Mortality Forecasts For Long-Term Care Subpopulations With Longevity Risk: A Bayesian Approach

This article aims to propose a new Bayesian methodology to forecast mortality rates of long-term care (LTC) subpopulations with longevity risk. A major obstacle to developing such a method is lack of data on the number of deaths in LTC subpopulations, which would prevent us from using conventional mortality models such as the Lee-Carter model. To overcome this difficulty, we propose an extended Lee-Carter model for mortality differential by LTC status that does not require data on the number of deaths in LTC subpopulations. We apply the proposed model to mortality forecasts for subpopulations under the public long-term care system in Japan. Our results show that the proposed method captures the heterogeneity in the mortality rates between the LTC statuses and provides reasonable forecasts.