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: (1) without either clinical data or an autopsy it is difficult to
be either specific or certain about the cause of death, and (2) each physician has unique training and experience and is
therefore biased in various ways when assigning causes of death. The first is a fundamental limitation of VA resulting
from the fact that VA data contain comparatively less information than clinical records and autopsy. A clear consequence
of the second is that traditional physician-assigned VA causes of death are biased and not comparable across either
single physicians or groups of physicians.
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:
(1) physicians are free to spend their time caring for patients, (2) VAs can be coded very quickly without having to wait
for the always-lengthy physician review process, and (3) physician-associated bias is removed from the process so
that cause assignments are reproducible and comparable.
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 'indeterminant' 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.
The team responsible for developing InSilicoVA includes
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 and Erick Axxe.