Transition Probability Matrices and Forecasting of Long-Term Care Demand

Abstract

To provide an actuarial basis for the long-term care security system, and to research health state transition probability and the size of population with long-term care demand. Methods- Based on actuarial theory to construct transition probability matrixes, adopt the Markov process with transition intensities as piecewise constants to forecast the size of population needing long-term care. Innovation-The transition probability matrixes based on the actuarial theory can avoid the subjectivity of variable selection in the common regression analysis method; the piecewise constant Markov method can overcome the defect that the Markov time invariant hypothesis is not in conformity with the reality of health state varying with age. Results- In the coming decade, the number of disabled population in China will be increased to 1.5 times the current number, and the number of female disabled population will be around 2 times that of male disabled population; the women who are advanced in age and poor in health are the main group with a demand for long-term care in the future. Significance-This study is beneficial to the accumulation of basic information for the construction of China’s long-term care security system for the elderly, thereby, providing a decision basis for the government to develop an aging strategy.