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Health Effects and Medicare Trajectories: Population-Based Analysis of Morbidity and Mortality Patterns

In: Biodemography of Aging

Author

Listed:
  • Igor Akushevich

    (Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging)

  • Julia Kravchenko

    (Duke University Medical Center, Department of Surgery)

  • Konstantin G. Arbeev

    (Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging)

  • Svetlana V. Ukraintseva

    (Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging)

  • Kenneth C. Land

    (Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging
    Duke University, Department of Sociology)

  • Anatoliy I. Yashin

    (Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging)

Abstract

The tremendous research potential of U.S. Medicare data for evaluation of current, and forecasting of future, patterns of aging-related diseases among older U.S. adults remains largely unexplored. In this chapter, we present and discuss the results of a series of epidemiologic and biodemographic measures that can be studied using the Medicare Files of Service Use. Specifically, we present analyses of age patterns of disease incidence, their time trends, recovery and long-term remission after disease onsets, interdependence of multiple coexisting disease risks, mortality at advanced ages, and multimorbidity patterns. Empirical analyses, regression models, and methods of mathematical modeling are used to evaluate their characteristics. U.S. Medicare data serve as an example of Big Data that is a powerful source of information about current and historic health of older U.S. adults.

Suggested Citation

  • Igor Akushevich & Julia Kravchenko & Konstantin G. Arbeev & Svetlana V. Ukraintseva & Kenneth C. Land & Anatoliy I. Yashin, 2016. "Health Effects and Medicare Trajectories: Population-Based Analysis of Morbidity and Mortality Patterns," The Springer Series on Demographic Methods and Population Analysis, in: Biodemography of Aging, chapter 0, pages 47-93, Springer.
    • Handle: RePEc:spr:ssdmcp:978-94-017-7587-8_3
    DOI: 10.1007/978-94-017-7587-8_3
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