Only about one-third of the deaths worldwide are assigned a medically-certified cause, and understanding the causes of deaths occurring outside of medical facilities is logistically and financially challenging. Verbal autopsy (VA) is a routinely used tool to collect information on cause of death in such settings. VA is a survey-based method where a structured questionnaire is conducted to family members or caregivers of a recently deceased person, and the collected information is used to infer the cause of death. As VA becomes an increasingly routine tool for cause-of-death data collection, the lengthy questionnaire has become a major challenge to the implementation and scale-up of VAs. In this paper, we propose a novel active questionnaire design approach that optimizes the order of the questions dynamically to achieve accurate cause-of-death assignment with the smallest number of questions. We propose a fully Bayesian strategy for adaptive question selection that is compatible with any existing probabilistic cause-of-death assignment methods. We also develop an early stopping criterion that fully accounts for the uncertainty in the model parameters. We also propose a penalized score to account for constraints and preferences of existing question structures. We evaluate the performance of our active designs using both synthetic and real data, demonstrating that the proposed strategy achieves accurate cause-of-death assignment using considerably fewer questions than the traditional static VA survey instruments.

翻译：全球仅有约三分之一的死亡病例获得了医学认证的死因，而了解非医疗机构内发生的死亡原因在后勤和财务上均面临巨大挑战。口头尸检（VA）是在此类场景下常规使用的死因信息收集工具。VA是一种基于调查问卷的方法，通过对近期逝者的家庭成员或照料者进行结构化访谈，利用收集到的信息推断死亡原因。随着VA日益成为死因数据收集的常规手段，冗长的问卷已成为实施和推广VA的重大障碍。本文提出了一种新颖的主动问卷设计方法，该方法动态优化问题顺序，旨在以最少的问题数量实现准确的死因判定。我们提出了一种完全贝叶斯策略用于自适应问题选择，该策略兼容任何现有的概率性死因判定方法。我们还开发了一个充分考虑模型参数不确定性的早停准则。此外，我们提出了一种惩罚得分来处理现有问题结构的约束和偏好。我们使用合成数据和真实数据评估了主动设计的性能，结果表明，相较于传统静态VA调查工具，所提出的策略能够以显著更少的问题实现准确的死因判定。