Three-Way Data Analysis Applied to Cause Specific Mortality Trends

Abstract

The costs of the social security public systems, in almost all developed countries, are affected by two phenomena: an increasing survival in higher ages and a smaller number of births. The combination of these two aspects largely impacts on the societies dealing with the rising pension and healthcare costs. In spite of the common trend given by the ageing population and the growing longevity, the mortality rates are also influenced by gender, countries, ethnicity, income, wealth, causes of death and so on. According to the WHO a “right” recognition of the causes of death is important for forecasting more accurately mortality. In this framework we intend to investigate the main causes of death impacting on the upcoming human survival, throughout a Multi-dimensional Data Analysis approach to the Lee Carter model of mortality trends. In a previous paper, we stated that the crude mortality data can be considered according to several criteria. In this contribution we take into account a three way array holding mortality data structured by time, age-group and causes of death. The model decomposition we propose is a modified version of the classical Lee Carter model allowing for three-way data treatment, analysis of residuals, graphical representation of the different components. A case study based on actual data will be discussed.