  Reliable mortality estimates at the subnational level are essential in the
study of health inequalities within a country. One of the difficulties in
producing such estimates is the presence of small populations, where the
stochastic variation in death counts is relatively high, and so the underlying
mortality levels are unclear. We present a Bayesian hierarchical model to
estimate mortality at the subnational level. The model builds on characteristic
age patterns in mortality curves, which are constructed using principal
components from a set of reference mortality curves. Information on mortality
rates are pooled across geographic space and smoothed over time. Testing of the
model shows reasonable estimates and uncertainty levels when the model is
applied to both simulated data which mimic US counties, and real data for
French departments. The estimates produced by the model have direct
applications to the study of subregional health patterns and disparities.
