Abstract
Monthly counts of medical visits across several years for persons identified to have alcoholism problems are modeled using two-state hidden Markov models (HMM) in order to describe the effect of alcoholism treatment on the likelihood of persons to be in a 'healthy' or 'unhealthy' state. The medical visits can be classified into different types leading to multivariate counts of medical visits each month. A multiple indicator HMM is introduced, which simultaneously fits the multivariate Poisson counts by assuming a shared hidden state underlying all of them. The multiple indicator HMM borrows information across different types of medical encounters. A univariate HMM based on the total count across types of medical visits each month is also considered. Comparisons between the multiple indicator HMM and the total count HMM are made, as well as comparisons with more traditional longitudinal models that directly model the counts. A Bayesian framework is used for the estimation of the HMM and implementation is in Winbugs.