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Trajectories and Predictors of Children's Early-Starting Conduct Problems: Child, Family, Genetic, and Intervention Effects

Published online by Cambridge University Press:  02 August 2019

Daniel S. Shaw ,
Chardée A. Galán ,
Kathryn Lemery-Chalfant ,
Thomas J. Dishion ,
Kit K. Elam ,
Melvin N. Wilson  and
Frances Gardner
Daniel S. Shaw*
Affiliation:
Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
Chardée A. Galán
Affiliation:
Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
Kathryn Lemery-Chalfant
Affiliation:
Department of Psychology, Arizona State University, Tempe, AZ, USA
Thomas J. Dishion
Affiliation:
Department of Psychology, Arizona State University, Tempe, AZ, USA
Kit K. Elam
Affiliation:
Department of Psychology, Arizona State University, Tempe, AZ, USA
Melvin N. Wilson
Affiliation:
Department of Psychology, University of Virginia, Charlottesville, VA, USA
Frances Gardner
Affiliation:
Department of Social Policy & Intervention, University of Oxford, Oxford, UK
*
Author for Correspondence: Daniel Shaw, Department of Psychology, University of Pittsburgh, 210 S. Bouquet Street, 4101 Sennott Square, Pittsburgh, PA, USA 15260-0001. E-mail: casey@pitt.edu
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Abstract

Several research teams have previously traced patterns of emerging conduct problems (CP) from early or middle childhood. The current study expands on this previous literature by using a genetically-informed, experimental, and long-term longitudinal design to examine trajectories of early-emerging conduct problems and early childhood discriminators of such patterns from the toddler period to adolescence. The sample represents a cohort of 731 toddlers and diverse families recruited based on socioeconomic, child, and family risk, varying in urbanicity and assessed on nine occasions between ages 2 and 14. In addition to examining child, family, and community level discriminators of patterns of emerging conduct problems, we were able to account for genetic susceptibility using polygenic scores and the study's experimental design to determine whether random assignment to the Family Check-Up (FCU) discriminated trajectory groups. In addition, in accord with differential susceptibility theory, we tested whether the effects of the FCU were stronger for those children with higher genetic susceptibility. Results augmented previous findings documenting the influence of child (inhibitory control [IC], gender) and family (harsh parenting, parental depression, and educational attainment) risk. In addition, children in the FCU were overrepresented in the persistent low versus persistent high CP group, but such direct effects were qualified by an interaction between the intervention and genetic susceptibility that was consistent with differential susceptibility. Implications are discussed for early identification and specifically, prevention efforts addressing early child and family risk.

Keywords

behavioral geneticsconduct disorderearly interventionmaternal depression
Type
Special Issue Articles
Information
Development and Psychopathology , Volume 31 , Special Issue 5: Honoring the Contributions and Legacy of Thomas Dishion , December 2019 , pp. 1911 - 1921
Copyright
Copyright © Cambridge University Press 2019 

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References

Achenbach, T. M., & Rescorla, L. A. (2001). Manual for the ASEBA School-Age Forms & Profiles. Burlington, VT: ASEBA.Google Scholar
Aguilar, B., Sroufe, L., Egeland, B., & Carlson, E. (2000). Distinguishing the early-onset/persistent and adolescence-onset antisocial behavior types: From birth to 16 years. Development and Psychopathology, 12, 109–132.Google Scholar
Andruff, H., Carraro, N., Thompson, A., & Gaudreau, P. (2009). Latent class growth modelling: A tutorial. Tutorials in Quantitative Methods for Psychology, 5(1), 11–24.Google Scholar
Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: differential susceptibility to environmental influences. Psychological Bulletin, 135(6), 885–908.Google Scholar
Bradley, R. H., Corwyn, R. F., McAdoo, H. P., & Garcia-Coll, C. (2001). The home environments of children in the United States part 1: Variations by age, ethnicity, and poverty status. Child Development, 72, 1844–1867.Google Scholar
Brame, B., Nagin, D.S., & Tremblay, R.E. (2001). Developmental trajectories of physical aggression from school entry to late adolescence. Journal of Child Psychology and Psychiatry, 42, 503–512.Google Scholar
Brennan, L., & Shaw, D. S. (2013). Revisiting data related to the age of onset and developmental course of female conduct problems. Clinical Child and Family Psychology Review, 16, 35–58, PMC3684009. http://dx.doi.org/10.1007/s10567-012-0125-8Google Scholar
Broidy, L. M., Nagin, D. S., Tremblay, R. E., Bates, J. E., Brame, R., Dodge, K. A., et al. (2003). Developmental trajectories of childhood disruptive behaviors and adolescent delinquency: A six-site, cross-national study. Developmental Psychology, 39, 222–245.Google Scholar
Brooks-Gunn, J., Duncan, G.J., & Aber, J.L. (1997). Lessons learned in future directions for research on the neighborhoods in which children live. In Brooks-Gunn, J., Duncan, G. J., Aber, J. L. (Eds.), Neighborhood poverty: Vol 1.Context and consequences for children (pp. 279–297). New York: Russell Sage Foundation.Google Scholar
Burt, S. A., & Mikolajewski, A. J. (2008). Preliminary evidence that specific candidate genes are associated with adolescent-onset antisocial behavior. Aggressive Behavior, 34(4), 437–445.Google Scholar
Campbell, S. B., Pierce, E. W., Moore, G., Marakovitz, S., and Newby, K. (1996). Boys’ externalizing problems at elementary school: Pathways from early behavior problems, maternal control, and family stress. Development and Psychopathology, 8, 701–720.Google Scholar
Caruthers, A. S., Van Ryzin, M. J., & Dishion, T. J. (2014). Preventing high-risk sexual behavior in early adulthood with family interventions in adolescence: Outcomes and developmental processes. Prevention Science, 15, 59–69.Google Scholar
Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., Craig, I. W. et al. (2002). Role of genotype in the cycle of violence in maltreated children. Science, 297, 851–854.Google Scholar
Chang, H., Shaw, D. S., Dishion, T. J., & Wilson, M. N. (2014). Direct and indirect effects of the Family Check-Up on self-regulation from toddlerhood to early school-age. Journal of Abnormal Child Psychology, 42, 1117–1128.Google Scholar
Connell, A., Bullock, B. M., Dishion, T. J., Shaw, D., Wilson, M., & Gardner, F. (2008). Family intervention effects on co-occurring behavior and emotional problems in early childhood: A latent transition analysis approach. Journal of Abnormal Child Psychology, 36, 1211–1225.Google Scholar
Connell, A., Klostermann, S., & Dishion, T. J. (2011). Family Check Up effects on adolescent arrest trajectories: Variation by developmental subtype. Journal of Research on Adolescence, 22, 367–380.Google Scholar
Dishion, T. J., Brennan, L. M., McEachern, A., Shaw, D. S., Wilson, M. N., & Weaver, C. M. (2014). The prevention of early onset problem behavior through periodic family check ups in early childhood: A health maintenance model with indigent, high risk families. Journal of Abnormal Child Psychology, 42, 343–354.Google Scholar
Dishion, T. J, & Kavanagh, K. (2003). Intervening in adolescent problem behavior: A family-centered approach. New York: Guilford Press.Google Scholar
Dishion, T. J., Shaw, D., Connell, A., Gardner, F., Weaver, C., & Wilson, M. (2008). The family check-up with high-risk indigent families: Preventing problem behavior by increasing parents’ positive behavior support in early childhood. Child Development, 79(5), 1395–1414.Google Scholar
Dishion, T. J., & Stormshak, E. (2007). Intervening in children's lives: An ecological, family-centered approach to mental health care. Washington, DC: American Psychological Association.Google Scholar
Elam, K. K., Chassin, L., & Pandika, D. (2018). Polygenic risk, family cohesion, and adolescent aggression in Mexican-American and European-Am4erican families: Developmental pathways to alcohol use. Development and Psychopathology. Epub ahead of print.Google Scholar
Elliott, D. S., Huizinga, D., & Ageton, S. S. (1985). Explaining delinquency and drug use. Thousand Oaks, CA: Sage.Google Scholar
Erickson, M.F., Sroufe, L.A., & Egeland, B. (1985). The relationship between quality of attachment and behavior problems in preschool in a high-risk sample. In Bretherton, I. & Waters, E. (Eds.), Growing points of attachment theory and research. Monographs of the Society for Research in Child Development, 50, Nos. 1–2, 147–167.Google Scholar
Eusden, J., Lewis, C. M., & O'Reilly, P. F. (2015). PRSice: Polygenic risk score software. Bioinformatics, 31(9), 1466–1468.Google Scholar
Fergusson, D.M. & Woodward, L.J. (1999). Maternal age and educational and psychosocial outcomes in early adulthood. The Journal of Child Psychology and Psychiatry, 40, 479–489.Google Scholar
Forbes, E. E., Brown, S. M., Kimak, M., Ferrell, R. E., Manuck, S. B., & Hariri, A. R. (2009). Genetic variation in components of dopamine neurotransmission impacts ventral striatal reactivity associated with impulsivity. Molecular Psychiatry, 14(1), 60–70.Google Scholar
Galán, C.A., Shaw, D.S., & Belsky, J. (in preparation). Taking stock of GxE Interaction involving dopaminergic Genes and alternative conceptual models: A meta-analytic investigation.Google Scholar
Gardner, F, Shaw, D. S, Dishion, T. J, Supplee, L. A, & Burton, J. (2007). Randomized prevention trial for early conduct problems: Effects on proactive parenting and links to toddler disruptive behavior. Journal of Family Psychology, 21, 398–406.Google Scholar
Gilliam, M., Forbes, E. E., Gianaros, P. J., Erickson, K. I., Brennan, L. M., & Shaw, D. S. (2015). Maternal depression in childhood and aggression in young adulthood: evidence for mediation by offspring amygdala–hippocampal volume ratio. Journal of Child Psychology and Psychiatry, 56, 1083–1091.Google Scholar
Goodman, S.H., Rouse, M.H., Connell, A., Broth, M.R., Hall, C.M., & Heyward, D. (2011). Maternal depression and child psychopathology: A meta-analytic review. Journal of Clinical Child and Family Psychology Review, 14, 1–27.Google Scholar
Greenberg, M.T., & Speltz, M.L. (1988). Contributions of attachment theory to the understanding of conduct problems during the preschool years. In Belsky, J. & Nezworski, T. (Eds.), Clinical implications of attachment (pp. 177–218). Hillsdale, NJ: Erlbaum.Google Scholar
Hyde, L. W., Shaw, D. S., Gardner, F., Cheong, J., Dishion, T. J., & Wilson, M. N. (2013). Dimensions of callousness in early childhood: Links to problem behavior and family intervention effectiveness. Development and Psychopathology, 25, 347–363.Google Scholar
Jabson, J. M., Dishion, T. J., Gardner, F. E. M., & Burton, J. (2004). Relationship Process Code v-2.0 Training Manual: A system for coding relationship interactions. Unpublished coding manual. Child and Family Center, University of Oregon, Eugene, Oregon.Google Scholar
Jung, T., & Wickrama, K. A. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302–317.Google Scholar
Keers, R., Coleman, J. R., Lester, K. J., Roberts, S., Breen, G., Thastum, M., … & Nauta, M. (2016). A genome-wide test of the differential susceptibility hypothesis reveals a genetic predictor of differential response to psychological treatments for child anxiety disorders. Psychotherapy and Psychosomatics, 85, 146–158.Google Scholar
Kochanska, G. (1991). Patterns of inhibition to the unfamiliar in children of normal and affectively ill mothers. Child Development, 62, 250–263.Google Scholar
Lemery-Chalfant, K., Clifford, S., Dishion, T., Shaw, D., & Wilson, M. (2018). Genetic moderation of the effects of the Family Check-Up intervention on children's internalizing symptoms: A longitudinal study with a racial/ethnically diverse sample. Development and Psychopathology.Google Scholar
Messer, L. C., Laraia, B. A., Kaufman, J. S., Eyster, J., Holzman, C., Culhane, J.,…O'Campo, P. (2006). The development of a standardized neighborhood deprivation index. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 83, 1041–1062. https://doi.org/10.1007/s11524-006-9094-xGoogle Scholar
Moffitt, T. E. (1990). Juvenile delinquency and Attention Deficit Disorder: Boys’ developmental trajectories from age 3 to age 15. Child Development, 61, 893–910.Google Scholar
Moffitt, T. E. (2006). Life-course-persistent and adolescent-limited antisocial behavior. In Cicchetti, D. & Cohen, D. J. (Eds.), Developmental psychopathology: Vol. 3. Risk, disorder, and adaptation (pp. 570–598). New York: Wiley.Google Scholar
Moffitt, T. E., & Caspi, A. (2001). Childhood predictors differentiate life-course persistent and adolescence-limited antisocial pathways among males and females. Development and Psychopathology, 13, 355–375.Google Scholar
Moffitt, T. E., Caspi, A., Harrington, H., & Milne, B. J. (2002). Males on the life-course-persistent and adolescence-limited antisocial pathways: Follow-up at age 26 years. Development and Psychopathology, 14, 179–207.Google Scholar
Moilanen, K. L., & Shaw, D. S., Dishion, T. J., Gardner, F., & Wilson, M. (2009). Longitudinal growth and predictors of inhibitory control in early childhood. Social Development, 19, 326–347.Google Scholar
Musci, R. J., Fairman, B., Masyn, K. E., Uhl, G., Maher, B., Sisto, D. Y., … & Ialongo, N. S. (2018). Polygenic score × intervention moderation: An application of discrete-time survival analysis to model the timing of first marijuana use among urban youth. Prevention Science, 19, 6–14.Google Scholar
Musci, R. J., Masyn, K. E., Uhl, G., Maher, B., Kellam, S. G., & Ialongo, N. S. (2015). Polygenic score × intervention moderation: An application of discrete-time survival analysis to modeling the timing of first tobacco use among urban youth. Development and Psychopathology, 27, 111–122.Google Scholar
Nagin, D. S. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.Google Scholar
Nagin, D. S., & Tremblay, R. E. (1999). Trajectories of boys’ physical aggression, opposition, and hyperactivity on the path to physically violent and nonviolent juvenile delinquency. Child Development, 70, 1181–1196.Google Scholar
Nagin, D. S., & Tremblay, R. E. (2001). Parental and early childhood predictors of persistent physical aggression in boys from kindergarten to high school. Archives of General Psychiatry, 58, 389–394.Google Scholar
NICHD Early Child Care Research Network. (2004). Are child developmental outcomes related to before-and after-school care arrangements? Results from the NICHD Study of Early Child Care. Child Development, 75, 280–295.Google Scholar
Odgers, C. L., Moffitt, T. E., Broadbent, J. M., Dickson, N., Hancox, R. J., Harrington, H. et al. (2008). Female and male antisocial trajectories: From childhood origins to adult outcomes. Development and Psychopathology, 20, 673–716.Google Scholar
Olson, S., Choe, D., & Sameroff, A. (2017). Trajectories of child externalizing problems between ages 3 and 10 years: Contributions of children's early effortful control, theory of mind, and parenting experiences. Development and Psychopathology, 29, 437–453.Google Scholar
Pappa, I., St Pourcain, B., Benke, K., Cavadino, A., Hakulinen, C., Nivard, M. G., & Tiemeier, H. (2016). A genome-wide approach to children's aggressive behavior: The EAGLE consortium. American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics, 171(5), 562–572.Google Scholar
Patterson, G. R., Reid, J. B., & Dishion, T. J. (1992). Antisocial boys. Eugene, OR: Castalia. Publishing Company.Google Scholar
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., & Bender, D. (2007). PLINK: tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3), 559–575.Google Scholar
Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.Google Scholar
Reuben, J., Shaw, D. S., Brennan, L. M., Dishion, T. J., & Wilson, M. N. (2015). A family-based intervention for improving children's emotional problems through effects on maternal depression. Journal of Consulting and Clinical Psychology, 83, 1142–1148.Google Scholar
Rhee, S. H., & Waldman, I. D. (2002). Genetic and environmental influences on antisocial behavior: A meta-analysis of twin and adoption studies. Psychological Bulletin, 128(3), 490–529.Google Scholar
Richman, M., Stevenson, J., & Graham, P. J. (1982). Preschool to school: A behavioral study. London: Academic Press.Google Scholar
Rothbart, M. K., Ahadi, S. A., Hershey, K. L., & Fisher, P. (2001). Investigations of temperament at three to seven years: The Children's Behavior Questionnaire. Child Development, 72, 1394–1408. https://doi.org/10.1111/1467-8624.00355Google Scholar
Sampson, R.J. (1997). Collective regulation of adolescent misbehavior: Validation results from eighty Chicago neighborhoods. Journal of Adolescent Research, 12, 227–244.Google Scholar
Sanson, A., Oberklaid, F., Pedlow, R., & Prior, M. (1991). Risk indicators: Assessment of infancy predictors of pre-school behavioural maladjustment. Journal of Child Psychology and Psychiatry, 32, 609–626.Google Scholar
Schwarz, G. (1978). Estimating the Dimension of a Model. The Annals of Statistics, 6(2), 461–464. Retrieved from http://links.jstor.org/sici?sici=0090-5364%28197803%296%3A2%3C461%3AETDOAM%3E2.0.CO%3B2-5Google Scholar
Shaw, D. S., & Bell, R. Q. (1993). Developmental theories of parental contributors to antisocial behavior. Journal of Abnormal Child Psychology, 21, 493–518.Google Scholar
Shaw, D. S., Dishion, T. J., Connell, A., Wilson, M. N., & Gardner, F. (2009). Improvements in maternal depression as a mediator of intervention effects on early child problem behavior. Development and Psychopathology, 21, 417–439.Google Scholar
Shaw, D.S., Dishion, T. J., Supplee, L. H., Gardner, F., & Arnds, K. (2006). A family-centered approach to the prevention of early-onset antisocial behavior: Two-year effects of the family check-up in early childhood. Journal of Consulting and Clinical Psychology, 74, 1–9.Google Scholar
Shaw, D.S., Gilliom, M., Ingoldsby, E.M., & Nagin, D (2003). Trajectories leading to school-age conduct problems. Developmental Psychology, 39, 189–200.Google Scholar
Shaw, D. S., Hyde, L. W., & Brennan, L. M. Early predictors of boys’ antisocial trajectories (2012). Development and Psychopathology, 24, 871–888.Google Scholar
Shaw, D.S., Owens, E.B., Vondra, J.I., Keenan, K. & Winslow, E.B. (1996). Early risk factors and pathways in the development of early disruptive behavior problems. Development and Psychopathology, 8, 679–699.Google Scholar
Shaw, D. S., Sitnick, S., Brennan, L. M., Choe, D. E., Dishion, T. J., Wilson, M. N., & Gardner, F. (2016). The long-term effectiveness of the Family Check-Up on school-age conduct problems: Moderation by neighborhood deprivation. Development and Psychopathology, 28, 1471–1487.Google Scholar
Shaw, D.S., Winslow, E.B., Owens, E. B., Vondra, J.I., Cohn, J.F., & Bell, R.Q. (1998). The development of early externalizing problems among children from low-income families: A transformational perspective. Journal of Abnormal Child Psychology, 26, 95–107.Google Scholar
Stevenson, J., & Goodman, R. (2001). Association between behaviour at age 3 years and adult criminality. British Journal of Psychiatry, 179, 197–202.Google Scholar
Stormshak, E. A., Connell, A., & Dishion, T. J. (2009). An adaptive approach to family-centered interventions in schools: Linking intervention engagement to academic outcomes in middle and high school. Prevention Science, 10, 221–235.Google Scholar
Supplee, L. H., Unikel, E., & Shaw, D. S. (2007). Physical environmental adversity and the protective role of maternal monitoring in relation to early child conduct problems. Journal of Applied Developmental Psychology, 28, 166-183.Google Scholar
Tremblay, R. E., Nagin, D. S., Seguin, J. R., Zoccolillo, M., Zelazo, P. D., Boivin, M., et al. (2004). Physical aggression during early childhood: Trajectories and predictors. Pediatrics, 114, E43–E50.Google Scholar
Vermunt, J. K., Vermunt, & K., J. (2010). Latent Class Modeling with Covariates: Two Improved Three-Step Approaches. Political Analysis, 18(4), 450–469. Retrieved from https://econpapers.repec.org/article/cuppolals/v_3a18_3ay_3a2010_3ai_3a04_3ap_3a450-469_5f01.htmGoogle Scholar
Wakschlag, L. S., & Hans, S. L. (1999). Relation of maternal responsiveness during infancy to the development of behavior problems in high-risk youths. Developmental Psychology, 35, 569–579.Google Scholar
Waller, R., Shaw, D. S., & Hyde, L. W. (2017). Observed fearlessness and positive parenting interact to predict childhood callous-unemotional behaviors among low-income boys. Journal of Child Psychology and Psychiatry, 58, 282–291.Google Scholar
Webster-Stratton, C. (1990). Long-term follow-up of families with young conduct problem children: From preschool to grade school. Journal оf Clinical Child Psychology, 19(2), 144–149.Google Scholar
Willoughby, M. T., Mills-Koonce, W. R., Gottfredson, N. C., & Wagner, N. J. (2014). Measuring callous unemotional behaviors in early childhood: Factor structure and the prediction of stable aggression in middle childhood. Journal of Psychopathology and Behavioral Assessment, 36, 30–42.Google Scholar
Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A., Dudbridge, F., & Middeldorp, C. M. (2014). Research review: polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry, 55(10), 1068–1087.Google Scholar
Xue, Y., Leventhal, T., Brooks-Gunn, J., & Earls, F. J. (2005). Neighborhood residence and mental health problems of 5-11 year olds. Achives of General Psychiatry, 62, 554–563.Google Scholar