Reinterpreting comorbidity among common mental disorders using latent class analysis.

Abstract

Comorbidity among mental disorders has long been a conundrum to researchers. While factor analytic models that divide psychopathology into internalizing and externalizing syndromes have helped to clarify the picture somewhat, much remains to be done. For example, evidence from factor analytic, behavior-genetic, and longitudinal modeling studies all suggest significant overlap between internalizing and externalizing disorders. Likewise, research also indicates that though structural models treat psychopathological syndromes as monothetic entities, far more heterogeneity exists among subjects with disorders such as depression, post-traumatic stress disorder, social phobia, and psychosis. These lines of work point to the need for alternative, complementary ways of examining psychopathology. In service of this, the current study examined psychopathology from a person-oriented approach. It utilized latent class analysis to characterize patterns of comorbidity among subjects from two different epidemiological samples - the National Comorbidity Survey (N=5877) and the National Comorbidity Survey-Replication (N=3197) - into groups (or classes) based on diagnoses of common mental disorders. Results from both samples indicated that subjects could be divided into 5 distinct latent classes, the profiles of which were almost identical across samples. Validation data including demographics, and medication and treatment-related variables also revealed distinct patterns of homogeneity and heterogeneity across latent classes. Results from this study provide a basis for understanding psychopathology from a novel perspective and have potential for a range of applications including understanding the genetics of psychopathological syndromes and even treatment research.