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Old-age Frailty Patterns and Implications for Long-term Care Programmes

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  • Joelle H. Fong

    (Nanyang Technological University)

Abstract

This paper examines patterns in old-age frailty within a multistate model that characterises the stochastic process of biological ageing. Using aggregate population-level U.S. mortality data, we study differences in frailty by gender and cohort. Our results show that, on average, women tend to be frailer than men at older ages with the male–female divergence growing considerably past age 80. We also find that average frailty levels have fluctuated over time with a distinct peak-and-trough pattern. These cohort trends in frailty and the subsequent dynamic forecasts of frailty among newer cohorts closely mirror how late-life disability has evolved among older Americans in recent decades, underscoring the important connection between frailty conditions and disability among older adults. The implications of these findings on spending for long-term care programmes within the broader health insurance system are discussed.

Suggested Citation

  • Joelle H. Fong, 2017. "Old-age Frailty Patterns and Implications for Long-term Care Programmes," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 42(1), pages 114-128, January.
    • Handle: RePEc:pal:gpprii:v:42:y:2017:i:1:d:10.1057_s41288-016-0006-3
    DOI: 10.1057/s41288-016-0006-3
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    References listed on IDEAS

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    2. Lin, S.-F. & Beck, A.N. & Finch, B.K. & Hummer, R.A. & Master, R.K., 2012. "Trends in US older adult disability: Exploring age, period, and cohort effects," American Journal of Public Health, American Public Health Association, vol. 102(11), pages 2157-2163.
    3. Congressional Budget Office, 2013. "Rising Demand for Long-Term Services and Supports for Elderly People," Reports 44363, Congressional Budget Office.
    4. Vicki Freedman & Eileen Crimmins & Robert Schoeni & Brenda Spillman & Hakan Aykan & Ellen Kramarow & Kenneth Land & James Lubitz & Kenneth Manton & Linda Martin & Diane Shinberg & Timothy Waidmann, 2004. "Resolving inconsistencies in trends in old-age disability: Report from a technical working group," Demography, Springer;Population Association of America (PAA), vol. 41(3), pages 417-441, August.
    5. Colvez, A. & Blanchet, M., 1981. "Disability trends in the United States population 1966-76: Analysis of reported causes," American Journal of Public Health, American Public Health Association, vol. 71(5), pages 464-471.
    6. Seeman, T.E. & Merkin, S.S. & Crimmins, E.M. & Karlamangla, A.S., 2010. "Disability trends among older Americans: National Health and Nutrition Examination surveys, 1988-1994 and 1999-2004," American Journal of Public Health, American Public Health Association, vol. 100(1), pages 100-107.
    7. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
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    Cited by:

    1. Martin Eling & Omid Ghavibazoo, 2019. "Research on long-term care insurance: status quo and directions for future research," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 44(2), pages 303-356, April.

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    More about this item

    Keywords

    population ageing; frail elderly; health systems; cohort analysis;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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