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A proposal for a dimensional classification system based on the shared features of the DSM-IV anxiety and mood
Timothy A Brown1, David H Barlow
1Center for Anxiety and Related Disorders, Department of Psychology, Boston University, MA 02215-2013, USA. tabrown@bu.edu
Abstract:
A wealth of evidence attests to the extensive current and lifetime diagnostic comorbidity of the Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM-IV) anxiety and mood disorders. Research has shown that the considerable cross-sectional covariation of DSM-IV emotional disorders is accounted for by common higher order dimensions such as neuroticism/behavioral inhibition (N/BI) and low positive affect/behavioral activation. Longitudinal studies indicate that the temporal covariation of these disorders can be explained by changes in N/BI and, in some cases, initial levels of N/BI are predictive of the temporal course of emotional disorders. The marked phenotypal overlap of the DSM-IV anxiety and mood disorders is a frequent source of diagnostic unreliability (e.g., temporal overlap in the shared features of generalized anxiety disorder and mood disorders, situation specificity of panic attacks in panic disorder and specific phobia). Although extant dimensional proposals may address some drawbacks associated with the DSM nosology (e.g., inadequate assessment of individual differences in disorder severity), these proposals do not reconcile key problems in current classification, such as modest reliability and high comorbidity. This article considers an alternative approach that emphasizes empirically supported common dimensions of emotional disorders over disorder-specific criteria sets. Selection and assessment of these dimensions are discussed along with how these methods could be implemented to promote more reliable and valid diagnosis, prognosis, and treatment planning. For instance, the advantages of this system are discussed in context of transdiagnostic treatment protocols that are efficaciously applied to a variety of disorders by targeting their shared features.
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