Gatekeeper trainings in community settings are successful in improving knowledge, reshaping attitudes, and boosting the confidence of gatekeepers. The most effective strategy to achieve the preferred objectives is to target those CF groups that are most in need of training and to tailor the content of the training program to the individual needs of the target group.

Characterization of long-term disease dynamics, from disease-free to end-stage, is integral to understanding the course of neurodegenerative diseases such as Parkinson's and Alzheimer's; and ultimately, how best to intervene. Natural history studies typically recruit multiple cohorts at different stages of disease and follow them longitudinally for a relatively short period of time. We propose a latent time joint mixed effects model to characterize longterm disease dynamics using this short-term data. Markov chain Monte Carlo methods are proposed for estimation, model selection, and inference. We apply the model to detailed simulation studies and data from the Alzheimer's Disease Neuroimaging Initiative.