mGLMM with a random intercept and slope was used for the correlation

This may be due to the effect of depression on behavioral pathways or physiological pathways. Less is known about the relationship between diabetes and other Benzoylmesaconine mental health disorders, but existing reports document similar impacts on outcomes including glycemic control, diabetic complications, quality of life, disability, and medication adherence. Comorbid diabetes and mental health disorders are also associated with increased Yunaconitine healthcare utilization and costs. We hypothesized that increased frequency of mental health visits would result in favorable net effects on healthcare costs over time. We also hypothesized that while outpatient and pharmacy costs might increase with mental health visits; there would be a corresponding decrease in inpatient costs associated with overall savings. The plots helped to examine trends over time in each source of cost by MHV before adjusting for any other covariates. To model the relationship between the three cost categories and covariates in a manner that accounted for differences in variance among the three outcomes, a joint model based on a multivariate generalized linear mixed model approach with shared random intercept and slope was used. Since the response variable is a vector of three correlated cost outcomes, mGLMM with a random intercept and slope was used to account for the correlation among the cost outcomes and estimate the joint effect of MHV on the three outcomes. Hence the exponent of the parameter estimates can be interpreted as the percent change in each type of cost as a function of unit change in the covariates. In the joint models, the random intercept shared by the three cost outcomes captures the association in the natural heterogeneity among the individual subjects�� inpatient, while the random slope captures the correlation in the trajectory over time of cost outcomes. Model goodness of fit was assessed using pseudo-AIC-type statistics and using residual plots. Potential savings were estimated by examining the adjusted mean cost differential between those with no mental health visits and those with one or more MHV in every cost category in every year and multiplying by the number of Veterans in that year.