; mortality; new pandemic; novel strategies; open source; pandemic disease; predictive modeling; public health intervention; response; sex; success; temporal measurement; theories; user friendly software; web site

Project Summary: The goal of the proposal is to develop a Bayesian statistical framework for mortality estimation from disparate data sources. Using this framework we will produce a suite of principled methods to be used in those situations in which vital registration data are lacking. We will emphasize efficient implementations that can be used by researchers in low- and middle-income countries (LMICs), who may have limited computing resources. In Aim 1, we will develop guidelines on a general statistical framework for mortality estimation. Aim 2 will focus on subnational child mortality with particular emphasis on the under-5 mortality rate (U5MR), which is a key indicator of the health of a population, and the neonatal mortality rate (NMR). Excess mortality estimation during the Covid-19 pandemic, by month, at the country level, will be the subject of Aim 3. We will disseminate results widely and provide software and training in the developed methods. We will produce yearly estimates of U5MR and NMR at the geographical level at which health decisions are made. To achieve this goal, household survey, VR and census data must be combined in a coherent way. Census data on child mortality typically provide summary birth history (SBH) data, which consist of mother's age along with the number of children born and the number who died, but without the times at which those events occurred. We will develop a framework for combining the different data sources, which will entail dealing with the design issues in the household survey, accounting for unknown birth and death times in the SBH data, and estimating the completeness of the VR data (births and deaths). We will also incorporate demographic information via a form of Bayesian benchmarking. Effective and appropriate use of the models will require rigorous model assessment, careful interpretation of results and meaningful and informative graphical summaries. We will develop robust models to evaluate the excess mortality, i.e., the difference between the deaths ob- served in the pandemic and those expected if the pandemic had not occurred. We will model the expected deaths, and incorporate the uncertainty in this endeavor in the excess mortality calculation. Completeness of mortality counts, that is, under-reporting and delays in reporting, will also be considered. For countries who do not report deaths in the pandemic, we must predict the mortality count using available country-level covariate data, and we will adopt flexible yet interpretable regression forms, and acknowledge uncertainty in the covariate data. We will produce user-friendly software for the methods, along with vignettes and training materials, including short courses. The endpoint is to have software that can be used by researchers in LMICs. All aims will be informed by the collaborative team's close links with the United Nations Inter-agency Group for Child Mortality Estimation (for the subnational child mortality aim) and the World Health Organization Division of Data, Analytics and Delivery for Impact (for the excess mortality aim). Together we will develop methods to highlight disparities and inform interventions.

项目摘要：该提案的目标是开发用于死亡率估计的贝叶斯统计框架 从不同的数据源。使用这个框架，我们将产生一套原则性的方法， 缺乏生命登记数据的情况。我们将强调有效的实施， 可供低收入和中等收入国家（LMIC）的研究人员使用，他们的计算能力可能有限 资源在目标1中，我们将制定关于死亡率估计的一般统计框架的指南。目的2 将侧重于国家以下各级的儿童死亡率，特别是5岁以下儿童死亡率， 人口健康的关键指标，以及新生儿死亡率。超额死亡率估计 在新冠肺炎大流行期间，在国家一级按月计算，将是目标3的主题。我们将传播 结果广泛，并提供软件和培训的开发方法。 我们将在做出健康决策的地理级别上对五岁以下儿童死亡率和核磁共振进行年度估计 进行了为了实现这一目标，必须以连贯的方式将住户调查、虚拟现实和人口普查数据结合起来。普查 关于儿童死亡率的数据通常提供简要出生史（SBH）数据，其中包括母亲的年龄沿着 有多少孩子出生，有多少孩子死亡，但没有这些事件发生的时间。 我们将开发一个框架来结合不同的数据源，这将需要处理设计 家庭调查中的问题，在SBH数据中解释未知的出生和死亡时间，并估计 VR数据的完整性（出生和死亡）。我们还将通过表格纳入人口统计信息 Bayesian Benchmarking的一部分。有效和适当地使用模型将需要严格的模型评估， 仔细解释结果和有意义和信息丰富的图形总结。 我们将开发强大的模型来评估超额死亡率，即，死亡人数的差异 在大流行期间以及在大流行未发生时预期的服务。我们将模拟预期的死亡， 并将这一奋进中的不确定性纳入超额死亡率计算中。死亡率的完全性 也将考虑到未充分报告和延迟报告的情况。对于未提交报告的国家 对于大流行中的死亡人数，我们必须使用现有的国家层面协变量数据来预测死亡人数，而且我们 将采用灵活但可解释的回归形式，并承认协变量数据的不确定性。 我们将为这些方法制作用户友好的软件，沿着插图和培训材料，包括 短期课程。最终目标是拥有可供中低收入国家研究人员使用的软件。所有的目标都将是 通过协作小组与联合国儿童死亡率机构间小组的密切联系， 估计（国家以下各级儿童死亡率目标）和世界卫生组织数据、分析司 和“交付以产生影响”（超额死亡率目标）。我们将共同制定方法， 并为干预提供信息。