Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-28T07:25:56.389Z Has data issue: false hasContentIssue false
  • English
  • Français

Longitudinal associations between depressive and anxiety disorders: a comparison of two trait models

Published online by Cambridge University Press:  06 September 2007

Thomas M. Olino ,
Daniel N. Klein ,
Peter M. Lewinsohn ,
Paul Rohde  and
John R. Seeley
Thomas M. Olino*
Affiliation:
Department of Psychology, Stony Brook University, Stony Brook, NY, USA Oregon Research Institute, Eugene, OR, USA
Daniel N. Klein
Affiliation:
Department of Psychology, Stony Brook University, Stony Brook, NY, USA Oregon Research Institute, Eugene, OR, USA
Peter M. Lewinsohn
Affiliation:
Oregon Research Institute, Eugene, OR, USA
Paul Rohde
Affiliation:
Oregon Research Institute, Eugene, OR, USA
John R. Seeley
Affiliation:
Oregon Research Institute, Eugene, OR, USA
*
*Address for correspondence: T. M. Olino, M.A., Department of Psychology, Stony Brook University, Stony Brook, NY 11794-2500, USA. (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Depression and anxiety are highly co-morbid disorders. Two latent trait models have been proposed to explain the nature of the relationship between these disorders. The first posits that depressive and anxiety disorders are both manifestations of a single internalizing factor. The second model, based on a tripartite model proposed by Clark & Watson [Journal of Abnormal Psychology (1991) 100, 316–336], proposes that depressive and anxiety disorders reflect a combination of shared and disorder-specific factors.

Method

We directly compared the two models in a sample of 891 individuals from the Oregon Adolescent Depression Project who participated in up to four diagnostic assessments over approximately 15 years. Structural equation models were used to examine the relationship between depressive and anxiety disorders across different developmental periods (<14, 14–18, 19–23, 24–30 years of age).

Results

The one- and three-factor models were hierarchically related. Thus, a direct comparison between the one- and three-factor models was possible using a χ2 difference test. The result found that the three-factor model fit the data better than the one-factor model.

Conclusions

The three-factor model, positing that depressive and anxiety disorders were caused by a combination of shared and disorder-specific factors, provided a significantly better fit to the data than the one-factor model postulating that a single factor influences the development of both depressive and anxiety disorders.

Keywords

Anxietyco-morbiditydepressioninternalizing psychopathologystructural equation modeling
Type
Original Articles
Information
Psychological Medicine , Volume 38 , Issue 3 , March 2008 , pp. 353 - 363
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Portions of these findings were presented at the Virtual Meeting of the Society for Research in Psychopathology, October 2005. The actual meeting was cancelled due to hurricanes.

References

Andrews, G (1996). Comorbidity and the general neurotic syndrome. British Journal of Psychiatry 168 (Suppl. 30), 76–84.CrossRefGoogle Scholar
APA (1987). Diagnostic and Statistical Manual of Mental Disorders, 3rd edn, revised. American Psychological Association: Washington, DC.Google Scholar
APA (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th edn, revised. American Psychological Association: Washington, DC.Google Scholar
Asparouhov, T, Muthén, B (2006). Robust chi square difference testing with mean and variance adjusted test statistics. Mplus Web Notes: No. 10 (http://www.statmodel.com/download/webnotes/webnote10.pdf).Google Scholar
Barlow, DA, Allen, LB, Choate, ML (2004). Toward a unified treatment for emotional disorders. Behavior Therapy 35, 205–230.CrossRefGoogle Scholar
Belzer, K, Schneier, FR (2004). Comorbidity of anxiety and depressive disorders: issues in conceptualization, assessment, and treatment. Journal of Psychiatric Practice 10, 296–306.CrossRefGoogle ScholarPubMed
Bentler, PM (1990). Comparative fit indexes in structural models. Psychological Bulletin 107, 238–246.CrossRefGoogle ScholarPubMed
Brady, EU, Kendall, PC (1992). Comorbidity of anxiety and depression in children and adolescents. Psychological Bulletin 111, 244–255.CrossRefGoogle ScholarPubMed
Brown, C, Schulberg, HC, Madonia, MJ, Shear, MK, Houck, PR (1996). Treatment outcomes for primary care patients with major depression and lifetime anxiety disorders. American Journal of Psychiatry 153, 1293–1300.Google ScholarPubMed
Brown, TA, Campbell, LA, Lehman, CL, Grisham, JR, Mancill, RB (2001). Current and lifetime comorbidity of the DSM-IV anxiety and mood disorders in a large clinical sample. Journal of Abnormal Psychology 110, 585–599.CrossRefGoogle Scholar
Brown, TA, Chorpita, BF, Barlow, DH (1998). Structural relationships among dimensions of the DSM-IV anxiety and mood disorders and dimensions of negative affect, positive affect, and autonomic arousal. Journal of Abnormal Psychology 107, 179–192.CrossRefGoogle ScholarPubMed
Byrne, BM, Shavelson, RJ, Muthén, B (1989). Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance. Psychological Bulletin 105, 456–466.CrossRefGoogle Scholar
Chorpita, BF, Daleiden, EL (2002). Tripartite dimensions of emotion in a child clinical sample: measurement strategies and implications for clinical utility. Journal of Consulting and Clinical Psychology 70, 1150–1160.CrossRefGoogle Scholar
Clark, LA (1989). The anxiety and depressive disorders: descriptive psychopathology and differential diagnosis. In Anxiety and Depression: Distinctive and Overlapping Features (ed. Kendall, P. C. and Watson, D.), pp. 83–129. Academic Press: San Diego.Google 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, Mineka, S (1994). Temperament, personality, and the mood and anxiety disorders. Journal of Abnormal Psychology 103, 103–116.CrossRefGoogle ScholarPubMed
Costello, EJ, Mustillo, S, Erkanli, A, Keeler, G, Angold, A (2003). Prevalence and development of psychiatric disorders in childhood and adolescence. Archives of General Psychiatry 60, 837–844.CrossRefGoogle ScholarPubMed
Fergusson, DM, Horwood, LJ, Boden, JM (2006). Structure of internalising symptoms in early adulthood. British Journal of Psychiatry 189, 540–546.CrossRefGoogle ScholarPubMed
Flora, DB, Curran, PJ (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods 9, 466–491.CrossRefGoogle ScholarPubMed
Hu, LT, Bentler, PM (1999). Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: An Interdisciplinary Journal 6, 1–55.CrossRefGoogle Scholar
Joiner, Jr. TE, Catanzaro, SJ, Laurent, J (1996). Tripartite structure of positive and negative affect, depression, and anxiety in child and adolescent psychiatric inpatients. Journal of Abnormal Psychology 105, 401–409.CrossRefGoogle ScholarPubMed
Keller, MB, Lavori, PW, Friedman, B, Nielsen, E, Endicott, J, McDonald-Scott, P, Andreasen, NC (1987). The Longitudinal Interval Follow-up Evaluation. A comprehensive method for assessing outcome in prospective longitudinal studies. Archives of General Psychiatry 44, 540–548.CrossRefGoogle ScholarPubMed
Kendler, KS, Prescott, CA, Myers, J, Neale, MC (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry 60, 929–937.CrossRefGoogle ScholarPubMed
Kessler, RC, Chiu, WT, Demler, O, Walters, EE (2005 a). 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, Berglund, P, Demler, O, Jin, R, Merikangas, KR, Walters, EE (2005 b). Lifetime prevalence and age of onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry 62, 593–602.CrossRefGoogle ScholarPubMed
Kraemer, HC, Wilson, KA, Hayward, C (2006). Lifetime prevalence and pseudocomorbidity in psychiatric research. Archives of General Psychiatry 63, 604–608.CrossRefGoogle ScholarPubMed
Krueger, RF (1999). The structure of common mental disorders. Archives of General Psychiatry 56, 921–926.CrossRefGoogle ScholarPubMed
Krueger, RF, Caspi, A, Moffitt, TE, Silva, PA (1998). The structure and stability of common mental disorders (DSM-III-R): a longitudinal-epidemiological study. Journal of Abnormal Psychology 107, 216–227.CrossRefGoogle ScholarPubMed
Krueger, RF, Finger, MS (2001). Using item response theory to understand comorbidity among anxiety and unipolar mood disorders. Psychological Assessment 13, 140–151.CrossRefGoogle ScholarPubMed
Lahey, BB, Applegate, B, Waldman, ID, Loft, JD, Hankin, BL, Rick, J (2004). The structure of child and adolescent psychopathology: generating new hypotheses. Journal of Abnormal Psychology 113, 358–385.CrossRefGoogle ScholarPubMed
Lewinsohn, PM, Hops, H, Roberts, RE, Seeley, JR, Andrews, JA (1993). Adolescent psychopathology: I. Prevalence and incidence of depression and other DSM-III-R disorders in high school students. Journal of Abnormal Psychology 102, 133–144.CrossRefGoogle ScholarPubMed
Lewinsohn, PM, Rohde, P, Seeley, JR, Klein, DN, Gotlib, IH (2003). Psychosocial characteristics of young adults who have experienced and recovered from major depressive disorder during adolescence. Journal of Abnormal Psychology 112, 353–363.CrossRefGoogle Scholar
MacCullum, RC, Browne, MW, Sugawara, HM (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods 1, 130–149.CrossRefGoogle Scholar
Marsh, HW, Hau, KT, Wen, Z (2004). In search of golden rules: comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's (1999) findings. Structural Equation Modeling: An Interdisciplinary Journal 11, 320–341.CrossRefGoogle Scholar
Merikangas, KR, Zhang, HP, Avenevoli, S, Acharyya, S, Neuenschwander, M, Angst, J (2003). Longitudinal trajectories of depression and anxiety in a prospective community study: the Zurich Cohort Study. Archives of General Psychiatry 60, 993–1000.CrossRefGoogle Scholar
Mineka, S, Watson, D, Clark, LA (1998). Comorbidity of anxiety and unipolar mood disorders. Annual Review of Psychology 49, 377–412.CrossRefGoogle ScholarPubMed
Muthén, LK, Muthén, BO (1998–2004). Mplus version 3.13. Muthén & Muthén: Los Angeles, CA.Google Scholar
Muthén, LK, Muthén, BO (2004). Mplus User's Guide, 3rd edn. Muthén & Muthén: Los Angeles, CA.Google Scholar
Nussbeck, FW, Eid, M, Lischetzke, T (2006). Analysing multitrait-multimethod data with structural equation models for ordinal variables applying the WLSMV estimator: what sample size is needed for valid results? British Journal of Mathematical and Statistical Psychology 59, 195–213.CrossRefGoogle ScholarPubMed
Orvaschel, H, Puig-Antich, J, Chambers, WJ, Tabrizi, MA, Johnson, R (1982). Retrospective assessment of prepubertal major depression with the Kiddie-SADS-E. Journal of the American Academy of Child and Adolescent Psychiatry 21, 392–397.Google ScholarPubMed
Pentz, MA, Chou, CP (1994). Measurement invariance in longitudinal clinical research assuming change from development and intervention. Journal of Consulting and Clinical Psychology 62, 450–462.CrossRefGoogle ScholarPubMed
Roberts, RE, Attkisson, CC, Rosenblatt, A (1998). Prevalence of psychopathology in children and adolescents. American Journal of Psychiatry 155, 715–725.Google ScholarPubMed
Rohde, P, Lewinsohn, PM, Seeley, JR (1997). Comparability of telephone and face-to-face interviews in assessing axis I and II disorders. American Journal of Psychiatry 154, 1593–1598.CrossRefGoogle ScholarPubMed
Slade, T, Watson, D (2006). The structure of common DSM-IV and ICD-10 mental disorders in the Australian general population. Psychological Medicine 36, 1593–1600.CrossRefGoogle ScholarPubMed
Sobin, C, Weissman, MM, Goldstein, RB, Adams, P, Wickramaratne, P, Warner, V, Lish, JD (1993). Diagnostic interviewing for family studies: comparing telephone and face-to-face methods for the diagnosis of lifetime psychiatric disorders. Psychiatric Genetics 3, 227–233.CrossRefGoogle Scholar
Steiger, JH (1989). Structural model evaluation and modification: an Interval estimation approach. Multivariate Behavioral Research 25, 173–180.CrossRefGoogle Scholar
Tyrer, P (2001). The case for cothymia: mixed anxiety and depression as a single diagnosis. British Journal of Psychiatry 179, 191–193.CrossRefGoogle ScholarPubMed
Vollebergh, WAM, Iedema, J, Bijl, RV, de Graaf, R, Smit, F, Ormel, J (2001). The structure and stability of common mental disorders: the NEMESIS Study. Archives of General Psychiatry 58, 597–603.CrossRefGoogle ScholarPubMed
Watson, D (2005). Rethinking mood and anxiety disorders: a quantitative hierarchical model for DSM-V. Journal of Abnormal Psychology 114, 522–536.CrossRefGoogle ScholarPubMed
Watson, D, Clark, LA, Weber, K, Assenheimer, JS, Strauss, ME, McCormick, RA (1995). Testing a tripartite model: II. Exploring the symptom structure of anxiety and depression in student, adult, and patient samples. Journal of Abnormal Psychology 104, 15–25.CrossRefGoogle ScholarPubMed
Watson, D, Kotov, R, Gamez, W (2006). Basic dimensions of temperament in relation to personality and psychopathology. In Personality and Psychopathology (ed. Krueger, R. F. and Tackett, J. L.), pp. 7–38. Guilford Press: New York.Google Scholar