 Descriptive analyses will be used to compare the characteristics of the participants in the intervention and control groups that we intend to compare with respect to any outcome of interests, e.g., blood pressure. Specifically, we will compare the groups with respect to demographic and socio-economic factors such as age, sex, ethnicity, and education level. Since the outcome measurements at baseline and the follow up quarterly visit at 3 months, 6 months, etc are expected to be correlated, we will use Generalized Estimating Equations GEE models. GEE takes into account the correlation of an outcome measured repeatedly over time by treating it as a nuisance parameter Zeger Liang, 1986. As a result, the within-subjects correlation is not modeled explicitly. GEE uses a working correlation to treat the within-subjects correlation, and produces standard error estimates that take into account the correlation of responses with subjects. In addition, we will use Random effects models Wolfinger OConnell, 1993 to adjust for the heterogeneity of subjects, which can be due to unmeasured predispositions, such as genetic factors, that account for the correlation within subjects across time. Estimated effects of a covariate of interest e.g., males vs. females are adjusted for these individual differences and model estimates should be interpreted in terms of the change due to the covariate in an individual at a given level of random subject effect. The random effects model is most useful when inferences about individual differences are the primary interest. Random effects models have been developed for both continuous and binary outcome variables Wolfinger et al., 1993. 