Predicting Youth Justice supervision among young people placed in out-of-home care before age 10
Dr Catia Malvoso2,3, Dr P Santiago2, Dr A Montgomerie2, Dr R Pilkington2, Professor John Lynch2,4
1Griffith University
2School of Public Health, University of Adelaide, Adelaide, Australia
3School of Psychology, University of Adelaide, Adelaide, Australia
4School of Population Health Sciences, University of Bristol, United Kingdom
Objective
To investigate how well we can predict which young people will be under Youth Justice (YJ) supervision by age 18, among children placed in out-of-home care (OOHC) before age 10.
Methods
Data were drawn from the Better Evidence Better Outcomes Linked Data (BEBOLD) platform, which includes whole-of-population linked administrative data on ~500,000 children in South Australia born 1991 onwards. In this study, children born 1991-1998 were followed from birth to age 18. Logistic regression models were used to predict the probability of a child placed in OOHC before age 10 transitioning into YJ by age 18. YJ contact was defined as at least one community- or custodial-based supervision order. Child and maternal sociodemographic and perinatal characteristics at birth, as well as maltreatment and placement characteristics were included as predictors.
Results
A total of 2,832 children experienced OOHC before age 10. Of these 13.5% (n=381) experienced contact with YJ by age 18. Model discrimination (AUROC) was 0.82. Using the top 30% of the predicted probabilities as the ‘high’ risk threshold: 518 children were classified as ‘high’ risk; sensitivity was 70.3%; specificity was 85.9%, and the positive predictive value was 32.2%. The prediction model improved classification of those children who go on to experience YJ supervision from 13.5% of all of the 2,832 eligible children, to 32.2% of those in the highest 30% of risk.
Conclusion
This analysis suggests there is potential to identify children in OOHC who are at higher risk of transitioning into YJ, and to provide these children with early supports that may prevent these transitions. However, there are ethical and practical considerations to using prediction models in this population, including the types of support programs employed and potential social and financial costs of inevitable false positive and negative predictions.
Biography:
Dr Catia Malvaso is research fellow who sits across the Schools of Psychology and Public Health at the University of Adelaide. Her position is funded through an Australian Research Council Discovery Early Career Researcher Award. Catia’s program of research focuses on better understanding pathways from childhood adversity to youth and adult offending behaviour with a focus on identifying opportunities for prevention. She was awarded the 2020 Early Career Researcher Prize from the Developmental and Life Course Criminology Division of the American Society of Criminology.