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Despite interest in psychosocial vulnerabilities to depression, little is known about reliable and valid individualized risk profiles that can be used to match individuals to evidence-based interventions for depression. This study investigated well-established cognitive and interpersonal vulnerabilities to depression among youth to discern an evidence-based risk classification approach which is being used in a personalized depression prevention randomized clinical trial. Data were drawn from a general community sample of adolescents (N = 467; ages 10–16, mean 13.14, SD = 1.62; 57% females) who were followed prospectively for 3 years. Youth completed measures of cognitive (negative cognitive style, dysfunctional attitudes, rumination) and interpersonal (support and conflict with peers and parents, excessive reassurance seeking, social competence, co-rumination) risks to depression, and then were followed longitudinally for onset of depression. Principal axis factor analyses showed that three latent factors--cognitive vulnerability, interpersonal support, and interpersonal conflict--optimally represented the structure of these risk factors. Clinically practical and meaningful cutoffs, based on tertile cut-off scores on cognitive and interpersonal risk measures, were used to categorize youth into relatively balanced high and low cognitive and interpersonal risk groups. These risk classification groups exhibited validity (AUC > 0.70) by predicting prospective onsets of depressive episodes at 18-months follow-ups. These findings demonstrate a reliable and valid approach to synthesize psychosocial vulnerabilities to depression, specifically cognitive and interpersonal risks. Results are discussed in terms of using these risk classifications profiles to test personalized prevention of depression during adolescence.
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Abela, J. R. Z., & Hankin, B. L. (2008). Cognitive vulnerability to depression in children and adolescents: A developmental psychopathology approach. In J. R. Z. Abela & B. L. Hankin (Eds.), Handbook of child and adolescent depression (pp. 35–78). New York: Guilford Press.
Abela, J. R. Z., & Sullivan, C. (2003). A test of Beck’s cognitive diathesis-stress theory of depression in early adolescents. Journal of Early Adolescence, 23(4), 384–404. CrossRef
Abela, J. R. Z., Vanderbilt, E., & Rochon, A. (2004). A test of the integration of the response styles and social support theories of depression in third and seventh grade children. Journal of Social and Clinical Psychology, 23(5), 653–674. CrossRef
Abramson, L. Y., Metalsky, G. I., & Alloy, L. B. (1989). Hopelessness depression: A theory-based subtype of depression. Psychological Review, 96(2), 358–372. CrossRef
Adams, P., Abela, J. R. Z., & Hankin, B. L. (2007). Factorial categorization of depression-related constructs in early adolescents. Journal of Cognitive Psychotherapy, 21(2), 123–139. CrossRef
Bray, B. C., Lanza, S. T., & Tan, X. M. (2015). Eliminating bias in classify-analyze approaches for latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal, 22, 1–11. CrossRef
Clarke, G. N., Hawkins, W., Murphy, M., Sheeber, L. B., Lewinsohn, P. M., & Seeley, J. R. (1995). Targeted prevention of unipolar depressive disorder in an at-risk sample of high school adolescents: A randomized trial of a group cognitive intervention. Journal of the American Academy of Child & Adolescent Psychiatry, 34(3), 312–321.
DeRubeis, R. J., Cohen, Z. D., Forand, N. R., Fournier, J. C., Gelfand, L. A., & Lorenzo-Luaces, L. (2014). The personalized advantage index: Translating research on prediction into individualized treatment recommendations. A demonstration. PLoS One, 9(1), 1–8. CrossRef
Flake, J. K., Pek, J., & Hehman, E. (2017). Construct validation in social and personality research: Current practice and recommendations. Social Psychological and Personality Science. https://doi.org/10.1177/1948550617693063
Hammen, C. L., & Shih, J. (2014). Depression and interpersonal processes. In I. H. Gotlib & C. L. Hammen (Eds.), Handbook of depression (3rd ed., pp. 277–295). New York: Guilford Press.
Hankin, B. L. (2012). Future directions in vulnerability to depression among youth: Integrating risk factors and processes across multiple levels of analysis. Journal of Clinical Child & Adolescent Psychology, 41(5), 695–718. CrossRef
Hankin, B. L., Lakdawalla, Z., Carter, I. L., Abela, J. R. Z., & Adams, P. (2007). Are neuroticism, cognitive vulnerabilities and self-esteem overlapping or distinct risks for depression? Evidence from exploratory and confirmatory factor analyses. Journal of Social and Clinical Psychology, 26(1), 29–63. CrossRef
Hankin, B. L., Young, J. F., Smolen, A., Jenness, J. L., Gulley, L. D., Technow, J. R., Barrocas Gottlieb, A., Cohen, J. R., & Oppenheimer, C. W. (2015). Depression from childhood in late adolescence: Influence of gender, development, genetic susceptibility, and peer stress. Journal of Abnormal Psychology, 124, 803–816. CrossRefPubMedPubMedCentral
Hankin, B. L., Snyder, H. R., & Gulley, L. D. (2016). Cognitive risks in developmental psychopathology. In D. Cicchetti (Ed.), Developmental psychopathology, maladaptation and psychopathology (3rd ed., pp. 312–385). Hoboken: Wiley.
Harter, S. (1985). Manual for the Self-perception Profile for Children: (Revision of the Perceived Competence Scale for Children). Denver: University of Denver.
Huibers, M. J. H., Cohen, Z. D., Lemmens, L. H. J. M., Arntz, A., Peeters, F. P. M. L., Cuijpers, P., & DeRubeis, R. J. (2015). Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the personalized advantage index approach. PLoS One, 10(11), 1–16. https://doi.org/10.1371/journal.pone.0140771 CrossRef
Kaufman, J., Birmaher, B., Brent, D., Rao, U. M. A., Flynn, C., Moreci, P., et al. (1997). Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): Initial reliability and validity data. Journal of the American Academy of Child & Adolescent Psychiatry, 36(7), 980–988. CrossRef
Kessler, R. C., van Loo, H. M., Wardenaar, K. J., Bossarte, R. M., Brenner, L. A., Cai, T., et al. (2016). Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Molecular Psychiatry, 21(10), 1366–1371. CrossRefPubMedPubMedCentral
Merry, S. N., Hetrick, S. E., Cox, G. R., Brudevold-Iversen, T., Bir, J. J., & McDowell, H. (2011). Psychological and educational interventions for preventing depression in children and adolescents. Evidence-Based Child Health: A Cochrane Review Journal, 7(5), 1409–1685. CrossRef
Niles, A. N., Loerinc, A. G., Krull, J. L., Roy-Byrne, P., Sullivan, G., Sherbourne, C. D., et al. (2017). Advancing personalized medicine: Application of a novel statistical method to identify treatment moderators in the coordinated anxiety learning and management study. Behavior Therapy, 48(4), 490–500. CrossRefPubMedPubMedCentral
Pintea, S., & Moldovan, R. (2009). The receiver-operating characteristic (ROC) analysis: Fundamentals and applications in clinical psychology. Journal of Cognitive and Behavioral Psychotherapies, 9(1), 49–66.
Rudolph, K. D., Lansford, J. E., & Rodkin, P. C. (2016). Interpersonal theories of developmental psychopathology. In D. Cicchetti (Ed.), Developmental psychopathology, maladaptation and psychopathology (3rd ed., pp. 312–385). Hoboken: Wiley.
World Health Organization. (2001). Mental health: A call for action by world health ministers. Geneva: World Health Organization.
Young, J. F., Mufron, L., & Schueler, C. M. (2016). Preventing adolescent depression: Interpersonal psychotherapy-adolescent skills training. NY: Oxford University Press. CrossRef
- Cognitive and Interpersonal Vulnerabilities to Adolescent Depression: Classification of Risk Profiles for a Personalized Prevention Approach
Benjamin L. Hankin
Jami F. Young
- Springer US