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Dependency Modeling Approach of Cause-Related Mortality and Longevity Risks: HIV/AIDS

Author

Listed:
  • Nicholas Bett

    (African Centre of Excellence in Data Science (ACEDS), College of Business and Economics, University of Rwanda, Kigali P.O. Box 4285, Rwanda
    Department of Mathematics, Physics, and Computing, School of Science and Aerospace Studies, Moi University, Eldoret P.O. Box 3900, Kenya)

  • Juma Kasozi

    (Department of Mathematics, College of Natural Sciences, Makerere University, Kampala P.O. Box 7062, Uganda)

  • Daniel Ruturwa

    (Department of Applied Statistics, School of Economics, University of Rwanda, Kigali P.O. Box 4285, Rwanda)

Abstract

Disaggregation of mortality by cause has advanced the development of life tables for life insurance and pension purposes. However, the assumption that the causes of death are independent is a challenge in reality. Furthermore, models that determine relationships among causes of death such as HIV/AIDS and their impact on mortality and longevity risks seem trivial or inflexible. To address these problems, we aim to determine and build an appropriate copula dependence model for HIV/AIDS against other causes of death in the presence of age, gender, and time. A bivariate copula model is proposed to capture the dependence structure of HIV/AIDS on life expectancy. This approach allows the fitting of flexible and interpretable bivariate copulas for a two-dimensional case. The dataset was derived from the World Health Organization database that constituted annualized death numbers, causes, age, gender, and years (2000 to 2019). Using Kendall’s tau and Pearson linear coefficient values, the survival Joe copulas proved to be a suitable model. The contribution and implication of this research are the quantification of the impact of HIV/AIDS on a life table, and, thus, the establishment of an alternative to the subjective actuarial judgment approach.

Suggested Citation

  • Nicholas Bett & Juma Kasozi & Daniel Ruturwa, 2023. "Dependency Modeling Approach of Cause-Related Mortality and Longevity Risks: HIV/AIDS," Risks, MDPI, vol. 11(2), pages 1-18, February.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:2:p:38-:d:1063096
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    References listed on IDEAS

    as
    1. Søren Kjærgaard & Yunus Emre Ergemen & Malene Kallestrup-Lamb & Jim Oeppen & Rune Lindahl-Jacobsen, 2019. "Forecasting Causes of Death using Compositional Data Analysis: the Case of Cancer Deaths," CREATES Research Papers 2019-07, Department of Economics and Business Economics, Aarhus University.
    2. Nader Fergany, 1971. "On the human survivorship function and life table construction," Demography, Springer;Population Association of America (PAA), vol. 8(3), pages 331-334, August.
    3. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    4. Allison Nall & Tiffany Chenneville & Lindsey M. Rodriguez & Jennifer L. O’Brien, 2019. "Factors Affecting HIV Testing among Youth in Kenya," IJERPH, MDPI, vol. 16(8), pages 1-14, April.
    5. Séverine Arnold (-Gaille) & Michael Sherris, 2015. "Causes-of-Death Mortality: What Do We Know on Their Dependence?," North American Actuarial Journal, Taylor & Francis Journals, vol. 19(2), pages 116-128, April.
    6. Marie Laure Delignette-Muller & Christophe Dutang, 2015. "fitdistrplus : An R Package for Fitting Distributions," Post-Print hal-01616147, HAL.
    7. Nicholas Bett & Juma Kasozi & Daniel Ruturwa, 2022. "Temporal Clustering of the Causes of Death for Mortality Modelling," Risks, MDPI, vol. 10(5), pages 1-34, May.
    8. Kaishev, Vladimir K. & Dimitrova, Dimitrina S. & Haberman, Steven, 2007. "Modelling the joint distribution of competing risks survival times using copula functions," Insurance: Mathematics and Economics, Elsevier, vol. 41(3), pages 339-361, November.
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