This comprehensive simulation study highlights some important points to be considered in interpreting any disease-mapping exercise based on hierarchical Bayesian procedures. First, the necessary control of false positives is indeed achieved using any of the models described. However, this is accompanied by a strong smoothing effect that renders the detection of localized increases in risk nearly impossible if these are not based on large (3-fold or more) excess risks or, in the case of more moderate (2-fold) excess risks, substantial expected counts of approximately 50 or more. Thus, in any study it is important to report the range of expected counts across the map and to calibrate any conclusions regarding the relative risks with respect to these expected counts.