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

Published online by Cambridge University Press:  02 July 2018

Katja Hanewald
Affiliation:
ARC Centre of Excellence in Population Ageing Research (CEPAR), University of New South Wales, Sydney, NSW 2052, Australia
Han Li*
Affiliation:
Department of Actuarial Studies and Business Analytics, Macquarie University, Sydney, NSW 2109, AustraliaARC Centre of Excellence in Population Ageing Research (CEPAR), University of New South Wales, Sydney, NSW 2052, Australia
Adam W. Shao
Affiliation:
ARC Centre of Excellence in Population Ageing Research (CEPAR), University of New South Wales, Sydney, NSW 2052, Australia
*
*Correspondence to: Han Li, Department of Actuarial Studies and Business Analytics, Macquarie University, Sydney, Australia. Tel: +61 (2) 9850 6460. E-mail: [email protected]

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.

Type
Paper
Copyright
© Institute and Faculty of Actuaries 2018 

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