The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data

中文翻译：

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通过口头尸检进行概率死因评估的贝叶斯因子模型


按原因划分的死亡分布为公共卫生规划、应对和评估提供了重要信息。全球约 60% 的死亡没有登记或没有给出原因，限制了我们了解疾病流行病学的能力。口头尸检 (VA) 调查越来越多地在此类环境中使用，以收集有关最近死亡者的体征、症状和病史的信息。本文开发了一种新颖的贝叶斯方法，用于使用口头尸检数据按原因估计死亡人口分布。所提出的方法基于多元概率模型，其中问卷中项目之间的关联是由潜在因素灵活诱导的。使用人口健康指标研究联盟标记的数据（包括 VA 和医学证明的死亡原因），我们评估了所提出方法的性能。此外，我们估计了与死亡原因高度相关的重要问卷项目。该框架提供了可简化未来数据的见解