Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
https://travis-ci.com/verbal-autopsy-software/pyCrossVA.svg?branch=master

Background

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

CrossVA is a python package for transforming verbal autopsy data collected using the 2016 WHO VA instrument (v1.5.1, or v1.4.1), 2012 WHO VA instrument, and the PHRMC short questionnaire into a format suitable for openVA.

The flagship function of this package is the transform() function, which prepares raw data 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 April 26, 2019) intended to demonstrate full concept and flexibility.

Simple Usage - Python

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 pycrossva.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 pycrossva.transform import transform

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

The transform function returns a Pandas DataFrame object. To write the Pandas DataFrame to a csv, you can do:

final_data.to_csv("filename.csv")

Command Line

pycrossva also contains a command line tool, pycrossva-transform that acts as a wrapper for the transform python function in the pycrossva package. Once you have installed pycrossva, you can run this from the command line in order to process verbal autopsy data without having to touch python code. If you have multiple input files to process from the same input type (or source format) to the same output type (or algorithm), you can run them all in a single command.

If no destination (--dst) is specified, the default behavior will be to write the resulting data to a csv in the current working directory with a name in the pattern of "output_type_from_src_mmddyy", where mmddyy is the current date. If dst is a directory, then the result file will still have the default name. If dst ends in '.csv' but multiple input files are given, then the output files will be written to dst_1.csv, dst_2.csv, etc.

pycrossva-transform takes 3 positional arguments:
  • input_type: source type of the input data (the special input type of 'AUTODETECT' specifies that the type should be detected automatically if possible)
  • output_type: format of output data (which algorithm the data should be prepared for)
  • src: filepath to the input data - can take multiple arguments, separated by a space

Examples:

$ pycrossva-transform 2012WHO InterVA4 path/to/mydata.csv
2012WHO 'path/to/my/data.csv' data prepared for InterVA4 and written to csv at 'my/current/directory/InterVA4_from_mydata_042319.csv'

$ pycrossva-transform 2012WHO InterVA4 path/to/mydata1.csv path/to/another/data2.csv --dst outputfolder
2012WHO 'path/to/mydata1.csv' data prepared for InterVA4 and written to csv at 'outputfolder/InterVA4_from_mydata1_042319.csv'
2012WHO 'path/to/another/data2.csv' data prepared for InterVA4 and written to csv at 'outputfolder/InterVA4_from_data2_042319.csv'

$ pycrossva-transform 2012WHO InterVA4 path/to/mydata1.csv path/to/another/data2.csv --dst outputfolder/results.csv
2012WHO 'path/to/mydata1.csv' data prepared for InterVA4 and written to csv at 'outputfolder/results_1.csv'
2012WHO 'path/to/another/data2.csv' data prepared for InterVA4 and written to csv at 'outputfolder/results_2.csv'

$ pycrossva-transform AUTODETECT InterVA4 path/to/mydata.csv
Detected input type: 2012WHO
2012WHO 'path/to/my/data.csv' data prepared for InterVA4 and written to csv at 'my/current/directory/InterVA4_from_mydata_042319.csv'

Running Tests

To run unit tests, first make sure all requirements are installed

pip install -r requirements.txt

Also make sure that pytest is installed

pip install pytest

Finally, run the tests

python setup.py install && cd pycrossva && python -m pytest --doctest-modules

Currently Supported

Inputs

  • 2021 WHO Questionnaire from ODK export
  • 2016 WHO Questionnaire from ODK export, v1.5.1
  • 2016 WHO Questionnaire from ODK export, v1.4.1
  • 2012 WHO Questionnaire from ODK export
  • PHRMC Shortened Questionnaire

Outputs

  • InSillicoVA
  • InterVA4
  • InterVA5

Roadmap

This is an alpha version of package functionality, with only limited support.

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:

  • Tariff
  • Tariff 2.0

Style

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

License

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.

About

No description or website provided.

Topics

Resources

License

Packages

No packages published

Languages