The study of the effects and impacts of exposure factors over the health pattern of people in an urbanized area is a key research issue for the actual, integral, and proper implementation of the three pillars of sustainable development (i.e., economic development, social development, and environmental protection). Earth observation (EO) data sets play a key role in investigating the interactions between human phenomena and natural events, because of the possibility to extract from remotely sensed data the physiochemical composition of the Earth surface or to characterize atmosphere constituents and water quality. Moreover, EO data analysis allows a more accurate assessment of the anthropogenic impact on the environment by monitoring the urbanization process. In this chapter, we introduce a study of the interplay between air pollution (as estimated by remotely sensed data processing) and clinical records, so that inferences and correlations among black particulate concentration, micro- and macrovascular disease onsets, and hospitalization tracks can be efficiently drawn. Experimental results show how effective connections between the estimated air quality and the clinical data behavior can be accurately derived by means of the proposed methods for data mining over large-scale heterogeneous records.