  The health impact of long-term exposure to air pollution is now routinely
estimated using spatial ecological studies, due to the recent widespread
availability of spatial referenced pollution and disease data. However, this
areal unit study design presents a number of statistical challenges, which if
ignored have the potential to bias the estimated pollution-health relationship.
One such challenge is how to control for the spatial autocorrelation present in
the data after accounting for the known covariates, which is caused by
unmeasured confounding. A second challenge is how to adjust the functional form
of the model to account for the spatial misalignment between the pollution and
disease data, which causes within-area variation in the pollution data. These
challenges have largely been ignored in existing long-term spatial air
pollution and health studies, so here we propose a novel Bayesian hierarchical
model that addresses both challenges, and provide software to allow others to
apply our model to their own data. The effectiveness of the proposed model is
compared by simulation against a number of state of the art alternatives
proposed in the literature, and is then used to estimate the impact of nitrogen
dioxide and particulate matter concentrations on respiratory hospital
admissions in a new epidemiological study in England in 2010 at the Local
Authority level.
