Predominant typologies of psychopathology in the United States: a latent class analysis.

BACKGROUND

Latent class analysis (LCA) offers a parsimonious way of classifying common typologies of psychiatric comorbidity. We used LCA to identify the nature and correlates of predominant typologies of Axis I and II disorders in a large and comprehensive population-based sample of U.S. adults.

METHODS

We analyzed data from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (2004-2005; n = 34,653), a population-based sample of U.S. adults. We derived latent classes based on all assessed Axis I and II disorders and examined the relationship between the identified Axis I classes and lifetime psychiatric disorders and suicide attempts, and physical and mental health-related quality of life.

RESULTS

A four-class solution was optimal in characterizing predominant typologies of both Axis I and II disorders. For Axis I disorders, these included low psychopathology (n = 28,935, 84.0%), internalizing (n = 3693, 9.9%), externalizing (n = 1426, 4.5%), and high psychopathology (n = 599, 1.6%) classes. For Axis II disorders, these included no/low personality disorders (n = 31,265, 90.9%), obsessive/paranoid (n = 1635, 4.6%), borderline/dysregulated (n = 1319, 3.4%), and highly comorbid (n = 434, 1.1%) classes. Compared to the low psychopathology class, all other Axis I classes had significantly increased odds of mental disorders, elevated Axis II classes, suicide attempts and poorer quality of life, with the high psychopathology class having the overall highest rates of these correlates, with the exception of substance use disorders. Compared to the low psychopathology class, the internalizing and externalizing classes had increased rates of mood and anxiety disorders, and substance use disorders, respectively.

CONCLUSION

Axis I and II psychopathology among U.S. adults may be best represented by four predominant typologies. Characterizing co-occurring patterns of psychopathology using person-based typologies represents a higher-order classification system that may be useful in clinical and research settings.