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.
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Acknowledgements
The research reported in this chapter was supported by the National Institute on Aging grants R01AG027019, R01AG030612, R01AG030198, R01AG032319, R01AG046860, R21 AG045245, and P01AG043352.
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Akushevich, I., Kravchenko, J., Arbeev, K.G., Ukraintseva, S.V., Land, K.C., Yashin, A.I. (2016). Health Effects and Medicare Trajectories: Population-Based Analysis of Morbidity and Mortality Patterns. In: Biodemography of Aging. The Springer Series on Demographic Methods and Population Analysis, vol 40. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7587-8_3
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