  In this work we present a spatial approach to model and investigate mortality
data referenced over a Lexis structure. We decompose the force of mortality
into two interpretable components: a Markov random field, smooth with respect
to time, age and cohort which explains the main pattern of mortality; and a
secondary component of independent shocks, accounting for additional non-smooth
mortality. Inference is based on a hierarchical Bayesian approach with Markov
chain Monte Carlo computations. We present an extensive application to data
from the Human Mortality Database about 37 countries. For each country the
primary smooth surface and the secondary surface of additional mortality are
estimated. The importance of each component is evaluated by the estimated value
of the respective precision parameter. For several countries we discovered a
band of extra mortality in the secondary surface across the time domain, in the
age interval between 60 and 90 years, with a slightly positive slope. The band
is significant in the most populated countries, but might be present also in
the others. The band represents a significant amount of extra mortality for the
elderly population, which is otherwise incompatible with a regular and smooth
dynamics in age, year and cohort.
