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Comorbidity: A network perspective

Published online by Cambridge University Press:  29 June 2010

Angélique O. J. Cramer
Affiliation:
Department of Psychology, University of Amsterdam, 1018 WB Amsterdam, The Netherlands A.O.J.Cramer@uva.nl www.aojcramer.com
Lourens J. Waldorp
Affiliation:
Department of Psychology, University of Amsterdam, 1018 WB Amsterdam, The Netherlands L.J.Waldorp@uva.nl http://users.fmg.uva.nl/lwaldorp
Han L. J. van der Maas
Affiliation:
Department of Psychology, University of Amsterdam, 1018 WB Amsterdam, The Netherlands H.L.J.vanderMaas@uva.nl http://users.fmg.uva.nl/hvandermaas/
Denny Borsboom
Affiliation:
Department of Psychology, University of Amsterdam, 1018 WB Amsterdam, The Netherlands D.Borsboom@uva.nl http://sites.google.com/site/borsboomdenny/dennyborsboom

Abstract

The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy.

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Copyright © Cambridge University Press 2010

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