There is increasing evidence, mainly from daily time series studies, linking air pollution and stroke. Small area level geographical correlation studies offer another means of examining the air pollution-stroke association. Populations within small areas may be more homogeneous than those within larger areal units, and census-based socioeconomic information may be available to adjust for confounding effects. Data on smoking from health surveys may be incorporated in spatial analyses to adjust for potential confounding effects but may be sparse at the small area level. Smoothing, using data from neighbouring areas, may be used to increase the precision of smoking prevalence estimates for small areas. We examined the effect of modelled outdoor NOx levels on stroke mortality using a Bayesian hierarchical spatial model to incorporate random effects, in order to allow for unmeasured confounders and to acknowledge sampling error in the estimation of smoking prevalence. We observed an association between NOx and stroke mortality after taking into account random effects at the small area level. We found no association between smoking prevalence and stroke mortality at the small area level after modelling took into account imprecision in estimating smoking prevalence. The approach we used to incorporate smoking as a covariate in a single large model is conceptually sound, though it made little difference to the substantive results.