Verbal autopsies (VAs) are extensively used to investigate the population-level distributions of deaths by cause in low-resource settings without well-organized vital statistics systems. Computer-based methods are often adopted to assign causes of death to deceased individuals based on the interview responses of their family members or caregivers. In this article, we develop a new Bayesian approach that extracts information about cause-of-death distributions from VA data considering the age- and sex-related variation in the associations between symptoms. Its performance is compared with that of existing approaches using gold-standard data from the Population Health Metrics Research Consortium. In addition, we compute the relevance of predictors to causes of death based on information-theoretic measures.

翻译：口头尸检（VA）被广泛用于研究资源匮乏地区（缺乏完善的居民生命统计系统）的死因人口分布。基于死者家属或照护者访谈问卷的计算机辅助死因判别方法常被采用。本文开发了一种新的贝叶斯方法，通过分析症状关联中随年龄和性别变化的差异，从VA数据中提取死因分布信息。利用人口健康度量研究联盟的金标准数据，将其性能与现有方法进行对比。此外，我们基于信息论度量计算了预测变量与死因的相关性。