Hostname: page-component-7c8c6479df-ph5wq Total loading time: 0 Render date: 2024-03-28T09:51:25.130Z Has data issue: false hasContentIssue false

Psychopathology and adversities from early- to late-adolescence: a general population follow-up study with the CBCL DSM-Oriented Scales

Published online by Cambridge University Press:  11 April 2012

M. Nobile*
Affiliation:
Department of Child Psychiatry, ‘Eugenio Medea’ Scientific Institute, Bosisio Parini, Italy
P. Colombo
Affiliation:
Department of Child Psychiatry, ‘Eugenio Medea’ Scientific Institute, Bosisio Parini, Italy
M. Bellina
Affiliation:
Department of Child Psychiatry, ‘Eugenio Medea’ Scientific Institute, Bosisio Parini, Italy
M. Molteni
Affiliation:
Department of Child Psychiatry, ‘Eugenio Medea’ Scientific Institute, Bosisio Parini, Italy
D. Simone
Affiliation:
Department of Child Psychiatry, ‘Eugenio Medea’ Scientific Institute, Bosisio Parini, Italy
F. Nardocci
Affiliation:
Department of Child Psychiatry, Azienda USL, Ravenna, Italy
O. Carlet
Affiliation:
Department of Child Psychiatry, ‘Eugenio Medea’ Scientific Institute, Conegliano, Italy
M. Battaglia
Affiliation:
Department of Child Psychiatry, ‘Eugenio Medea’ Scientific Institute, Bosisio Parini, Italy Centre for the Study of Behavioural Plasticity, Vita-Salute San Raffaele University, Milan, Italy Laval University & Institut universitaire en santé mentale de Québec, Canada
*
*Address for correspondence: Maria Nobile, M.D., Department of Child Psychiatry, ‘Eugenio Medea’ Scientific Institute, Via Don Luigi Monza 20, 23842 Bosisio Parini (LC), Italy. (Email: maria.nobile@bp.lnf.it)

Abstract

Aims.

Adolescence is a critical transition phase between childhood and adulthood, when the burden of mental disorder may still be prevented. The aim of this study was to evaluate the continuity and discontinuity of behavioural problems in adolescence while taking into account the multiple co-variation of psychopathological traits and the complex role of recent stressful life events (SLEs).

Methods.

This is a 5-year follow-up investigation of emotional and behavioural problems assessed by the newly developed Child Behavior Checklist (CBCL) DSM-Oriented Scales (DOSs) in 420 general population subjects aged 15–19 years.

Results.

The DOSs showed good stability, even when multiple co-variation was taken into account. Longitudinal data showed that homotypic evolution of psychopathology was to be expected in the first place. Equifinality and multifinality were also found. Oppositional Defiant Problems emerged to be polyvalent predictors of both internalizing and externalizing problems. Furthermore, Oppositional Defiant Problems predicted more SLEs, which in turn predicted more Depression, Anxiety and Oppositional Defiant Problems. Mediational analyses confirmed the role of SLEs in partially accounting for the continuity of Oppositional Defiant Problems and for the heterotypic progression towards Affective Problems.

Conclusions.

These data underscore early adolescence behavioural problems as an important focus for primary and secondary intervention.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2012

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

Achenbach, TM, Rescorla, LA (2001). Manual for ASEBA School-Age Forms and Profiles. University of Vermont, Research Center for Children, Youth, & Families: Burlington, VT.Google Scholar
Achenbach, TM, Howell, CT, McConaughy, SH, Stanger, C (1995). Six-year predictors of problems in a national sample of children and youth: I. Cross-informant syndromes. Journal of the American Academy of Child and Adolescent Psychiatry 34, 336347.Google Scholar
Amone-P'Olak, K, Ormel, J, Huisman, M, Verhulst, FC, Oldehinkel, AJ, Burger, H (2009). Life stressors as mediators of the relation between socioeconomic position and mental health problems in early adolescence: the TRAILS study. Journal of the American Academy of Child and Adolescent Psychiatry 48, 10311038.Google Scholar
Angold, A, Costello, EJ, Erkanli, A (1999). Comorbidity. Journal of Child Psychology and Psychiatry, and Allied Disciplines 40, 5787.Google Scholar
Baron, RM, Kenny, DA (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51, 11731182.CrossRefGoogle ScholarPubMed
Bot, M, de Leeuw den Bouter, BJE, Adriaanse, MC (2011). Prevalence of psychosocial problems in Dutch children aged 8–12 years and its association with risk factors and quality of life. Epidemiology and Psychiatric Sciences 20, 357365.Google Scholar
Boylan, K, Georgiades, K, Szatmari, P (2010). The longitudinal association between oppositional and depressive symptoms across childhood. Journal of the American Academy of Child and Adolescent Psychiatry 49, 152161.Google Scholar
Brown, GW, Bifulco, A, Harris, TO (1987). Life events, vulnerability and onset of depression: some refinements. British Journal of Psychiatry 150, 3042.CrossRefGoogle ScholarPubMed
Caspi, A, Moffitt, TE, Newman, DL, Silva, PA (1996). Behavioral observations at age 3 years predict adult psychiatric disorders. Longitudinal evidence from a birth cohort. Archives of General Psychiatry 53, 10331039.Google Scholar
Cicchetti, D (1990). A historical perspective on the discipline of developmental psychopathology. In Risk and Protective Factors in the Development of Psychopathology (ed. Rolf, J. E., Masten, A. S., Cicchetti, D., Nuechterlein, K. H. and Weintraub, S.), pp. 228. Cambridge University Press: New York.Google Scholar
Cohen, J (1988). Set correlation and contingency tables. Applied Psychological Measurement 12, 425434.CrossRefGoogle Scholar
Copeland, WE, Shanahan, L, Costello, EJ, Angold, A (2009 a). Childhood and adolescent psychiatric disorders as predictors of young adult disorders. Archives of General Psychiatry 66, 764772.Google Scholar
Copeland, WE, Shanahan, L, Costello, EJ, Angold, A (2009 b). Configurations of common childhood psychosocial risk factors. Journal of Child Psychology and Psychiatry 50, 451459.Google Scholar
Costello, EJ, Angold, A, Burns, BJ, Stangl, DK, Tweed, DL, Erkanli, A, Worthman, CM (1996). The Great Smoky Mountains Study of Youth. Goals, design, methods, and the prevalence of DSM-III-R disorders. Archives of General Psychiatry 53, 11291136.CrossRefGoogle ScholarPubMed
de Girolamo, G, Dagani, J, Purcell, R, Cocchi, A, McGorry, PD (2012). Age of onset of mental disorders and use of mental health services: needs, opportunities and obstacles. Epidemiology and Psychiatric Science 21, 4757.Google Scholar
Essex, MJ, Kraemer, HC, Armstrong, JM, Boyce, WT, Goldsmith, HH, Klein, MH, Woodward, H, Kupfer, DJ (2006). Exploring risk factors for the emergence of children's mental health problems. Archives of General Psychiatry 63, 12461256.Google Scholar
Ferdinand, RF (2008). Validity of the CBCL/YSR DSM-IV scales anxiety problems and affective problems. Journal of Anxiety Disorders 22, 126134.Google Scholar
Ford, T, Goodman, R, Meltzer, H (2003). The British Child and Adolescent Mental Health Survey 1999: the prevalence of DSM-IV disorders. Journal of the American Academy of Child and Adolescent Psychiatry 42, 12031211.Google Scholar
Frigerio, A, Vanzin, L, Pastore, V, Nobile, M, Giorda, R, Marino, C, Molteni, M, Rucci, P, Ammaniti, M, Lucarelli, L, Lenti, C, Walder, M, Martinuzzi, A, Carlet, O, Muratori, F, Milone, A, Zuddas, A, Cavolina, P, Nardocci, F, Tullini, A, Morosini, P, Polidori, G, De Girolamo, G (2006). The Italian preadolescent mental health project (PrISMA): rationale and methods. International Journal of Methods in Psychiatric Research 15, 2235.Google Scholar
Frigerio, A, Rucci, P, Goodman, R, Ammaniti, M, Carlet, O, Cavolina, P, De Girolamo, G, Lenti, C, Lucarelli, L, Mani, E, Martinuzzi, A, Micali, N, Milone, A, Morosini, P, Muratori, F, Nardocci, F, Pastore, V, Polidori, G, Tullini, A, Vanzin, L, Villa, L, Walder, M, Zuddas, A, Molteni, M (2009). Prevalence and correlates of mental disorders among adolescents in Italy: the PrISMA study. European Child and Adolescent Psychiatry 18, 217226.CrossRefGoogle ScholarPubMed
Grant, KE, Compas, BE, Thurm, AE, McMahon, SD, Gipson, PY, Campbell, AJ, Krochock, K, Westerholm, RI (2006). Stressors and child and adolescent psychopathology: evidence of moderating and mediating effects. Clinical Psychology Review 26, 257283.Google Scholar
Haggerty, RL, Sherrod, LR, Garmezy, N, Rutter, M (1994) Stress, Risk and Resilience in Children and Adolescents: Process, Mechanisms and Interventions. Cambridge University Press: London.Google Scholar
Hammen, C (2006). Stress generation in depression: reflections on origins, research, and future directions. Journal of Clinical Psychology 62, 10651082.Google Scholar
Hatch, SL, Harvey, SB, Maughan, B (2010). A developmental–contextual approach to understanding mental health and well-being in early adulthood. Social Science and Medicine 70, 261268.CrossRefGoogle ScholarPubMed
Hofstra, MB, Van der Ende, J, Verhulst, FC (2000). Continuity and change of psychopathology from childhood into adulthood: a 14-year follow-up study. Journal of the American Academy of Child and Adolescent Psychiatry 39, 850858.Google Scholar
Hofstra, MB, van der Ende, J, Verhulst, FC (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 and Adolescent Psychiatry 41, 182189.Google Scholar
Kraemer, HC, Blasey, CM (2004). Centring in regression analyses: a strategy to prevent errors in statistical inference. International Journal of Methods in Psychiatric Research 13, 141151.CrossRefGoogle ScholarPubMed
Krol, NP, De Bruyn, EE, Coolen, JC, van Aarle, EJ (2006). From CBCL to DSM: a comparison of two methods to screen for DSM-IV diagnoses using CBCL data. Journal of Clinical Child and Adolescent 35, 127135.Google Scholar
Lengua, LJ, Sadowski, CA, Friedrich, WN, Fisher, J (2001). Rationally and empirically derived dimensions of children's symptomatology: expert ratings and confirmatory factor analyses of the CBCL. Journal of Consulting and Clinical Psychology 69, 683698.Google Scholar
Mash, EJ, Barkley, RA (1996). Child Psychopathology. Guilford Press: New York.Google Scholar
Meltzer, H, Gatward, R, Corbin, T, Goodman, R, Ford, T (2003). Persistence, Onset, Risk Factors and Outcomes of Childhood Mental Disorders. TSO: London.Google Scholar
Nobile, M, Giorda, R, Marino, C, Carlet, O, Pastore, V, Vanzin, L, Bellina, M, Molteni, M, Battaglia, M (2007). Socioeconomic status mediates the genetic contribution of the dopamine receptor D4 and serotonin transporter linked promoter region repeat polymorphisms to externalization in preadolescence. Development and Psychopathology 19, 11471160.Google Scholar
Nobile, M, Rusconi, M, Bellina, M, Marino, C, Giorda, R, Carlet, O, Vanzin, L, Molteni, M, Battaglia, M (2009). The influence of family structure, the TPH2 G-703T and the 5-HTTLPR serotonergic genes upon affective problems in children aged 10–14 years. Journal of Child Psychology and Psychiatry, and Allied Disciplines 50, 317325.Google Scholar
Nobile, M, Rusconi, M, Bellina, M, Marino, C, Giorda, R, Carlet, O, Vanzin, L, Molteni, M, Battaglia, M (2010). COMT Val158Met polymorphism and socioeconomic status interact to predict attention deficit/hyperactivity problems in children aged 10–14. European Child and Adolescent Psychiatry 19, 549557.Google Scholar
Plomin, R, DeFries, JC, Loehlin, JC (1977). Genotype–environment interaction and correlation in the analysis of human behavior. Psychological Bulletin 84, 309322.Google Scholar
Preacher, KJ, Hayes, AF (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods 40, 879891.Google Scholar
Rowe, R, Maughan, B, Eley, TC (2006). Links between antisocial behavior and depressed mood: the role of life events and attributional style. Journal of Abnormal Child Psychology 34, 293302.Google Scholar
Roza, SJ, Hofstra, MB, van der Ende, J, Verhulst, FC (2003). Stable prediction of mood and anxiety disorders based on behavioral and emotional problems in childhood: a 14-year follow-up during childhood, adolescence, and young adulthood. American Journal of Psychiatry 160, 21162121.Google Scholar
Rudolph, KD, Flynn, M, Abaied, JL, Groot, A, Thompson, R (2009). Why is past depression the best predictor of future depression? Stress generation as a mechanism of depression continuity in girls. Journal of Clinical Child and Adolescent Psychology 38, 473485.Google Scholar
Rutter, M, Moffitt, TE, Caspi, A (2006). Gene–environment interplay and psychopathology: multiple varieties but real effects. Journal of Child Psychology and Psychiatry, and Allied Disciplines 47, 226261.Google Scholar
Spatola, CA, Fagnani, C, Pesenti-Gritti, P, Ogliari, A, Stazi, MA, Battaglia, M (2007). A general population twin study of the CBCL/6–18 DSM-oriented scales. Journal of the American Academy of Child and Adolescent Psychiatry 46, 619627.Google Scholar
Spencer, TJ, Biederman, J, Mick, E (2007). Attention-deficit/hyperactivity disorder: diagnosis, lifespan, comorbidities, and neurobiology. Journal of Pediatric Psychology 32, 631642.CrossRefGoogle ScholarPubMed
Stanger, C, McConaughy, SH, Achenbach, TM (1992). Three-year course of behavioral/emotional problems in a national sample of 4- to 16-year-olds: II. Predictors of syndromes. Journal of the American Academy of Child and Adolescent Psychiatry 31, 941950.Google Scholar
Verhulst, FC, Van der Ende, J (1992). Six-year stability of parent-reported problem behavior in an epidemiological sample. Journal of Abnormal Child Psychology 20, 695–610.Google Scholar
Verhulst, FC, Van der Ende, J (1995). The eight-year stability of problem behavior in an epidemiologic sample. Pediatric Research 38, 612617.Google Scholar