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This study examined which dynamic risk factors for recidivism play an important role during adolescence. The sample consisted of 13,613 American juveniles who had committed a criminal offense. The results showed that the importance of almost all dynamic risk factors, both in the social environment domain (school, family, relationships) and in the individual domain (attitude, skills, aggressiveness), decreased as juveniles grew older. Therefore, the potential effect of an intervention aimed at these factors will also decrease as juveniles grow older. The relative importance of the risk factors also changed: In early adolescence, risk factors in the family domain showed the strongest association with recidivism, whereas in late adolescence risk factors in the attitude, relationships, and school domain were more strongly related to recidivism. These results suggest that the focus of an intervention needs to be attuned to the age of the juvenile to achieve the maximum potential effect on recidivism.

Objectives Recent evolutions in actuarial research have revealed the potential increased utility of machine learning and data-mining strategies to develop statistical models such as classification/decision-tree analysis and neural networks, which are said to mimic the decision-making of practitioners. The current article compares such actuarial modeling methods with a traditional logistic regression risk-assessment development approach. Methods Utilizing a large purposive sample of Washington State offenders (N= 297,600), the current study examines and compares the predictive validity of the currently used Washington State Static Risk Assessment (SRA) instrument to classification tree analysis/random forest and neural network models. Results Overall findings varied, being dependent on the outcome of interest, with the best model for each method resulting in AUCs ranging from 0.732 to 0.762. Findings reveal some predictive performance improvements with advanced machine-learning methodologies, yet the logistic regression models demonstrate comparable predictive performance.Conclusions The study concluded that while data-mining techniques hold potential for improvements over traditional methods, regression-based models demonstrate comparable, and often improved, prediction performance with noted parsimony and greater interpretability.

Birth to teenage or unmarried mothers are strongly associated with later risk of juvenile delinquency. Although there are multiple, interrelated risk factors for juvenile delinquency, prevention of births to teenage and/or unmarried mothers may help to prevent subsequent juvenile delinquency.