Intervening with Adolescent Substance User: What do we know so far about and where do we go from here Michael Dennis, Ph.D. Chestnut Health Systems, Normal, IL October 29, 2009 Presentation for Washington State and Regional Policy Makers at the Puget Sound Educational School District, Renton, WA, October 27-30, This presentation was supported by PSESD, ESD113, and King County. The author would like to thank Dennis Deck for providing the tables of 2009 SAPISP data. The presentation also reports on treatment & research funded by the Center for Substance Abuse Treatment (CSAT), Substance Abuse and Mental Health Services Administration (SAMHSA) under contracts and , as well as several individual CSAT, NIAAA, NIDA and private foundation grants. The opinions are those of the author and do not reflect official positions of the consortium or government. Available on line at or by contacting Michael Dennis, Chestnut Health Systems, 448 Wylie Drive, Normal, IL 61761, phone , fax , Questions about the GAIN can also be sent to

Crime & Violence by Substance Severity Source: NSDUH 2006 Adolescents Substance use severity is related to crime and violence

Family, Vocational & MH by Substance Severity Source: NSDUH 2006 Adolescents as well as family, school and mental health problems

People Entering Publicly Funded Treatment Generally Use For Decades P e r c e n t s t i l l u s i n g Years from first use to 1+ years of abstinence Source: Dennis et al., % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% It takes 27 years before half reach 1 or more years of abstinence or die

Percent still using Years from first use to 1+ years of abstinence under Age of First Use* Source: Dennis et al., % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 60% longer The Younger They Start, The Longer They Use * p<.05

Percent still using Years from first use to 1+ years of abstinence Years to first Treatment Admission* Source: Dennis et al., % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 20 or more years 0 to 9 years 10 to 19 years 57% quicker The Sooner They Get The Treatment, The Quicker They Get To Abstinence p<.05

After Initial Treatment… Relapse is common, particularly for those who: – Are Younger – Have already been to treatment multiple times – Have more mental health issues or pain It takes an average of 3 to 4 treatment admissions over 9 years before half reach a year of abstinence Yet over 2/3rds do eventually abstain Treatment predicts who starts abstinence Self help engagement predicts who stays abstinent Source: Dennis et al., 2005, Scott et al 2005

The Likelihood of Sustaining Abstinence Another Year Grows Over Time 36% 66% 86% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 to 12 months1 to 3 years4 to 7 years Duration of Abstinence % Sustaining Abstinence Another Year. After 1 to 3 years of abstinence, 2/3rds will make it another year After 4 years of abstinence, about 86% will make it another year Source: Dennis, Foss & Scott (2007) Only a third of people with 1 to 12 months of abstinence will sustain it another year But even after 7 years of abstinence, about 14% relapse each year

The Cyclical Course of Relapse, Incarceration, Treatment and Recovery: Adolescents Probability of Going to Using vs. Early “Recovery” (+ good) -- Baseline Substance Use Severity (0.74) + Baseline Total Symptom Count (1.46) -- Past Month Substance Problems (0.48)+ Times Urine Screened (1.56) -- Substance Frequency (0.48)+ Recovery Environment (r)* (1.47) + Positive Social Peers (r)** (1.69) * Average days during transition period of participation in self help, AOD free structured activities and inverse of AOD involved activities, violence, victimization, homelessness, fighting at home, alcohol or drug use by others in home ** Proportion of social peers during transition period in school/work, treatment, recovery, and inverse of those using alcohol, drugs, fighting, or involved in illegal activity. In the Community Using (75% stable) In Treatment (48 v 35% stable) In Recovery (62% stable) Source: 2006 CSAT AT data set 26% 19%

In the Community Using (75% stable) In Recovery (62% stable) The Cyclical Course of Relapse, Incarceration, Treatment and Recovery: Adolescents * Average days during transition period of participation in self help, AOD free structured activities and inverse of AOD involved activities, violence, victimization, homelessness, fighting at home, alcohol or drug use by others in home 20% 10% Incarcerated (46% stable) Probability of Going to Using vs. Early “Recovery” (+ good) + Recovery Environment (r)* (3.33) Source: 2006 CSAT AT data set

Recovery* by Level of Care * Recovery defined as no past month use, abuse, or dependence symptoms while living in the community. Percentages in parentheses are the treatment outcome (intake to 12 month change) and the stability of the outcomes (3months to 12 month change) Source: CSAT Adolescent Treatment Outcome Data Set (n-9,276) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Pre-IntakeMon 1-3Mon 4-6Mon 7-9Mon Percent in Past Month Recovery* Outpatient (+79%, -1%) Residential(+143%, +17%) Post Corr/Res (+220%, +18%) OP & Resid Similar CC better

Source: French et al., 2008; Chandler et al., 2009; Capriccioso, 2004 Cost of Substance Abuse Treatment Episode $22,000 / year to incarcerate an adult $30,000/ child-year in foster care $70,000/year to keep a child in detention $750 per night in Detox $1,115 per night in hospital $13,000 per week in intensive care for premature baby $27,000 per robbery $67,000 per assault

Investing in Treatment has a Positive Annual Return on Investment (ROI) Substance abuse treatment has been shown to have a ROI of between $1.28 to $7.26 per dollar invested Even year long treatment drug courts have an average ROI of $2.14 to $2.71 per dollar invested Source: Bhati et al., (2008); Ettner et al., (2006) This also means that for every dollar treatment is cut, we lose more money than we saved.

Washington Youth Served by Treatment & SAP are already costing society Using the GAIN we are able estimate the cost to society of tangible services (e.g., health care utilization, days in detention, probation, parole, days of missed school) in 2009 dollars for the 90 days before intake The 258 adolescents served by ESD113 in the school year… – cost society $229,830 ($ per year) – an average of $891 per adolescent ($3,663 per year) The 2,733 adolescents served in King County between … – cost society $4,609,580 ($18.438,321 per year) – an average of $1,687 per adolescent ($6,747 per year) Thus both are targeting groups with a high potential to offset their costs to society (or cost you more if you cut back on them)

Substance Use Disorders are Common, But Treatment Participation Rates Are Low: United States (US) Source: OAS, 2006 – 2003, 2004, and 2005 NSDUH Over 88% of adolescent and young adult treatment and over 50% of adult treatment is publicly funded Few Get Treatment: 1 in 17 adolescents, 1 in 22 young adults, 1 in 12 adults Much of the private funding is limited to 30 days or less and authorized day by day or week by week

Substance Use Disorders are Common, But Treatment Participation Rates Are Low: Washington State Source: OAS, 2006 – 2003, 2004, and 2005 NSDUH Similar rates for adolescents : 1 in 18 Higher problem rate for young adults, but higher treatment rate : 1 in 7 Higher problems rate, and less treatment participation for adults: 1 in 19

Substance Use Disorders are Common, But Treatment Participation Rates Are Low: Seattle & King County, WA Source: OAS, 2006 – 2003, 2004, and 2005 NSDUH High higher problems rate, but similar treatment rates: 1 in 19 young adults 1 in 12 adults Similar problem rate but much lower Treatment Rate: 1 in 40 adolescents

Adolescent Rates of High (2+) Scores on Mental Health (MH) or Substance Abuse (SA) Screener by Setting in Washington State Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders Among DSHS Clients. Olympia, WA: Department of Social and Health Services. Retrieved from Problems could be easily identified Comorbidity is common

Where in the System are the Adolescents with Mental Health, Substance Abuse and Co-ocurring? Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders Among DSHS Clients. Olympia, WA: Department of Social and Health Services. Retrieved from

Where in the System are the Adolescents with Mental Health, Substance Abuse and Co-ocurring? Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders Among DSHS Clients. Olympia, WA: Department of Social and Health Services. Retrieved from <1%

Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders Among DSHS Clients. Olympia, WA: Department of Social and Health Services. Retrieved from Adolescent Client Validation of Hi Co-occurring from GAIN Short Screener vs Clinical Records by Setting in Washington State Two page measure closely approximated all found in the clinical record after the next two years

GAIN SS Can Also be Used for Monitoring Intake3 Mon Mon 15 Mon 18 Mon 21 Mon 24 Mon Total Disorder Screener (TDScr) 12+ Mon.s ago (#1s) 2-12 Mon.s ago (#2s) Past Month (#3s) Lifetime (#1,2,or 3) Track Gap Between Prior and current Lifetime Problems to identify “under reporting” Track progress in reducing current (past month) symptoms) Monitor for Relapse

23 SAPISP Results: State Wide (n=10,924) Source: SAPISP 2009 Data WA State dichotomizes as 0-1=Low 2+=High GAIN SS uses triage: 0=Low 1-2=Mod 3+=High

24 Total Disorder Screener Severity by Level of Care Source: SAPISP 2009 Data and Dennis et al 2006 Residential Median (10.5) is higher Outpatient & Student Asst. Prog. are Similar (Median 6.0 vs. 6.4) Well Targeted 95% 1+ 85% 3+ About 30% of OP & SAP are in the high severity range more typical of residential

Internalizing Disorder Screener by Level of Care Source: SAPISP 2009 Data and CSAT 2008 Full subset to Adolescent Intakes SAP Higher on Internalizing Disorders

Externalizing Disorder Screener by Level of Care Source: SAPISP 2009 Data and CSAT 2008 Full subset to Adolescent Intakes SAP Mod-Hi on Externalizing Disorders

Substance Disorder Screener by Level of Care Source: SAPISP 2009 Data and CSAT 2008 Full subset to Adolescent Intakes SAP Lower on Substance Disorders

Crime/Violence Screener by Level of Care Source: SAPISP 2009 Data and CSAT 2008 Full subset to Adolescent Intakes SAP Lower on Crime/Violence

29 King County: Pattern of Weekly Use Source: King County 08/31/09 (n=3102)

30 King County: Substance Use Disorder Severity Source: King County 08/31/09 (n=3102)

31 King County: Co-Occurring Psychiatric Problems Source: King County 08/31/09 (n=3102) Externalizing Disorders Internalizing Disorders

32 King County: Recovery Environment Source: King County 08/31/09 (n=3102)

33 King County: Past Year Violence & Crime *Dealing, manufacturing, prostitution, gambling (does not include simple possession or use) Source: King County 08/31/09 ( n=3102 )

34 King County: Intensity of Juvenile Justice System Involvement Source: King County 08/31/09 ( n=3102 )

35 King County: Count Number of Problems Mod/Hi* * (Alcohol, cannabis, or other drug disorder, depression, anxiety, trauma, suicide, ADHD, CD, victimization, violence/ illegal activity) Source: King County 08/31/09 (n=3102) Over 90% self report one or more major clinical problems Over half report 5 or more major clinical problems

So what does it mean to move the field towards Evidence Based Practice (EBP)? Introducing explicit intervention protocols that are – Targeted at specific problems/subgroups and outcomes – Having explicit quality assurance procedures to cause adherence at the individual level and implementation at the program level Having the ability to evaluate performance and outcomes – For the same program over time, – Relative to other interventions Introducing reliable and valid assessment that can be used – At the individual level to immediately guide clinical judgments about diagnosis/severity, placement, treatment planning, and the response to treatment – At the program level to drive program evaluation, needs assessment, performance monitoring and long term program planning

Major Predictors of Bigger Effects 1. Chose a strong intervention protocol based on prior evidence 2. Used quality assurance to ensure protocol adherence and project implementation 3. Used proactive case supervision of individual 4. Used triage to focus on the highest severity subgroup

Impact of the numbers of Favorable features on Recidivism (509 JJ studies) Source: Adapted from Lipsey, 1997, 2005 Average Practice Recidivism Drops the more factors present

553/771=72% unmet need 218/224=97% to targeted 771/982=79% in need Exploring Need, Unmet Need, & Targeting of Mental Health Services in AAFT Size of the Problem Extent to which services are currently being targeted Extent to which services are not reaching those in most need At Intake. After 3 mon No/Low Need Mod/High Need Total Any Treatment No Treatment Total

Mental Health Problem (at intake) vs. Any MH Treatment by 3 months *3+ on ASAM dimension B3 criteria Source: 2008 CSAT AAFT Summary Analytic Dataset

Why Do We Care About Unmet Need? If we subset to those in need, getting mental health services predicts reduced mental health problems Both psychosocial and medication interventions are associated with reduced problems If we subset to those NOT in need, getting mental health services does NOT predict change in mental health problems Conversely, we also care about services being poorly targeted to those in need.

Residential Treatment need (at intake) vs. 7+ Residential days at 3 months Opportunity to redirect existing funds through better targeting Source: 2008 CSAT AAFT Summary Analytic Dataset