  Recently we developed a new framework in Hirz et al (2015) to model
stochastic mortality using extended CreditRisk$^+$ methodology which is very
different from traditional time series methods used for mortality modelling
previously. In this framework, deaths are driven by common latent stochastic
risk factors which may be interpreted as death causes like neoplasms,
circulatory diseases or idiosyncratic components. These common factors
introduce dependence between policyholders in annuity portfolios or between
death events in population. This framework can be used to construct life tables
based on mortality rate forecast. Moreover this framework allows stress testing
and, therefore, offers insight into how certain health scenarios influence
annuity payments of an insurer. Such scenarios may include improvement in
health treatments or better medication. In this paper, using publicly available
data for Australia, we estimate the model using Markov chain Monte Carlo method
to identify leading death causes across all age groups including long term
forecast for 2031 and 2051. On top of general reduced mortality, the proportion
of deaths for certain certain causes has changed massively over the period 1987
to 2011. Our model forecasts suggest that if these trends persist, then the
future gives a whole new picture of mortality for people aged above 40 years.
Neoplasms will become the overall number-one death cause. Moreover, deaths due
to mental and behavioural disorders are very likely to surge whilst deaths due
to circulatory diseases will tend to decrease. This potential increase in
deaths due to mental and behavioural disorders for older ages will have a
massive impact on social systems as, typically, such patients need long-term
geriatric care.
