Probabilistic Cause-of-Death Assignment Using Verbal Autopsies

Tyler H. McCormick, Zehang Richard Li, Clara Calvert, Amelia C. Crampin, Kathleen Kahn, Samuel J. Clark

Research output: Contribution to journalArticlepeer-review

Abstract

ABSTRACTIn 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.
Original languageEnglish
Pages (from-to)1036-1049
Number of pages14
JournalJournal of the American Statistical Association
Volume111
Issue number515
Early online date18 Oct 2016
DOIs
Publication statusE-pub ahead of print - 18 Oct 2016

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