ABSTRACT
Healthy life expectancy (HLE) is an indicator that measures the number of years individuals at a given age are expected to live free of disease or disability. HLE forecasting is essential for planning the provision of health care to elderly populations and appropriately pricing Long Term Care insurance products. In this paper, we propose a methodology that simultaneously forecasts HLE for groups of countries and allows for investigating similarities in their HLE patterns. We firstly apply a functional data clustering to the multivariate time series of HLE at birth of different countries for the years 1990-2019 provided by the Global Burden of Disease Study. Three clusters are identified for both genders. Then, we carry out the HLE simultaneous forecasting of the populations within each cluster by a multivariate random walk with drift. Numerical results and the statistical significance of the parameters of the identified multivariate processes are shown. Demographic evidences on the different evolution of HLE between countries are commented.
ABSTRACT
This study investigates the long-term dynamics of longevity by taking into account the specific contribution of each country, and how this has changed over time, thus highlighting different timing and speeds of the evolution of life expectancy among the low-lowest mortality countries. Leveraging on quantile regression, we analyze the specific position of countries that have recorded the maximum (BPLE) and second-best life expectancy value at least once in the period 1960-2014, both at ages 0 and 65. Moving in this direction, the purpose of our contribution is to provide new perspectives on the untracked behavior that may be overshadowed by the maximum longevity levels. Our results provide a comprehensive picture of the different phases and transitions experienced by developed countries in the evolution of life expectancy that has led to a continuous increase in the BPLE. This study is a prominent practice in detecting untracked behaviors, providing imminent onsets on the maximum and sub-maximum values, thus contributing to new clues for future longevity.
ABSTRACT
Life expectancy at birth has attracted interest in various fields, as a health indicator that measures the quality of life. Its appeal relies on the ability to enclose and summarize all the factors affecting longevity. However, more granular information, provided by social indicators such as cause-of-death mortality rates, plays a crucial role in defining appropriate policies for governments to achieve well-being and sustainability goals. Unfortunately, their availability is not always guaranteed. Exploiting the relationship between life expectancy at birth and cause-of-death mortality rates, in this paper we propose an indirect model to produce estimates of death rates due to specific causes using the summary indicator of life expectancy at birth, thus the general levels of the observed mortality. By leveraging on a constrained optimization procedure, we ensure a robust framework where the cause-specific mortality rates are coherent to the aggregate mortality. The main advantage is that indirect estimations allow us to overcome the data availability problem: very often the cause-specific mortality data are incomplete, whereas data on the aggregate mortality are not. Using data from the Human Cause-of-Death Database, we show a numerical application of our model to two different countries, Russia and Spain, which have experienced a different evolution of life expectancy and different leading causes of death. In Spain, we detected the impact of several public health policies on the lowered levels of cancer deaths and related life expectancy increases. As regards the Russia, our results catch the effects of the anti-alcohol campaign of 1985-1988 on longevity changes.
