  Diabetes prevalence is on the rise in the UK, and for public health strategy,
estimation of relative disease risk and subsequent mapping is important. We
consider an application to London data on diabetes prevalence and mortality. In
order to improve the estimation of relative risks we analyse jointly prevalence
and mortality data to ensure borrowing strength over the two outcomes. The
available data involves two spatial frameworks, areas (middle level super
output areas, MSOAs), and general practices (GPs) recruiting patients from
several areas. This raises a spatial misalignment issue that we deal with by
employing the multiple membership principle. Specifically we translate area
spatial effects to explain GP practice prevalence according to proportions of
GP populations resident in different areas. A sparse implementation in Stan of
both the MCAR and GMCAR allows the comparison of these bivariate priors as well
as exploring the different implications for the mapping patterns for both
outcomes. The necessary causal precedence of diabetes prevalence over mortality
allows a specific conditionality assumption in the GMCAR, not always present in
the context of disease mapping.
