Probabilistic Cause-of-Death Assignment Using Verbal Autopsies

ABSTRACT

In regions without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In such regions, the majority of deaths occur outside hospitals and are not recorded. Worldwide, fewer than one-third of deaths are assigned a cause, with the least information available from the most impoverished nations. In populations like this, verbal autopsy (VA) is a commonly used tool to assess cause of death and estimate cause-specific mortality rates and the distribution of deaths by cause. VA uses an interview with caregivers of the decedent to elicit data describing the signs and symptoms leading up to the death. This article develops a new statistical tool known as InSilicoVA to classify cause of death using information acquired through VA. InSilicoVA shares uncertainty between cause of death assignments for specific individuals and the distribution of deaths by cause across the population. Using side-by-side comparisons with both observed and simulated data, we demonstrate that InSilicoVA has distinct advantages compared to currently available methods. Supplementary materials for this article are available online.

Keywords:

• Bayesian methods
• cause of death
• Demography
• Verbal autopsy
• Vital records

Acknowledgments

The authors are grateful to Peter Byass, Basia Zaba, Laina Mercer, Stephen Tollman, Adrian Raftery, Philip Setel, Osman Sankoh, and Jon Wakefield for helpful discussions. We are also grateful to the MRC/Wits Rural Public Health and Health Transitions Research Unit and the Karonga Prevention Study for sharing their data for this project.

Funding

Preparation of this article was supported by the Bill and Melinda Gates Foundation, with partial support from a seed grant from the Center for the Studies of Demography and Ecology at the University of Washington along with grant K01 HD057246 to Clark and K01 HD078452 to McCormick, both from the National Institute of Child Health and Human Development (NICHD). The Agincourt health and socio-demographic surveillance system (HDSS) is supported by the Medical Research Council and University of the Witwatersrand, South Africa, and the Wellcome Trust, UK (grants 058893/Z/99/A; 069683/Z/02/Z; 085477/Z/08/Z; 085477/B/08/Z).