Faced with these nonstandard characteristics, statistical models have been developed to address these issues and make best use of small-area health data. In connection with generic developments in a flexible modeling strategy using the paradigm of Bayesian hierarchical models, hierarchical disease-mapping models based on conditional autoregressions (CAR) were proposed in the 1990s through the work of Besag et al. (1991), Clayton and Bernardinelli (1992), and Clayton et al. (1993). These CAR models are now commonly used both by statisticians and epidemiologists, and their implementation is facilitated by existing software such as WinBUGS (Spiegelhalter et al. 2002). Alternative semiparametric formulations to CAR have also been proposed recently (Denison and Holmes 2001; Green and Richardson 2002; Knorr-Held and Rasser 2000) to model more heterogeneous risk surfaces and particularly to allow for potential discontinuities in the risk. The main characteristic of all these models is to provide some shrinkage and spatial smoothing of the raw relative risk estimates that otherwise would be computed separately in each area. Such shrinkage gives a more stable estimate of the pattern of underlying risk of disease than that provided by the raw estimates. The pattern of the raw risks, strongly influenced by the size of the population at risk, leads to a noisy and blurred picture of the true unobserved risks.