Probabilistic Cause-of-Death Assignment Using Verbal Autopsies. Issue 515 (2nd July 2016)

• Record Type: Journal Article
• Authors: McCormick, Tyler H.; Li, Zehang Richard; Calvert, Clara; Crampin, Amelia C.; Kahn, Kathleen; Clark, Samuel J.
• Is Part Of: Journal of the American Statistical Association. Volume 111:Issue 515(2016)
• Page Start: 1036
• Page End: 1049
• Publication Date: 2016-07-02
• Subjects: Bayesian methods -- cause of death -- Demography -- Verbal autopsy -- Vital records; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2016.1152191
• Languages: English
• ISSNs: 0162-1459

Abstract

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.