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: 10576014
• 负责人: Zehang Li
• 金额: $ 7.7万
• 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/10576014
• 关键词: Address; Adopted; Algorithms; Archives; Autopsy; Bayesian Modeling; Biological Markers; Cause of Death; Cessation of life; Collection; Complex; Computer software; Country; Data; Data Reporting; Data Set; Death Certificates; Dependence; Effectiveness; Failure; Foundations; Future; Gold; Health; Heterogeneity; Individual; Intervention; Light; Measurement; Medical History; Methods; Modeling; Monitor; Physicians; Population; Questionnaires; Reproducibility; Research; Signs and Symptoms; Standardization; Statistical Algorithm; Statistical Data Interpretation; Statistical Models; Structure; Surveys; Symptoms; System; Target Populations; Tissue Sample; Uncertainty; Vital Statistics; Work; World Health Organization; analysis pipeline; analytical method; base; 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

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 死亡原因数据对于理解疾病的燃烧，新兴健康需求以及 公共卫生干预的有效性。很少有低收入和中等收入国家（LMIC）具有足够的至关重要 产生有关死亡原因的高质量统计的统计系统。口头尸检（VA）是广泛的 当无法进行全面尸检和死亡证书时，收集有关死亡原因的信息的工具。这 VA的当前分析方法受到缺乏普遍性的显着限制。现有的VA方法产量 部署时死亡原因的不准确和死亡分布的偏见估计值 与基于建立模型的人群不同的人群。在这个项目中， 我们将开发强大的，域的自适应和计算可行的方法，以将原因分配给个人 通过完成以下目的，死亡和估计特定原因的死亡率：（i）开发统计模型 表征多个异质VA数据集； （ii）开发和评估域自适应算法 用于新人群的死亡原因； （iii）扩展统一的域适应框架 进行常规VA分析管道。该新框架将通过完整的 来自多个人群的参考死亡中的信息可实现稳健性到跨越数据的转移 人群。该框架还将在收集的符号中纳入复杂的依赖关系和 以可解释的方式出现症状，并允许快速，简化的实施与标准兼容 VA问卷。我们将开发针对域自适应死亡原因的第一个统一框架 使用VA数据，并提供有关VA和VA收集的标志和符号之间关系的关键见解 死亡原因。该项目将为未来的研究奠定基础，例如将VA与其他 从病史或组织样本收集的协变量和生物标志物信息，并设计系统性 使用大规模VA调查的死亡原因监测和监视。