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 死亡原因数据对于了解疾病负担、新出现的健康需求和 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）拥有足够的重要基础设施。 口头尸检 (VA) 是一种广泛采用的统计系统，可生成有关死亡原因的高质量统计数据。 在无法进行全面尸检和死亡证明时收集有关死亡原因信息的工具。 目前的 VA 分析方法由于缺乏通用性而受到严重限制。 死因分配不准确，部署时对死亡分布的估计存在偏差 与本项目中开发模型所依据的人群不同的人群， 我们将开发稳健的、领域自适应的、计算上可行的方法，将原因分配给个人 通过完成以下目标来估计死亡人数和估计特定原因死亡率： (i) 开发统计模型 (ii) 开发和评估域自适应算法 用于新人群的死因分配；以及 (iii) 扩展统一领域适应框架 这个新框架将利用完整的 VA 方法来改进现有的 VA 方法。 来自多个人群的参考死亡中可用的信息，以实现数据转移的稳健性 该框架还将纳入收集的迹象和复杂的依赖关系。 以可解释的方式症状，并允许与标准兼容的快速和简化的实施 我们将开发第一个统一的领域自适应死因分配框架。 使用 VA 数据，并对 VA 收集的体征和症状之间的关系提供重要见解 该项目将为未来的研究奠定基础，例如将 VA 与其他研究相结合。 从病史或组织样本中收集的协变量和生物标志物信息，以及系统设计 使用大规模 VA 调查进行死因监测和监测。