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Washington State Institute for Public Policy
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Other substance use disorder treatment for juveniles (non-therapeutic communities)

Juvenile Justice
Benefit-cost methods last updated December 2018.  Literature review updated August 2017.
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Other substance use disorder treatment is a broadly defined category which includes a variety of substance use disorder treatment modalities targeted and delivered to youth who are involved in the juvenile justice system. Substance use disorder treatments seek to reduce substance use issues (e.g., general use and frequency) and its related effects (e.g., recidivism). These modalities include residential treatment, cognitive behavioral therapy, Treatment Alternatives to Street Crime (TASC), social learning interventions, and others.

These interventions refer youth associated with the juvenile justice system as either a condition of probation or as a caveat to treatment received in detention. Treatments occur both in detention and the community. Treatments range in length of stay from three to eight months and require anywhere from 1.5 to 6 hours of weekly sessions with a trained professional per participant.

Therapeutic communities were excluded from this meta-analysis and analyzed separately.
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2017). The chance the benefits exceed the costs are derived from a Monte Carlo risk analysis. The details on this, as well as the economic discount rates and other relevant parameters are described in our Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $413 Benefits minus costs ($2,479)
Participants ($307) Benefit to cost ratio ($0.04)
Others $2,668 Chance the program will produce
Indirect ($2,864) benefits greater than the costs 48 %
Total benefits ($90)
Net program cost ($2,389)
Benefits minus cost ($2,479)
1In addition to the outcomes measured in the meta-analysis table, WSIPP measures benefits and costs estimated from other outcomes associated with those reported in the evaluation literature. For example, empirical research demonstrates that high school graduation leads to reduced crime. These associated measures provide a more complete picture of the detailed costs and benefits of the program.

2“Others” includes benefits to people other than taxpayers and participants. Depending on the program, it could include reductions in crime victimization, the economic benefits from a more educated workforce, and the benefits from employer-paid health insurance.

3“Indirect benefits” includes estimates of the net changes in the value of a statistical life and net changes in the deadweight costs of taxation.
Detailed Monetary Benefit Estimates Per Participant
Benefits from changes to:1 Benefits to:
Taxpayers Participants Others2 Indirect3 Total
Crime $1,282 $0 $3,502 $638 $5,422
Labor market earnings associated with cannabis abuse or dependence $71 $157 $0 $0 $228
Labor market earnings associated with illicit drug abuse or dependence ($29) ($63) $0 $0 ($92)
Health care associated with illicit drug abuse or dependence ($802) ($142) ($845) ($404) ($2,193)
Health care associated with cannabis abuse or dependence $9 $3 $11 $4 $27
Mortality associated with illicit drugs ($119) ($261) $0 ($1,907) ($2,286)
Adjustment for deadweight cost of program $0 $0 $0 ($1,196) ($1,196)
Totals $413 ($307) $2,668 ($2,864) ($90)
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $2,253 2012 Present value of net program costs (in 2017 dollars) ($2,389)
Comparison costs $0 2012 Cost range (+ or -) 10 %
This cost estimate is weighted by the treatment types included in the meta-analysis. The costs represent the average length of the treatment for the included interventions, approximately one to four months. Treatment costs were provided by the Washington State Rehabilitation Administration.
The figures shown are estimates of the costs to implement programs in Washington. The comparison group costs reflect either no treatment or treatment as usual, depending on how effect sizes were calculated in the meta-analysis. The cost range reported above reflects potential variation or uncertainty in the cost estimate; more detail can be found in our Technical Documentation.
Estimated Cumulative Net Benefits Over Time (Non-Discounted Dollars)
The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in non-discounted dollars to simplify the “break-even” point from a budgeting perspective. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.

^WSIPP’s benefit-cost model does not monetize this outcome.

^^WSIPP does not include this outcome when conducting benefit-cost analysis for this program.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic in order to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the types of program impacts that were measured in the research literature (for example, crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases.

Adjusted effect sizes are used to calculate the benefits from our benefit cost model. WSIPP may adjust effect sizes based on methodological characteristics of the study. For example, we may adjust effect sizes when a study has a weak research design or when the program developer is involved in the research. The magnitude of these adjustments varies depending on the topic area.

WSIPP may also adjust the second ES measurement. Research shows the magnitude of some effect sizes decrease over time. For those effect sizes, we estimate outcome-based adjustments which we apply between the first time ES is estimated and the second time ES is estimated. We also report the unadjusted effect size to show the effect sizes before any adjustments have been made. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured Treatment age No. of effect sizes Treatment N Adjusted effect sizes(ES) and standard errors(SE) used in the benefit - cost analysis Unadjusted effect size (random effects model)
First time ES is estimated Second time ES is estimated
ES SE Age ES SE Age ES p-value
Alcohol use disorder^^ 16 1 25 0.029 0.292 18 n/a n/a n/a 0.079 0.788
Cannabis use disorder 16 2 61 -0.012 0.209 18 -0.012 0.209 18 0.002 0.991
Crime 16 5 839 -0.047 0.090 18 -0.047 0.090 28 -0.047 0.598
Illicit drug use disorder 16 1 225 0.177 0.098 18 0.000 0.187 21 0.177 0.070
Substance use disorder^ 16 2 101 0.177 0.250 18 n/a n/a n/a 0.048 0.892
Technical violations^ 16 1 251 0.248 0.175 18 n/a n/a n/a 0.248 0.156

Citations Used in the Meta-Analysis

Anglin, M.D., Longshore, D., & Turner, S. (1999). Treatment alternatives to street crime: An evaluation of five programs. Criminal Justice and Behavior, 26(2), 168-195.

Chassin, L., Knight, G., Vargas-Chanes, D., Losoya, S. H., & Naranjo, D. (2009). Substance use treatment outcomes in a sample of male serious juvenile offenders. Journal of Substance Abuse Treatment, 36(2), 183-194.

Friedman, A.S., Terras, A., & Glassman, K. (2002). Multimodal substance use intervention program for male deliquents. Journal of Child and Adolescent Substance Abuse, 11(4), 43-65.

Henderson, C.E., Dakof, G.A., Liddle, H.A., & Greenbaum, P.E. (2010). Effectiveness of multidimensional family therapy with higher severity substance-abusing adolescents: Report from two randomized controlled trials. Journal of Consulting and Clinical Psychology, 78(6), 885-897.

Henderson, C.E., Wevodau, A.L., Henderson, S.E., Colbourn, S.L., Gharagozloo, L., North, L.W., & Lotts, V.A. (2016). An independent replication of the adolescent-community reinforcement approach with justice-involved youth. The American Journal on Addictions, 25(3), 233-240.

Kelly, W.R. (2001). An outcome evaluation of the Texas Youth Commission's chemical dependency treatment program, final report. Austin, TX: University of Texas.

Stanger, C., Budney, A.J., Kamon, J.L., & Thostensen, J. (2009). A randomized trial of contingency management for adolescent marijuana abuse and dependence. Drug and Alcohol Dependence, 105(3), 240-247.

Tolou-Shams, M., Dauria, E., Conrad, S.M., Kemp, K., Johnson, S., & Brown, L.K. (2017). Outcomes of a family-based HIV prevention intervention for substance using juvenile offenders. Journal of Substance Abuse Treatment, 77, 115-125.