The openVA Toolkit for Verbal Autopsies

TL;DR

The openVA toolkit offers a standardized, open-source R framework for analyzing verbal autopsy data, enabling consistent application and comparison of multiple cause-of-death classification methods in regions lacking complete vital statistics.

Contribution

It introduces the first unified, compatible framework for VA data analysis, integrating multiple existing algorithms within an open-source R package.

Findings

Supports various data formats and customizable cause-symptom associations

Facilitates model fitting, comparison, and visualization in R

Enhances reproducibility and standardization of VA analysis

Abstract

Verbal autopsy (VA) is a survey-based tool widely used to infer cause of death (COD) in regions without complete-coverage civil registration and vital statistics systems. In such settings, many deaths happen outside of medical facilities and are not officially documented by a medical professional. VA surveys, consisting of signs and symptoms reported by a person close to the decedent, are used to infer the cause of death for an individual, and to estimate and monitor the cause of death distribution in the population. Several classification algorithms have been developed and widely used to assign cause of death using VA data. However, The incompatibility between different idiosyncratic model implementations and required data structure makes it difficult to systematically apply and compare different methods. The openVA package provides the first standardized framework for analyzing VA data that is compatible with all openly available methods and data structure. It provides an open-sourced, R implementation of several most widely used VA methods. It supports different data input and output formats, and customizable information about the associations between causes and symptoms. The paper discusses the relevant algorithms, their implementations in R packages under the openVA suite, and demonstrates the pipeline of model fitting, summary, comparison, and visualization in the R environment.