This article investigates risk of Youth Justice (YJ) supervision by age 18, among children who had experienced Out-Of-Home Care (OOHC) before age 10. Data were drawn from the Better Evidence Better Outcomes Linked Data platform. Children born 1991–1998 who experienced at least one placement in OOHC before age 10 (N = 2,832) were followed to age 18 for YJ supervision. Logistic regression models including child and maternal sociodemographic and perinatal characteristics and child protection characteristics were used to predict the probability of (a) any YJ supervision; (b) any custodial YJ supervision by age 18. Of children in OOHC before age 10, 13.5% (n = 381) experienced any YJ supervision by age 18, and 10.6% (n = 300) experienced custodial YJ supervision. Using all 42 predictors, model discrimination (Area Under the Receiver Operator Characteristic Curve (AUROC) and Area Under the Precision Recall Curve (AUPRC)) was ∼0.8 and ∼0.4, respectively, for both outcomes. We used the top 30% of the predicted probabilities to create a ″high″ risk threshold. At this high-risk threshold, sensitivity was 69.8% and 75.3%, respectively; specificity was 76.5% and 75.5%; and the positive predictive value was 32.3% and 26.8%. These risk prediction models have reasonable discrimination to identify children in OOHC who are at higher risk of transitioning into YJ, and are technically feasible. However, predicting risk implies providing opportunities for early supports that may prevent transitions from OOHC to YJ. There are ethical and practical considerations to using prediction models in this population.
BaidawiS.SheehanS. (2019). "Crossover kids": Offending by child-protection-involved youth. Trends & Issues in Crime & Criminal Justice, 582, 1–22.
3.
BoydK.EngK. H.PageC. D. (2013). Area under the precision-recall curve: point estimates and confidence intervals. Paper presented at the Joint European conference on machine learning and knowledge discovery in databases.
4.
CashmoreJ. (2011). The link between child maltreatment and adolescent offending: Systems neglect of adolescents. Family Matters, 89, 31–41.
Cerdeña, J. P., Plaisime, M. V., & Borrell, L. N. (2024). Race as a risk marker, not a risk factor: Revising race-based algorithms to protect racially oppressed patients. Journal of General Internal Medicine, 39(13), 2565–2570.
7.
Clancey, G., Wang, S., & Lin, B. (2020). Youth justice in Australia: Themes from recent inquiries. Trends and Issues in Crime and Criminal Justice, 605, 1–19.
8.
CollinsG. S.ReitsmaJ. B.AltmanD. G.MoonsK. G. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. Journal of British Surgery, 102(3), 148–158. https://doi.org/10.1002/bjs.9736
9.
Commission for Children and Young People. (2021). Our youth, our way: inquiry into the over-representation of Aboriginal children and young people in the Victorian youth justice system. Commission for Children and Young People.
10.
CookJ. (2019). PRTAB: Stata module to compute Precision-recall curves.
11.
Day, A., Malvaso, C., Boyd, C., Hawkins, K., & Pilkington, R. (2023). The effectiveness of trauma-informed youth justice: a discussion and review. Frontiers in Psychology, 14, 1157695.
12.
DeGueS.WidomC. S. (2009). Does out-of-home placement mediate the relationship between child maltreatment and adult criminality?Child Maltreatment, 14(4), 344–355. https://doi.org/10.1177/1077559509332264
13.
FarringtonD. P. (1995). The development of offending and antisocial behaviour from childhood: Key findings from the Cambridge study in delinquent development. Journal of Child Psychology and Psychiatry, 360(6), 929–964. https://doi.org/10.1111/j.1469-7610.1995.tb01342.x
14.
FarringtonD. P. (2007). Origins of violent behavior over the life span. In FlanneryD.FlanneryA.WaldmanL. (Eds.), The Cambridge Handbook of Violent Behavior and Aggression. Cambridge University Press.
15.
FuG.-H.XuF.ZhangB.-Y.YiL.-Z. (2017). Stable variable selection of class-imbalanced data with precision-recall criterion. Chemometrics and Intelligent Laboratory Systems, 171, 241–250. https://doi.org/10.1016/j.chemolab.2017.10.015
16.
HolmanC. D.BassA. J.RouseI. L.HobbsM. S. T. (1999). Population-based linkage of health records in western Australia: Development of a health services research linked database. Australian and New Zealand Journal of Public Health, 23(5), 453–459. https://doi.org/10.1111/j.1467-842X.1999.tb01297.x
17.
HomelR.FreibergK.BranchS. (2015). CREATE-ing capacity to take developmental crime prevention to scale: A community-based approach within a national framework. Australian & New Zealand Journal of Criminology, 48(3), 367–385. https://doi.org/10.1177/0004865815589826
18.
HomelR.FreibergK.LambC.LeechM.BatchelorS.CarrA.HayI.TeagueR.EliasG. (2006). The Pathways to Prevention project: doing developmental prevention in a disadvantaged community. Trends & Issues in Crime & Criminal Justice, 323.
19.
Hunkin, H., Malvaso, C. G., Chittleborough, C. R., Gialamas, A., Montgomerie, A., Falster, K., ... & Pilkington, R. M. (2025). Systematic review and meta-analysis: Multisystemic therapy and functional family therapy targeting antisocial behavior in adolescence.. Journal of the American Academy of Child & Adolescent Psychiatry, 64(4), 427–446.
20.
KoeglC. J.FarringtonD. P.AugimeriL. K. (2019). Predicting future criminal convictions of children under age 12 using the early assessment risk lists. Journal of Developmental and Life-Course Criminology, 7(1), 17–40. https://doi.org/10.1007/s40865-019-00129-0
21.
Krakouer, J., Wu Tan, W., & Parolini, A. (2021). Who is analysing what? The opportunities, risks and implications of using predictive risk modelling with Indigenous Australians in child protection: A scoping review. Australian Journal of Social Issues, 56(2), 173–197.
22.
Littell, J. H., Pigott, T. D., Nilsen, K. H., Green, S. J., & Montgomery, O. L. (2021). Multisystemic Therapy® for social, emotional, and behavioural problems in youth age 10 to 17: An updated systematic review and meta-analysis. Campbell Systematic Reviews, 17(4), e1158.
23.
MalvasoC. G.DelfabbroP. (2015). Offending behaviour among young people with complex needs in the Australian out-of-home care system. Journal of Child and Family Studies, 24(12), 3561–3569. https://doi.org/10.1007/s10826-015-0157-z
24.
MalvasoC. G.DelfabbroP.DayA. (2017). The child protection and juvenile justice nexus in Australia: A longitudinal examination of the relationship between maltreatment and offending. Child Abuse & Neglect, 64, 32–46. https://doi.org/10.1016/j.chiabu.2016.11.028
25.
MalvasoC. G.SantiagoP.PilkingtonR.MontgomerieA.DelfabbroP. H.DayA.LynchJ. (2020). The intersection between the Child Protection and Youth Justice systems in South Australia. BetterStart Child Health and Development Group, The University of Adelaide.
26.
ManningM.HomelR.SmithC. (2010). A meta-analysis of the effects of early developmental prevention programs in at-risk populations on non-health outcomes in adolescence. Children and Youth Services Review, 32(4), 506–519. https://doi.org/10.1016/j.childyouth.2009.11.003
27.
McFarlaneK. (2017). Care-criminalisation: The involvement of children in out-of-home care in the New South Wales criminal justice system. Australian & New Zealand Journal of Criminology, 51(3), 412–433. https://doi.org/10.1177/0004865817723954
28.
McGrathA.GerardA.ColvinE. (2020). Care-experienced children and the criminal justice system. Trends & Issues in Crime & Criminal Justice, 600, 1–14.
29.
MoffittT. (1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100, 674–701. https://doi.org/10.1037/0033-295X.100.4.674
30.
OsbornA. L.DelfabbroP.BarberJ. G. (2008). The psychosocial functioning and family background of children experiencing significant placement instability in Australian out-of-home care. Children and Youth Services Review, 30(8), 847–860. https://doi.org/10.1016/j.childyouth.2007.12.012
31.
OzenneB.SubtilF.Maucort-BoulchD. (2015). The precision–recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases. Journal of Clinical Epidemiology, 68(8), 855–859. https://doi.org/10.1016/j.jclinepi.2015.02.010
32.
PilkingtonR.GrantJ.ChittleboroughC.GialamasA.MontgomerieA.LynchJ. (2017). Child Protection in South Australia. Retrieved from BetterStart Child Health and Development Research Group.
33.
RavivT.TaussigH. N.CulhaneS. E.GarridoE. F. (2010). Cumulative risk exposure and mental health symptoms among maltreated youth placed in out-of-home care. Child Abuse & Neglect, 34(10), 742–751. https://doi.org/10.1016/j.chiabu.2010.02.011
RyanJ.MarshallJ. M.HerzD.HernandezP. M. (2008). Juvenile delinquency in child welfare: Investigating group home effects. Children and Youth Services Review, 30(9), 1088–1099. https://doi.org/10.1016/j.childyouth.2008.02.004
36.
RyanJ.TestaM. F. (2005). Child maltreatment and juvenile delinquency: Investigating the role of placement and placement instability. Children and Youth Services Review, 27(3), 227–249. https://doi.org/10.1016/j.childyouth.2004.05.007
37.
SaitoT.RehmsmeierM. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PloS One, 10(3), e0118432. https://doi.org/10.1371/journal.pone.0118432
38.
SawyerM.CarboneJ.SearleJ. A.RobinsonP. (2007). The mental health and wellbeing of children and adolescents in home-based foster care. The Medical Journal of Australia, 186, 181–184. https://doi.org/10.5694/j.1326-5377.2007.tb00857.x
39.
StataCorpL. (2007). Stata data analysis and statistical software. Special Edition Release, 10, 733.
40.
SteyerbergE. W.VickersA. J.CookN. R.GerdsT.GonenM.ObuchowskiN.PencinaM. J.KattanM. W. (2010). Assessing the performance of prediction models: A framework for some traditional and novel measures. Epidemiology (Cambridge, Mass.), 21(1), 128–138. https://doi.org/10.1097/EDE.0b013e3181c30fb2
41.
TaussigH. N. (2002). Risk behaviors in maltreated youth placed in foster care: A longitudinal study of protective and vulnerability factors. Child Abuse & Neglect, 26, 1179–1199. https://doi.org/10.1016/S0145-2134(02)00391-5
42.
Van CalsterB.McLernonD. J.Van SmedenM.WynantsL.SteyerbergE. W. (2019). Calibration: The achilles heel of predictive analytics. BMC Medicine, 17(1), 1–7. https://doi.org/10.1186/s12916-019-1466-7
43.
VinnerljungB.SallnäsM. (2008). Into adulthood: A follow-up study of 718 young people who were placed in out-of-home care during their teens. Child & Family Social Work, 13(2), 144–155. https://doi.org/10.1111/j.1365-2206.2007.00527.x
44.
Walter, M., Lovett, R., Maher, B., Williamson, B., Prehn, J., Bodkin-Andrews, G., & Lee, V. (2021). Indigenous data sovereignty in the era of big data and open data. Australian Journal of Social Issues, 56(2), 143–156.
45.
Walter, M., & Suina, M. (2019). Indigenous data, Indigenous methodologies and Indigenous data sovereignty. International Journal of Social Research Methodology, 22(3), 233–243.
46.
WidomC. S. (2003). Understanding child maltreatment and juvenile delinquency: The research. In WiligJ.WidomC. S.TuellJ. (Eds.), Understanding child maltreatment and juvenile delinquency: from research to effective program, practice, and systematic solutions. CWLA Press.
47.
YampolskayaS.ArmstrongM. I.McNeishR. (2011). Children placed in out-of-home care: Risk factors for involvement with the juvenile justice system. Violence and Victims, 26(2), 231–245. https://doi.org/10.1891/0886-6708.26.2.231
48.
Young Offenders Act, South Australia. (1993).
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