Ya-Fen Chan, Ph.D., Rod Funk, B.S., & Michael Dennis, Ph.D.

1. Trajectories of criminal behavior among adolescent substance users during treatment and thirty-month follow-up Ya-Fen Chan, Ph.D., Rod Funk, B.S., & Michael Dennis, Ph.D. Chestnut Health Systems, Bloomington, Illinois

2. Adolescent Criminal Behavior • In the US, in 1999, 2.4 million juvenile arrests; 104,000 juvenile violent arrests; 1,400 arrests for murder. • Young offenders persistently and frequently involved in different types of offenses (Farrington, 1996; Stattin et al., 1991). • High proportion of juvenile violent offenders were drug users, yet a considerable proportion of juvenile drug users were those who manifested with more serious form of delinquent behaviors (Huizinga et al.,1998). • About 70% of youths in drug abuse treatment were involved in justice system at the same time (Dennis et al., 2005).

4. Objective • To identify the trajectories of criminal behavior and its correlates among adolescent drug users from treatment entry to 3, 6, 9, 12, and 30 months follow-up

5. Sample • 800 adolescents entering outpatient and residential substance abuse treatment in 6 cities (Farmington, CT; Madison County, IL; Oakland, CA; Philadelphia, PA; St. Petersburg, FL; Tucson, AZ) as part of the Persistent Effects of Treatment Study of Adolescents (PETS-A) were sampled. • Adolescents were interviewed by research staff at intake and 3, 6, 9, 12 and 30 months after intake, using the Global Appraisal of Individual Needs (GAIN).

6. Outcome Measure • Criminal behaviors include indices related property crime, substance use related crime, and violent crime. • At intake, the past 12 month criminal behavior were assessed. For 3, 6, 9, 12 and 30 months follow-up, the past three month criminal behavior were asked. • Participants with intake and at least four follow-up data were included. The missing wave was imputed using interpolation of the prior and posterior waves and the regression to project the missing wave.

7. Cluster Analysis • A technique to develop typologies and identify patters of association in a heterogeneous population • Use Ward’s minimum distance with the measure of Squared Euclidean Distance • Cluster on different types of crime, days of illegal activity, days of illegal activity for money and days in a controlled environment • Once the cluster solutions are identified, the correlates can be examined

8. Past Year Crime and Activityat Intake

9. Demographic Characteristics

10. Past Year Drug Use at Intake

11. Past Year Psychiatric and Behavioral Disorders at Intake

12. 1. change from intake to average of months 3 to 12 2. change from intake to 30 months Average Legal Outcomes and Time in Controlled Environments (n=800) 1.00 Percentages 0.50 Z-score (from total mean at intake) 0.00 Days in a Controlled Environ. -0.50 (46% , -2%) Average Crime Outcome (-63.2% , -65%) -1.00 3 6 9 15 18 21 24 27 30 12 Intake Months from Intake

20. Association of Correlates and Clusters *p<0.05

21. Association of Correlates and Clusters **p<0.001

22. Association of Correlates and Clusters **p<0.001

23. Association of Correlates and Clusters *p<0.05,**p<0.001

24. Association of Correlates and Clusters **p<0.001

25. Association of Correlates and Clusters *p<0.05,**p<0.001

26. Conclusions • Treatment is associated with reductions in illegal activity and violence. • Without continuing care, these effects deteriorate over time and detention rates go back up for the moderate to high severity adolescents. • Adolescents with high crime/violence are particularly prone to relapse and recidivism and are the most likely to be back in treatment, trouble or incarcerated at 30 months. • This fourth group is more likely to be male, non-white, victimized, involved with the juvenile justice system, and the most likely to be surrounded by other people using. • The two moderate groups are more likely to have higher rates of psychiatric disorders.

27. Limitations, Strength & Next Steps • Limitations • self report • descriptive/observational • Strengths • Detailed assessment • Large sample • High follow-up rates • Next Steps • Replicate with additional data • Predict trajectory likelihood based on intake and/or initial response to treatment. • Evaluate the impact of additional continuing care (e.g.., Godley experiment) on longer term trajectories.

28. Acknowledgment The content of this presentations are based on treatment & research funded by the Center for Substance Abuse Treatment (CSAT), Substance Abuse and Mental Health Services Administration (SAMHSA) under contract 270-2003-00006 using data provided by the CYT and AMT grantees: (TI11320, TI11324, TI11317, TI11321, TI11323, TI11874, TI11424, TI11894, TI11871, TI11433, TI11423, TI11432, TI11422, TI11892, TI11888). The opinions are those of the author and do not reflect official positions of the consortium or government. Available on line at www.chestnut.org/LI/Posters or by contacting Joan Unsicker at 720 West Chestnut, Bloomington, IL 61701, phone: (309) 827-6026, fax: (309) 829-4661, e-Mail: junsicker@Chestnut.Org