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Guide for data entry.xlsx



About Verbal Autopsy

From Wikipedia:

A verbal autopsy (VA) is a method of gathering health information about a deceased individual to determine his or her cause of death. Health information and a description of events prior to death are acquired from conversations or interviews with a person or persons familiar with the deceased and analyzed by health professional or computer algorithms to assign a probable cause of death.

Verbal autopsy is used in settings where most deaths are undocumented. Estimates suggest a majority of the 60 million annual global deaths occur without medical attention or official medical certification of the cause of death. The VA method attempts to establish causes of death for previously undocumented subjects, allowing scientists to analyze disease patterns and direct public health policy decisions.

Noteworthy uses of the verbal autopsy method include the Million Death Study in India, China's national program to document causes of death in rural areas, and the Global Burden of Disease Study 2010.


CrossVA is a python package for transforming verbal autopsy data collected using the 2016 WHO VA instrument (currently, only version 1.5.1) into a format suitable for openVA.

The flagship function of this package is the transform() function, which prepares raw data from a verbal autopsy questionnaire for use in a verbal autopsy algorithm. The user can either choose to use a default mapping, or create a custom one of their own design. The default mappings are listed in Currently Supported and can be invoked by passing in a tuple as the mapping argument in (input, output) format.

Project Status

This package is a fleshed out prototype of the framework MTIRE is proposing for the open source CrossVA project going forward. This is an alpha version (as of Jan 7, 2018) intended to demonstrate full concept and flexibility, not for use in research or verbal autopsy evaluations.

Simple Usage

The simplest way to get started with CrossVA is to invoke the transform function with a default mapping, and the path to a csv containing your raw verbal autopsy data.

from transform import transform

transform(("2016WHOv151", "InterVA4"), "path/to/data.csv")

You can also call the transform function on a Pandas DataFrame, if you wanted to read in and process the data before calling the function.

from transform import transform

data = pd.read_csv("path/to/data.csv")
data = some_special_function(data)
transform(("2016WHOv151", "InterVA4"), data)

Currently Supported


  • 2016 WHO Questionnaire from ODK export, v1.5.1

2016 WHO documentation can be found here.


  • InSillicoVA
  • InterVA


This is an alpha version of package functionality, with only limited support. Future versions and updates will include expanding inputs and outputs, as well as creating more user-facing features.

Supporting more inputs

One component of moving to a production version will be to offer additional mapping files to support more input formats. The package currently supports the 2016 WHO v1.5.1 odk export.

The following is a list of four additional inputs already in our sights:

  • PHRMC short
  • PHRMC long
  • WHO 2012
  • WHO 2016 v1.4.1

Expanding outputs

One component of moving to a production version will be to offer additional mapping files to support more output formats. The package currently supports mapping to the InterVA4 and InsillicoVA format.

The following is a list of additional outputs for other algorithms to be supported in future versions:

  • Tarrif
  • Tarrif 2.0
  • InterVA5

Expanding user-facing features

Some of the user-facing features in this version are sparser than we would like for a production-level package. In this vein, we want to prioritize creating both good documentation and intuitive features for the user, so that the package is easy to understand and use.

  • Better error messages

    Adding exception classes to distinguish between mapping, configuration, and data errors, so that it will be more immediately obvious to the user what the root cause of the error is.

  • Improving speed

    Adding additional validation checks has slowed down the algorithm from its original proof of concept speed. We believe this can be further improved before the package is in a production version.

  • More - and more detail - in validation checks

    Being able to convey to the end-user when the data has unexpected properties or an incorrect format will be essential to allow the user to understand and correct the issue.


This package was written using google style guide for Python and PEP8 standards. Tests have been written using doctest.


This package is licensed under the GNU GENERAL PUBLIC LICENSE (v3, 2007). Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

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