Most epidemiological risk indicators strongly depend on the age composition of populations, which makes the direct comparison of raw (unstandardized) indicators misleading because of the different age structures of the spatial units of study. Age-standardized rates (ASRs) are a common solution for overcoming this confusing effect. The main drawback of ASRs is that they depend on age-specific rates which, when working with small areas, are often based on very few, or no, observed cases for most age groups. A similar effect occurs with life expectancy at birth and many more epidemiological indicators, which makes standardized mortality ratios the omnipresent risk indicator for small areas epidemiologic studies. To deal with this issue, a multivariate smoothing model is proposed. This age-space dependence structure allows information to be transferred between neighbouring age groups and regions at the same time, providing more reliable age-specific rates estimates that can be later used to calculate enhanced epidemiological indicators.