The k-medoids cluster analysis partitioned the 5,390 subjects into 100 mutually exclusive clusters based on the 10 MCA output dimensions. The hierarchical clustering method aggregated the 100 clusters into a hierarchy from 1 to 100, producing a dendrogram with pseudo F and t2 statistics. These statistics suggested that between three and eight clusters were optimal for this sample. In the final step, we visually inspected the features of the clusters to identify the clinically different characteristics of the clusters. Five mutually exclusive clusters were finally identified. As shown in Table 2, these clusters (subtypes) differed significantly on age, sex, race, education and marital status. Specifically, Groups 2–5 included significantly more men than women and these groups were less educated than Group 1. Group 5 had the lowest level of education. Groups 3 to 5 included a significantly higher proportion of EAs than the other two groups and Group 5 had the fewest married participants.
