Developing an innovative statistical framework to integrate multiple verbal autopsy datasets to estimate cause-specific mortality

开发创新的统计框架来整合多个口头尸检数据集，以估计特定原因的死亡率

• 批准号: 10710402
• 负责人: Zehang Li
• 金额: $ 7.3万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10710402
• 关键词: Address; Adopted; Algorithms; Archives; Autopsy; Bayesian Modeling; Biological Markers; Cause of Death; Certification; Cessation of life; Classification; Collection; Complex; Computer software; Country; Data; Data Reporting; Data Set; Dependence; Effectiveness; Failure; Foundations; Future; Health; Heterogeneity; Individual; Intervention; Measurement; Medical History; Methods; Modeling; Monitor; Physicians; Population; Probability; Questionnaires; Reproducibility; Research; Signs and Symptoms; Standardization; Statistical Algorithm; Statistical Models; Structure; Surveys; Symptoms; System; Target Populations; Tissue Sample; Uncertainty; Vital Statistics; Work; World Health Organization; analysis pipeline; analytical method; burden of illness; computer framework; data quality; design; flexibility; global health; health goals; heterogenous data; improved; innovation; insight; low and middle-income countries; mortality; novel; open source; population based; population health; prevent; public health intervention; statistics; tool; validation studies; verbal

Project Abstract Cause of death data are essential for understanding the burden of disease, emerging health needs, and the effectiveness of public health interventions. Few low- and middle-income countries (LMIC) have adequate vital statistics systems that produce high quality statistics on causes of death. Verbal autopsy (VA) is a widely adopted tool to collect information on causes of death when full autopsy and death certification are not possible. The current analytical methods for VA are significantly limited by the lack of generalizability. Existing VA methods yield inaccurate cause-of-death assignment and biased estimates of the distribution of deaths when they are deployed to populations that are different than the populations based on which the models are developed. In this project, we will develop robust, domain adaptive, and computationally feasible methods to assign causes to individual deaths and estimate cause-specific mortality, by completing the following aims: (i) to develop statistical models to characterize multiple heterogeneous VA datasets; (ii) to develop and evaluate domain adaptive algorithms for cause-of-death assignment in new populations; and (iii) to extend the unified domain adaptation framework to routine VA analysis pipeline. This new framework will improve on existing VA methods by utilizing the full information available in reference deaths from multiple populations to achieve robustness to data shift across populations. The framework will also incorporate the complex dependence relationship in the collected signs and symptoms in an interpretable manner, and allow fast and streamlined implementation compatible with standard VA questionnaires. We will develop the first unified framework for domain adaptive cause-of-death assignment using VA data and offer critical insights into the relationship between the signs and symptoms collected by VA and causes of death. The project will lay the groundwork for future research, such as integrating VAs with additional covariates and biomarker information collected from medical history or tissue samples, and designing systematic cause-of-death monitoring and surveillance using large-scale VA surveys.

项目摘要 死因数据对于了解疾病负担、新出现的健康需求以及 公共卫生干预措施的有效性。很少有低收入和中等收入国家(LMIC)有足够的关键 提供高质量的死因统计数据的统计系统。口头尸检(VA)是一种被广泛采用的 在无法进行全面尸检和死亡fi确认的情况下收集死因信息的工具。这个 现有的VA分析方法由于缺乏通用性而受到很大的限制。fi。现有VA方法的收益率 死因分配不准确，部署时对死亡分布的估计有偏差 到与开发模型所基于的种群不同的种群。在这个项目中， 我们将开发健壮的、领域自适应的和计算上可行的方法来将原因分配给个人 通过完成以下目标，死亡和估计死因fic死亡率：(1)建立统计模型 描述多个不同类型的VA数据集；(Ii)开发和评估域自适应算法 在新人口中进行死因分配；以及(3)扩展统一的领域适应框架 至常规VA分析管道。这个新的框架将通过充分利用现有的VA方法 多个人口的参考死亡人数中提供的信息，以实现对数据转移的稳健性 人口。该框架还将在收集的标志中纳入复杂的依赖关系，并 症状以可解释的方式，并允许快速和简化的实施与标准兼容 退伍军人问卷调查。我们将开发用于领域自适应死因分配的fi第一个统一fi框架 使用VA数据，并对VA收集的体征和症状之间的关系提供关键见解 死因。该项目将为未来的研究奠定基础，例如将VAS与其他 从病史或组织样本收集的协变量和生物标记物信息，并设计系统 死因监测和使用大规模退伍军人管理局调查的监测。