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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Tsuyoshi Kunihama & Zehang Richard Li & Samuel J. Clark & Tyler H. McCormick, 2024. "Bayesian analysis of verbal autopsy data using factor models with age- and sex-dependent associations between symptoms," Discussion Paper Series 266, School of Economics, Kwansei Gakuin University.
  • Handle: RePEc:kgu:wpaper:266
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    File URL: http://192.218.163.163/RePEc/pdf/kgdp266.pdf
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    More about this item

    Keywords

    Bayesian factor models; Causes of death distribution; Multivariate data; Verbal autopsies; Survey data;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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