Transcript What do we know and where do we go from here

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 27, 2009
Presentation for the King County and Student Assistance Prevention and Intervention
Services Program (SAPISP) providers at the Puget Sound Educational School District,
Renton, WA, October 27-30, 2009. 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 270-2003-00006
and 270-07-0191, 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
www.chestnut.org/LI/Posters or by contacting Michael Dennis, Chestnut Health
Systems, 448 Wylie Drive, Normal, IL 61761, phone 309-451-7801, fax 309-451-7765,
e-Mail: [email protected] Questions about the GAIN can also be sent to
[email protected]
Goals of this workshop are to
1.
2.
3.
4.
5.
6.
7.
Describe the prevalence, course, and consequences of
adolescent substance use and recovery
Summarize the Move Towards Screening, Brief
Intervention, and Referral to (Long Term) Treatment
Discuss Trends in the Adolescent Substance Abuse
Treatment System in the United States (US)
Highlight what it takes to move the field towards
evidenced-based practice related to assessment,
treatment, program evaluation and planning
Provide an overview of the GAIN Short Screener and
Results from the Student Assistance Prevention and
Intervention Services Program (SAPISP) in Washington
State
Summarize the Results from King County Substance
Treatment System
Stimulate a discussion of the implications for further
program planning and evaluation
2
Part 1.
Prevalence, course, and
consequences of adolescent
substance use and recovery
3
Severity of Past Year Substance Use/Disorders
(2002 U.S. Household Population age 12+= 235,143,246)
Dependence 5%
Abuse 4%
Regular AOD
Use 8%
Any Infrequent
Drug Use 4%
No Alcohol or
Drug Use
32%
Light Alcohol
Use Only 47%
Source: 2002 NSDUH, Dennis & Scott 2007
Problems Vary by Age
NSDUH Age Groups
100
90
80
70
60
Over 90% of
use and
problems
start between
the ages of
12-20
People with drug
dependence die an
average of 22.5 years
sooner than those
without a diagnosis
It takes decades before
most recover or die
Severity Category
50
No Alcohol or Drug Use
Light Alcohol Use Only
Any Infrequent Drug Use
Regular AOD Use
40
30
20
Abuse
Dependence
10
0
65+
50-64
35-49
30-34
21-29
18-20
16-17
14-15
12-13
Source: 2002 NSDUH and Dennis & Scott 2007
Crime & Violence by Substance Severity
60%
Substance use severity is
related to crime and violence
50%
Adolescents 12-17
40%
30%
20%
10%
0%
Serious Fight Fighting with
At School
Group
Dependence (3.9%)
Weekly AOD Use (6.4%)
Light Alc Use (12.4%)
Source: NSDUH 2006
Sold Drugs
Attacked with Stole (>$50)
intent to harm
Carried
Handgun
Abuse (4.2%)
Any Drug or Heavy Alc Use (8.8%)
No PY AOD Use (64.3%)
Family, Vocational & MH by Substance Severity
60%
..as well as family, school
and mental health
problems
50%
Adolescents 12-17
40%
30%
20%
10%
0%
10 or More
Disliked School
Arguments with
Parents
Dependence (3.9%)
Weekly AOD Use (6.4%)
Light Alc Use (12.4%)
Source: NSDUH 2006
GPA = D or
lower
Major
Depression
Any MH
Treatment
Abuse (4.2%)
Any Drug or Heavy Alc Use (8.8%)
No PY AOD Use (64.3%)
Brain Activity on PET Scan After
Using Cocaine
Rapid rise in brain
activity after taking
cocaine
Actually ends up
lower than they
started
1-2 Min
3-4
5-6
6-7
7-8
8-9
9-10
10-20
20-30
Photo courtesy of Nora Volkow, Ph.D. Mapping cocaine binding sites in human and baboon
brain in vivo. Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ, Macgregor RIR,
Hitzemann R, Logan J, Bendreim B, Gatley ST. et al. Synapse 1989;4(4):371-377.
Prolonged Substance Use Injures The Brain:
Healing Takes Time
Normal levels of
brain activity in PET
scans show up in
yellow to red
Reduced brain
activity after regular
use can be seen
even after 10 days
of abstinence
Normal
10 days of abstinence
After 100 days of
abstinence, we can
see brain activity
“starting” to recover
100 days of abstinence
Source: Volkow ND, Hitzemann R, Wang C-I, Fowler IS, Wolf AP, Dewey SL. Long-term frontal brain metabolic changes in cocaine
abusers. Synapse 11:184-190, 1992; Volkow ND, Fowler JS, Wang G-J, Hitzemann R, Logan J, Schlyer D, Dewey 5, Wolf AP.
Decreased dopamine D2 receptor availability is associated with reduced frontal metabolism in cocaine abusers. Synapse 14:169-177,
1993.
Image courtesy of Dr. GA Ricaurte, Johns Hopkins University School of Medicine
Adolescent Brain
Development Occurs from the
Inside to Out and
Front
Photo courtesy offrom
the NIDABack
Web site.to
From
A
Slide Teaching Packet: The Brain and the
Actions of Cocaine, Opiates, and Marijuana.
pain
Percent still using
People Entering Publicly Funded
Treatment Generally Use For Decades
It takes 27 years
before half reach
1 or more years of
abstinence or die
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
5
10
15
20
25
Years from first use to 1+ years of abstinence
Source: Dennis et al., 2005
30
Percent still using
The Younger They Start,
The Longer They Use
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Age of
First Use*
under 15
60% longer
15-20
21+
0
5
10
15
20
25
Years from first use to 1+ years of abstinence
Source: Dennis et al., 2005
30
* p<.05
Percent still using
The Sooner They Get The Treatment,
The Quicker They Get To Abstinence
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Years to first
Treatment
Admission*
20 or
more
years
57% quicker
10 to 19
years
0
5
10
15
20
25
Years from first use to 1+ years of abstinence
Source: Dennis et al., 2005
30
0 to 9
years
•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
After 1 to 3 years of
abstinence, 2/3rds will
make it another year
100%
% Sustaining Abstinence
Another Year
90%
80%
70%
60%
Only a third of
people with
1 to 12 months of
abstinence will
sustain it
another year
86%
After 4 years of
abstinence,
about 86% will
make it
another year
66%
50%
40%
36%
30%
20%
10%
0%
1 to 12 months
1 to 3 years
Duration of Abstinence
Source: Dennis, Foss & Scott (2007)
4 to 7 years
But even after 7 years
of abstinence, about
14% relapse each year
What does recovery look like on average?
1-12 Months
Duration of Abstinence
1-3 Years
4-7 Years
• More clean and sober friends
• Less illegal activity and
incarceration
• Less homelessness, violence and
victimization
• Less use by others at home, work,
and by social peers
• Virtual elimination of illegal activity and illegal
income
• Better housing and living situations
• Increasing employment and income
• More social and spiritual support
• Better mental health
• Housing and living situations continue to improve
• Dramatic rise in employment and income
• Dramatic drop in people living below the poverty line
Source: Dennis, Foss & Scott (2007)
Deaths in the next 12 months
Sustained Abstinence Also Reduces
The Risk of Death
The Risk of Death
goes down with
years of sustained
abstinence
Users/Early
Abstainers
more likely
to die in
the next 12
months
It takes 4 or
more years of
abstinence for
risk to get
down to
community
levels
(Matched on Gender,
Race & Age)
Source: Scott, Dennis, Simeone & Funk (forthcoming)
The Cyclical Course of Relapse, Incarceration,
Treatment and Recovery: Adolescents
P not the same in
both directions
Incarcerated
(46% stable)
5%
10%
20%
In the
Community
Using
(75% stable)
7%
Avg of 39% change
status each quarter
12%
3%
More likely than
adults to be diverted
to treatment
(OR=4.0)
24%
In Recovery
(62% stable)
27%
7%
19%
26%
In Treatment
(48% stable)
More likely than adults to stay 90
Source: 2006 CSAT AT data set
days in treatment (OR=1.7)
7%
Treatment is the
most likely path
to recovery
The Cyclical Course of Relapse, Incarceration,
Treatment and Recovery: Adolescents
Probability of Going from Use to Early “Recovery” (+ good)
-Age (0.8)
+ Female (1.7),
- Frequency Of Use (0.23)
+ Non-White (1.6)
+ Self efficacy to resist relapse (1.4)
+ Substance Abuse Treatment Index (1.96)
In the
Community
Using
(75% stable)
12%
In Recovery
(62% stable)
27%
Probability of from Recovery to “Using” (+ good)
- Freq. Of Use (0.0002)
+ Initial Weeks in Treatment (1.03)
- Illegal Activity (0.70)
+ Treatment Received During Quarter (2.00)
- Age (0.81)
+ Recovery Environment (r)* (1.45)
+ Positive Social Peers (r) (1.43)
* 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.
The Cyclical Course of Relapse, Incarceration,
Treatment and Recovery: Adolescents
Probability of Going from Use to “Treatment” (+ good)
-Age (0.7)
+ Times urine Tested (1.7),
+ Treatment Motivation (1.6)
+ Weeks in a Controlled Environment (1.4)
In the
Community
Using
(75% stable)
7%
In Treatment
(48 v 35% stable)
Source: 2006 CSAT AT data set
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)
In the
Community
Using
(75% stable)
In Recovery
(62% stable)
26%
19%
In Treatment
(48 v 35% stable)
Source: 2006 CSAT AT data set
* 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.
The Cyclical Course of Relapse, Incarceration,
Treatment and Recovery: Adolescents
Incarcerated
(46% stable)
20%
In the
Community
Using
(75% stable)
10%
In Recovery
(62% stable)
Probability of Going to Using vs. Early “Recovery” (+ good)
+ Recovery Environment (r)* (3.33)
* 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
Source: 2006 CSAT AT data set
Cumulative Recovery Pattern at 30 months
5% Sustained
Recovery
37% Sustained
Problems
19% Intermittent,
currently in
recovery
39% Intermittent,
currently not in
recovery
The Majority of Adolescents
Cycle in and out of Recovery
Source: Dennis et al, forthcoming
Percent in Past Month Recovery*
Recovery* by Level of Care
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Outpatient (+79%, -1%)
Residential(+143%, +17%)
Post Corr/Res (+220%, +18%)
CC
better
OP &
Resid
Similar
Pre-Intake
Mon 1-3
Mon 4-6
Mon 7-9
Mon 10-12
* 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)
Early Re-Intervention Experiment 2 (ERI2)

Recruitment of 448 adults from Community Based
Treatment in Chicago in 2000 (84% of eligible recruited)

Quarterly follow-up for 4 years (95-97% follow-up)

Random assignment to Outcome Monitoring (OM)
Control quarterly interviewing Control) or Recovery
Management Checkups (RMC) Experiment

Measures include the GAIN, CEST, CAI, NEO, CRI,
urine tests, and staff logs

Proximal Outcomes: Reduced time readmission,
increased treatment, reduce successive quarters using in
community

Distal Outcomes: Reduced substance use, emotional
problems and HIV risk behaviors
100%
80%
60%
40%
20%
0%
Sample Characteristics of ERI-2 Experiment
African American
Age 30-49
Female
Current CJ Involved
Past Year Dependence
Prior Treatment
Residential Treatment
Other Mental Disorders
Homeless
Physical Health Problems
* No significant differences by condition
ERI 2 (n=446)
Recovery Management Checkup (RMC)




Quarterly Screening to determining “Eligibility”
and “Need”
Linkage meeting/motivational interviewing to:
– provide personalized feedback to participants
about their substance use and related problems,
– help the participant recognize the problem and
consider returning to treatment,
– address existing barriers to treatment, and
– schedule an assessment.
Linkage assistance
– reminder calls and rescheduling
– Transportation and being escorted as needed
Treatment Engagement Specialist
Early Re-Intervention (ERI) Experiment and
Hypotheses
Monitoring
and
Early ReIntervention
Reduce
Time to Readmission
Less
Successive
Quarters
Using
Less Risk
Behaviors,
MH and
Crime
Relative to Control, RMC will reduce the
time from relapse to readmission
The quicker the return to treatment, the less
successive quarters using in the community
The less quarters using in the community, the less HIV
Risk Behaviors, Mental Health and Crime Problems
Source: Dennis et al 2003, 2007; Scott et al 2005, 2009
ERI-2 Time to Treatment Re-Entry at Year 4
The size of the effect is
growing every quarter
100%
Percent Readmitted 1+ Times
90%
80%
45-13 = -32 months
(d=-.41)
70%
RMC increases the
odds of re-entering
treatment over
4 years by 3.1
74% ERI-2 RMC*
(n=198)
60%
48% ERI-2 OM
(n=195)
50%
40%
30%
20%
10%
0%
0
3
Wilcoxon-Gehen
6 9 12 15 18 21 24 27 30 33 36 39 42 45 statistic (df=1)
= 28.60, p<.001
Months from 1st Follow-up In Need for Treatment
,
Source: Dennis & Scott, 2009
OR=3.1, p<.05
ERI-2: Impact on Outcomes at 45 Months
100%
90%
RMC Increased
Treatment Participation
80%
74%
71%
Percentage
70%
60%
More
days of
abstinent
61%
67%
55%
50%
OM
Fewer Seq.
Quarters
in Need
50%
41%
RMC
Less likely
to be in
Need at 45m
56%
47%
38%
40%
30%
20%
10%
0%
Re-entered
Treatment
(d=0.22)*
of 180 Days
of Treatment
(d= 0.26) *
Source: Scott & Dennis (2009)
of 1260 Days
Abstinent
(d= 0.26)*
of 14 Subsequent Still in need of
Quarters in Need Tx at Mon 45
(d= -0.32)*
(d= -0.22) *
* p<.05
Positive Consequences of Early Readmission


Checkups and Early Readmission to Treatment
were associated with:
– Less substance use and problems
– Longer periods of abstinence
– More attendance and engagement in self help
activities
Above were associated with:
– Fewer HIV risk behaviours
– Less illegal activity, arrests, and time
incarcerated
– Fewer mental health problems
– Less utilization and costs to society
Source: Dennis & Scott (2009)
Screening & Brief Inter.(1-2 days)
In-prison Therap. Com. (28 weeks)
Outpatient (18 weeks)
Intensive Outpatient (12 weeks)
Treatment Drug Court (46 weeks)
Residential (13 weeks)
Methadone Maintenance (87 weeks)
Therapeutic Community (33 weeks)
$70,000
$60,000
$50,000
$40,000
$30,000
$20,000
$10,000
$0
Cost of Substance Abuse Treatment Episode
$407
• $750 per night in Detox
$1,249
• $1,115 per night in hospital
$1,132
• $13,000 per week in intensive
care for premature baby
$1,384
• $27,000 per robbery
$2,486
• $67,000 per assault
$2,907
$4,277
$14,818
$22,000 / year
to incarcerate
an adult
$30,000/
child-year in
foster care
Source: French et al., 2008; Chandler et al., 2009; Capriccioso, 2004
$70,000/year to
keep a child in
detention
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

Treatment drug courts have an average ROI of
$2.14 to $2.71 per dollar invested
This also means that for every dollar treatment
is cut, we lose more money than we saved.
Source: Bhati et al., (2008); Ettner et al., (2006)
Part 2. No Wrong Door:
the Move Towards
Screening, Brief
Intervention, and Referral
to (Long Term) Treatment
35
Substance Use Disorders are Common,
But Treatment Participation Rates Are Low:
United States (US)
Over 88% of adolescent and
Few Get Treatment:
1 in 17 adolescents,
1 in 22 young adults,
25%
1 in 12 adults
young adult treatment and
over 50% of adult treatment
is publicly funded
21.2%
Much of the private
funding is limited to 30
days or less and
authorized day by day
or week by week
7.3%
20%
15%
10%
8.9%
5%
0.5%
1.0%
0.6%
0%
12 to 17
18 to 25
26 or older
Abuse or Dependence in past year
Treatment in past year
Source: OAS, 2006 – 2003, 2004, and 2005 NSDUH
Substance Use Disorders are Common,
But Treatment Participation Rates Are Low:
Washington State
23.1%
25%
20%
15%
10%
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
Similar rates for
adolescents :
1 in 18
9.0%
8.0%
3.40%
5%
0.50%
0.6%
0%
12 to 17
18 to 25
26 or older
Abuse or Dependence in past year
Treatment in past year
Source: OAS, 2006 – 2003, 2004, and 2005 NSDUH
Substance Use Disorders are Common,
But Treatment Participation Rates Are Low:
Seattle & King County, WA
25%
20%
15%
10%
Similar problem
rate but much
lower Treatment
Rate: 1 in 40
adolescents
High higher problems rate, but
similar treatment rates:
23.3%
1 in 19 young adults
1 in 12 adults
8.4%
8.2%
5%
0.2%
1.2%
0.6%
0%
12 to 17
18 to 25
26 or older
Abuse or Dependence in past year
Treatment in past year
Source: OAS, 2006 – 2003, 2004, and 2005 NSDUH
The Movement to Increase Screening


Screening, Brief Intervention and Referral to Treatment
(SBIRT) has been shown to be effective in identifying people
not currently in treatment, initiating treatment/change and
improving outcomes (see http://sbirt.samhsa.gov/ )
The US Preventive Services Task Force (USPSTF, 2004;
2007), National Quality Forum (NQF, 2007), and Healthy
People 2010 have each recommended
–
–


regular screening, brief intervention, and referral to treatment (SBIRT)
for tobacco and alcohol abuse in general medical settings for everyone
SBIRT for drug use in high risk populations (e.g., adolescents,
pregnant and post partum women, people with HIV, and people with
co-occurring psychiatric conditions)
CSAT and NIDA are both funding several demonstration and
research projects to develop and evaluate models for doing
this
Washington State mandated screening in all adolescent and
adult substance abuse treatment, mental health, justice, and
child welfare programs
39
Problems could be easily identified
Comorbidity
is common
75%
75%
12%
12%
Substance Abuse Student Assistance
Treatment
Programs
(n=8,213)
(n=8,777)
Either
Juvenile Justice
(n=2,024)
High on Mental Health
Mental Health
Treatment (10,937)
High on Substance
12%
11%
46%
35%
61%
60%
73%
62%
40%
37%
86%
83%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
77%
67%
57%
47%
Adolescent Rates of High (2+) Scores on Mental Health
(MH) or Substance Abuse (SA) Screener by Setting
in Washington State
Children's
Administration
(n=239)
High on Both
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 http://publications.rda.dshs.wa.gov/1392/
4%
3%
17%
17%
18%
17%
Lower than expected
rates of SA in Mental
Health & Children’s
Admin
69%
69%
44%
51%
31%
64%
43%
53%
31%
Eastern State
Hospital
(n=422)
65%
Substance
Abuse
Treatment
(n=75,208)
51%
46%
78%
73%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
81%
68%
69%
56%
Adult Rates of High (2+) Scores on Mental Health
(MH) or Substance Abuse (SA) Screener
by Setting in Washington State
Either
Corrections:
Community
(n=2,723)
High on Mental Health
Corrections:
Prison
(n=7,881)
Mental Health
Treatment
(55,847)
High on Substance
Childrens
Administration
(n=1,238)
High on Both
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 http://publications.rda.dshs.wa.gov/1392/
Adolescent Client Validation of Hi Co-occurring from
GAIN Short Screener vs Clinical Records
by Setting in Washington State
Substance Abuse
Treatment (n=8,213)
Juvenile Justice
(n=2,024)
GAIN Short Screener
Mental Health
Treatment (10,937)
9%
11%
15%
12%
34%
35%
56%
Two page measure closely approximated all found
in the clinical record after the next two years
47%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Children's
Administration
(n=239)
Clinical Indicators
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 http://publications.rda.dshs.wa.gov/1392/
Higher rate in clinical record in Mental Health and
Children’s Administration. But that was based on
-“any use” vs. “week use + abuse/dependence”
- and 2 years vs. past year
3%
17%
22%
39%
59%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
56%
Adult Client Validation of Hi Co-occurring from
GAIN Short Screener vs Clinical Records
by Setting in Washington State
Substance Abuse Treatment
(n=75,208)
Mental Health Treatment
(55,847)
GAIN Short Screener
Childrens Administration
(n=1,238)
Clinical Indicators
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 http://publications.rda.dshs.wa.gov/1392/
Several Recent Reviews, Experiments and QuasiExperiments Have Demonstrated That

Screening, brief intervention and referral to treatment are
being found effective and cost effective

More assertive continuing care can increase adherence
with continuing care expectations

Recovery management checkups can identify people who
have relapsed and get them back to treatment faster

That doing each improves short and long term outcomes

That the rate of improve effects went up as interventions
when from less than 3 months (38%) to 3 to 12 months
(44%) to more than 12 months (100%)
Source: Bhati et al 2008; Dennis et al 2003, 2007, Godley et al 2002, 2007; Marlowe, 2008; McKay, in
press; National Quality Forum, 2007; Scott et al 2005, in press; USPSTF, 2004; 2007,
Part 3. Trends in the Adolescent
Substance Abuse Treatment
System in the
United States (US)
130000
110000
98,713
90000
70000
30000
133,723
17% drop off from
161,424 in 2002 to
133,723 in 2007
62% increase from
98,952 in 1992 to
161,424 in 2002
50000
142,768
145,579
151,321
160,352
161,424
141,350
140,583
142,580
134,898
133,331
126,533
150000
112,335
170000
98,952
Number of Admissions Age 12-17 .
190000
148,935
Trends in Adolescent (Age 12-17) Treatment
Admissions in the U.S.: 1992-2006
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
10000
Year of Admission
Source: Office of Applied Studies 1992- 2007 Treatment Episode Data Set (TEDS) http://www.samhsa.gov/oas/dasis.htm
Change in Adolescent Admissions by Level of
Care in US Public Treatment 1992-2007
180,000
Outpatient the most
common modality
Outpatient the most
common modality
160,000
140,000
120,000
OP (37%)
100,000
80,000
IOP (224%)
60,000
Residential
(23%)
40,000
Detox
(26%)
20,000
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
-
Source: Office of Applied Studies 1992- 2007 Treatment Episode Data Set (TEDS) http://www.samhsa.gov/oas/dasis.htm
Change in Adolescent Referral Source in
US Public Treatment 1992-2007
180,000
160,000
Other (-8%)
Juvenile Justice is the
largest source of referral
140,000
Other Health
Provider (21%)
120,000
School (-7%)
100,000
80,000
Other
Community
Referral (43%)
60,000
Other AOD
Provider (9%)
40,000
Self/Family
(34%)
20,000
Juvenile Justice
(102%)
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
-
Source: Office of Applied Studies 1992- 2007 Treatment Episode Data Set (TEDS) http://www.samhsa.gov/oas/dasis.htm
Change in Adolescent Prior Tx Admissions in
US Public Treatment 1992-2007
160,000
140,000
27% of Adolescents
have been in
treatment before
5 or more Tx
(44%)
4 Prior Tx
(66%)
120,000
100,000
3 Prior Tx
(63%)
80,000
2 Prior Tx
(59%)
60,000
40,000
1 Prior Tx
(62%)
20,000
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
-
No Prior Tx
(46%)
Source: Office of Applied Studies 1992- 2007 Treatment Episode Data Set (TEDS) http://www.samhsa.gov/oas/dasis.htm
Change in Adolescent Focal Problems in
US Public Treatment 1992-2007
140,000
120,000
Primarily Marijuana
and Alcohol
Marijuana (134%)
Alcohol (7%)
Cocaine (33%)
100,000
Methamphetamine
(576%)
80,000
Hallucinogens
(-72%)
But rapid growth in
Methampethamine, Opioids
and Psychotropic's (exceeds
1000% in several states)
60,000
40,000
Stimulants (27%)
Psychotropics
(300%)
Opioids (307%)
20,000
Inhalants (-66%)
-
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
Other (197%)
Source: Office of Applied Studies 1992- 2007 Treatment Episode Data Set (TEDS) http://www.samhsa.gov/oas/dasis.htm
Adolescent Length of Stay 2006
100%
Median of 71 days
90%
366 DAYS OR MORE
80%
181 TO 365 DAYS
70%
121 TO 180 DAYS
60%
50%
91 TO 120 DAYS
40%
61 TO 90 DAYS
30%
46 TO 60 DAYS
20%
31 TO 45 DAYS
10%
0 TO 30 DAYS
0%
Detox (1,565 Residential
IOP
Outpatient
Total
admissions)
(20,868
(17,795
(75,650
(115,878
admissions) admissions) admissions) admissions)
Less than 41% stay the
90 days or longer
recommended by NIDA
Source: Office of Applied Studies 2006 Discharge – Treatment Episode Data Set (TEDS) http://www.samhsa.gov/oas/dasis.htm
Adolescent 2006 Discharge Status US
100%
90%
80%
Only 14% step
down after
intensive treatment
70%
60%
Other
50%
40%
AMA/ASA
30%
TRANSFERRED
20%
TREATMENT
COMPLETED
10%
0%
Detox (1,563 Residential
IOP
Outpatient
Total
admissions)
(20,826
(17,743
(73,828
(113,960
admissions) admissions) admissions) admissions)
56% Successful
Discharge
Source: Office of Applied Studies 2006 Discharge – Treatment Episode Data Set (TEDS) http://www.samhsa.gov/oas/dasis.htm
Days or %
But things are changing: 2002 vs. 2006
100
90
80
70
60
50
40
30
20
10
0
71
56
50
47
10
Median Length of
Stay (Days)
Postive Discharge
(%)
2002
14
Transfers (%)
2006
Source: Data received through August 4, 2004 from 23 States (CA, CO, GA, HI, IA, IL, KS, MA, MD, ME, MI, MN, MO, MT, NE, NJ, OH, OK, RI, SC, TX,
UT, WY) as reported in Office of Applied Studies (OAS; 2005). Treatment Episode Data Set (TEDS): 2002. Discharges from Substance Abuse Treatment
Services, DASIS Series: S-25, DHHS Publication No. (SMA) 04-3967, Rockville, MD: Substance Abuse and Mental Health Services Administration.
Retrieved from http://wwwdasis.samhsa.gov/teds02/2002_teds_rpt_d.pdf and Office of Applied Studies 2006 Discharge – Treatment Episode Data Set
(TEDS) http://www.samhsa.gov/oas/dasis.htm .
Programs often LACK Standardized
Assessment for…






Substance use disorders (e.g., abuse, dependence,
withdrawal), readiness for change, relapse potential
and recovery environment
Common mental health disorders (e.g., conduct,
attention deficit-hyperactivity, depression, anxiety,
trauma, self-mutilation and suicidality)
Crime and violence (e.g., inter-personal violence,
drug related crime, property crime, violent crime)
HIV risk behaviors (needle use, sexual risk,
victimization)
Recovery environment and peer risk
Child maltreatment (physical, sexual, emotional)
Number of Problems is Related to
Level of Care
100%
90%
0 to 1
80%
2 to 4
70%
60%
5 or more
50%
40%
67%
30%
20%
50%
78%
55%
39%
10%
0%
Outpatient
(OR=1)
Intensive
Outpatient
(OR=1.6)
Long Term
Residential
(OR=1.9)
Source: Dennis et al 2009; CSAT 2007 Adolescent
Treatment Outcome Data Set (n=12,824)
Med. Term
Residential
(OR=3.2)
*
Short Term
Residential
(OR=5.5)
Clients entering
Short Term
Residential
(usually dual
diagnosis) have
5.5 times higher
odds of having 5+
major problems*
(Alcohol, cannabis, or other drug disorder,
depression, anxiety, trauma, suicide, ADHD, CD,
victimization, violence/ illegal activity)
55
No. of Problems*
by Severity of Victimization
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
None
One
Two
Three
Four
Five+
70%
45%
15%
Low
(OR 1.0)
Mod.
(OR=4.8)
High
(OR=13.8)
Severity of Victimization
* (Alcohol, cannabis, or other drug disorder, depression, anxiety, trauma, suicide, ADHD, CD,
victimization, violence/ illegal activity)
Source: CSAT AT 2007 dataset subset to adolescent studies (N=15,254)
Those with
high lifetime
levels of
victimization
have 117 times
higher odds of
having 5+
major
problems*
Victimization and Level of Care Interact to
Predict Outcomes
Marijuana Use (Days of 90)
40
CHS Outpatient
CHS Residential
Traumatized groups have
35 higher severity
30
25
20
15
10
High trauma group does
not respond to CHS OP
5
0
Intake
OP -High
6 Months
OP - Low/Mod
Source: Funk, et al., 2003
Intake
Resid-High
Both groups respond to
residential treatment
6 Months
Resid - Low/Mod.
57
Summary of Problems in the US Treatment System

Less than 41% of Adolescents in US stay the 3
months recommended by NIDA researchers

Only about 56% have positive discharges

After intensive treatment, less than 14% step
down to outpatient care

Problems are often assessed in an unstandardized
way that leads to under identification

More than fourth of the adolescents are
“returning” to treatment – suggesting a need for
better continuing care protocols
Part 4.
Highlight what it takes to move
the field towards evidencedbased practice related to
assessment, treatment,
program evaluation and
planning
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
The Current Renaissance of
Adolescent Treatment Research
Feature
1930-1997
1997-2009
Tx Studies*
16
Over 300
Random/Quasi
9
Over 66
Tx Manuals*
0
30+
QA/Adherence
Rare
Common
Std Assessment*
Rare
Common
Under 50%
Over 80%
40-50%
85-95%
Methods
Descriptive/Simple
More Advanced
Economic
Some Cost
Cost, CEA, BCA
Participation Rates
Follow-up Rates
* Published and publicly available
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)
Average
Practice
Source: Adapted from Lipsey, 1997, 2005
Recidivism
Drops the
more factors
present
Cognitive Behavioral Therapy (CBT) Interventions
that Typically do Better than Usual Practice in
Reducing Recidivism (29% vs. 40%)












Adolescent Community Reinforcement Approach (ACRA)
Aggression Replacement Training
Assertive Continuing Care
Brief Strategic Family Therapy (BSFT)
Interpersonal Social Problem Solving
Functional Family Therapy (FFT)
MET/CBT combinations and Other manualized CBT
Moral Reconation Therapy
Multidimensional Family Therapy (MDFT)
Multisystemic Therapy (MST)
Reasoning & Rehabilitation
Thinking for a Change
NOTE: There is generally little or no differences in mean
effect size between these brand names
Source: Adapted from Lipsey et al 2001, Waldron et al, 2001, Dennis et al, 2004
Other Protocols Targeted at Specific Issues:









Detoxification services and medication, particularly
related to opioid and methamphetamine use
Tobacco cessation
Adolescent psychiatric services related to depression,
anxiety, ADHD, and conduct disorder
Trauma, suicide ideation, & parasuicidal behavior
Need for child maltreatment interventions (not just
reporting protocols)
HIV Intervention to reduce high risk pattern of sexual
behavior
Anger Management
Problems with family, school, work, and probation
Recovery coaches, recovery schools, recovery housing
and other adolescent oriented self help groups / services
On-site proactive urine testing can be used to
reduce false negatives by more than half
Reduction in
false negative
reports at no
additional cost
Effects grow
when
protocol is
repeated
Implementation is Essential
(Reduction in Recidivism from .50 Control Group Rate)
The best is to
have a strong
program
implemented
well
Thus one should optimally pick the
strongest intervention that one can
implement well
Source: Adapted from Lipsey, 1997, 2005
The effect of a well
implemented weak program is
as big as a strong program
implemented poorly
Range of Effect Sizes (d) for Change in Days
of Abstinence (intake to 12 months) by Site
1.40
Cohen’s d
1.20
6 programs
completely
above CYT
EAT Programs did
Better than CYT on
average
1.40
1.20
1.00
1.00
0.80
0.80
0.60
0.60
0.40
0.40
0.20
0.20
75% above CYT
median
0.00
4 CYT Sites (f=0.39)
(median within site d=0.29)
Source: Dennis, Ives, & Muck, 2008
36 EAT Sites (f=0.21)
(median within site d=0.49)
0.00
Change in Abstinence (6 mo-Intake)
After Adolescent Community Reinforcement Approach
(A-CRA) by degree of Implementation Monitoring
% Point Change in Abstinence
100%
90%
Effects associated with
intensity of quality
assurance and
monitoring
80%
70%
60%
50%
40%
36%
24%
30%
20%
4%
10%
0%
CYT
AAFT
Other
(high monitoring)
(mod. monitoring)
(training only)
Source: CSAT 2008 SA Dataset subset to 6 Month Follow up (n=1,961)
69
Implications of Implementation Science

Can identify complex and simple protocols that
improve outcomes

Interventions have to be reliably delivered in
order to achieve reliable outcomes

Simple targeted protocols can make a big
difference

Need for reliable assessment of need,
implementation, and outcomes
The Global Appraisal of Individual
Needs (GAIN) is ..





A family of instruments ranging from screening,
to quick assessment to a full Biopsychosocial and
monitoring tools
Designed to integrate clinical and research
assessment
Designed to support clinical decision making at
the individual client level
Designed to support evaluation and planning at
program level and secondary analysis
A key piece of infrastructure in the move
towards evidenced based practice and a key
source of practice based evidence
Progressive Continuum of Measurement
(Common Measures)
Quick
Comprehensive Special
More Extensive / Longer/ Expensive
Screener

Screening to Identify Who Needs to be “Assessed” (5-10 min)
–
–
–
–
–
–
Focus on brevity, simplicity for administration & scoring
Needs to be adequate for triage and referral
GAIN Short Screener for SUD, MH & Crime
ASSIST, AUDIT, CAGE, CRAFT, DAST, MAST for SUD
SCL, HSCL, BSI, CANS for Mental Health
LSI, MAYSI, YLS for Crime

Quick Assessment for Targeted Referral (20-30 min)
– Assessment of who needs a feedback, brief intervention or referral for
more specialized assessment or treatment
– Needs to be adequate for brief intervention
– GAIN Quick
– ADI, ASI, SASSI, T-ASI, MINI

Comprehensive Biopsychosocial (1-2 hours)
– Used to identify common problems and how they are interrelated
– Needs to be adequate for diagnosis, treatment planning and placement
of common problems
– GAIN Initial (Clinical Core and Full)
– CASI, A-CASI, MATE

Specialized Assessment (additional time per area)
–
–
Additional assessment by a specialist (e.g., psychiatrist, MD, nurse,
spec ed) may be needed to rule out a diagnosis or develop a treatment
plan or individual education plan
CIDI, DISC, KSADS, PDI, SCAN
As of June 30, 2009, there were 1127 administrative
units (agencies, grantees, counties, states)
collaborating to use the GAIN in the U.S.,
State or County System
GAIN-Short Screener
GAIN-Quick
GAIN-Full
Canada and other countries
1-10 Sites in
Other Countries:
Brazil
China
Mexico
Japan
Key Issues that we try to address with the
GAIN Instruments and Coordinating Center
1. High turnover workforce with variable education
background related to diagnosis, placement,
treatment planning and referral to other services
2. Heterogeneous needs and severity characterized
by multiple problems, chronic relapse, and multiple
episodes of care over several years
3. Lack of access to or use of data at the program
level to guide immediate clinical decisions, billing
and program planning
4. Missing, bad or misrepresented data that needs to
be minimized and incorporated into interpretations
5. Lack of Infrastructure that is needed to support
implementation and fidelity
1. High Turnover Workforce with Variable Education





Questions spelled out and
simple question format
Lay wording mapped onto
expert standards for given area
Built in definitions, transition
statements, prompts, and
checks for inconsistent and
missing information.
Standardized approach to
asking questions across
domains
Range checks and skip logic
built into electronic
applications




Formal training and certification
protocols on administration,
clinical interpretation, data
management, coordination, local,
regional, and national “trainers”
Above focuses on consistency
across populations, level of care,
staff and time
On-going quality assurance and
data monitoring for the
reoccurrence or problems at the
staff (site or item) level
Availability of training resources,
responses to frequently asked
questions, and technical
assistance
Outcome: Improved Reliability and Efficiency
2. Heterogeneous Needs and Severity






Multiple domains
Focus on most common
problems
Participant self description of
characteristics, problems,
needs, personal strengths and
resources
Behavior problem recency,
breadth , and frequency
Utilization lifetime, recency
and frequency
Dimensional measures to
measure change with
interpretative cut points to
facilitate decisions




Items and cut points mapped
onto DSM for diagnosis,
ASAM for placement, and to
multiple standards and
evidence- based practices for
treatment planning
Computer generated scoring
and reports to guide decisions
Treatment planning
recommendations and links to
evidence-based practice
Basic and advanced clinical
interpretation training and
certification
Outcome: Comprehensive Assessment
3. Lack of Access to or use of Data at the Program Level




Data immediately available to
support clinical decision
making for a case
Data can be transferred to
other clinical information
system to support billing,
progress reports, treatment
planning and on-going
monitoring
Data can be exported and
cleaned to support further
analyses
Data can be pooled with other
sites to facilitate comparison
and evaluation




PC and web based software
applications and support
Formal training and
certification on using data at
the individual level and data
management at the program
level
Data routinely pooled to
support comparisons across
programs and secondary
analysis
Over three dozen scientists
already working with data to
link to evidence-based practice
Outcome: Improved Program Planning and Outcomes
4. Missing, Bad or Misrepresented Data





Assurances, time anchoring,
definitions, transition, and
question order to reduce
confusion and increase valid
responses
Cognitive impairment check
Validity checks on missing,
bad, inconsistency and
unlikely responses
Validity checks for atypical
and overly random symptom
presentations
Validity ratings by staff





Training on optimizing
clinical rapport
Training on time anchoring
Training answering questions,
resolving vague or
inconsistent responses,
following assessment protocol
and accurate documentation.
Utilization and documentation
of other sources of
information
Post hoc checks for on-going
site, staff or item problems
Outcome: Improved Validity
CSAT Adolescent Treatment GAIN Data
from 203 level of care x site combinations
Levels of Care
Long-term Residential
Moderate-Term Residential
Short-Term Residential
Source: Dennis, Funk & Hanes-Stevens, 2008
Outpatient Continuing Care
Intensive Outpatient
Outpatient
Early Intervention
General Group Home
Corrections
Other
Ratings of Problem Severity (x-axis) by Treatment
Utilization (y-axis) by Population Size (circle size)
Utilization
Average Current Treatment
.
1.00
F. HiHi (CC)
12%
0.80
0.60
0.40
B
Low- Mod
0.20
0.00
C
Mod-Mod
20%
A
Low-Low
D
Hi-Low
8%
12%
-0.20
-0.20
G. Hi-Mod
(Env Sx/
PH Tx)
9%
E
HiMod
14%
14%
H. Hi-Hi
(Intx Sx;
PH/MH Tx)
12%
0.00
0.20
0.40
0.60
Average Current Problem Severity
0.80
1.00
Variance Explained in 10 NOMS Outcomes
Percent of Variance Explained
0%
5%
10%
15%
20%
25%
24%
No AOD related Prob.
11%
No Health Problems
25%
No Mental Health Prob.
15%
No Illegal Activity
33%
No JJ System Involve.
26%
Living in Community
18%
No Family Prob.
14%
Vocationally Engaged
8%
Count of above
\1
35%
26%
No AOD Use
Social Support
30%
\2 Past 90 days *All statistically Significant
Past
month
Source: CSAT 2007 AT Outcome Data Set (n=11,013)
24%
Predicted Count of Positive Outcomes by Level
Predicted Count of Positive Outcomes by Level of Care:
of Care: Cluster
A
Low
Low
(n=1,025)
Cluster A Low - Low (n=1,025)
10
10
9
9
8
8
7
7
6
6
5
5
4
Person “A” does
better in Outpatient
3
Person “B” does
better in Higher
Levels of Care
2
4
3
2
Outpatient
Higher LOC
Best Level of Care*:
Level
of Care*:
Cluster A LowBest
- Low
(n=1,025)
Cluster A Low - Low (n=1,025)
120%
% Best Predicted Outcomes
99.6%
100%
80%
60%
40%
20%
0.4%
0%
Outpatient
* Based on Maximum Predicted Count of Positive Outcomes
Source: CSAT 2007 AT Outcome Data Set (n=11,013)
Higher LOC
Best Level of Care*:
Cluster B Low - Mod (n=1,654)
90%
% Best Predicted Outcomes
80%
75.1%
70%
60%
50%
40%
30%
20%
14.1%
10.5%
10%
0.4%
0%
Outpatient
IOP
* Based on Maximum Predicted Count of Positive Outcomes
Source: CSAT 2007 AT Outcome Data Set (n=11,013)
OPCC
Residential
Best Level of Care*:
Best Level
of Care*:
Cluster C Mod-Mod
(n=1209)
Cluster C Mod-Mod (n=1209)
90%
% Best Predicted Outcomes
80%
70%
60%
50%
40%
38.6%
30.2%
30%
23.6%
20%
7.6%
10%
0%
Outpatient
IOP
* Based on Maximum Predicted Count of Positive Outcomes
Source: CSAT 2007 AT Outcome Data Set (n=11,013)
OPCC
Residential
Best Level of Care*:
Cluster D Hi-Low (n=687)
90%
80%
70%
60%
50%
40%
38.3%
33.8%
27.9%
30%
20%
10%
0%
Outpatient
* Based on Maximum Predicted Count of Positive Outcomes
Source: CSAT 2007 AT Outcome Data Set (n=11,013)
IOP/OPCC
Residential
Best Level of Care*:
Best Level of Care*:
Cluster F Hi-Hi
(n=968)
Cluster(CC)
F Hi-Hi
(CC) (n=968)
90%
81.5%
% Best Predicted Outcomes
80%
70%
60%
50%
40%
30%
20%
10%
9.9%
8.6%
0.0%
0%
Outpatient
IOP
* Based on Maximum Predicted Count of Positive Outcomes
Source: CSAT 2007 AT Outcome Data Set (n=11,013)
OPCC
Residential
Best Level of Care*:
Cluster Cluster H Hi-Hi (Intx/PH/MH) (n=1,017)
90%
78.2%
% Best Predicted Outcomes
80%
70%
60%
50%
40%
30%
17.2%
20%
10%
0.0%
4.6%
0%
Outpatient
IOP
* Based on Maximum Predicted Count of Positive Outcomes
Source: CSAT 2007 AT Outcome Data Set (n=11,013)
OPCC
Residential
Best Level of Care*:
Cluster E Hi-Mod (n=1,190)
88.3%
90%
% Best Predicted Outcomes
80%
70%
60%
50%
40%
30%
20%
10.6%
10%
0.0%
1.1%
IOP
OPCC
0%
Outpatient
* Based on Maximum Predicted Count of Positive Outcomes
Source: CSAT 2007 AT Outcome Data Set (n=11,013)
Residential
Best Level of Care*:
Best Level
of Care*:
Cluster G Hi-Mod
(Env/PH)
(n=749)
Cluster G Hi-Mod (Env/PH) (n=749)
100%
94.1%
90%
80%
70%
60%
50%
40%
30%
20%
10%
5.9%
0.0%
0%
Outpatient
IOP/OPCC
* Based on Maximum Predicted Count of Positive Outcomes
Source: CSAT 2007 AT Outcome Data Set (n=11,013)
Residential
Best (x) by Actual (y) Level of Care Placement
3500
3132
3000
2339
2500
1968
2000
Higher
Best
1500
Lower
1000
797
500
0
Outpatient
(n=3132)
Intensive Outpatient
(n=797)
OP - Continuing
Care (n=1968)
Residential
(n=2339)
Exploring Need, Unmet Need, & Targeting of
Mental Health Services in AAFT
At Intake . No/Low
After 3 mon
Any Treatment
Mod/High
Need
Need
6
218
Total
224
218/224=97% to targeted
No Treatment
205
553
758
553/771=72%
unmet need
Total
211
771
982
771/982=79% in need
Size of the Problem
Extent to which services are not reaching those in most need
Extent to which services are currently being targeted
Mental Health Problem (at intake) vs.
Any MH Treatment by 3 months
97%
100%
90%
80%
79%
72%
70%
60%
50%
40%
30%
20%
10%
0%
% of Clients With
Mod/High Need
(n=771/982)*
% w Need but No Service % of Services Going to
After 3 months
Those in Need
(n=553/771)
(n=218/224)
*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
100%
90%
80%
70%
60%
50%
40%
30%
90%
Opportunity to
redirect
existing funds
through better
targeting
52%
36%
20%
10%
0%
% of Clients With
Mod/High Need
(n=349/980)*
% w Need but No
% of Services Going to
Service After 3 months Those in Need (n=34/66)
(n=315/349)
Source: 2008 CSAT AAFT Summary Analytic Dataset
Part 5. Overview of the GAIN Short
Screener and Results from the
Student Assistance Prevention
and Intervention Services
Program (SAPISP) in
Washington State
GAIN-Short Screener (GSS)




Administration Time: A 3- to 5-minute screener
Purpose: Used in general populations to
– identify or rule-out clients who will be identified as
having any behavioral health disorders on the 60-120 min
versions of the GAIN
– triage area of problem
– serve as a simple measure of change
– Easy for administration and interpretation by staff with
minimal training or direct supervision
Mode: Designed for self- or staff-administration, with paper
and pen, computer, or on the web
Scales: Four screeners for Internalizing Disorders,
Externalizing Disorders, Substance Disorders,
Crime/Violence, and a Total
GAIN-Short Screener (GSS) (continued)



Response Set: Recency of 20 problems rated past month (3), 212 months ago (2), more than a year ago (1), never (0)
Interpretation: Combined by cumulative time period as:
– Past month count (3s) to measure of change
– Past year count (2s or 3s) to predict diagnosis
– Lifetime count (1s, 2s or 3s) as a measure of peak severity
– Can be classified within time period low (0), moderate (1-2)
or high (3)
– Can also be used to classify remission as
– Early (lifetime but not past month)
– Sustained (lifetime but not past year)
Reports: Narrative, tabular, and graphical reports built into web
based GAIN ABS and/or ASP application for local hosting
GAIN-Short Screener (GSS)
Expected Factor Structure of
Psychopathology and Psychopathy
Source: Dennis, Chan, and Funk (2006)
101
Co-occurring Mental Health Problems are Common,
Prevalence
of co-occurring
problems
but the Type
of Problems
also Changes
with Age
by age groups
Internalizing
Disorders go up
with age
100
Prevalence (%)
80
Any
Internalizing
60
Externalizing
Both internalizing
and externalizing
40
20
0
<15
15-17
18-25
25-39
Age groups
40+
Externalizing
Disorders go down
with age (but do
NOT go away)
Source: Chan, YF; Dennis, M L.; Funk, RR. (2008). Prevalence and comorbidity of
major internalizing and externalizing problems among adolescents and adults presenting to
substance abuse treatment. Journal of Substance Abuse Treatment, 34(1) 14-24 .
Any Illegal Activity in the Next Six Months by Intake
Severity on Crime/Violence and Substance Problem Scales
Knowing both is a better predictor
(high –high group is 5.5 times more
likely than low low)
Any Ilegal Activity
(months1-6)
Intake Crime/
Violence Severity
Predicts Recidivism
60%
58%
40%
20%
46%
53%
33%
44%
27%
36%
26%
20%
High
0%
Intake Substance
Problem Severity
Predicts
Recidivism
While there is
risk, most (4280%) actually
do not commit
additional crime
Mod
High
Mod
Low
Crime/Violence Scale
(Intake)
Low
Substance
Problem Scale
(Intake)
Source: CSAT 2008 V5 dataset Adolescents aged 12-17 with 3 and/or 6 month follow-up (N=9006)
GAIN SS Psychometric Properties
100%
Low Mod.
High
Prevalence (% 1+ disorder)
90%
Sensitivity (% w disorder above)
80%
Specificity (% w/o disorder below)
(n=6194 adolescents)
70%
60%
50%
40%
20%
At 3 or more
symptoms we get
99% prevalence,
10%
91% sensitivity, &
89% specificity
30%
Using a higher cut
point increases
prevalence and
specificity, but
decreases sensitivity
0%
0
1
2
3
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Total Disorder Screener (TDScr)
Total score has alpha of
.85 and is correlated .94
Source: Dennis et al 2006
with full GAIN version
GSS Performance by Subscale and Disorders
Screener/Disorder
Internal Disorder Screener (0-5)
Any Internal Disorder
Major Depression
Generalized Anxiety
Suicide Ideation
Mod/High Traumatic Stress
External Disorder Screener (0-5)
Any External Disorder
AD, HD or Both
Conduct Disorder
Substance Use Disorder Screener (0-5)
Any Substance Disorder
Dependence
Abuse
Crime Violence Screener (0-5)
Any Crime/Violence
High Physical Conflict
Mod/High General Crime
Total Disorder Screener (0-5)
Any Disorder
Any Internal Disorder
Any External Disorder
Any Substance Disorder
Any Crime/Violence
Prevalence
1+
3+
Sensitivity
1+
3+
Specificity
1+
3+
81%
56%
32%
24%
60%
99%
87%
56%
43%
82%
94%
98%
100%
100%
94%
55%
72%
83%
84%
60%
71%
54%
44%
41%
55%
99%
94%
83%
79%
90%
88%
65%
78%
97%
82%
91%
98%
99%
98%
67%
78%
70%
75%
51%
62%
96%
85%
90%
96%
65%
30%
100%
87%
13%
96%
100%
89%
68%
91%
25%
73%
30%
14%
100%
82%
28%
88%
31%
85%
99%
46%
100%
94%
100%
94%
49%
70%
51%
76%
38%
71%
99%
77%
100%
97%
58%
68%
89%
68%
99%
63%
75%
92%
73%
99%
100%
100%
99%
100%
91%
98%
99%
92%
96%
47%
8%
10%
20%
10%
89%
28%
37%
51%
32%
Moderate
(1+) gives
best result
for
sensitivity
High (3+) gives
best result for
specificity
Recommend
Triage as
0=Not likely
1-2 Possible
3+=Likely
Full GAIN measure
Construct Validity of
GSS Internalizing Disorder Screener
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
% Days with MH
problem
Mod/High on
Emotional Problem
Scale (EPS)
Mod/High on
Internal Mental
Distress Scale
(IMDS)
Internalizing Disorder Screener (IDScr)
0
1
2
3
4
5
Source: Dennis 2009, Education Service District 113 (n=979) and King County (n=1002)
Construct Validity of
GSS Externalizing Disorder Screener
Full GAIN measure
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
% Days with
behavioral
problems
Mod/High on
High on Behavior
Emotional Problem Complexity Scale
Scale (EPS)
(BCS)
Externalizing Disorder Screener (EDScr)
0
1
2
3
4
5
Source: Dennis 2009, Education Service District 113 (n=979) and King County (n=1002)
Construct Validity of
GSS Substance Disorder Screener
100%
90%
Full GAIN measure
80%
70%
60%
50%
40%
30%
20%
10%
0%
% Days of
AOD use
Past Year Abuse or
Dependence
Past Year
Dependence
Substance Disorder Screener (SDScr)
0
1
2
3
4
5
Source: Dennis 2009, Education Service District 113 (n=979) and King County (n=1002)
Construct Validity of
GSS Crime/Violence Screener
Full GAIN measure
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
% Days of illegal
activities
Mod/High on
Illegal Activity
Scale (IAS)
High on
Crime/Violence
Scale (CVS)
Crime and Violence Screener (CVScr)
0
1
2
3
4
5
Source: Dennis 2009, Education Service District 113 (n=979) and King County (n=1002)
Other Validations
Confirmatory Factor Analysis
 Dennis, Chan & Funk (2006) found that the 20 item GSS and its four
subscales were highly correlated (.84 to .94) with the full scale, had 90%
sensitivity and over 90% area under the curve relative to the full GAIN;
Confirmatory factors analysis also found it to be consistent with the overall
model of psychopathology after allowing for age (CFI=.92; RMSEA=.06).
Substance Disorders:
 McDonnell and colleagues (2009) found that the 5-item GAIN SS Substance
Disorder Screener had 92% sensitivity and 85% correct classification relative
to the Diagnostic Inventory Scale for Children (DISC) Predictive Scales
(DPS; Lucas et al 2001) and 88% sensitivity and 88% correct classification
relative to the CRAFFT (Knight et al 2001)
Internalizing Disorders:
 McDonnell and colleagues (2009) found that the 5-item GAIN SS
Internalizing Disorder Screener had 100% sensitivity and 75% correct
classification relative to the Youth Self Report (YSR; Achenbach et al, 2001)
and that the 5-item GAIN SS Externalizing Disorder Screener had 89%
sensitivity and 65% correct classification to the YSR.
 Riley and colleagues (2009) found that the 5-item GAIN SS’s Internalizing
Disorder Screener had 92% sensitivity and 80% area under the curve relative
to the Structured Clinical Interview for DSM (SCID) and was more efficient
relative to 11 item Addiction Severity Index (ASI) psychiatric composite
score (McLellan et al., 1992), 10 item K10 (Kessler et al., 2002) and the 87
item Psychiatric Diagnostic Screening Questionnaire (PDSQ; Zimmerman
and Mattia, 2001)
Total Disorder Screener Severity
Disorder Screener for Adolescents
by Level ofTotal
Care:
Adolescents
Outpatient
Median=6.0
(30% at 10+)
% within Level of Care
11%
Lo Mod. High ->
Residential (n=1,965)
10%
w
9%
OP/IOP (n=2,499)
8%
7%
6%
5%
4%
Residential
3%
Median= 10.5
2%
(59% at 10+)
1%
0%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Few
missed
Total Disorder Sceener (TDScr) Score
(1/2-3%)
Source: SAPISP 2009 Data and Dennis et al 2006
111
Total Disorder Screener Severity
Total Disorder Screener for Adults
by Level of Care:
Adults
% within Level of Care
12%
Outpatient
Lo
Mod.
High
->
Residential (n=1,965)
11%
Median=4.5
10% w
(29% at 10+)
OP/IOP (n=2,499)
9%
8%
7%
6%
5%
4%
3%
Residential
2%
Median= 8.5
1%
(59% at 10+)
0%
10% of 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
adult OP
Total Disorder Sceener (TDScr) Score
missed)
Source: SAPISP 2009 Data and Dennis et al 2006
112
GAIN SS Can Also be Used for Monitoring
20
12+ Mon.s ago (#1s)
2-12 Mon.s ago (#2s)
Past Month (#3s)
Lifetime (#1,2,or 3)
16
12
10
11
9
9
10
Track Gap Between
Prior and current
Lifetime Problems to
identify “under
reporting”
8
8
3
4
2
2
0
Intake
3
6
9
12
15
18
21
24
Mon Mon Mon Mon Mon Mon Mon Mon
Track progress in
reducing current
(past month)
symptoms)
Total Disorder Screener (TDScr)
Monitor for Relapse
Status of Translations
Short
Screener
Other
Instruments Software
Reports
English
Spanish
Done
Done
Done
Done
Done
Done
In progress
In progress
French
In progress
In progress
In progress
In progress
Portuguese Done
Starting
Not yet
Not Yet
Done
Hmong,
Japanese,
Russian,
Pilipino,
Punjabi,
Vietnamese
Not yet
Not yet
Not Yet
Language
114
Student Assistance Prevention and
Intervention Services Program (SAPISP)






Core funding is funneled from DASA via OSPI and combined
with a variety of other local, state, and federal funding sources
(eg, DFSCA, SSHS, SPF-SIG).
13 grantees (the 9 ESDs and 4 largest school districts) hire
specialists to serve about 75% of MS and HS statewide.
Specialists conduct some primary prevention activities and
serve about 16,000 students specifically referred for
assistance related to mental health, alcohol or drug use,
tobacco use or other behavioral problems
Screening using the GAIN-SS was first implemented in the
2007-2008 school year.
Reporting is optional for “Quick” referrals that are seen only
once or twice.
Data Presented here are for the 2008 to 2009 school year
SAPISP Results: State Wide (n=10,924)
15%
17%
44%
40%
6%
6%
8%
23%
13%
72%
16%
3
11%
17%
4
9%
6%
8%
5+
13%
4%
1%
No of Prob.
8%
9%
Source: SAPISP 2009 Data
30%
Crime/
Violence (CV)
19%
28%
1
Substance
Disorder
18%
0
2
20%
Externalizing
Disorder
WA State
dichotomizes as
0-1=Low
2+=High
18%
5%
4%
12%
Internalizing
Disorder
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
GAIN SS
uses triage:
0=Low
1-2=Mod
3+=High
116
Total Disorder Screener Severity
Disorder Screener for Adolescents
by Level ofTotal
Care
Outpatient & Student
Asst. Prog. are Similar
% within Level of Care
11%
(Median
Lo Mod. High ->
Residential (n=1,965)
10%
6.0
vs.
6.4)
w
OP/IOP (n=2,499)
9%
SAP (n=10,649)
8%
7%
6%
5%
4%
Residential
3%
Median
2%
(10.5) is
higher
1%
0%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Well
Targeted
Total Disorder Sceener (TDScr) Score
95% 1+
85% 3+
About 30% of OP & SAP are in the high
Source: SAPISP 2009 Data and Dennis et al 2006 severity range more typical of residential
117
Total Disorder Screener by Level of Care
Out patient (N=8873)
77%
Intensive OP (N=1193)
17%
81%
Ear ly Intervention (N=1112)
6%
14% 5%
86%
11% 4%
Post Resid-CC (N=597)
91%
5% 4%
Mod. Term Resid. (N= 539)
93%
5%1%
Long Term Resid. (N=686)
95%
4%1%
Short Term Resid. (N=237)
99%
Student Asst. Prog. (N= 10,649)
86%
0%
High
Moderate
Low
20%
40%
1%0%
10% 5%
60%
80%
SAP Similar to OP/IOP on Total
Source: SAPISP 2009 Data and CSAT 2008 Full subset to Adolescent Intakes
100%
Internalizing Disorder Screener by Level of Care
Out patient (N=8873)
22%
Intensive OP (N=1193)
28%
Ear ly Intervention (N=1112)
23%
Post Resid-CC (N=597)
37%
Mod. Term Resid. (N= 539)
35%
Long Term Resid. (N=686)
34%
45%
33%
39%
36%
41%
36%
36%
43%
33%
Short Term Resid. (N=237)
48%
35%
Student Asst. Prog. (N= 10,649)
45%
37%
0%
High
Moderate
Low
20%
40%
60%
27%
29%
23%
18%
18%
80%
100%
SAP Higher on Internalizing Disorders
Source: SAPISP 2009 Data and CSAT 2008 Full subset to Adolescent Intakes
Externalizing Disorder Screener by Level of Care
Out patient (N=8873)
37%
27%
Intensive OP (N=1193)
42%
Ear ly Intervention (N=1112)
44%
Post Resid-CC (N=597)
51%
0%
Low
19%
20%
22%
25%
21%
67%
Student Asst. Prog. (N= 10,649)
Moderate
29%
23%
62%
Short Term Resid. (N=237)
32%
26%
51%
Long Term Resid. (N=686)
High
27%
59%
Mod. Term Resid. (N= 539)
36%
17%
22% 12%
37%
40%
60%
80%
12%
100%
SAP Mod-Hi on Externalizing Disorders
Source: SAPISP 2009 Data and CSAT 2008 Full subset to Adolescent Intakes
Substance Disorder Screener by Level of Care
Out patient (N=8873)
39%
40%
20%
Intensive OP (N=1193)
50%
33%
17%
Ear ly Intervention (N=1112)
52%
31%
17%
Post Resid-CC (N=597)
73%
Mod. Term Resid. (N= 539)
70%
Long Term Resid. (N=686)
71%
Short Term Resid. (N=237)
26%
0%
Moderate
Low
26% 4%
20% 9%
82%
Student Asst. Prog. (N= 10,649)
High
14% 14%
20%
17%1%
29%
40%
44%
60%
80%
SAP Lower on Substance Disorders
Source: SAPISP 2009 Data and CSAT 2008 Full subset to Adolescent Intakes
100%
Crime/Violence Screener by Level of Care
Out patient (N=8873)
24%
Intensive OP (N=1193)
38%
33%
Ear ly Intervention (N=1112)
35%
41%
Post Resid-CC (N=597)
45%
Mod. Term Resid. (N= 539)
40%
53%
Short Term Resid. (N=237)
55%
13%
0%
High
Moderate
Low
33%
37%
22%
31%
24%
36%
Long Term Resid. (N=686)
Student Asst. Prog. (N= 10,649)
38%
29%
17%
31%
47%
20%
24%
40%
14%
40%
60%
80%
SAP Lower on Crime/Violence
Source: SAPISP 2009 Data and CSAT 2008 Full subset to Adolescent Intakes
100%
Count of Problems (0-4) with Mod/High
Severity by Level of Care
Out patient (N=8873)
29%
29%
22% 15%
6%
Intensive OP (N=1193)
38%
26%
17% 14% 5%
Ear ly Intervention (N=1112)
38%
30%
19% 10% 4%
Post Resid-CC (N=597)
49%
27%
14%7% 4%
Mod. Term Resid. (N= 539)
47%
30%
16% 7% 1%
Long Term Resid. (N=686)
56%
Short Term Resid. (N=237)
65%
Student Asst. Prog. (N= 10,649)
36%
0%
4
3
2
1
26%
20%
22% 8%5%0%
31%
40%
0
Source: CSAT 2008 Full subset to Adolescents and Intake
12%5%1%
60%
21% 7% 5%
80%
100%
Count of Problems (0-4) with Mod/High
Severity by Demographics (n=10,924)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
4%
7%
5%
8%
23%
18%
8%
9%
19%
31%
31%
5%
7%
5%
8%
21%
20%
30%
32%
4%
9%
4%
6%
6%
9%
0
19%
21%
25%
1
29%
31%
2
32%
34%
3
C
au
ca
s
42%
4
G
ra
d
es
9
-1
2
-8
26%
es
6
ra
d
th
er
33%
O
isp
an
ic
isp
H
36%
G
io
n
,n
ot
isp
32%
no
tH
al
e
ck
,
M
Bl
a
e
al
Fe
m
37%
H
38%
35%
Source: SAPISP 2009 Data
124
Count of Problems (0-4) with Mod/High
Severity by County (n=10,924)
5%
10%
7%
6%
6%
13%
4%
4%
15%
6%
10%
10%
21%
22%
22%
21%
29%
11%
24%
11%
3%
7%
5%
7%
17%
21%
21%
0
1
18%
17%
28%
38%
23%
25%
42%
31%
33%
29%
31%
2
23%
3
19%
37%
26%
33%
29%
31%
32%
31%
38%
36%
15%
4
K
G
ra
ys
in
H
gar
N
bo
ot
Se r
at
tle
*
Le
w
is
M
as
on
Pi
er Pac
ifi
ce
Pi
c
-T
er
ce
ac
-N
om
ot
Ta a
co
m
a
Th
O
ur
th
st
er
on
C
ou
nt
ie
s
ST
A
TE
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Source: SAPISP 2009 Data
125
SAPISP % with Tobacco Goal
by Total Disorder Screener Score
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
% with Tobacco Goal
(OR=4.3)*
Need for Tobacco
Cessation Goal is
related to other
behavioral health
issues
0 1 2 3 4 5 6 7 8 9 1011121314151617181920
Total Disorder Screener (TDScr) Score
Source: SAPISP 2009 Data
126
SAPISP % with Tobacco Goal
by 4 sub-Screeners
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
% with Tobacco Goal
Closely Related to
Other Drug Use,
then Crime, then
Externalizing
Disorders
0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5
Internalizing
Disorder
(OR=1.2)
Source: SAPISP 2009 Data
Externalizing
Disorder
(OR=2.7)
Substance
Disorder
(OR=6.1)
Crime and
Violence
(OR=3.1)
127
Implications
 The GAIN Short Screener can readily identify youth
in need of behavioral health treatment and
distinguish the type of need
 While there is some variation, this holds across
gender, race, age, and geographic location
 While the system was originally set up largely
targeted at substance use, mental health problems are
more common
 SAP are doing a good job of targeting adolescents
with behavioral health issues – but most are actually
in the treatment range (not early intervention)
 Other things like tobacco use are often related to the
the severity of behavioral health problems
128
Part 6. Preliminary Findings from
King County
129
Demographics
29%
Female
African American
12%
53%
Caucasian
27%
Mixed/Other
Hispanic*
12 to 14 years old
14%
17%
65%
15 to 17 years old
18+ years
18%
Single Parent Family
56%
37%
Ever Homeless /Run Away
0%
20%
40%
60%
80%
100%
*Any Hispanic ethnicity separate from race group.
Source: King County 08/31/09 (n=3102)
130
Employed
100%
90%
76%
60%
Any JJ Involvement
Source: King County 08/31/09 (n=3102)
80%
28%
In School
In Controlled
Environment
70%
60%
50%
40%
30%
20%
10%
0%
Environment
34%
131
Anything
100%
90%
80%
70%
58%
Alcohol
17%
Cannabis
39%
Cocaine
3%
Opioid
3%
Other Drugs
Needle Use
60%
50%
40%
30%
20%
0%
10%
Pattern of Weekly Use (13+/90 days) :
7%
2%
Tobacco
Controlled Environment
Source: King County 08/31/09 (n=3102)
47%
18%
132
48%
Any Past Year Dependence
24%
Any withdrawal symptoms in the past week
3%
89%
Can Give 1+ Reasons to Quit*
Client believes Need ANY Treatment
29%
Acknowledges having an AOD problem
29%
Source: King County 08/31/09 (n=3102)
100%
60%
3 or More Years of Use
Any prior substance abuse treatment
90%
79%
Past Year Substance Diagnosis
Severe withdrawal (11+ symptoms) in past week
80%
70%
60%
50%
40%
30%
20%
10%
0%
Substance Use Disorder Severity
63%
133
43%
Conduct Disorder
63%
43%
37%
Ever Homeless or Runaway
Source: King County 08/31/09 (n=3102)
100%
10%
High severity victimization (GVS>3)
Prior Mental Health Treatment
90%
Internalizing
Disorders
21%
Ever Physical, Sexual or Emotional Victimization
Any Self Mutilation*
80%
28%
Major Depressive Disorder
Any homicidal/suicidal thoughts past year
Externalizing
Disorders
38%
Attention Deficit/Hyperactivity Disorder
General Anxiety Disorder
70%
59%
Any Co-occurring Psychiatric
Traumatic Stress Disorder
60%
50%
40%
30%
20%
10%
0%
Co-Occurring Psychiatric Problems
17%
11%
37%
134
Social Peers Getting Drunk Weekly+
100%
90%
80%
70%
42%
Others at Home Getting Drunk
Weekly+
28%
Social Peers Using Drugs
70%
School/Work Peers Using Drugs
Source: King County 08/31/09 (n=3102)
60%
49%
School/Work Peers Getting Drunk
Weekly+
Others at Home Using Drugs
50%
40%
30%
20%
10%
0%
Recovery Environment
59%
10%
135
Sexually active
100%
90%
80%
70%
60%
50%
55%
Any Unprotected Sex
25%
Multiple Sex partners
21%
Victimized Physically, Sexually, or
Emotionally
21%
Any Needle use
40%
30%
20%
0%
10%
Past 90 day HIV Risk Behaviors
2%
Source: King County 08/31/09 (n=3102)
136
Any violence or illegal activity
100%
39%
Any Property Crimes
31%
24%
21%
Prior Juvenile Justice Involvement
60%
Current Juvenile Justice involvement
1+/90 days In Controlled Environment
90%
53%
Any Illegal Activity
Other Drug Related Crimes*
80%
62%
Physical Violence
Any Interpersonal/ Violent Crime
70%
60%
50%
40%
30%
20%
10%
0%
Past Year Violence & Crime
47%
37%
*Dealing, manufacturing, prostitution, gambling (does not include simple possession or use)
Source: King County 08/31/09 (n=3102)
137
Intensity of Juvenile Justice System
Involvement
In detention/jail 1429 days
On prob/parole
6%
14+ days w/ 1+
drug screens
11%
In detention/jail
30+ days
1%
Past year illegal
activity/SA use
45%
Other prob/parole/
detention
11%
Other JJ/CJ status
18%
Source: King County 08/31/09 (n=3102)
Past arrest/JJ/CJ
status
8%
138
Count Number of Problems Mod/Hi*: King County
Over 90%
self report
one or more
major
clinical
problems
100%
90%
80%
70%
60%
10%
11%
11%
1 Prob.
13%
2 Probs.
50%
Over half
report 5 or
more major
clinical
problems
No SR prob
40%
30%
55%
3 Probs.
20%
4+ Probs.
10%
0%
Total (n=3102)
Source: King County 08/31/09 (n=3102)
* (Alcohol, cannabis, or other drug disorder,
depression, anxiety, trauma, suicide, ADHD,
CD, victimization, violence/ illegal activity)
139
GAIN Treatment Planning/Placement Grid
Problem Recency/Severity
None
Current (past 90 days)*
Past
Low-Mod
| High Severity
None
1. No Problem
Consider monitoring
and relapse prevention.
Past
Consider initial or low
invasive treatment.
4. Severe problems;
Not in treatment
Consider a more intensive
treatment or intervention
strategies.
0. Not Logical
Current
Treatment History
2. Past problem
3. Low/Moderate
problems;
Not in treatment
Check under- standing
of problem or lying and
recode.
5. No current
problems;
Currently in
treatment
Review for step down or
discharge.
6. Low/Moderate
problems;
Currently in treatment
Review need to continue or
step up.
7. Severe problems;
Currently in treatment
Review need
for more intensive or
assertive levels.
.
* Current for Dimension B1 = Past 7 days
140
King County ASAM Cells
0%
20%
40%
60%
80%
100%
B1.Intox/Withd.
B2 Biomedical
B3.Psych/Beh
B4.Readiness
B5.Rel. Pot.
B6.Environ.
Inconsistent
High Prob
No problem
No Prob in Tx
Source: King County 08/31/09 (n=2990)
Past Prob
L/M Prob in Tx
Low/Mod Prob
H Prob in Tx
141
75%
Environmental risk
73%
Dual diagnosis
72%
School problems
67%
Risky sexual behavior
64%
Behavior control
63%
Child maltreatment
62%
Need for change
62%
Recovery Coach
60%
Public insurance
60%
Anger management
Source: King County 08/31/09 (n=2645)
100%
70%
Case management
Tobacco cessation
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Coping with stress
Other
Common
Treatment
Planning
Needs
53%
51%
142
ESD113 ASAM Cells by County
Similar overall
Distribution
0%
20%
40%
60%
More variation
by program
80% 100%
Total (n=3102)
Asian CRS (n=26)
United Indians of ATF (n=37)
Ruth Dykeman YFS. (n=118)
Cen for Human Serv. (n=838)
Auburn Youth Res. (n=360)
Kent YFS. (n=183)
Central YFS (n=64)
Friends of Youth (n=91)
Youth Eastside Sv (n=540)
Renton Area YFS (n=26)
Wa. Asian Pacific Islander FASA (n=206)
Integrated Counseling Sv (n=106)
Seattle MH (n=52)
A Low-Low
Outpatient
B Low-Mod
Outpatient
C Mod-Mod
Mixed
D Hi-Low
Mixed
F Hi-Hi (CC)
OP Cont Care
H Hi-Hi (TX)
OP Cont. Care
G Hi-Mod (Env/Tx
Residential
E Hi-Mod
Residential
Source: King County 08/31/09 (n=3102)
143
Implications
 The GAIN Provides a more detailed sense of the
problems and can be used to link these problems to
treatment planning and placement recommendations
 As a whole the system is very similar to the U.S.
treatment system, but the case mix and needs of
programs vary widely
 Adolescents face a wide range of challenges to their
recovery environment that need to be addressed
 There is a need for integration with other mental and
health (e.g., tobacco, STI) services to address
adolescent needs
 Ideally there is a need for treatment record and post
treatment outcome data on these programs & clients
144
References
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