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Modelling multi-state health transitions in China: a generalised linear model with time trends

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  • Hanewald, Katja
  • Li, Han
  • Shao, Adam W.

Abstract

Rapid population ageing in China has urged the need to understand health transitions of older Chinese to assist the development of social security programmes and financial products aimed at funding long-term care. In this paper, we develop a new flexible approach to modelling health transitions in a multi-state Markov model that allows for age effects, time trends and age-time interactions. The model is implemented in the generalised linear modelling framework. We apply the model to evaluate health transitions of Chinese elderly using individual-level panel data from the Chinese Longitudinal Healthy Longevity Survey for the period 1998–2012. Our results confirm that time trends and age–time interactions are important factors explaining health transitions in addition to the more commonly used age effects. We document that differences in disability and mortality rates continue to persist between urban and rural older Chinese. We also compute life expectancies and healthy life expectancies based on the proposed model as inputs for the development of aged care and financial services for older Chinese.

Suggested Citation

  • 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.
    • Handle: RePEc:cup:anacsi:v:13:y:2019:i:01:p:145-165_00
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    Cited by:

    1. Yu, Dandan & Lu, Bei & Piggott, John, 2022. "Alcohol consumption as a predictor of mortality and life expectancy: Evidence from older Chinese males," The Journal of the Economics of Ageing, Elsevier, vol. 22(C).
    2. Deng, Yuanyuan & Fang, Hanming & Hanewald, Katja & Wu, Shang, 2023. "Delay the Pension Age or Adjust the Pension Benefit? Implications for Labor Supply and Individual Welfare in China," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 1192-1215.
    3. Chen, An & Chen, Yusha & Xu, Xian, 2022. "Care-dependent tontines," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 69-89.
    4. 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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