Patterns of comorbidity among mental disorders: a person-centered approach

Abstract

Objective-Comorbidity poses a major challenge to conventional methods of diagnostic classification. While dimensional models of psychopathology have shed some light on this issue, the reason for inter-relationships among dimensions is unclear. The current study attempted to utilize an alternative approach to characterizing patterns of comorbidity among common mental disorders by modeling them instead as clusters by using latent class analysis.Method-Latent class analyses (LCA) of DSM diagnoses from two nationally representative epidemiological samples -the National Comorbidity Survey (NCS) and National Comorbidity Survey -Replication (NCS-R) datasets were undertaken.Results-Within each dataset, LCA yielded five latent classes exhibiting distinctive profiles of diagnostic comorbidity: a fear class (all phobias and panic disorder); a distress class (depression, generalized anxiety disorder, dysthymia); an externalizing class (alcohol and drug dependence, conduct disorder); a multimorbid class (highly elevated rates of all disorders); and a few disorders class (very low probability of all disorders). While some disorders were relatively specific to certain classes, others (major depression, PTSD, social phobia) appeared to be evident across all classes. Profiles for the five classes were highly similar across the two samples. When bipolar I disorder was added to the LCA models, in both samples, it occurred almost exclusively in the multimorbid class.Conclusions-Comorbidity among mental disorders in the general population appears to occur in a finite number of distinct patterns. This finding has important implications for efforts to refine existing diagnostic classification schemes, as well as for research directed at elucidating the etiology of mental disorders.The phenomenon of comorbidity poses a serious challenge to traditional psychiatric classification systems such as the DSM and the ICD, which conceptualize mental disorders as discrete pathologic conditions. Factor analytic studies have characterized observed co-© 2010 Elsevier Inc. All rights reserved.Correspondence to: Uma Vaidyanathan. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. occurrence among common mental disorders in terms of two correlated but distinct factors of internalizing (subsuming two interrelated subdimensions of "fear" and "anxious-misery") and externalizing (antisocial and addictive disorders) [1]. However, the basis of the moderately large correlation between factors of internalizing and externalizing (i.e., the sources of overlap between disorders in one domain and the other) remains uncl...

The Research Domain Criteria (RDoC): An analysis of methodological and conceptual challenges

“…There is extensive cooccurrence and, even more troublingly, covariation, among many putatively separable psychological conditions, suggesting that these conditions are often slightly different variants of shared etiological processes (Cramer, Waldrop, van der Maas, & Borsboom, 2010;Vaidyanathan, Patrick, & Iacono, 2011). For many DSM disorders, such as posttraumatic stress disorder (PTSD; Brady, Killeen, Brewerton, & Lucerini, 2000), childhood externalizing and internalizing disorders, and all personality disorders (Grove & Tellegen, 1991), comorbidity e in the sense of co-occurrence e is the rule rather than the exception, with the substantial majority of individuals with a given condition meeting criteria for one or more additional conditions (Lilienfeld, 2007).…”

Latent class profile of psychiatric symptoms and treatment utilization in a sample of patients with co-occurring disorders

“…These results are in line with a body of work which points toward the integration of both a categorical and a dimensional (understood as symptom severity, expressed by the score of a particular subscale item) approach to patient classification. For instance, studies seeking to find subgroups based on combinations of psychiatric comorbidities in adolescents, 21 patients with posttraumatic stress disorder (PTSD), 22 patients with schizophrenia, 23 and the general population [24][25][26] obtained solutions with quantitative and qualitative differences between classes, suggesting that subgroups are mostly based on combinations of specific disorders and symptom severity. In another study, 27 carried out with adults seeking treatment for substance use, the best fit was obtained by a three-class model with quantitative differences only (classes were labeled as SUD-only, co-occurring major depressive disorder, and multimorbidity).…”

The Hierarchical Taxonomy of Psychopathology (HiTOP): A Dimensional Alternative to Traditional Nosologies

“…However, when structural findings are translated to practical application, these results are operationalized as scales or other composites of variables, regardless of whether they were derived by class-based or factor analytic methods. Recent studies that used class-based methods (e.g., latent class analysis) found classes that represent extreme levels of dimensions identified in factor analytic research (Olino et al, 2012;Vaidyanathan, Patrick, & Iacono, 2011), but older studies produced different sets of clusters (Kessler et al, 2005). Dimensional models have shown better fit to the data than latent classes or hybrid models (Eaton et al, 2013;Carragher et al, 2014;Haslam et al, 2012;Vrieze, Perlman, Krueger, & Iacono, 2012;Walton et al, 2011;Wright et al, 2013).…”