Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-23T22:32:15.187Z Has data issue: false hasContentIssue false
  • English
  • Français

New insights into the correlation structure of DSM-IV depression symptoms in the general population v. subsamples of depressed individuals

Published online by Cambridge University Press:  09 January 2017

S. Foster  and
M. Mohler-Kuo
S. Foster*
Affiliation:
Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
M. Mohler-Kuo
Affiliation:
Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
*
*Address for correspondence: S. Foster, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, 8001 Zürich, Switzerland. (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Aims.

Previous research failed to uncover a replicable dimensional structure underlying the symptoms of depression. We aimed to examine two neglected methodological issues in this research: (a) adjusting symptom correlations for overall depression severity; and (b) analysing general population samples v. subsamples of currently depressed individuals.

Methods.

Using population-based cross-sectional and longitudinal data from two nations (Switzerland, 5883 young men; USA, 2174 young men and 2244 young women) we assessed the dimensions of the nine DSM-IV depression symptoms in young adults. In each general-population sample and each subsample of currently depressed participants, we conducted a standardised process of three analytical steps, based on exploratory and confirmatory factor and bifactor analysis, to reveal any replicable dimensional structure underlying symptom correlations while controlling for overall depression severity.

Results.

We found no evidence of a replicable dimensional structure across samples when adjusting symptom correlations for overall depression severity. In the general-population samples, symptoms correlated strongly and a single dimension of depression severity was revealed. Among depressed participants, symptom correlations were surprisingly weak and no replicable dimensions were identified, regardless of severity-adjustment.

Conclusions.

First, caution is warranted when considering studies assessing dimensions of depression because general population-based studies and studies of depressed individuals generate different data that can lead to different conclusions. This problem likely generalises to other models based on the symptoms’ inter-relationships such as network models. Second, whereas the overall severity aligns individuals on a continuum of disorder intensity that allows non-affected individuals to be distinguished from affected individuals, the clinical evaluation and treatment of depressed individuals should focus directly on each individual's symptom profile.

Keywords

Classificationdepressive disorderdiagnosisepidemiologyfactor analysis
Type
Original Articles
Information
Epidemiology and Psychiatric Sciences , Volume 27 , Issue 3 , June 2018 , pp. 288 - 300
Copyright
Copyright © Cambridge University Press 2017 

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.)

References

Aggen, SH, Neale, MC, Kendler, KS (2005). DSM criteria for major depression: evaluating symptom patterns using latent-trait item response models. Psychological Medicine 35, 475–487.CrossRefGoogle ScholarPubMed
Aggen, SH, Kendler, KS, Kubarych, TS, Neale, MC (2011). Differential age and sex effects in the assessment of major depression: a population-based twin item analysis of the DSM criteria. Twin Research and Human Genetics 14, 524–538.CrossRefGoogle ScholarPubMed
American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th edn. DSM-5. American Psychiatric Publishing: Washington, DC.Google Scholar
Barendse, MT, Oort, FJ, Timmerman, ME (2014). Using Exploratory Factor Analysis to Determine the Dimensionality of Discrete Responses. Structural Equation Modeling: A Multidisciplinary Journal 22, 87–101.Google Scholar
Baumeister, H, Parker, G (2012). Meta-review of depressive subtyping models. Journal of Affective Disorders 139, 126–140.Google Scholar
Beard, C, Millner, AJ, Forgeard, MJ, Fried, EI, Hsu, KJ, Treadway, MT, Leonard, CV, Kertz, SJ, Bjorgvinsson, T (2016). Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychological Medicine Sep 14 [Epub ahead of print].Google Scholar
Bech, P, Rasmussen, NA, Olsen, LR, Noerholm, V, Abildgaard, W (2001). The sensitivity and specificity of the Major Depression Inventory, using the Present State Examination as the index of diagnostic validity. Journal of Affective Disorders 66, 159–164.Google Scholar
Belzung, C, Billette de Villemeur, E, Lemoine, M, Camus, V (2010). Latent variables and the network perspective. The Behavioral and Brain Sciences 33, 150–151.Google Scholar
Boschloo, L, van Borkulo, CD, Rhemtulla, M, Keyes, KM, Borsboom, D, Schoevers, RA (2015). The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PLoS ONE 10, e0137621.CrossRefGoogle Scholar
Bringmann, LF, Vissers, N, Wichers, M, Geschwind, N, Kuppens, P, Peeters, F, Borsboom, D, Tuerlinckx, F (2013). A network approach to psychopathology: new insights into clinical longitudinal data. PLoS ONE 8, e60188.Google Scholar
Bringmann, LF, Lemmens, LH, Huibers, MJ, Borsboom, D, Tuerlinckx, F (2015). Revealing the dynamic network structure of the beck depression inventory-II. Psychological Medicine 45, 747–757.CrossRefGoogle ScholarPubMed
Bromet, E, Andrade, LH, Hwang, I, Sampson, NA, Alonso, J, de Girolamo, G, de Graaf, R, Demyttenaere, K, Hu, C, Iwata, N, Karam, AN, Kaur, J, Kostyuchenko, S, Lepine, JP, Levinson, D, Matschinger, H, Mora, ME, Browne, MO, Posada-Villa, J, Viana, MC, Williams, DR, Kessler, RC (2011). Cross-national epidemiology of DSM-IV major depressive episode. BMC Medicine 9, 90.Google Scholar
Brown, TA, Moore, MT (2012). Confirmatory factor analysis. In Handbook of Structural Equation Modeling (ed. Hoyle, RH), pp. 361–379. The Guilford Press: New York.Google Scholar
Buhler, J, Seemuller, F, Lage, D (2014). The predictive power of subgroups: an empirical approach to identify depressive symptom patterns that predict response to treatment. Journal of Affective Disorders 163, 81–87.CrossRefGoogle ScholarPubMed
Carragher, N, Adamson, G, Bunting, B, McCann, S (2009). Subtypes of depression in a nationally representative sample. Journal of Affective Disorders 113, 88–99.Google Scholar
Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Data. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. Available at https://www.cdc.gov/nchs/nhanes/index.htmGoogle Scholar
Chen, L, Eaton, WW, Gallo, JJ, Nestadt, G (2000). Understanding the heterogeneity of depression through the triad of symptoms, course and risk factors: a longitudinal, population-based study. Journal of Affective Disorders 59, 1–11.Google Scholar
Cole, DA, Cai, L, Martin, NC, Findling, RL, Youngstrom, EA, Garber, J, Curry, JF, Hyde, JS, Essex, MJ, Compas, BE, Goodyer, IM, Rohde, P, Stark, KD, Slattery, MJ, Forehand, R (2011). Structure and measurement of depression in youths: applying item response theory to clinical data. Psychological Assessment 23, 819–833.Google Scholar
Coryell, W, Winokur, G, Shea, T, Maser, JD, Endicott, J, Akiskal, HS (1994). The long-term stability of depressive subtypes. The American Journal of Psychiatry 151, 199–204.Google Scholar
Cramer, AO, Borsboom, D, Aggen, SH, Kendler, KS (2012). The pathoplasticity of dysphoric episodes: differential impact of stressful life events on the pattern of depressive symptom inter-correlations. Psychological Medicine 42, 957–965.Google Scholar
Cuijpers, P, Vogelzangs, N, Twisk, J, Kleiboer, A, Li, J, Penninx, BW (2014). Comprehensive meta-analysis of excess mortality in depression in the general community v. patients with specific illnesses. The American Journal of Psychiatry 171, 453–462.CrossRefGoogle Scholar
Danks, D, Fancsali, S, Glymour, C, Scheines, R (2010). Comorbid science? The Behavioral and Brain Sciences 33, 153–155.Google Scholar
Eaton, NR, Krueger, RF, Markon, KE, Keyes, KM, Skodol, AE, Wall, M, Hasin, DS, Grant, BF (2013). The structure and predictive validity of the internalizing disorders. Journal of Abnormal Psychology 122, 86–92.Google Scholar
Familiar, I, Ortiz-Panozo, E, Hall, B, Vieitez, I, Romieu, I, Lopez-Ridaura, R, Lajous, M (2015). Factor structure of the Spanish version of the Patient Health Questionnaire-9 in Mexican women. International Journal of Methods in Psychiatric Research 24, 74–82.Google Scholar
Ferrari, AJ, Charlson, FJ, Norman, RE, Patten, SB, Freedman, G, Murray, CJ, Vos, T, Whiteford, HA (2013). Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Medicine 10.CrossRefGoogle ScholarPubMed
Fried, EI, Nesse, RM (2015). Depression is not a consistent syndrome: an investigation of unique symptom patterns in the STAR*D study. Journal of Affective Disorders 172, 96–102.Google Scholar
Fried, EI, Nesse, RM, Guille, C, Sen, S (2015). The differential influence of life stress on individual symptoms of depression. Acta Psychiatrica Scandinavica 131, 465–471.CrossRefGoogle ScholarPubMed
Fried, EI, Nesse, RM, Zivin, K, Guille, C, Sen, S (2014). Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors. Psychological Medicine 44, 2067–2076.CrossRefGoogle Scholar
Fried, EI, van Borkulo, CD, Epskamp, S, Schoevers, RA, Tuerlinckx, F, Borsboom, D (2016). Measuring depression over time…or not? lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression. Psychological Assessment Jan 28 [Epub ahead of print].Google Scholar
Goekoop, R, Goekoop, JG (2014). A network view on psychiatric disorders: network clusters of symptoms as elementary syndromes of psychopathology. PLoS ONE 9, e112734.Google Scholar
Goldberg, DP, Krueger, RF, Andrews, G, Hobbs, MJ (2009). Emotional disorders: cluster 4 of the proposed meta-structure for DSM-V and ICD-11. Psychological Medicine 39, 2043–2059.Google Scholar
Haig, BD, Vertue, FM (2010). Extending the network perspective on comorbidity. The Behavioral and Brain Sciences 33, 158.Google Scholar
Haslam, N, Holland, E, Kuppens, P (2012). Categories v. dimensions in personality and psychopathology: a quantitative review of taxometric research. Psychological Medicine 42, 903–920.Google Scholar
Humphry, SM, McGrane, JA (2010). Is there a contradiction between the network and latent variable perspectives? The Behavioral and Brain Sciences 33, 160–161.Google Scholar
Hybels, CF, Landerman, LR, Blazer, DG (2013). Latent subtypes of depression in a community sample of older adults: can depression clusters predict future depression trajectories? Journal of Psychiatric Research 47, 1288–1297.Google Scholar
Jennrich, RI, Bentler, PM (2011). Exploratory bi-factor analysis. Psychometrika 76, 537–549.CrossRefGoogle ScholarPubMed
Jennrich, RI, Bentler, PM (2012). Exploratory bi-factor analysis: the oblique case. Psychometrika 77, 442–454.Google Scholar
Keller, MC, Nesse, RM (2006). The evolutionary significance of depressive symptoms: different adverse situations lead to different depressive symptom patterns. Journal of Personality and Social Psychology 91, 316–330.Google Scholar
Keller, MC, Neale, MC, Kendler, KS (2007). Association of different adverse life events with distinct patterns of depressive symptoms. The American Journal of Psychiatry 164, 1521–1529.Google Scholar
Kendler, KS, Aggen, SH, Neale, MC (2013). Evidence for multiple genetic factors underlying DSM-IV criteria for major depression. JAMA Psychiatry 70, 599–607.Google Scholar
Kessler, RC, Bromet, EJ (2013). The epidemiology of depression across cultures. Annual Review of Public Health 34, 119–138.Google Scholar
Kline, RB (2011). Principles and Practice of Structural Equation Modelling. Guilford Press: New York.Google Scholar
Kroenke, K, Spitzer, RL, Williams, JB (2001). The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine 16, 606–613.Google Scholar
Krueger, RF, Deyoung, CG, Markon, KE (2010). Toward scientifically useful quantitative models of psychopathology: the importance of a comparative approach. The Behavioral and Brain Sciences 33, 163–164.Google Scholar
Lewinsohn, PM, Pettit, JW, Joiner, TE Jr., Seeley, JR (2003). The symptomatic expression of major depressive disorder in adolescents and young adults. Journal of Abnormal Psychology 112, 244–252.Google Scholar
Li, Y, Aggen, S, Shi, S, Gao, J, Tao, M, Zhang, K, Wang, X, Gao, C, Yang, L, Liu, Y, Li, K, Shi, J, Wang, G, Liu, L, Zhang, J, Du, B, Jiang, G, Shen, J, Zhang, Z, Liang, W, Sun, J, Hu, J, Liu, T, Miao, G, Meng, H, Hu, C, Huang, G, Li, G, Ha, B, Deng, H, Mei, Q, Zhong, H, Gao, S, Sang, H, Zhang, Y, Fang, X, Yu, F, Yang, D, Chen, Y, Hong, X, Wu, W, Chen, G, Cai, M, Song, Y, Pan, J, Dong, J, Pan, R, Zhang, W, Shen, Z, Liu, Z, Gu, D, Liu, X, Zhang, Q, Flint, J, Kendler, KS (2014 a). The structure of the symptoms of major depression: exploratory and confirmatory factor analysis in depressed Han Chinese women. Psychological Medicine 44, 1391–1401.Google Scholar
Li, Y, Aggen, S, Shi, S, Gao, J, Tao, M, Zhang, K, Wang, X, Gao, C, Yang, L, Liu, Y, Li, K, Shi, J, Wang, G, Liu, L, Zhang, J, Du, B, Jiang, G, Shen, J, Zhang, Z, Liang, W, Sun, J, Hu, J, Liu, T, Miao, G, Meng, H, Hu, C, Huang, G, Li, G, Ha, B, Deng, H, Mei, Q, Zhong, H, Gao, S, Sang, H, Zhang, Y, Fang, X, Yu, F, Yang, D, Chen, Y, Hong, X, Wu, W, Chen, G, Cai, M, Song, Y, Pan, J, Dong, J, Pan, R, Zhang, W, Shen, Z, Liu, Z, Gu, D, Liu, X, Zhang, Q, Flint, J, Kendler, KS (2014 b). Subtypes of major depression: latent class analysis in depressed Han Chinese women. Psychological Medicine 44, 3275–3288.Google Scholar
Lux, V, Kendler, KS (2010). Deconstructing major depression: a validation study of the DSM-IV symptomatic criteria. Psychological Medicine 40, 1679–1690.Google Scholar
MacCallum, RC, Roznowski, M, Necowitz, LB (1992). Model modifications in covariance structure analysis: the problem of capitalization on chance. Psychological Bulletin 111, 490–504.Google Scholar
Markon, KE, Chmielewski, M, Miller, CJ (2011). The reliability and validity of discrete and continuous measures of psychopathology: a quantitative review. Psychological Bulletin 137, 856–879.Google Scholar
Markus, KA (2010). Questions about networks, measurement, and causation. The Behavioral and Brain Sciences 33, 164–165.Google Scholar
Melartin, T, Leskela, U, Rytsala, H, Sokero, P, Lestela-Mielonen, P, Isometsa, E (2004). Co-morbidity and stability of melancholic features in DSM-IV major depressive disorder. Psychological Medicine 34, 1443–1452.Google Scholar
Mezuk, B, Kendler, KS (2012). Examining variation in depressive symptoms over the life course: a latent class analysis. Psychological Medicine 42, 2037–2046.Google Scholar
Molenaar, PC (2010). Latent variable models are network models. The Behavioral and Brain Sciences 33, 166.Google Scholar
Murray, CJ, Lopez, AD (1996). Evidence-based health policy – lessons from the global burden of disease study. Science 274, 740–743.Google Scholar
Muthén, BO (1989). Dichotomous factor analysis of symptom data. Sociological Methods and Research 18, 19–65.Google Scholar
Olsen, LR, Jensen, DV, Noerholm, V, Martiny, K, Bech, P (2003). The internal and external validity of the major depression inventory in measuring severity of depressive states. Psychological Medicine 33, 351–356.Google Scholar
Oquendo, MA, Barrera, A, Ellis, SP, Li, S, Burke, AK, Grunebaum, M, Endicott, J, Mann, JJ (2004). Instability of symptoms in recurrent major depression: a prospective study. The American Journal of Psychiatry 161, 255–261.Google Scholar
Pornprasertmanit, S, Miller, P, Schoemann, K, Rosseel, Y (2013). sem-Tools: Useful tools for structural equation modeling (R package version 0.4-0). Available at http://CRAN.R-project.org/package=semToolsGoogle Scholar
Prisciandaro, JJ, Roberts, JE (2009). A comparison of the predictive abilities of dimensional and categorical models of unipolar depression in the National Comorbidity Survey. Psychological Medicine 39, 1087–1096.Google Scholar
R Core Team (2014). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna.Google Scholar
Reise, SP, Moore, TM, Haviland, MG (2010). Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment 92, 544–559.Google Scholar
Revelle, W (2013). psych: Procedures for Personality and Psychological Research. Version 1.3.10. Northwestern University, Evanston, Illinois, USA.Google Scholar
Rindskopf, D (1984). Structural equation models: empirical identification, Heywood cases, and related problems. Sociological Methods and Research 13, 109–119.Google Scholar
Rodgers, S, Grosse Holtforth, M, Muller, M, Hengartner, MP, Rossler, W, Ajdacic-Gross, V (2014). Symptom-based subtypes of depression and their psychosocial correlates: a person-centered approach focusing on the influence of sex. Journal of Affective Disorders 156, 92–103.Google Scholar
Ross, D (2010). Some mental disorders are based on networks, others on latent variables. The Behavioral and Brain Sciences 33, 166–167.Google Scholar
Rosseel, Y (2012). lavaan: an R package for structural equation modeling. Journal of Statistical Software 48, 1–36.Google Scholar
Shafer, AB (2006). Meta-analysis of the factor structures of four depression questionnaires: beck, CES-D, Hamilton, and Zung. Journal of Clinical Psychology 62, 123–146.Google Scholar
Simon, GE, Perlis, RH (2010). Personalized medicine for depression: can we match patients with treatments? The American Journal of Psychiatry 167, 1445–1455.Google Scholar
Slade, T, Andrews, G (2005). Latent structure of depression in a community sample: a taxometric analysis. Psychological Medicine 35, 489–497.Google Scholar
Steiger, JH (1980). Testing pattern hypotheses on correlation matrices: alternative statistics and some empirical results. Multivariate Behavioral Research 15, 335–352.Google Scholar
Studer, J, Baggio, S, Mohler-Kuo, M, Dermota, P, Gaume, J, Bertholet, N, Daeppen, JB, Gmel, G (2013 a). Examining non-response bias in substance use research–are late respondents proxies for non-respondents? Drug and Alcohol Dependence 132, 316–323.Google Scholar
Studer, J, Mohler-Kuo, M, Dermota, P, Gaume, J, Bertholet, N, Eidenbenz, C, Daeppen, JB, Gmel, G (2013 b). Need for informed consent in substance use studies – harm of bias? Journal of Studies on Alcohol and Drugs 74, 931–940.Google Scholar
van Borkulo, CD, Borsboom, D, Epskamp, S, Blanken, TF, Boschloo, L, Schoevers, RA, Waldorp, LJ (2014). A new method for constructing networks from binary data. Scientific Reports 4, 5918.Google Scholar
van Borkulo, C, Boschloo, L, Borsboom, D, Penninx, BWJH, Waldorp, LJ, Schoevers, RA (2015). Association of symptom network structure with the course of longitudinal depression. JAMA Psychiatry 72, 1219–1219.Google Scholar
van Loo, HM, de Jonge, P, Romeijn, JW, Kessler, RC, Schoevers, RA (2012). Data-driven subtypes of major depressive disorder: a systematic review. BMC Medicine 10, 156.CrossRefGoogle ScholarPubMed
World Health Organization (2012). Taschenführer zur ICD-10 Klassifikation psychischer Störungen nach dem Pocket Guide von J.E. Cooper [Pocket guide to the ICD-10 classification of mental and behavioural disorders with glossary and diagnostic criteria for research: ICD-10 DCR-10]. Verlag Hans Huber: Bern.Google Scholar
Zimmerman, M, Ellison, W, Young, D, Chelminski, I, Dalrymple, K (2015). How many different ways do patients meet the diagnostic criteria for major depressive disorder? Comprehensive Psychiatry 56, 29–34.Google Scholar