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Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study.
J Affect Disord 2019; 262:90-98JA

Abstract

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.

Authors+Show Affiliations

Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; National and Specialist CAMHS Trauma, Anxiety, and Depression Clinic, South London and Maudsley NHS Foundation Trust, London, UK. Electronic address: andrea.danese@kcl.ac.uk.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31715391

Citation

Meehan, Alan J., et al. "Developing an Individualized Risk Calculator for Psychopathology Among Young People Victimized During Childhood: a Population-representative Cohort Study." Journal of Affective Disorders, vol. 262, 2019, pp. 90-98.
Meehan AJ, Latham RM, Arseneault L, et al. Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study. J Affect Disord. 2019;262:90-98.
Meehan, A. J., Latham, R. M., Arseneault, L., Stahl, D., Fisher, H. L., & Danese, A. (2019). Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study. Journal of Affective Disorders, 262, pp. 90-98. doi:10.1016/j.jad.2019.10.034.
Meehan AJ, et al. Developing an Individualized Risk Calculator for Psychopathology Among Young People Victimized During Childhood: a Population-representative Cohort Study. J Affect Disord. 2019 Nov 5;262:90-98. PubMed PMID: 31715391.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study. AU - Meehan,Alan J, AU - Latham,Rachel M, AU - Arseneault,Louise, AU - Stahl,Daniel, AU - Fisher,Helen L, AU - Danese,Andrea, Y1 - 2019/11/05/ PY - 2019/05/28/received PY - 2019/09/23/revised PY - 2019/10/25/accepted PY - 2019/11/13/pubmed PY - 2019/11/13/medline PY - 2019/11/13/entrez KW - Psychopathology KW - Resilience KW - Risk calculator KW - Risk prediction KW - Victimization SP - 90 EP - 98 JF - Journal of affective disorders JO - J Affect Disord VL - 262 N2 - 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. SN - 1573-2517 UR - https://www.unboundmedicine.com/medline/citation/31715391/Developing_an_individualized_risk_calculator_for_psychopathology_among_young_people_victimized_during_childhood:_A_population-representative_cohort_study L2 - https://linkinghub.elsevier.com/retrieve/pii/S0165-0327(19)31406-5 DB - PRIME DP - Unbound Medicine ER -