Spatio-temporal health data is now routinely available. Often when time augments space, the focus is on modeling global spatio-temporal effects. However, temporal effects are often localized spatially and so it could be important to disaggregate these effects. This leads to spatial clustering of temporal effects. Often this disaggregation is approached via latent mixture component models. Extending this approach to multiple disease incidence is the focus of this presentation. The specific example that is explored, and motivates the detailed modeling, is incidence of mild cognitive impairment (MCI) and Alzheimers disease (AD). MCI is considered a pre-cursor of AD and so there is a temporal latent link between these outcomes. Our models address latent component mixtures for each disease but also coupled components shared between diseases. A case study in annual county level incidence in South Carolina is presented.