In low-resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause-specific mortality fraction (CSMF) for populations. Post-mortem autopsies, considered the gold standard for COD assignment, are often difficult or impossible to implement due to deaths occurring outside the hospital, expense and/or cultural norms. For this reason, verbal autopsies (VAs) are commonly conducted, consisting of a questionnaire administered to next of kin recording demographic information, known medical conditions, symptoms and other factors for the decedent. This article proposes a novel class of hierarchical factor regression models that avoid restrictive assumptions of standard methods, allow both the mean and covariance to vary with COD category, and can include covariate information on the decedent, region or events surrounding death. Taking a Bayesian approach to inference, this work develops an MCMC algorithm and validates the FActor Regression for Verbal Autopsy (FARVA) model in simulation experiments. An application of FARVA to real VA data shows improved goodness-of-fit and better predictive performance in inferring COD and CSMF over competing methods. Code and a user manual are made available at https://github.com/kelrenmor/farva.

中文翻译：

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贝叶斯分层因子回归模型从口头尸检数据推断死亡原因


在资源匮乏的环境中，死亡人口动态登记并不常见，确定和研究个人的死因 (COD) 和人群的特定原因死亡率 (CSMF) 通常至关重要。尸检被认为是 COD 分配的黄金标准，但由于死亡发生在医院外、费用和/或文化规范，通常很难或不可能实施。因此，通常会进行口头尸检（VA），包括向近亲进行问卷调查，记录死者的人口统计信息、已知的医疗状况、症状和其他因素。本文提出了一类新颖的分层因子回归模型，该模型避免了标准方法的限制性假设，允许均值和协方差随 COD 类别而变化，并且可以包含有关死者、死亡区域或死亡事件的协变量信息。本文采用贝叶斯方法进行推理，开发了 MCMC 算法，并在模拟实验中验证了 FActor Regression for Verbal Autopsy (FARVA) 模型。将 FARVA 应用于真实 VA 数据表明，与竞争方法相比，在推断 COD 和 CSMF 方面具有更高的拟合优度和更好的预测性能。代码和用户手册可在 https://github.com/kelrenmor/farva 获取。