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The Impact of Systematic Trend and Uncertainty on Mortality and Disability in a Multistate Latent Factor Model for Transition Rates

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  • Zixi Li
  • Adam W. Shao
  • Michael Sherris

Abstract

Multiple state functional disability models do not generally include systematic trend and uncertainty. We develop and estimate a multistate latent factor intensity model with transition and recovery rates depending on a stochastic frailty factor to capture trend and uncertainty. We estimate the model parameters using U.S. Health and Retirement Study data between 1998 and 2012 with Monte Carlo maximum likelihood estimation method. The model shows significant reductions in disability and mortality rates during this period and allows us to quantify uncertainty in transition rates arising from the stochastic frailty factor. Recovery rates are very sensitive to the stochastic frailty. There is an increase in expected future lifetimes as well as an increase in future healthy life expectancy. The proportion of lifetime spent in disability on average remains stable with no strong support in the data for either morbidity compression or expansion. The model has widespread application in costing of government-funded aged care and pricing and risk management of long-term-care insurance products.

Suggested Citation

  • Zixi Li & Adam W. Shao & Michael Sherris, 2017. "The Impact of Systematic Trend and Uncertainty on Mortality and Disability in a Multistate Latent Factor Model for Transition Rates," North American Actuarial Journal, Taylor & Francis Journals, vol. 21(4), pages 594-610, October.
  • Handle: RePEc:taf:uaajxx:v:21:y:2017:i:4:p:594-610
    DOI: 10.1080/10920277.2017.1330157
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    Citations

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    Cited by:

    1. Michael Sherris, 2021. "On Sustainable Aged Care Financing in Australia," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 54(2), pages 275-284, June.
    2. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    3. Qiqi Wang & Katja Hanewald & Xiaojun Wang, 2022. "Multistate health transition modeling using neural networks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(2), pages 475-504, June.
    4. Kabuche, Doreen & Sherris, Michael & Villegas, Andrés M. & Ziveyi, Jonathan, 2024. "Pooling functional disability and mortality in long-term care insurance and care annuities: A matrix approach for multi-state pools," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 165-188.
    5. 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.
    6. Ventura-Marco, Manuel & Vidal-Meliá, Carlos & Pérez-Salamero González, Juan Manuel, 2023. "Joint life care annuities to help retired couples to finance the cost of long-term care," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 122-139.

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