Verbal autopsy (VA) is an old and well established approach to ascertain cause of death when it is not feasible or practical to conduct medical certification and full autopsies. After a death is identified, a specially-trained fieldworker interviews the caregivers (usually family members) of the decedent. A typical VA interview includes a set of structured questions with categorical/quantitative responses and a narrative section that records the ‘story’ of the death from the respondent’s point of view. The resulting data are interpreted by various means to assign causes to the death.
The most common practice is to have clinically trained, experienced physicians read the interviews and determine causes. To address the fact that physicians frequently do not agree on causes, VA interviews are often read by two physicians, and sometimes three, and the final causes are determined through a consensus mechanism. This implicitly acknowledges two of the challenges inherent to VA:
An alternative to physician review is the use of an algorithmic method that processes the categorical/quantitative responses in VA interviews to identify causes of death. The algorithmic approach has three important advantages:
I have led a small team to develop a new algorithmic method for coding verbal autopsies. Building on InterVA - 'Interpret VA', the first VA algorithm - we developed InSilicoVA. InSilicoVA assigns a cause to all deaths (no 'indeterminate' causes), provides consistent estimates of uncertainty for both individual causes and population-level cause-specific mortality fractions, utilizes both 'yes' and 'no' answers, and is fully probability-based so that results are presented as true probability distributions.
Software has been developed to implement InSilicoVA including R packages and universal executables delivered in a Docker container. This is openVA and has been developed by a team including Richard Li, Jason Thomas, Peter Choi, Tyler McCormick, and myself, with increasing help from Yue Chu.