Context Patterns of comorbidity among mental disorders are thought to reflect the natural organization of mental illness. Factor analysis can be used to investigate this structure and construct a quantitative classification system. Prior studies identified 3 dimensions of psychopathology: internalizing, externalizing, and thought disorder. However, research has largely relied on common disorders and community samples. Consequently, it is unclear how well the identified organization applies to patients and how other major disorders fit into it.
Objective To analyze comorbidity among a wide range of Axis I disorders and personality disorders (PDs) in the general outpatient population.
Design Clinical cohort study.
Setting A general outpatient practice, the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project.
Participants Outpatients (N = 2900) seeking psychiatric treatment.
Main Outcome Measures The Structured Clinical Interview for DSM-IV and the Structured Interview for DSM-IV Personality.
Results We tested several alternative groupings of the 25 target disorders. The DSM-IV organization fit the data poorly. The best-fitting model consisted of 5 factors: internalizing (anxiety and eating disorders, major depressive episode, and cluster C, borderline, and paranoid PDs), externalizing (substance use disorders and antisocial PD), thought disorder (psychosis, mania, and cluster A PDs), somatoform (somatoform disorders), and antagonism (cluster B and paranoid PDs).
Conclusions We confirmed the validity of the 3 previously found spectra in an outpatient population. We also found novel somatoform and antagonism dimensions, which this investigation was able to detect because, to our knowledge, this is the first study to include a variety of somatoform and personality disorders. The findings suggest that many PDs can be placed in Axis I with related clinical disorders. They also suggest that unipolar depression may be better placed with anxiety disorders than with bipolar disorders. The emerging quantitative nosology promises to provide a more useful guide to clinicians and researchers.