  The analysis of population-wide datasets can provide insight on the health
status of large populations so that public health officials can make
data-driven decisions. The analysis of such datasets often requires highly
parameterized models with different types of fixed and randoms effects to
account for risk factors, spatial and temporal variations, multilevel effects
and other sources on uncertainty. To illustrate the potential of Bayesian
hierarchical models, a dataset of about 500 000 inhabitants released by the
Polish National Health Fund containing information about ischemic stroke
incidence for a 2-year period is analyzed using different types of models.
Spatial logistic regression and survival models are considered for analyzing
the individual probabilities of stroke and the times to the occurrence of an
ischemic stroke event. Demographic and socioeconomic variables as well as drug
prescription information are available at an individual level. Spatial
variation is considered by means of region-level random effects.
