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Multistate health transition modeling using neural networks

Author

Listed:
  • Qiqi Wang
  • Katja Hanewald
  • Xiaojun Wang

Abstract

This article proposes a new model that combines a neural network with a generalized linear model (GLM) to estimate and predict health transition intensities. We introduce neural networks to health transition modeling to incorporate socioeconomic and lifestyle factors and to allow for linear and nonlinear links between these variables. We use transfer learning to link the models for different health transitions and improve the model estimation for health transitions with limited data. We apply the model to individual‐level data from the Chinese Longitudinal Healthy Longevity Survey from 1998 to 2018. The results show that our model performs better in estimation and prediction than standalone GLM and neural network models. We provide new estimates of the life expectancies for a range of population subgroups. We also describe a wide range of possible applications for further health‐related research, including risk prediction using health claim data and mortality prediction based on individual‐level mortality data.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jrinsu:v:89:y:2022:i:2:p:475-504
    DOI: 10.1111/jori.12364
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    References listed on IDEAS

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    1. 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.
    2. Renshaw, A. E. & Haberman, S., 1995. "On the graduations associated with a multiple state model for permanent health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 17(1), pages 1-17, August.
    3. Cheng, Xiang & Jin, Zhuo & Yang, Hailiang, 2020. "Optimal Insurance Strategies: A Hybrid Deep Learning Markov Chain Approximation Approach," ASTIN Bulletin, Cambridge University Press, vol. 50(2), pages 449-477, May.
    4. Marcus Christiansen, 2012. "Multistate models in health insurance," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(2), pages 155-186, June.
    5. Patrick L. Brockett & Linda L. Golden & Jaeho Jang & Chuanhou Yang, 2006. "A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(3), pages 397-419, September.
    6. Goldman, Noreen & Korenman, Sanders & Weinstein, Rachel, 1995. "Marital status and health among the elderly," Social Science & Medicine, Elsevier, vol. 40(12), pages 1717-1730, June.
    7. Danan Gu & Yi Zeng, 2004. "Sociodemographic Effects on the Onset and Recovery of ADL Disability among Chinese Oldest-old," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 11(1), pages 1-42.
    8. Gabrielli, Andrea, 2020. "A Neural Network Boosted Double Overdispersed Poisson Claims Reserving Model," ASTIN Bulletin, Cambridge University Press, vol. 50(1), pages 25-60, January.
    9. Joelle H. Fong & Adam W. Shao & Michael Sherris, 2015. "Multistate Actuarial Models of Functional Disability," North American Actuarial Journal, Taylor & Francis Journals, vol. 19(1), pages 41-59, January.
    10. Hanewald, Katja & Li, Han & Shao, Adam W., 2019. "Modelling multi-state health transitions in China: a generalised linear model with time trends," Annals of Actuarial Science, Cambridge University Press, vol. 13(1), pages 145-165, March.
    11. 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.
    12. Eberhardt, M.S. & Pamuk, E.R., 2004. "The importance of place of residence: Examining health in rural and nonrural areas," American Journal of Public Health, American Public Health Association, vol. 94(10), pages 1682-1686.
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