Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-25T07:11:29.198Z Has data issue: false hasContentIssue false
  • English
  • Français

Joint developmental trajectories of internalizing and externalizing disorders between childhood and adolescence

Published online by Cambridge University Press:  18 July 2016

Michel G. Nivard ,
Gitta H. Lubke ,
Conor V. Dolan ,
David M. Evans ,
Beate St. Pourcain ,
Marcus R. Munafò  and
Christel M. Middeldorp
Michel G. Nivard*
Affiliation:
Vrije Universiteit
Gitta H. Lubke
Affiliation:
Vrije Universiteit University of Notre Dame
Conor V. Dolan
Affiliation:
Vrije Universiteit
David M. Evans
Affiliation:
University of Queensland Diamantina Institute University of Bristol
Beate St. Pourcain
Affiliation:
Max Planck Institute for Psycholinguistics
Marcus R. Munafò
Affiliation:
University of Bristol
Christel M. Middeldorp
Affiliation:
Vrije Universiteit VU University Medical Centre
*
Address correspondence and reprint requests to: Michel G. Nivard, van der Boechorststraat 1, 1081BT, Amsterdam, The Netherlands; E-mail: m.g.nivard@vu.nl.
Get access Rights & Permissions [Opens in a new window]

Abstract

This study sought to identify trajectories of DSM-IV based internalizing (INT) and externalizing (EXT) problem scores across childhood and adolescence and to provide insight into the comorbidity by modeling the co-occurrence of INT and EXT trajectories. INT and EXT were measured repeatedly between age 7 and age 15 years in over 7,000 children and analyzed using growth mixture models. Five trajectories were identified for both INT and EXT, including very low, low, decreasing, and increasing trajectories. In addition, an adolescent onset trajectory was identified for INT and a stable high trajectory was identified for EXT. Multinomial regression showed that similar EXT and INT trajectories were associated. However, the adolescent onset INT trajectory was independent of high EXT trajectories, and persisting EXT was mainly associated with decreasing INT. Sex and early life environmental risk factors predicted EXT and, to a lesser extent, INT trajectories. The association between trajectories indicates the need to consider comorbidity when a child presents with INT or EXT disorders, particularly when symptoms start early. This is less necessary when INT symptoms start at adolescence. Future studies should investigate the etiology of co-occurring INT and EXT and the specific treatment needs of these severely affected children.

Type
Regular Articles
Information
Development and Psychopathology , Volume 29 , Issue 3 , August 2017 , pp. 919 - 928
Copyright
Copyright © Cambridge University Press 2016 

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

We are extremely grateful to all of the families who took part in this study, the midwives for their help in recruiting them, and the whole Avon Longitudinal Study of Parents and Children (ALSPAC) team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. The UK Medical Research Council and the Wellcome Trust (Grants 092731 and 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors, and Michel G. Nivard serves as guarantor for the contents of this paper. This project was supported by ZonMW 912-10-020 and ERC-230374. Support was also provided by Royal Netherlands Academy of Science Professor Award PAH/6635 (to M.G.N.) and ACTION (to G.H.L.). ACTION receives funding from the European Union Seventh Framework Program (FP7/2007-013) under Grant 602768.

References

Achenbach, T., & Rescorla, L. A. (2001). Manual for the ASEBA School-Age Forms and Profiles. Burlington, VT: University of Vermont, Research Center for Children, Youth, and Families.Google Scholar
Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40, 57–87.Google Scholar
Barker, E. D., Séguin, J. R., White, H. R., Bates, M. E., Lacourse, E., Carbonneau, R., et al. (2007). Developmental trajectories of male physical violence and theft: Relations to neurocognitive performance. Archives of General Psychiatry, 64, 592–599.CrossRefGoogle ScholarPubMed
Beauchaine, T. P., & McNulty, T. (2013). Comorbidities and continuities as ontogenic processes: Toward a developmental spectrum model of externalizing psychopathology. Development and Psychopathology, 25, 1505–1528.Google Scholar
Biederman, J., Mick, E., & Faraone, S. V. (1998). Depression in attention deficit hyperactivity disorder (ADHD) children: “True” depression or demoralization? Journal of Affective Disorders, 47, 113–122.Google Scholar
Boyd, A., Golding, J., Macleod, J., Lawlor, D. A., Fraser, A., Henderson, J., et al. (2012). Cohort profile: The “Children of the 90s”—The index offspring of the Avon Longitudinal Study of Parents and Children. International Journal of Epidemiology. Advance online publication.Google Scholar
Brezo, J., Barker, E. D., Paris, J., Hébert, M., Vitaro, F., Tremblay, R. E., et al. (2008). Childhood trajectories of anxiousness and disruptiveness as predictors of suicide attempts. Archives of Pediatrics and Adolescent Medicine, 162, 1015–1021.Google Scholar
Broeren, S., Muris, P., Diamantopoulou, S., & Baker, J. R. (2013). The course of childhood anxiety symptoms: Developmental trajectories and child-related factors in normal children. Journal of Abnormal Child Psychology, 41, 81–95.CrossRefGoogle ScholarPubMed
Brown, R. T., Borden, K. A., Clingerman, S. R., & Jenkins, P. (1988). Depression in attention deficit-disordered and normal children and their parents. Child Psychiatry and Human Development, 18, 119–132.Google Scholar
Chase, R. M., & Eyberg, S. M. (2008). Clinical presentation and treatment outcome for children with comorbid externalizing and internalizing symptoms. Journal of Anxiety Disorders, 22, 273–282.Google Scholar
Copeland, W. E., Shanahan, L., Costello, E. J., & Angold, A. (2009). Childhood and adolescent psychiatric disorders as predictors of young adult disorders. Archives of General Psychiatry, 66, 764–772.CrossRefGoogle ScholarPubMed
Cosgrove, V. E., Rhee, S. H., Gelhorn, H. L., Boeldt, D., Corley, R. C., Ehringer, M. A., et al. (2011). Structure and etiology of co-occurring internalizing and externalizing disorders in adolescents. Journal of Abnormal Child Psychology, 39, 109–123.Google Scholar
Costello, E. J., Copeland, W., & Angold, A. (2011). Trends in psychopathology across the adolescent years: What changes when children become adolescents, and when adolescents become adults? Journal of Child Psychology and Psychiatry, 52, 1015–1025.Google Scholar
Costello, E. J., Compton, S. N., Keeler, G., & Angold, A. (2003). Relationships between poverty and psychopathology: A natural experiment. Journal of the American Medical Association, 290, 2023–2029.Google Scholar
Dekker, M. C., Ferdinand, R. F., Van Lang, N. D., Bongers, I. L., van der Ende, J., & Verhulst, F. C. (2007). Developmental trajectories of depressive symptoms from early childhood to late adolescence: Gender differences and adult outcome. Journal of Child Psychology and Psychiatry, 48, 657–666.CrossRefGoogle ScholarPubMed
Dolan, C. V., Geels, L., Vink, J. M., van Beijsterveldt, C. E. M., Neale, M. C., Bartels, M., et al. (2015). Testing causal effects of maternal smoking during pregnancy on offspring's externalizing and internalizing behavior. Behavior Genetics. Advance online publication.Google Scholar
Ekstrom, J. (2011). A generalized definition of the polychoric correlation coefficient. Unpublished manuscript, University of California, Los Angeles, Department of Statistics.Google Scholar
Fanti, K. A., & Henrich, C. C. (2010). Trajectories of pure and co-occurring internalizing and externalizing problems from age 2 to age 12: Findings from the National Institute of Child Health and Human Development Study of Early Child Care. Developmental Psychology, 46, 1159–1175.Google Scholar
Golding, J., Pembrey, M., & Jones, R. (2001). ALSPAC—The Avon Longitudinal Study of Parents and Children: I. Study methodology. Paediatric and Perinatal Epidemiology , 15, 74–87.CrossRefGoogle ScholarPubMed
Goodman, A., Heiervang, E., Collishaw, S., & Goodman, R. (2011). The DAWBA bands as an ordered-categorical measure of child mental health: Description and validation in British and Norwegian samples. Social Psychiatry and Psychiatric Epidemiology, 46, 521–532.Google Scholar
Goodman, R., Ford, T., Richards, H., Gatward, R., & Meltzer, H. (2000). The development and well being assessment: Description and initial validation of an integrated assessment of child and adolescent psychopathology. Journal of Child Psychology and Psychiatry, 41, 645–655.Google Scholar
Granic, I. (2014). The role of anxiety in the development, maintenance, and treatment of childhood aggression. Development and Psychopathology, 26, 1515–1530.Google Scholar
Groen-Blokhuis, M. M., Middeldorp, C. M., van Beijsterveldt, C. E., & Boomsma, D. I. (2011). Evidence for a causal association of low birth weight and attention problems. Journal of the American Academy of Child & Adolescent Psychiatry, 50, 1247–1254.Google Scholar
Hack, M., Youngstrom, E. A., Cartar, L., Schluchter, M., Taylor, H. G., Flannery, D., et al. (2004). Behavioral outcomes and evidence of psychopathology among very low birth weight infants at age 20 years. Pediatrics, 114, 932–940.Google Scholar
Haltigan, J. D., Roisman, G. I., Susman, E. J., Barnett-Walker, K., & Monahan, K. C. (2011). Elevated trajectories of externalizing problems are associated with lower awakening cortisol levels in midadolescence. Developmental Psychology, 47, 472–478.Google Scholar
Hofstra, M. B., Van der Ende, J., & Verhulst, F. C. (2000). Continuity and change of psychopathology from childhood into adulthood: A 14-year follow-up study. Journal of the American Academy of Child & Adolescent Psychiatry, 39, 850–858.Google Scholar
Hofstra, M. B., Van der Ende, J., & Verhulst, F. C. (2002). Child and adolescent problems predict DSM-IV disorders in adulthood: A 14-year follow-up of a Dutch epidemiological sample. Journal of the American Academy of Child & Adolescent Psychiatry, 41, 182–189.Google Scholar
Jung, T., & Wickrama, K. A. S. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2, 302–317.Google Scholar
Kendall, P. C., Brady, E. U., & Verduin, T. L. (2001). Comorbidity in childhood anxiety disorders and treatment outcome. Journal of the American Academy of Child & Adolescent Psychiatry, 40, 787–794.Google Scholar
Larsson, H., Dilshad, R., Lichtenstein, P., & Barker, E. D. (2011). Developmental trajectories of DSM IV symptoms of attention deficit/hyperactivity disorder: Genetic effects, family risk and associated psychopathology. Journal of Child Psychology and Psychiatry, 52, 954–963.CrossRefGoogle ScholarPubMed
Letcher, P., Smart, D., Sanson, A., & Toumbourou, J. W. (2009). Psychosocial precursors and correlates of differing internalizing trajectories from 3 to 15 years. Social Development, 18, 618–646.Google Scholar
Linnet, K. M., Dalsgaard, S., Obel, C., Wisborg, K., Henriksen, T. B., Rodriguez, A., et al. (2003). Maternal lifestyle factors in pregnancy risk of attention deficit hyperactivity disorder and associated behaviors: Review of the current evidence. American Journal of Psychiatry, 160, 1028–1040.Google Scholar
Little, R. J., & Rubin, D. B. (2014). Statistical analysis with missing data. Hoboken, NJ: Wiley.Google Scholar
Lubke, G. (2012). Old issues in a new jacket: Power and validation in the context of mixture modeling. Measurement: Interdisciplinary Research and Perspectives, 10, 212–216.Google Scholar
Lubke, G. H., Miller, P. J., Verhulst, B., Bartels, M., van Beijsterveldt, T., Willemsen, G., et al. (2015). A powerful phenotype for gene-finding studies derived from trajectory analyses of symptoms of anxiety and depression between age seven and 18. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. Advance online publication.Google ScholarPubMed
McAdams, T. A., Neiderhiser, J. M., Rijsdijk, F. V., Narusyte, J., Lichtenstein, P., & Eley, T. C. (2014). Accounting for genetic and environmental confounds in associations between parent and child characteristics: A systematic review of children-of-twins studies. Psychological Bulletin, 140, 1138–1173.CrossRefGoogle ScholarPubMed
McGue, M., Osler, M., & Christensen, K. (2010). Causal inference and observational research the utility of twins. Perspectives on Psychological Science, 5, 546–556.Google Scholar
Muthen, B., & Muthen, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882–891.Google Scholar
Muthén, L. K., & Muthén, B. O. (2007). Mplus users guide (5th ed.). Los Angeles: Author.Google Scholar
Nagin, D. S. (1999). Analyzing developmental trajectories: A semiparametric, group-based approach. Psychological Methods, 4, 139–157.Google Scholar
Newman, D., Moffitt, T., Caspi, A., & Silva, P. (1998). Comorbid mental disorders: Implications for treatment and sample selection. Journal of Abnormal Psychology, 107, 305–311.CrossRefGoogle ScholarPubMed
Ormel, J., Raven, D., van Oort, F., Hartman, C. A., Reijneveld, S. A., Veenstra, R., et al. (2014). Mental health in Dutch adolescents: A TRAILS report on prevalence, severity, age of onset, continuity and co-morbidity of DSM disorders. Psychological Medicine, 27, 345–360.Google Scholar
Petersen, I. T., Bates, J. E., Dodge, K. A., Lansford, J. E., & Pettit, G. S. (2015). Describing and predicting developmental profiles of externalizing problems from childhood to adulthood. Development and Psychopathology, 27, 791–818.Google Scholar
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.Google Scholar
Stringaris, A., Lewis, G., & Maughan, B. (2014). Developmental pathways from childhood conduct problems to early adult depression: Findings from the ALSPAC cohort. British Journal of Psychiatry, 205, 17–23.CrossRefGoogle ScholarPubMed
Toumbourou, J. W., Williams, I., Letcher, P., Sanson, A., & Smart, D. (2011). Developmental trajectories of internalising behaviour in the prediction of adolescent depressive symptoms. Australian Journal of Psychology, 63, 214–223.Google Scholar
van Dongen, J., Slagboom, P. E., Draisma, H. H., Martin, N. G., & Boomsma, D. I. (2012). The continuing value of twin studies in the omics era. Nature Reviews Genetics, 13, 640–653.Google Scholar
Van Lier, P. A., Der Ende, J. V., Koot, H. M., & Verhulst, F. C. (2007). Which better predicts conduct problems? The relationship of trajectories of conduct problems with ODD and ADHD symptoms from childhood into adolescence. Journal of Child Psychology and Psychiatry, 48, 601–608.Google Scholar
Weissman, M. M., Warner, V., Wickramaratne, P. J., & Kandel, D. B. (1999). Maternal smoking during pregnancy and psychopathology in offspring followed to adulthood. Journal of the American Academy of Child & Adolescent Psychiatry, 38, 892–899.CrossRefGoogle ScholarPubMed
Wiggins, J. L., Mitchell, C., Hyde, L. W., & Monk, C. S. (2015). Identifying early pathways of risk and resilience: The codevelopment of internalizing and externalizing symptoms and the role of harsh parenting. Development and Psychopathology. Advance online publication.Google Scholar
Wolke, D., Waylen, A., Samara, M., Steer, C., Goodman, R., Ford, T., et al. (2009). Selective drop-out in longitudinal studies and non-biased prediction of behaviour disorders. British Journal of Psychiatry, 195, 249–256.Google Scholar