Bayesian analysis of verbal autopsy data using factor models with age- and sex-dependent associations between symptoms

Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark and Tyler H. McCormick
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Tsuyoshi Kunihama: Department of Economics, Kwansei Gakuin University
Zehang Richard Li: Department of Statistics, University of California, Santa Cruz
Samuel J. Clark: Department of Sociology, Ohio State University
Tyler H. McCormick: Department of Statistics and Department of Sociology, University of Washington

No 266, Discussion Paper Series from School of Economics, Kwansei Gakuin University

Abstract: Verbal autopsies (VAs) are extensively used to investigate the population-level distributions of deaths by cause in low-resource settings without well-organized vital statistics systems. Computer-based methods are often adopted to assign causes of death to deceased individuals based on the interview responses of their family members or caregivers. In this article, we develop a new Bayesian approach that extracts information about cause-of-death distributions from VA data considering the age- and sex-related variation in the associations between symptoms. Its performance is compared with that of existing approaches using gold-standard data from the Population Health Metrics Research Consortium. In addition, we compute the relevance of predictors to causes of death based on information-theoretic measures.

Keywords: Bayesian factor models; Causes of death distribution; Multivariate data; Verbal autopsies; Survey data (search for similar items in EconPapers)
JEL-codes: C11 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2024-03
New Economics Papers: this item is included in nep-inv
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