  Among the proposals for joint disease mapping, the shared component model has
become more popular. Another recent advance to strengthen inference of disease
data has been the extension of purely spatial models to include time and
space-time interaction. Such analyses have additional benefits over purely
spatial models. However, only a few proposed spatio-temporal models could
address analysing multiple diseases jointly.
  In the proposed model, each component is shared by different subsets of
diseases, spatial and temporal trends are considered for each component, and
the relative weight of these trends for each component for each relevant
disease can be estimated. We present an application of the proposed method on
incidence rates of seven prevalent cancers in Iran. The effect of the shared
components on the individual cancer types can be identified. Regional and
temporal variation in relative risks is shown. We present a model which
combines the benefits of shared-components with spatio-temporal techniques for
multivariate data. We show, how the model allows to analyse geographical and
temporal variation among diseases beyond previous approaches.
