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Long-term care models and dependence probability tables by acuity level: New empirical evidence from Switzerland

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  • Fuino, Michel
  • Wagner, Joël

Abstract

Due to the demographic changes and population aging occurring in many countries, the financing of long-term care (LTC) poses a systemic threat. The scarcity of knowledge about the probability of an elderly person needing help with activities of daily living has hindered the development of insurance solutions that complement existing social systems. In this paper, we consider two models: a frailty level model that studies the evolution of a dependent person through mild, moderate and severe dependency states to death and a type of care model that distinguishes between care received at home and care received in an institution. We develop and interpret the expressions for the state- and time-dependent transition probabilities in a semi-Markov framework. Then, we empirically assess these probabilities using a novel longitudinal dataset covering all LTC needs in Switzerland over a 20-year period. As a key result, we are the first to derive dependence probability tables by acuity level, gender and age for the Swiss population. We find that the transition probabilities differ significantly by gender, age and time spent in the frailty level and type of care states.

Suggested Citation

  • Fuino, Michel & Wagner, Joël, 2018. "Long-term care models and dependence probability tables by acuity level: New empirical evidence from Switzerland," Insurance: Mathematics and Economics, Elsevier, vol. 81(C), pages 51-70.
    • Handle: RePEc:eee:insuma:v:81:y:2018:i:c:p:51-70
    DOI: 10.1016/j.insmatheco.2018.05.002
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    Cited by:

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    2. 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.
    3. Guibert, Quentin & Planchet, Frédéric, 2018. "Non-parametric inference of transition probabilities based on Aalen–Johansen integral estimators for acyclic multi-state models: application to LTC insurance," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 21-36.
    4. Michel Fuino & Andrey Ugarte Montero & Joël Wagner, 2022. "On the drivers of potential customers' interest in long‐term care insurance: Evidence from Switzerland," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 25(3), pages 271-302, September.
    5. Fuino, Michel & Wagner, Joël, 2020. "Duration of long-term care: Socio-economic factors, type of care interactions and evolution," Insurance: Mathematics and Economics, Elsevier, vol. 90(C), pages 151-168.
    6. Manuel Ventura-Marco & Carlos Vidal-Meliá & Juan Manuel Pérez-Salamero González, 2022. "Life care annuities to help couples cope with the cost of long-term care," Documentos de Trabajo del ICAE 2022-03, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    7. Ugarte Montero, Andrey & Wagner, Joël, 2023. "On potential information asymmetries in long-term care insurance: A simulation study using data from Switzerland," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 230-241.
    8. Manuel L. Esquível & Gracinda R. Guerreiro & Matilde C. Oliveira & Pedro Corte Real, 2021. "Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care," Risks, MDPI, vol. 9(2), pages 1-17, February.
    9. 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.

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