  Health surveys allow exploring health indicators that are of great value from
a public health point of view and that cannot normally be studied from regular
health registries. These indicators are usually coded as ordinal variables and
may depend on covariates associated with individuals. In this paper, we propose
a Bayesian individual-level model for small-area estimation of survey-based
health indicators. A categorical likelihood is used at the first level of the
model hierarchy to describe the ordinal data, and spatial dependence among
small areas is taken into account by using a conditional autoregressive (CAR)
distribution. Post-stratification of the results of the proposed
individual-level model allows extrapolating the results to any administrative
areal division, even for small areas. We apply this methodology to the analysis
of the Health Survey of the Region of Valencia (Spain) of 2016 to describe the
geographical distribution of a self-perceived health indicator of interest in
this region.
