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

Igor Akushevich (), Julia Kravchenko (), Konstantin G. Arbeev (), Svetlana V. Ukraintseva (), Kenneth C. Land () and Anatoliy I. Yashin ()
Additional contact information
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
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

Chapter Chapter 3 in Biodemography of Aging, 2016, pp 47-93 from Springer

Abstract: 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.

Keywords: Incidence Rate; Disease Incidence; Cardiovascular Health Study; Medicare Data; Asthma Incidence (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-94-017-7587-8_3

Ordering information: This item can be ordered from
http://www.springer.com/9789401775878

DOI: 10.1007/978-94-017-7587-8_3

Access Statistics for this chapter

More chapters in The Springer Series on Demographic Methods and Population Analysis from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().