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Theory of partitioning of disease prevalence and mortality in observational data

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  • Akushevich, I.
  • Yashkin, A.P.
  • Kravchenko, J.
  • Fang, F.
  • Arbeev, K.
  • Sloan, F.
  • Yashin, A.I.

Abstract

In this study, we present a new theory of partitioning of disease prevalence and incidence-based mortality and demonstrate how this theory practically works for analyses of Medicare data. In the theory, the prevalence of a disease and incidence-based mortality are modeled in terms of disease incidence and survival after diagnosis supplemented by information on disease prevalence at the initial age and year available in a dataset. Partitioning of the trends of prevalence and mortality is calculated with minimal assumptions. The resulting expressions for the components of the trends are given by continuous functions of data. The estimator is consistent and stable. The developed methodology is applied for data on type 2 diabetes using individual records from a nationally representative 5% sample of Medicare beneficiaries age 65+. Numerical estimates show excellent concordance between empirical estimates and theoretical predictions. Evaluated partitioning model showed that both prevalence and mortality increase with time. The primary driving factors of the observed prevalence increase are improved survival and increased prevalence at age 65. The increase in diabetes-related mortality is driven by increased prevalence and unobserved trends in time-periods and age-groups outside of the range of the data used in the study. Finally, the properties of the new estimator, possible statistical and systematical uncertainties, and future practical applications of this methodology in epidemiology, demography, public health and health forecasting are discussed.

Suggested Citation

  • Akushevich, I. & Yashkin, A.P. & Kravchenko, J. & Fang, F. & Arbeev, K. & Sloan, F. & Yashin, A.I., 2017. "Theory of partitioning of disease prevalence and mortality in observational data," Theoretical Population Biology, Elsevier, vol. 114(C), pages 117-127.
    • Handle: RePEc:eee:thpobi:v:114:y:2017:i:c:p:117-127
    DOI: 10.1016/j.tpb.2017.01.003
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    References listed on IDEAS

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    1. James Vaupel & Vladimir Romo, 2003. "Decomposing change in life expectancy: A bouquet of formulas in honor of Nathan Keyfitz’s 90th birthday," Demography, Springer;Population Association of America (PAA), vol. 40(2), pages 201-216, May.
    2. Shiro Horiuchi & John Wilmoth & Scott Pletcher, 2008. "A decomposition method based on a model of continuous change," Demography, Springer;Population Association of America (PAA), vol. 45(4), pages 785-801, November.
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    1. Akushevich, I. & Yashkin, A. & Kovtun, M. & Stallard, E. & Yashin, A.I. & Kravchenko, J., 2023. "Decomposition of disparities in life expectancy with applications to administrative health claims and registry data," Theoretical Population Biology, Elsevier, vol. 153(C), pages 50-68.

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