Dealing with multigranular spatio-temporal databases to manage psychiatric epidemiology data

In epidemiology spatio-temporal data may represent surveillance data and origins of diseases. In order to better exploit these data, temporal and spatial dimensions could be managed considering them as meta-data useful to retrieve classical data. In this paper, we propose to use a framework for spatio-temporal granularities with the aim to improve the querying of clinical spatio-temporal data. We show how granularities can be used to enrich a psychiatric case register. We exemplify our approach reporting spatio-temporal queries, based on granularities, useful for epidemiological studies.