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; 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：该提案的目的是为死亡率估算开发贝叶斯统计框架 来自不同的数据源。使用此框架，我们将生产一套主要的方法 缺乏重要的注册数据的情况。我们将强调有效的实现 低收入国家（LMIC）的研究人员可以使用，计算机可能有限 资源。在AIM 1中，我们将制定有关死亡率估算的一般统计框架的准则。目标2 将重点关注次国儿童死亡率，特别着重于5岁以下死亡率（U5MR） 人口健康的关键指标和新生儿死亡率（NMR）。过多的死亡率估计 在Covid-19-19大流行期间，按月在国家一级将成为AIM 3的主题。我们将传播 结果广泛，并在开发方法中提供软件和培训。 我们将在健康决策的地理层面上对U5MR和NMR的年度估算 制成。为了实现这一目标，家庭调查，VR和人口普查数据必须以连贯的方式合并。人口普查 有关儿童死亡率的数据通常提供摘要出生历史（SBH）数据，该数据包括母亲的年龄 随着孩子的数量和死亡的人数，但没有发生这些事件的时间。 我们将开发一个结合不同数据源的框架，这将需要处理设计 家庭调查中的问题，占SBH数据中未知的出生和死亡时间，并估算 VR数据的完整性（出生和死亡）。我们还将通过表格合并人口信息 贝叶斯基准测试。有效且适当使用模型将需要严格的模型评估， 仔细解释结果以及有意义且内容丰富的图形摘要。 我们将开发强大的模型来评估过剩的死亡率，即死亡人数之间的差异 如果没有发生大流行，则在大流行中服役。我们将建模预期的死亡， 并将这项工作的不确定性纳入过多的死亡率计算中。死亡率的完整性 还将考虑计数，即报告不足和报告的延迟。对于不报告的国家 大流行中的死亡，我们必须使用可用国家 /地区的协变量数据来预测死亡率计数，我们 将采用灵活但可解释的回归形式，并承认协变量数据中的不确定性。 我们将生产用于方法的用户友好软件，以及小插曲和培训材料，包括 短课程。终点是拥有可以在LMIC中使用的软件。所有目标都是 由合作团队与联合国儿童死亡人际机构间的密切联系所告知 估计（针对次国儿童死亡率目标）和世界卫生组织数据部，分析 和撞击的交付（对于过剩的死亡率目标）。我们将共同开发突出显示分布的方法 并告知干预措施。