Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-27T02:44:27.641Z Has data issue: false hasContentIssue false
  • English
  • Français

Childhood exposure to interpersonal violence is associated with greater transdiagnostic integration of psychiatric symptoms

Published online by Cambridge University Press:  09 November 2020

Justin D. Russell Open the ORCID record for Justin D. Russell [Opens in a new window] ,
Taylor J. Keding ,
Quanfa He ,
James J. Li  and
Ryan J. Herringa
Justin D. Russell
Affiliation:
Department of Psychiatry, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
Taylor J. Keding
Affiliation:
Department of Psychiatry, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA Neuroscience Training Program, University of Wisconsin, Madison, WI, USA
Quanfa He
Affiliation:
Department of Psychology, University of Wisconsin, Madison, WI, USA
James J. Li
Affiliation:
Department of Psychology, University of Wisconsin, Madison, WI, USA Waisman Center, University of Wisconsin, Madison, WI, USA
Ryan J. Herringa*
Affiliation:
Department of Psychiatry, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
*
Author for correspondence: Ryan J. Herringa, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Childhood exposure to interpersonal violence (IPV) may be linked to distinct manifestations of mental illness, yet the nature of this change remains poorly understood. Network analysis can provide unique insights by contrasting the interrelatedness of symptoms underlying psychopathology across exposed and non-exposed youth, with potential clinical implications for a treatment-resistant population. We anticipated marked differences in symptom associations among IPV-exposed youth, particularly in terms of ‘hub’ symptoms holding outsized influence over the network, as well as formation and influence of communities of highly interconnected symptoms.

Methods

Participants from a population-representative sample of youth (n = 4433; ages 11–18 years) completed a comprehensive structured clinical interview assessing mental health symptoms, diagnostic status, and history of violence exposure. Network analytic methods were used to model the pattern of associations between symptoms, quantify differences across diagnosed youth with (IPV+) and without (IPV–) IPV exposure, and identify transdiagnostic ‘bridge’ symptoms linking multiple disorders.

Results

Symptoms organized into six ‘disorder’ communities (e.g. Intrusive Thoughts/Sensations, Depression, Anxiety), that exhibited considerably greater interconnectivity in IPV+ youth. Five symptoms emerged in IPV+ youth as highly trafficked ‘bridges’ between symptom communities (11 in IPV– youth).

Conclusion

IPV exposure may alter mutually reinforcing symptom co-occurrence in youth, thus contributing to greater psychiatric comorbidity and treatment resistance. The presence of a condensed and unique set of bridge symptoms suggests trauma-enriched nodes which could be therapeutically targeted to improve outcomes in violence-exposed youth.

Keywords

Adverse childhood experienceschild psychiatryecophenotypeinterpersonal violencenetwork analysis
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 10 , July 2022 , pp. 1883 - 1891
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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

Agnew-Blais, J., & Danese, A. (2016). Childhood maltreatment and unfavourable clinical outcomes in bipolar disorder: A systematic review and meta-analysis. The Lancet Psychiatry, 3(4), 342–349. https://doi.org/10.1016/S2215-0366(15)00544-1.CrossRefGoogle ScholarPubMed
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.Google Scholar
Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5–13. https://doi.org/10.1002/wps.20375.CrossRefGoogle ScholarPubMed
Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608.CrossRefGoogle Scholar
Boschloo, L., Schoevers, R. A., van Borkulo, C. D., Borsboom, D., & Oldehinkel, A. J. (2016). The network structure of psychopathology in a community sample of preadolescents. Journal of Abnormal Psychology, 125(4), 599–606. https://doi.org/10.1037/abn0000150.CrossRefGoogle Scholar
Boschloo, L., van Borkulo, C. D., Rhemtulla, M., Keyes, K. M., Borsboom, D., & Schoevers, R. A. (2015). The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PLoS ONE, 10(9), e0137621. https://doi.org/10.1371/journal.pone.0137621.CrossRefGoogle Scholar
Calkins, M. E., Merikangas, K. R., Moore, T. M., Burstein, M., Behr, M. A., Satterthwaite, T. D., … Mentch, F. D. (2015). The Philadelphia neurodevelopmental cohort: Constructing a deep phenotyping collaborative. Journal of Child Psychology and Psychiatry, 56(12), 1356–1369. https://doi.org/10.1111/jcpp.12416.CrossRefGoogle ScholarPubMed
De Ron, J., Fried, E. I., & Epskamp, S. (2019). Psychological networks in clinical populations: A tutorial on the consequences of Berkson's bias. Psychological Medicine, 1–9. https://doi.org/10.1017/S0033291719003209.Google Scholar
Dvir, Y., Ford, J. D., Hill, M., & Frazier, J. A. (2014). Childhood maltreatment, emotional dysregulation, and psychiatric comorbidities. Harvard Review of Psychiatry, 22(3), 149–161. https://doi.org/10.1097/HRP.0000000000000014.CrossRefGoogle ScholarPubMed
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. https://doi.org/10.3758/s13428-017-0862-1.CrossRefGoogle ScholarPubMed
Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1–18. https://doi.org/10.18637/jss.v048.i04.CrossRefGoogle Scholar
Fonseca-Pedrero, E., Ortuño, J., Debbané, M., Chan, R. C., Cicero, D., Zhang, L. C., … Kwapil, T. (2018). The network structure of schizotypal personality traits. Schizophrenia Bulletin, 44(suppl_2), S468–S479. https://doi.org/10.1093/schbul/sby044.CrossRefGoogle Scholar
Fried, E. I., & Cramer, A. O. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science, 12(6), 999–1020. https://doi.org/10.1177/1745691617705892.CrossRefGoogle ScholarPubMed
Fried, E. I., van Borkulo, C. D., Cramer, A. O. J., Boschloo, L., Schoevers, R. A., & Borsboom, D. (2017). Mental disorders as networks of problems: A review of recent insights. Social Psychiatry and Psychiatric Epidemiology, 52(1), 1–10. https://doi.org/10.1007/s00127-016-1319-z.CrossRefGoogle ScholarPubMed
Fritz, J., Fried, E. I., Goodyer, I. M., Wilkinson, P. O., & Harmelen, A.-L. van. (2018). A network model of resilience factors for adolescents with and without exposure to childhood adversity. Scientific Reports, 8(1), 1–13. https://doi.org/10.1038/s41598-018-34130-2.CrossRefGoogle ScholarPubMed
Fritz, J., Stochl, J., Goodyer, I. M., van Harmelen, A.-L., & Wilkinson, P. O. (2020). Embracing the positive: An examination of how well resilience factors at age 14 can predict distress at age 17. Translational Psychiatry, 10. https://doi.org/10.1038/s41398-020-00944-w.CrossRefGoogle ScholarPubMed
Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12(6), e0174035. https://doi.org/10.1371/journal.pone.0174035.CrossRefGoogle ScholarPubMed
Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576. https://doi.org/10.1146/annurev.psych.58.110405.085530.CrossRefGoogle ScholarPubMed
Green, J. G., McLaughlin, K. A., Berglund, P. A., Gruber, M. J., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2010). Childhood adversities and adult psychiatric disorders in the national comorbidity survey replication I: Associations with first onset of DSM-IV disorders. Archives of General Psychiatry, 67(2), 113–123. https://doi.org/10.1001/archgenpsychiatry.2009.186.CrossRefGoogle ScholarPubMed
Herringa, R. J. (2017). Trauma, PTSD and the developing brain. Current Psychiatry Reports, 19(10), 69. https://doi.org/10.1007/s11920-017-0825-3.CrossRefGoogle ScholarPubMed
Isvoranu, A.-M., van Borkulo, C. D., Boyette, L.-L., Wigman, J. T. W., Vinkers, C. H., & Borsboom, D. (2017). A network approach to psychosis: Pathways between childhood trauma and psychotic symptoms. Schizophrenia Bulletin, 43(1), 187–196. https://doi.org/10.1093/schbul/sbw055.CrossRefGoogle ScholarPubMed
Jones, P. J. (2019). Networktools: Tools for Identifying Important Nodes in Networks (Version 1.2.1) [R]. Retrieved from https://cran.r-project.org/package=networktools.Google Scholar
Jones, P. J., Ma, R., & McNally, R. J. (2019). Bridge centrality: A network approach to understanding comorbidity [published online ahead of print June 10 2019]. Multivariate Behavioral Research, 1–15. https://doi.org/10.1080/00273171.2019.1614898.Google Scholar
Kaufman, J., Birmaher, B., Brent, D., Rao, U. M. A., Flynn, C., Moreci, P., … Ryan, N. (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. https://doi.org/10.1097/00004583-199707000-00021.CrossRefGoogle ScholarPubMed
Keyes, K. M., Eaton, N. R., Krueger, R. F., McLaughlin, K. A., Wall, M. M., Grant, B. F., & Hasin, D. S. (2012). Childhood maltreatment and the structure of common psychiatric disorders. The British Journal of Psychiatry, 200(2), 107–115. https://doi.org/10.1192/bjp.bp.111.093062.CrossRefGoogle ScholarPubMed
Kilpatrick, D. G., Ruggiero, K. J., Acierno, R., Saunders, B. E., Resnick, H. S., & Best, C. L. (2003). Violence and risk of PTSD, major depression, substance abuse/dependence, and comorbidity: Results from the national survey of adolescents. Journal of Consulting and Clinical Psychology, 71(4), 692–700. https://doi.org/10.1037/0022-006X.71.4.692.CrossRefGoogle ScholarPubMed
Merikangas, K., Avenevoli, S., Costello, J., Koretz, D., & Kessler, R. C. (2009). National comorbidity survey replication adolescent supplement (NCS-A): I. Background and measures. Journal of the American Academy of Child & Adolescent Psychiatry, 48(4), 367–369. https://doi.org/10.1097/CHI.0b013e31819996f1.CrossRefGoogle ScholarPubMed
Nanni, V., Uher, R., & Danese, A. (2012). Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: A meta-analysis. The American Journal of Psychiatry, 169(2), 141–151. https://doi.org/10.1176/appi.ajp.2011.11020335.CrossRefGoogle ScholarPubMed
Pinheiro, P. S. (2006). World Report on Violence Against Children. Geneva: United Nations, General Assembly. Retrieved from United Nations, General Assembly website: https://www.ohchr.org/EN/HRBodies/CRC/Study/Pages/StudyViolenceChildren.aspx.Google Scholar
Rhemtulla, M., Fried, E. I., Aggen, S. H., Tuerlinckx, F., Kendler, K. S., & Borsboom, D. (2016). Network analysis of substance abuse and dependence symptoms. Drug and Alcohol Dependence, 161, 230–237. https://doi.org/10.1016/j.drugalcdep.2016.02.005.CrossRefGoogle ScholarPubMed
Russell, J. D., Neill, E. L., Carrión, V. G., & Weems, C. F. (2017). The network structure of posttraumatic stress symptoms in children and adolescents exposed to disasters. Journal of the American Academy of Child & Adolescent Psychiatry, 56, 669–677. https://doi.org/10.1016/j.jaac.2017.05.021.CrossRefGoogle Scholar
Satterthwaite, T. D., Connolly, J. J., Ruparel, K., Calkins, M. E., Jackson, C., Elliott, M. A., … Gur, R. E. (2016). The Philadelphia neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth. NeuroImage, 124, Part B, 1115–1119. https://doi.org/10.1016/j.neuroimage.2015.03.056.CrossRefGoogle Scholar
Teicher, M. H., & Samson, J. A. (2013). Childhood maltreatment and psychopathology: A case for ecophenotypic variants as clinically and neurobiologically distinct subtypes. American Journal of Psychiatry, 170(10), 1114–1133. https://doi.org/10.1176/appi.ajp.2013.12070957.CrossRefGoogle ScholarPubMed
van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports, 4, 5918. https://doi.org/10.1038/srep05918.CrossRefGoogle ScholarPubMed
Williams, J. K., Smith, D. C., An, H., & Hall, J. A. (2008). Clinical outcomes of traumatized youth in adolescent substance abuse treatment: A longitudinal multisite study. Journal of Psychoactive Drugs, 40(1), 77–84. https://doi.org/10.1080/02791072.2008.10399763.CrossRefGoogle ScholarPubMed
Supplementary material: File

Russell et al. supplementary material

Russell et al. supplementary material

Download Russell et al. supplementary material(File)
File 50.5 KB