Index Topic Background Key Papers TZA MOH Endorses InSilicoVA Software and openVA Team VA Interview Additional Papers Gratuitous Critique of InSilicoVA

Background ▲

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 or 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 '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.

The team responsible for developing InSilicoVA includes Richard Li, Tyler McCormick, and myself.

Key papers ▲

• The openVA Toolkit for Verbal Autopsies
• Probabilistic Cause-of-Death Assignment using Verbal Autopsies
• Bayesian Nested Latent Class Models for Cause-of-Death Assignment using Verbal Autopsies Across Multiple Domains
• Bayesian Joint Spike-and-Slab Graphical Lasso
• Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies
• Bayesian Factor Models for Probabilistic Cause of Death Assessment with Verbal Autopsies
• Estimating Causes of Death Where There Is No Medical Certification: Evolution and State of the Art of Verbal Autopsy
• Agreement between cause of death assignment by computer-coded verbal autopsy methods and physician coding of verbal autopsy interviews in South Africa
• Temporal Changes in Cause of Death Among Adolescents and Adults in Six Countries in Eastern And Southern Africa: A Multi-Country Cohort Study using Verbal Autopsy Data

Independent Endorsement by the Tanzanian Ministry of Health ▲

A report from the Ministry of Health of Tanzania (June, 2024) that evaluates VA cause-coding methods for use in Tanzania's civil registration and vital statistics system endorses InSilicoVA as the recommended VA cause coding approach for WHO standard VAs. For more on this, see News item 'Tanzania Ministry of Health endorses InSilicoVA'.

Software: openVA and the openVA Team ▲

We have created and released open source, freely available software for InSilicoVA and all of the other commonly-used verbal autopsy cause-coding algorithms (except Tariff 2.0) in the statistical programming environment R. All of the packages are available at the Comprehensive R Archive Network (CRAN). The openVA package operates the others. We also maintain a publicly-accessible Github repository with the code and a variety of additional resources. For users, we published a tutorial and user manual in the The R Journal: The openVA Toolkit for Verbal Autopsies. This software has been developed by members of the openVA Team: of Richard Li, Jason Thomas, Peter Choi, Tyler McCormick, and myself, with increasing help from Yue Chu.

pyOpenVA

To make the VA cause-coding algorithms fast and easily available using a familiar interface, we have created pyOpenVA. This is standard (i.e. non-research) software that installs using a traditional installer, does not require supporting software (e.g. R, Java, etc.) to be installed on your computer, and operates through a graphical user interface. pyOpenVA is written in Python and C++ so it is fast.

Links to Key openVA Software

• pyOpenVA - 'Assets' contains installers for Windows and MacOS
• openVA Team site and openVA Toolkit paper
• openVA R Package
• openVA App
• Video for installation of openVA App
• Video for example analysis using openVA App

The VA Interview ▲

Clarissa Surek-Clark has led the openVA Team's thinking around the VA interview. Drawing on her experience as a professional translator and interpreter and training as a sociolinguist, she is curious to know how VA interviews are conducted across a variety of cultural and linguistic settings. VAs are conducted in many different languages, and sometimes multiple languages are used in a single interview; however, the questionnaire is typically in English or other official language. This results in (a lot of) ad hoc translation and interpreting. We want to know how this affects the VA data and the eventual comparability of causes of death ascertained through VA.

• Verbal Autopsy Interview Standardization Study: A report from the field

Additional papers ▲

• Mortality Disparities by Age and Causes of Death in Rural South Africa
• Cause-specific mortality rates in sub-Saharan Africa and Bangladesh
• Strengthening Standardised Interpretation of Verbal Autopsy Data: the New InterVA-4 tool
• The WHO 2016 verbal autopsy instrument: An international standard suitable for automated analysis by InterVA, InSilicoVA, and Tariff 2.0
• Automated versus physician assignment of cause of death for verbal autopsies: randomized trial of 9374 deaths in 117 villages in India
• An integrated approach to processing WHO-2016 verbal autopsy data: the InterVA-5 model
• Direct maternal deaths attributable to HIV in the era of antiretroviral therapy: evidence from three population-based HIV cohorts with verbal autopsy

The gratuitous critique of InSilicoVA and our response ▲

In 2018 several authors associated the Institute for Health Metrics and Evaluation (IHME) published a paper gratuitously attacking InSilicoVA - publication 1 below. They tested InSilicoVA using algorithm parameters set in such a way as to make it perform as poorly as possible, and based on results produced in that way, they concluded that InSilicoVA is flawed.

Our team replicated their work to discover what they had done and published a rebuttal explaining why InSilicoVA performed so poorly in their hands - publication 2 below.

Comment 3 below is their frustrated response to our rebuttal.

1. Flaxman, A.D., J.C. Joseph, C.J.L. Murray, I.D. Riley and A.D. Lopez. (2018). Performance of InSilicoVA for Assigning Causes of Death to Verbal Autopsies: Multisite Validation study Using Clinical Diagnostic Gold Standards. BMC Medicine 16 (56). [ DOI ]

2. Li, Z.R., T.H. McCormick, and S.J. CLARK (2020). Non-confirming Replication of `Performance of InSilicoVA for Assigning Causes of Death to Verbal Autopsies: Multisite Validation Study using Clinical Diagnostic Gold Standards', BMC Medicine 2018; 16:56. BMC Medicine 18 (69). [ DOI ]

3. Flaxman, A.D., R. Hazard, I. Riley, A.D. Lopez and C.J.L. Murray. (2020). Born to Fail: Flaws in Replication Design Produce Intended Results. BMC Medicine 18 (73). [ DOI ]