Verbal Autopsy in Civil Registration and Vital Statistics: The Symptom-Cause Information Archive

TL;DR

This paper discusses creating a global archive of verbal autopsy data to improve cause-of-death assignment and burden of disease estimation in developing countries through machine learning validation.

Contribution

It proposes establishing a symptom-cause archive to enhance machine-based cause assignment and comparability in verbal autopsy analysis.

Findings

Development of a reference archive for verbal autopsy data

Potential for improved cause-of-death accuracy

Enhanced validation and training for machine algorithms

Abstract

The burden of disease is fundamental to understanding, prioritizing, and monitoring public health interventions. Cause of death is required to calculate the burden of disease, but in many parts of the developing world deaths are neither detected nor given a cause. Verbal autopsy is a feasible way of assigning a cause to a death based on an interview with those who cared for the person who died. As civil registration and vital statistics systems improve in the developing world, verbal autopsy is playing and increasingly important role in providing information about cause of death and the burden of disease. This note motivates the creation of a global symptom-cause archive containing reference deaths with both a verbal autopsy and a cause assigned through an independent mechanism. This archive could provide training and validation data for refining, developing, and testing machine-based algorithms to automate cause assignment from verbal autopsy data. This, in turn, would improve the comparability of machine-assigned causes and provide a means to fine-tune individual cause assignment within specific contexts.