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The health impact of long-term exposure to air pollution is now routinelyestimated using spatial ecological studies, due to the recent widespreadavailability of spatial referenced pollution and disease data. However, thisareal unit study design presents a number of statistical challenges, which ifignored have the potential to bias the estimated pollution-health relationship.One such challenge is how to control for the spatial autocorrelation present inthe data after accounting for the known covariates, which is caused byunmeasured confounding. A second challenge is how to adjust the functional formof the model to account for the spatial misalignment between the pollution anddisease data, which causes within-area variation in the pollution data. Thesechallenges have largely been ignored in existing long-term spatial airpollution and health studies, so here we propose a novel Bayesian hierarchicalmodel that addresses both challenges, and provide software to allow others toapply our model to their own data. The effectiveness of the proposed model iscompared by simulation against a number of state of the art alternativesproposed in the literature, and is then used to estimate the impact of nitrogendioxide and particulate matter concentrations on respiratory hospitaladmissions in a new epidemiological study in England in 2010 at the LocalAuthority level.

Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies