Disease mapping studies summarize spatial and spatiotemporal variations in disease risk. This information may be used for simple descriptive purposes, to assess whether health targets are being met or whether new policies are successful, to provide the context for further studies (by providing information on the form and size of the spatial variability in risk) or, by comparing the estiamted risk map with an exposure map, to obtain clues to aetiology. There are well-known problems with mapping raw risks and relative risks for rare diseases and/or small areas since sampling variability tends to dominate the subsequent maps. To alleviate these difficulties a multilevel modelling approach may be followed in which estimates based on small numbers are ‘shrunk’ towards a common value. In this paper we extend these models to investigate the effects of inaccuracies in the health and population data. In terms of the health data we consider the effects of errors that occur due to the imperfect collection procedures that are used by disease registers. For cancers in particular, this is a major problem, with case underascertainment (i.e. undercount) being the common type of error. The populations that are used for estimating disease risks have traditionally been treated as known quantities. In practice, however, these counts are often based on sources of data such as the census which are subject to error (in particular underenumeration) and are only available for census years. Intercensual population counts must consider not only the usual demographic changes (e.g. births and deaths) but migration also. We propose several approaches for modelling population counts and investigate the sensitivity of inference to the sizes of these errors. We illustrate the methods proposed using data for breast cancer in the Thames region of the UK and we compare our results with those obtained from more conventional approaches.