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Investigating differential symptom profiles in major depressive episode with and without generalized anxiety disorder: true co-morbidity or symptom similarity?

Published online by Cambridge University Press:  06 November 2009

M. Sunderland ,
L. Mewton ,
T. Slade  and
A. J. Baillie
M. Sunderland*
Affiliation:
Clinical Research Unit for Anxiety and Depression (CRUfAD), University of New South Wales, Sydney, Australia
L. Mewton
Affiliation:
National Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia
T. Slade
Affiliation:
National Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia
A. J. Baillie
Affiliation:
Centre for Emotional Health, Department of Psychology, Macquarie University, Sydney, Australia
*
*Address for correspondence: M. Sunderland, Clinical Research Unit for Anxiety and Depression, 299 Forbes Street, Darlinghurst, NSW2010, Australia. (Email: matthews@student.unsw.edu.au)
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Abstract

Background

Large community-based epidemiological surveys have consistently identified high co-morbidity between major depressive episode (MDE) and generalized anxiety disorder (GAD). Some have suggested that this co-morbidity may be artificial and the product of the current diagnostic system. Because of the added direct and indirect costs associated with co-morbidity, it is important to investigate whether methods of diagnostic classification are artificially increasing the level of observed co-morbidity.

Method

The item response theory (IRT) log-likelihood ratio procedure was used to test for differential item functioning (DIF) of MDE symptoms between respondents with and without a diagnosis of GAD in the 2001–2002 National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

Results

The presence of GAD significantly increased the chances of reporting any symptom of MDE, with odds ratios ranging from 2.54 to 5.36. However, there was no indication of significant DIF of MDE symptoms in respondents with and without GAD.

Conclusions

The lack of any significant DIF indicates that cases with GAD do not present with a distinct MDE symptom profile, one that is consistent with the endorsement of symptoms that are conceptually similar in nature between the two disorders, compared to cases without GAD. This does not support the hypothesis that co-morbidity between MDE and GAD is artificially inflated because of the similar symptom criteria required by the current diagnostic system. Instead, MDE and GAD may be thought of as two distinct diagnostic entities that frequently co-occur because of a shared underlying trait.

Keywords

Co-morbiditydifferential item functioninggeneralized anxiety disorderitem response theorymajor depressive episode
Type
Original Articles
Information
Psychological Medicine , Volume 40 , Issue 7 , July 2010 , pp. 1113 - 1123
Copyright
Copyright © Cambridge University Press 2009

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References

Andrews, G, Slade, T, Issakidis, C (2002). Deconstructing current comorbidity: data from the Australian National Survey of Mental Health and Well-Being. British Journal of Psychiatry 181, 306–314.CrossRefGoogle ScholarPubMed
Angold, A, Costello, EJ, Erkanli, A (1999). Comorbidity. Journal of Child Psychology and Psychiatry 40, 57–87.CrossRefGoogle ScholarPubMed
APA (2000). The Diagnostic and Statistical Manual of Mental Disorders, 4th edn, text revision (DSM-IV-TR). American Psychiatric Association: Washington, DC.Google Scholar
Benjamini, Y, Hochberg, Y (1995). Controlling for false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57, 289–300.Google Scholar
Brown, TA, Antony, MM, Barlow, DH (1995 a). Diagnostic comorbidity in panic disorder: effect on treatment outcome and course of comorbid diagnoses following treatment. Journal of Consulting and Clinical Psychology 63, 408–418.CrossRefGoogle ScholarPubMed
Brown, TA, Barlow, DH (1992). Comorbidity among anxiety disorders: implications for treatment and DSM-IV. Journal of Consulting and Clinical Psychology 60, 835–844.Google Scholar
Brown, TA, Marten, PA, Barlow, DH (1995 b). Discriminant validity of the symptoms constituting the DSM-III-R and DSM-IV associated symptom criterion of generalized anxiety disorder. Journal of Anxiety Disorders 9, 317–328.CrossRefGoogle Scholar
Browne, MW, MacCallum, RC, Kim, CT, Anderson, BL, Glaser, R (2002). When fit indices and the residuals are incompatible. Psychological Methods 7, 403–421.CrossRefGoogle ScholarPubMed
Carter, AS, Pollock, RA, Suvak, MK, Pauls, DL (2004). Anxiety and major depression comorbidity in a family study of obsessive-compulsive disorder. Depression and Anxiety 20, 165–174.CrossRefGoogle Scholar
Clark, LA, Watson, D (1991). Tripartite model of anxiety and depression: psychometric evidence and taxonomic implications. Journal of Abnormal Psychology 100, 316–336.CrossRefGoogle ScholarPubMed
Clark, LA, Watson, D (2006). Distress and fear disorders: an alternative empirically based taxonomy of the ‘mood’ and ‘anxiety’ disorders. British Journal of Psychiatry 189, 481–483.CrossRefGoogle ScholarPubMed
Clarkin, JF, Kendall, PC (1992). Comorbidity and treatment planning: summary and future directions. Journal of Consulting and Clinical Psychology 60, 904–908.CrossRefGoogle ScholarPubMed
Drasgow, F, Parsons, CK (1983). Application of unidimensional item response theory models to multidimensional data. Applied Psychological Measurement 7, 189–199.CrossRefGoogle Scholar
Edelen, MO, Thissen, D, Teresi, JA, Kleinman, M, Ocepek-Welikson, K (2006). Identification of differential item functioning using item response theory and the likelihood-based model comparison approach. Medical Care 44, S134–S142.Google Scholar
Embretson, SE, Reise, SP (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates: Mahwah, NJ.Google Scholar
Frances, AJ, Manning, D, Marin, D, Kocsis, J, McKinney, K, Hall, W, Kline, M (1992). Relationship of anxiety and depression. Psychopharmacology 106, S82–S86.CrossRefGoogle ScholarPubMed
Frances, AJ, Widiger, TA, Fyer, MR (1990). The influence of classification methods on comorbidity. In Comorbidity of Mood and Anxiety Disorders (ed. Maser, J. and Cloninger, C. R.), pp. 41–60. American Psychiatric Press: Washington, DC.Google Scholar
Grant, BF, Dawson, DA, Stinson, FS, Chou, PS, Kay, W, Pickering, R (2003 b). The Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS-IV): reliability of alcohol consumption, tobacco use, family history of depression and psychiatric diagnostic modules in a general population sample. Drug and Alcohol Dependence 71, 7–16.CrossRefGoogle Scholar
Grant, BF, Hasin, DS, Stinson, FS, Dawson, DA, Ruan, WJ, Goldstein, RB, Smith, SM, Saha, TD, Huang, B (2005). Prevalence, correlates, co-morbidity, and comparative disability of DSM-IV generalized anxiety disorder in the USA: results from the National Epidemiologic Survey of Alcohol and Related Conditions. Psychological Medicine 35, 1747–1759.CrossRefGoogle Scholar
Grant, BF, Kaplan, K, Shepard, J, Moore, T (2003 a). Source and Accuracy Statement for Wave 1 of the 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions. National Institute on Alcohol Abuse and Alcoholism: Bethesda, MD.Google Scholar
Hays, RD, Morales, LS, Reise, SP (2000). Item response theory and health outcomes measurement in the 21st century. Medical Care 39, II28–II42.Google Scholar
Hettema, JM, Neale, MC, Myers, JM, Prescott, CA, Kendler, KS (2006). A population-based twin study of the relationship between neuroticism and internalizing disorders. American Journal of Psychiatry 163, 857–864.CrossRefGoogle ScholarPubMed
Holland, PW, Wainer, H (1993). Differential Item Functioning. Lawrence Erlbaum Associates: Mahwah, NJ.Google Scholar
Hunt, C (2000). The diagnosis and nature of generalized anxiety disorder. Current Opinion in Psychiatry 13, 157–161.CrossRefGoogle Scholar
Hunt, C, Issakidis, C, Andrews, G (2002). DSM-IV generalized anxiety disorder in the Australian National Survey of Mental Health and Well-being. Psychological Medicine 32, 649–659.CrossRefGoogle ScholarPubMed
Hunt, C, Slade, T, Andrews, G (2004). Generalized anxiety disorder and major depressive disorder comorbidity in the National Survey of Mental Health and Well-Being. Depression and Anxiety 20, 23–31.CrossRefGoogle ScholarPubMed
Jorm, AF (2006). National surveys of mental disorders: are they researching scientific facts or constructing useful myths? Australian and New Zealand Journal of Psychiatry 40, 830–834.CrossRefGoogle ScholarPubMed
Judd, LL, Kessler, RC, Paulus, MP, Zeller, PV, Wittchen, HU, Kunovac, JL (1998). Comorbidity as a fundamental feature of generalized anxiety disorders: results from the National Comorbidity Study (NCS). Acta Psychiatrica Scandinavica 98, 6–11.CrossRefGoogle Scholar
Kendler, KS, Gardner, CO, Gatz, M, Pedersen, NL (2007). The sources of co-morbidity between major depression and generalized anxiety disorder in a Swedish national twin sample. Psychological Medicine 37, 453–462.CrossRefGoogle Scholar
Kessler, RC, Chiu, WT, Demler, O, Walters, EE (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry 62, 617–627.CrossRefGoogle ScholarPubMed
Kessler, RC, Ustun, B (eds) (2008). The WHO World Mental Health Survey: Global Perspectives on the Epidemiology of Mental Disorders. Cambridge University Press: New York, NY.Google Scholar
Krueger, RF (1999). The structure of common mental disorders. Archives of General Psychiatry 56, 921–926.CrossRefGoogle ScholarPubMed
Krueger, RF, Bezdjian, S (2009). Enhancing research and treatment of mental disorders with dimensional concepts: toward DSM-V and ICD-11. World Psychiatry 8, 3–6.CrossRefGoogle ScholarPubMed
Krueger, RF, Finger, MS (2001). Using item response theory to understand comorbidity among anxiety and unipolar mood disorder. Psychological Assessment 13, 140–151.CrossRefGoogle Scholar
Kruijshaar, ME, Barendregt, J, Vos, T, de Graaf, R, Spijker, J, Andrews, G (2005). Lifetime prevalence estimates of major depression: an indirect estimation method and a quantification of recall bias. European Journal of Epidemiology 20, 103–111.Google Scholar
Maj, M (2005). ‘Psychiatric comorbidity’: an artefact of current diagnostic systems? British Journal of Psychiatry 186, 182–184.CrossRefGoogle ScholarPubMed
Maser, JD (1998). Generalized anxiety disorder and its comorbidities: dispute at the boundaries. Acta Psychiatrica Scandinavica 98, 12–22.CrossRefGoogle Scholar
Millsap, RE, Everson, HT (1993). Methodology review: statistical approaches for assessing measurement bias. Applied Psychological Measurement 17, 297–334.Google Scholar
Moussavi, S, Chatterji, S, Verdes, E, Tandon, A, Patel, V, Ustun, B (2007). Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet 370, 851–858.CrossRefGoogle ScholarPubMed
Murray, CJL, Lopez, AD (eds) (1996). The Global Burden of Disease. Harvard University Press: Harvard, MA.Google ScholarPubMed
Muthen, LK, Muthen, BO (2007). Mplus Users' Guide Fifth Edition. Muthen & Muthen: Los Angeles, CA.Google Scholar
Navas-Ara, MJ, Gomez-Benito, J (2002). Effects of ability scale purification on the identification of DIF. European Journal of Psychological Assessment 18, 9–15.CrossRefGoogle Scholar
Proudfoot, H, Baillie, AJ, Teesson, M (2006). The structure of alcohol dependence in the community. Drug and Alcohol Dependence 81, 21–26.CrossRefGoogle ScholarPubMed
Sanderson, WC, Beck, AT, Beck, J (1990). Syndrome comorbidity in patients with major depression or dysthymia: prevalence and temporal relationships. American Journal of Psychiatry 147, 1025–1028.Google Scholar
Shah, BV, Barnwell, BG, Biegler, GS (1997). SUDAAN User's Manual. Research Triangle Institute: Research Triangle Park, NC.Google Scholar
Simon, GE, Von Korff, M (1992). Reevaluation of secular trends in depression rates. American Journal of Epidemiology 135, 1411–1422.CrossRefGoogle ScholarPubMed
Simon, GE, Von Korff, M, Ustun, TB, Gater, R, Gureje, O, Sartorius, N (1995). Is the lifetime risk of depression actually increasing? Journal of Clinical Epidemiology 48, 1109–1118.CrossRefGoogle ScholarPubMed
Slade, T, Watson, D (2006). The structure of common DSM-IV and ICD-10 mental disorder in the Australian general population. Psychological Medicine 36, 1593–1600.CrossRefGoogle ScholarPubMed
Small, DM, Simons, AD, Yovanoff, P, Silva, SG, Lewis, CC, Murakami, JL, March, J (2008). Depressed adolescents and comorbid psychiatric disorders: are there differences in the presentation of depression? Journal of Abnormal Child Psychology 36, 1015–1028.CrossRefGoogle ScholarPubMed
Teesson, M, Lynskey, M, Manor, B, Baillie, A (2002). The structure of cannabis dependence in the community. Drug and Alcohol Dependence 68, 255–262.CrossRefGoogle ScholarPubMed
Teresi, JA, Ocepek-Welikson, K, Kleinman, M, Cook, KF, Crane, PK, Gibbons, LE, Morales, LS, Orlando-Edelen, M, Cella, D (2007). Evaluating measurement equivalence using the item response theory log-likelihood ratio (IRTLR) method to assess difference item functioning (DIF): applications to measures of physical functioning ability and general distress. Quality of Life Research 16, 43–68.CrossRefGoogle ScholarPubMed
Thissen, D (2001). IRTLRDIF v. 2. Software for the computation of the statistics involved in item response theory likelihood-ratio tests for differential item functioning (www.unc.edu/~dthissen/dl.html).Google Scholar
Thissen, D, Steinberg, L, Kuang, D (2002). Quick and easy implementation of the Benjamini–Hochberg procedure for controlling the false positive rate in multiple comparisons. Journal of Educational and Behavioral Statistics 27, 77–83.CrossRefGoogle Scholar
Thissen, D, Steinberg, L, Wainer, H (1993). Detection of differential item functioning using the parameters of item response models. In Differential Item Functioning (ed. Holland, P. W. and Wainer, H.), pp. 67–114. Lawrence Erlbaum Associates: Hillsdale, NJ.Google Scholar
Yu, CY (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Doctoral thesis, University of California, Los Angeles.Google Scholar
Woods, C (2008 a). Likelihood-ratio DIF testing: effects of nonnormality. Applied Psychological Measurement 32, 511–526.CrossRefGoogle Scholar
Woods, C (2008 b). IRT-LR-DIF with estimation of the focal-group density as an empirical histogram. Educational and Psychological Measurement 88, 571–586.CrossRefGoogle Scholar
Woods, C (2009). Empirical selection of anchor for tests of differential item functioning. Applied Psychological Measurement 33, 42–57.CrossRefGoogle Scholar
Zimmerman, M, McGlinchey, JB, Young, D, Chelminski, I (2006). Diagnosing major depressive disorder. IV. Relationship between number of symptoms and the diagnosis of disorder. Journal of Nervous and Mental Disease 194, 450–453.CrossRefGoogle ScholarPubMed