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Modeling Age-Specific Mortality for Countries with Generalized HIV Epidemics

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  • David J Sharrow
  • Samuel J Clark
  • Adrian E Raftery

Abstract

Background: In a given population the age pattern of mortality is an important determinant of total number of deaths, age structure, and through effects on age structure, the number of births and thereby growth. Good mortality models exist for most populations except those experiencing generalized HIV epidemics and some developing country populations. The large number of deaths concentrated at very young and adult ages in HIV-affected populations produce a unique ‘humped’ age pattern of mortality that is not reproduced by any existing mortality models. Both burden of disease reporting and population projection methods require age-specific mortality rates to estimate numbers of deaths and produce plausible age structures. For countries with generalized HIV epidemics these estimates should take into account the future trajectory of HIV prevalence and its effects on age-specific mortality. In this paper we present a parsimonious model of age-specific mortality for countries with generalized HIV/AIDS epidemics. Methods and Findings: The model represents a vector of age-specific mortality rates as the weighted sum of three independent age-varying components. We derive the age-varying components from a Singular Value Decomposition of the matrix of age-specific mortality rate schedules. The weights are modeled as a function of HIV prevalence and one of three possible sets of inputs: life expectancy at birth, a measure of child mortality, or child mortality with a measure of adult mortality. We calibrate the model with 320 five-year life tables for each sex from the World Population Prospects 2010 revision that come from the 40 countries of the world that have and are experiencing a generalized HIV epidemic. Cross validation shows that the model is able to outperform several existing model life table systems. Conclusions: We present a flexible, parsimonious model of age-specific mortality for countries with generalized HIV epidemics. Combined with the outputs of existing epidemiological and demographic models, this model makes it possible to project future age-specific mortality profiles and number of deaths for countries with generalized HIV epidemics.

Suggested Citation

  • David J Sharrow & Samuel J Clark & Adrian E Raftery, 2014. "Modeling Age-Specific Mortality for Countries with Generalized HIV Epidemics," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-10, May.
    • Handle: RePEc:plo:pone00:0096447
    DOI: 10.1371/journal.pone.0096447
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    References listed on IDEAS

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    1. Mark C. Wheldon & Adrian E. Raftery & Samuel J. Clark & Patrick Gerland, 2013. "Reconstructing Past Populations With Uncertainty From Fragmentary Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 96-110, March.
    2. Ian Timæus & Momodou Jasseh, 2004. "Adult mortality in sub-Saharan Africa: Evidence from demographic and health surveys," Demography, Springer;Population Association of America (PAA), vol. 41(4), pages 757-772, November.
    3. Ševčíková, Hana & Alkema, Leontine & Raftery, Adrian, 2011. "bayesTFR: An R package for Probabilistic Projections of the Total Fertility Rate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i01).
    4. John Wilmoth & Sarah Zureick & Vladimir Canudas-Romo & Mie Inoue & Cheryl Sawyer, 2012. "A flexible two-dimensional mortality model for use in indirect estimation," Population Studies, Taylor & Francis Journals, vol. 66(1), pages 1-28.
    5. Leontine Alkema & Adrian Raftery & Patrick Gerland & Samuel Clark & François Pelletier & Thomas Buettner & Gerhard Heilig, 2011. "Probabilistic Projections of the Total Fertility Rate for All Countries," Demography, Springer;Population Association of America (PAA), vol. 48(3), pages 815-839, August.
    6. David Sharrow & Samuel J. Clark & Mark Collinson & Kathleen Kahn & Stephen Tollman, 2013. "The age pattern of increases in mortality affected by HIV," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(39), pages 1039-1096.
    7. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    8. Adrian Raftery & Jennifer Chunn & Patrick Gerland & Hana Ševčíková, 2013. "Bayesian Probabilistic Projections of Life Expectancy for All Countries," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 777-801, June.
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    Cited by:

    1. Jessica Godwin & Adrian E. Raftery, 2017. "Bayesian projection of life expectancy accounting for the HIV/AIDS epidemic," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(48), pages 1549-1610.
    2. Samuel J. Clark, 2019. "A General Age-Specific Mortality Model With an Example Indexed by Child Mortality or Both Child and Adult Mortality," Demography, Springer;Population Association of America (PAA), vol. 56(3), pages 1131-1159, June.
    3. Athena Pantazis & Samuel J Clark, 2018. "A parsimonious characterization of change in global age-specific and total fertility rates," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-19, January.

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