Epidemiological studies of categorical mental disorders consistently report gender and ethnicity differences in many disorder prevalence rates. Further, these disorders are often comorbid. Can a dimensional multivariate liability model be developed to clarify how gender and ethnicity are associated with diverse, comorbid mental disorders? I pursued this possibility in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; N = 43,093). Gender and ethnicity differences in prevalence rates showed systematic patterns; for instance, women showed higher rates of all internalizing (mood and anxiety) disorders, and men showed higher rates of all externalizing (antisocial and substance use) disorders. I next investigated the latent associations underpinning disorder comorbidity and found that a dimensional internalizing-externalizing liability model fit the data well in all sub-populations. This model was gender and ethnicity invariant, indicating that observed gender and ethnicity differences in prevalence rates originated from the groups' different average standings on latent internalizing and externalizing liability dimensions. I discuss implications of these findings for understanding gender and ethnicity differences in psychopathology and for classification and intervention.
University of Minnesota Ph.D. dissertation. August 2012. Major: Psychology. Advisor: Robert Frank Krueger, Ph.D. 1 computer file (PDF); vi, 86 pages.
Eaton, Nicholas Robert.
Testing gender and ethnicity invariance of the comorbidity structure of common mental disorders..
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