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Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)90-98
Number of pages9
JournalJournal of Affective Disorders
Publication statusPublished - 1 Feb 2020


  • Meehan_et_al_JAD_2020

    Meehan_et_al_JAD_2020.pdf, 0.99 MB, application/pdf


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King's Authors


Background: Victimized children are at greater risk for psychopathology than non-victimized peers. However, not all victimized children develop psychiatric disorders, and accurately identifying which victimized children are at greatest risk for psychopathology is important to provide targeted interventions. This study sought to develop and internally validate individualized risk prediction models for psychopathology among victimized children.

Methods: Participants were members of the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative British birth cohort of 2,232 twins born in 1994-1995. Victimization exposure was measured prospectively between ages 5 and 12 years, alongside a comprehensive range of individual-, family-, and community-level predictors of psychopathology. Structured psychiatric interviews took place at age-18 assessment. Logistic regression models were estimated with Least Absolute Shrinkage and Selection Operator (LASSO) regularization to avoid over-fitting to the current sample, and internally validated using 10-fold nested cross-validation.

Results: 26.5% (n = 591) of E-Risk participants had been exposed to at least one form of severe childhood victimization, and 60.4% (n = 334) of victimized children met diagnostic criteria for any psychiatric disorder at age 18. Separate prediction models for any psychiatric disorder, internalizing disorders, and externalizing disorders selected parsimonious subsets of predictors. The three internally validated models showed adequate discrimination, based on area-under-the-curve estimates (range = 0.66-0.73), and good calibration.

Limitations: External validation in wholly-independent data is needed before clinical implementation.

Conclusions: Findings offer proof-of-principle evidence that prediction modeling can be useful in supporting identification of victimized children at greatest risk for psychopathology. This has the potential to inform targeted interventions and rational resource allocation.

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