The Clustering of Psychopathology Among Adults Seeking Treatment for Alcohol and Drug Addiction

• We examined patterns of psychopathology among adults entering addiction treatmen.t
• Clients were grouped into 3 latent classes, one with a high degree of multimorbidity.
• The patterns of disorders are consistent with those seen in the general population.
• This method is a useful way to measure the burden of psychopathology on individuals.

Beyond the high prevalence of co-occurring mental and substance use disorders, little is known about more complex patterns of psychopathology and multimorbidity, particularly in treatment populations. We sought to identify a parsimonious set of latent classes to describe the structure of mental disorder comorbidity among adults entering outpatient addiction treatment, and explore differences in the structure and prevalence of classes across sociodemographic characteristics. Participants (N = 544) completed the Psychiatric Diagnostic Screening Questionnaire at treatment admission. We used latent class analysis to identify classes of clients with specific patterns of co-occurring mental disorders. The best-fitting solution identified 3 classes, characterized by no comorbidity (i.e., substance use disorders only), co-occurring major depression, and multimorbidity or a high degree of psychopathology. Older age was associated with lower probability of being in the class with co-occurring major depression, women were more likely than men to be in the multimorbid class, and being married or partnered was associated with a lower probability of being in either of the comorbid classes. These results are consistent with general population research on the patterning of psychiatric disorders, implying that while clients in addiction treatment may have extraordinarily high levels of psychopathology, the patterns of symptoms and the groups most affected are not markedly different than in other settings. By capturing the complexity of interrelationships among the many factors that are known to influence prognosis and outcomes, latent class analysis offers a useful way to examine and represent case-mix in clinical populations.