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Briefing for the SAMHSA Workgroup on Underage and
Problem Drinking
Michael Dennis, Ph.D.Chestnut Health Systems, Normal, IL
Presentation for the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Workgroup on Underage and Problem Drinking, Rockville, MD. This presentation reports on treatment & research funded by
the SAMHSA contract 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 at 448 Wylie Drive, Normal, IL 61761, phone: (309) 451-7801, Fax: (309) 451-7763, e-mail: [email protected]
1. Estimate the size and correlates of underage and problem alcohol use
2. Demonstrate how under age drinking is particularly problematic for youth in the short and long run
3. Show how even a short screener can be used to quickly identify behavioral health problems and impact program planning
4. Describe how the GAIN has been used as a key piece of infrastructure to support the move towards evidenced based practice
5. Illustrate what we have learned by pooling data from CSAT adolescent/young adult grantees and its implications for program planning
Goals of this Presentation are to
There 41.4 Million Under Age or Problem Drinkers in the U.S.
54%
31%
34%
58%
3%
4%
9%
7%
0% 20% 40% 60% 80% 100%
Age 12 to 20(38.1mil)
Age 21+(207.9mil)
No use in past yearOnly light alcohol use in the past yearHeavy alcohol use in the past monthAlcohol abuse or dependence in the past year
17.6 Million under age
drinkers (46% of 38.1 Mil)
28.4 Million (12%) Problem Drinkers
(4.6m/12% of youth, 23.8m/11% of adult)
Source: SAMHSA 2006. National Survey On Drug Use And Health, 2006 [Computer file]
Heavy and Problem Alcohol Use is More Common Among Males
52%40%
49% 47%
67%77%
54%
68%
0%
20%
40%
60%
80%
100%
Age 12 to 20 (38.1mil) Age 21+ (207.9mil)
% M
ale
No use in past yearOnly light alcohol use in the past yearHeavy alcohol use in the past monthAlcohol abuse or dependence in the past year
Source: SAMHSA 2006. National Survey On Drug Use And Health, 2006 [Computer file]
Total Population
52% 48%
Underage, Heavy and Youth Problem Alcohol Use is More Common Among Caucasians
55%61%64%
74%83%
77%72% 70%
0%
20%
40%
60%
80%
100%
Age 12 to 20 (38.1mil) Age 21+ (207.9mil)
% C
auca
sian
No use in past yearOnly light alcohol use in the past yearHeavy alcohol use in the past monthAlcohol abuse or dependence in the past year
Source: SAMHSA 2006. National Survey On Drug Use And Health, 2006 [Computer file]
60%70%
Total Population
Alcohol Use Severity is associated with moreCo-occurring Cannabis Abuse or Dependence
1% 0%6% 1%
11%2%
26%
8%
0%
20%
40%
60%
80%
100%
Age 12 to 20 (38.1mil) Age 21+ (207.9mil)
% C
ann
abis
Dis
ord
er
No use in past yearOnly light alcohol use in the past yearHeavy alcohol use in the past monthAlcohol abuse or dependence in the past year
Source: SAMHSA 2006. National Survey On Drug Use And Health, 2006 [Computer file]
5% 1%
Total Population
Odd Ratio=34.8 Odd Ratio=17.7
Alcohol Use Severity is associated with Co-occurring Other Drug Abuse or Dependence
1% 1%2% 1%5% 2%15%
8%
0%
20%
40%
60%
80%
100%
Age 12 to 20 (38.1mil) Age 21+ (207.9mil)
% O
ther
Dru
g D
isor
der
No use in past yearOnly light alcohol use in the past yearHeavy alcohol use in the past monthAlcohol abuse or dependence in the past year
Source: SAMHSA 2006. National Survey On Drug Use And Health, 2006 [Computer file]
3% 1%
Total Population
Odd Ratio=17.5 Odd Ratio=8.6
Alcohol Disorders are associated with Co-occurring Depression
14% 10%19%
9%11% 8%
30% 25%
0%
20%
40%
60%
80%
100%
Age 12 to 20 (38.1mil) Age 21+ (207.9mil)
% D
epre
ssio
n
No use in past yearOnly light alcohol use in the past yearHeavy alcohol use in the past monthAlcohol abuse or dependence in the past year
Source: SAMHSA 2006. National Survey On Drug Use And Health, 2006 [Computer file]
19% 11%
Total Population
NOTE: NSDUH does not ask about other
disorders or ask about them for those under 18
Odd Ratio=2.6 Odd Ratio=3.0
Only Alcohol Abuse/Dependence
associated with higher Psychiatric Comorbidity
National Comorbidity Study Replication (NCSR) Shows Comorbidity is Actually More Common
Source: Dennis, Scott, Funk & Chan forthcoming; National Co morbidity Study Replication
Lifetime Number of Disorders
Lifetime Pattern of Disorders
None54%
1 Disorder18%
2 Disorders10%
3 to 16 Disorders
18%
Substance Only3%
None48%
Sub.+Int4%
Ext.+Int.10%
Sub. + Ext. + Int. 8%
Sub.+Ext1%
Internalizing Only21%
Externalizing Only5%
(28%/46% Any)=61% Co-occurring
(13%/16% SUD)=81% Co-occurring
NOTE: Not asked about work if under age 15 in NSDUH
Potential Screening/ Intervention Sites: Age 12 to 20 (38.1 million)
Source: SAMHSA 2006. National Survey On Drug Use And Health, 2006 [Computer file]
5% 8%
0%
30%
52%
90%
6% 10%
2%
36%
75%
95%
7% 9% 5%
38%
89% 96
%
7%
15%
10%
41%
81%
95%
0%
20%
40%
60%
80%
100%
Hosptial MentalHealth Tx
SubstanceAbuse Tx
EmergencyRoom
Workplace School
% A
ny C
onta
ct
No use in past yearOnly light alcohol use in the past yearHeavy alcohol use in the past monthAlcohol abuse or dependence in the past year
Key potential of Workplace (e.g., EAP, Wellness, HRA) and School (e.g., SAP,
EI, Prevention) Programs
Potential Screening/ Intervention Sites: Age 21+ (207.9 million)
Source: SAMHSA 2006. National Survey On Drug Use And Health, 2006 [Computer file]
16%
12%
1%
32%
58%
10%
13%
1%
27%
80%
7% 8%
1%
26%
87%
8%
21%
8%
34%
89%
0%
20%
40%
60%
80%
100%
Hosptial Mental HealthTx
SubstanceAbuse Tx
EmergencyRoom
Workplace
% A
ny C
onta
ct
No use in past yearOnly light alcohol use in the past yearHeavy alcohol use in the past monthAlcohol abuse or dependence in the past year
Key potential of Workplace Programs
NOTE: Not asked about School if over age 18 in NSDUH
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%
Light Alcohol Use Only 47%
No Alcohol or Drug Use
32%
Source: 2002 NSDUH, Dennis & Scott, 2007
Higher Severity is Associated with Higher Annual Cost to Society Per Person
Source: 2002 NSDUH
$0$231 $231
$725$406
$0$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
No Alcohol orDrug Use
Light Alcohol
Use Only
AnyInfrequentDrug Use
Regular AODUse
Abuse Dependence
Median (50th percentile)
$948
$1,613
$1,078$1,309
$1,528
$3,058Mean (95% CI)
This includes people who are in recovery, elderly, or do not use
because of health problems Higher Costs
Adults & Adolescents
Severity of Past Year Substance Use/Disorders by Age
Source: 2002 NSDUH and Dennis & Scott 2007
0
10
20
30
40
50
60
70
80
90
100
12-13
14-15
16-17
18-20
21-29
30-34
35-49
50-64
65+
No Alcohol or Drug Use
Light Alcohol Use Only
Any Infrequent Drug Use
Regular AOD Use
Abuse
Dependence
NSDUH Age Groups
Severity Category
Over 90% of use and
problems start between the ages of
12-20
It takes decades before most recover or die
People with drug dependence die an
average of 22.5 years sooner than those
without a diagnosis
(2002 U.S. Household Population age 12+=
235,143,246)
Photo courtesy of the NIDA Web site. From A Slide Teaching Packet: The Brain and the Actions of Cocaine, Opiates, and Marijuana.
pain
Adolescent Brain Development Occurs from the
Inside to Out and from Back to Front
Crime & Violence by Substance Severity
0%
10%
20%
30%
40%
50%
60%
Serious FightAt School
Fighting withGroup
Sold Drugs Attacked withintent to harm
Stole (>$50) CarriedHandgun
Dependence (3.9%) Abuse (4.2%)
Weekly AOD Use (6.4%) Any Drug or Heavy Alc Use (8.8%)
Light Alc Use (12.4%) No PY AOD Use (64.3%)
Source: NSDUH 2006
Adolescents 12-17Substance use severity is related to crime and violence
Family, Vocational & MH by Substance Severity
Source: NSDUH 2006
0%
10%
20%
30%
40%
50%
60%
10 or MoreArguments with
Parents
Disliked School GPA = D orlower
MajorDepression
Any MHTreatment
Dependence (3.9%) Abuse (4.2%)
Weekly AOD Use (6.4%) Any Drug or Heavy Alc Use (8.8%)
Light Alc Use (12.4%) No PY AOD Use (64.3%)
Adolescents 12-17..as well as family, school
and mental health problems
Age of First Use Predicts Symptoms of Dependence an Average of 22 years Later
Source: Dennis, Babor, Roebuck & Donaldson (2002) and 1998 NHSDA
3945
63
71
3734
51
62
3023
4148
0
10
20
30
40
50
60
70
80
90
100
Pop.=151,442,082Pop.=176,188,916Pop.=71,704,012Pop.=38,997,916
% w
ith
1+ P
ast Y
ear
Sym
ptom
s
Under Age 15
Aged 15-17
Aged 18 or older
Tobacco: OR=1.49*
Alcohol: OR=2.74*
* p<.05
Marijuana:OR=2.45*
Other Drugs:OR=2.65*
People Entering Publicly Funded Treatment Generally Use For Decades
Per
cen
t st
ill u
sin
g
Years from first use to 1+ years of abstinence302520151050
Source: Dennis et al., 2005
100%
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
Per
cen
t st
ill u
sin
g
Years from first use to 1+ years of abstinence
under 15
21+
15-20
Age of First Use*
302520151050
Source: Dennis et al., 2005
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
60% longer
The Younger They Start, The Longer They Use
* p<.05
Per
cen
t st
ill u
sin
g
Years from first use to 1+ years of abstinence
Years to first Treatment Admission*
302520151050
Source: Dennis et al., 2005
100%
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
7.8%
20.9%
7.2%
0.5%1.0%0.4%0%
5%
10%
15%
20%
25%
12 to 17 18 to 25 26 or older
Abuse or Dependence in past yearTreatment in past year
Why we need to be expand beyond specialty care into school, work place, and health care..
Source: OAS, 2009 – 2006, 2007, and 2008 NSDUH
Over 88% of adolescent and young adult treatment and
over 50% of adult treatment is publicly funded
Few Get Treatment: 1 in 19 adolescents,
1 in 21 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
Health care reform
(including school based health
care, prevention care, and equity) may change this
Source: French et al., 2008; Chandler et al., 2009; Capriccioso, 2004
Cost of Substance Abuse Treatment Episode
$407
$1,249$1,132$1,384$2,486$2,907$4,277
$14,818
$0
$1
0,0
00
$2
0,0
00
$3
0,0
00
$4
0,0
00
$5
0,0
00
$6
0,0
00
$7
0,0
00
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)
$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
Many SBIRT, School, Workplace and other early
intervention programs focus on brief intervention
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
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.
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 SBIRT for tobacco, alcohol and increasingly drugs
CSAT, CSAP, OJJDP, BJS NIAAA and NIDA are funding several projects to develop and evaluate models for doing this in primary care, trauma, emergency departments, schools, workplaces, and justice programs
Washington State mandated screening in all adolescent and adult substance abuse treatment, mental health, justice, and child welfare programs with the 5 minute Global Appraisal of Individual Needs (GAIN) short screener
77% 86
%
73%
75%
61%67
%
83%
62%
75%
60%
57%
40% 46
%
12%
12%
47%
37%
35%
12%
11%
0%10%20%30%40%50%60%70%80%90%
100%
Substance AbuseTreatment(n=8,213)
Student AssistancePrograms(n=8,777)
Juvenile Justice(n=2,024)
Mental HealthTreatment (10,937)
Children'sAdministration
(n=239)
Either High on Mental Health High on Substance 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/
Washington State Results with GAIN Short Screener: Adolescent
Problems could be easily identified & Comorbidity common
35%
12%
11%
56%
34%
15%
9%
47%
0%10%20%30%40%50%60%70%80%90%
100%
Substance AbuseTreatment (n=8,213)
Juvenile Justice(n=2,024)
Mental HealthTreatment (10,937)
Children'sAdministration
(n=239)
GAIN Short Screener 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/
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
0 5,000 10,000 15,000 20,000 25,000
Any BehavioralHealth (n=22,879)
Mental Health(21,568)
Substance AbuseNeed (10,464)
Co-occurring(9,155)
Substance Abuse Treatment Student Assistance ProgramJuvenile Justice Mental Health TreatmentChildren's Administration
Where in the System are the Adolescents with Mental Health, Substance Abuse and Co-occurring?
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/
School Assistance Programs (SAP) largest part of BH/MH system
SAP+ SA Treatment
Over half of system
Washington State Results with GAIN Short Screener: Adults
81%
78%
65%
64% 69
%
18%
68% 73
%
43%
44%
69%
17%
69%
51%
53%
51%
17%
4%
56%
46%
31%
31%
17%
3%
0%10%20%30%40%50%60%70%80%90%
100%
SubstanceAbuse
Treatment(n=75,208)
Eastern StateHospital(n=422)
Corrections:Community(n=2,723)
Corrections:Prison
(n=7,881)
Mental HealthTreatment(55,847)
ChildrensAdministration
(n=1,238)
Either High on Mental Health High on Substance 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/
Problems could be easily identified & Comorbidity common
Washington State Validation of Co-occurring: GAIN Short Screener vs Clinical Records
17%
3%
59%
39%
22%
56%
0%
10%20%
30%40%
50%
60%70%
80%90%
100%
Substance Abuse Treatment(n=75,208)
Mental Health Treatment(55,847)
Childrens Administration(n=1,238)
GAIN Short Screener 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.
(Important of considering urine tests and other sources of information)
0 20,0
00
40,0
00
60,0
00
80,0
00
100,
000
120,
000
Any Behavioral Health (n=106,818)
Mental Health (n=94,832)
Substance Abuse (n=67,115)
Co-Occurring (n=55,128)
Substance Abuse Treatment Eastern State HospitalCorrections: Community Corrections: PrisonMental Health Treatment Childrens Administration
Where in the System are the Adults with Mental Health, Substance Abuse and Co-occurring?
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/
Substance Abuse Treatment is over half of treatment system for substance disorders, other mental disorders, and co-occurring
0%1%2%3%4%5%6%7%8%9%
10%11%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Total Disorder Sceener (TDScr) Score
% w
ithi
n L
evel
of
Car
e
Residential (n=1,965)
OP/IOP (n=2,499)
SAP (n=10,649)
Low
Mod. High ->
32
Total Disorder Screener Severity by Level of Care: Adolescents
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
0%1%2%3%4%5%6%7%8%9%
10%11%12%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Total Disorder Sceener (TDScr) Score
% w
ithi
n L
evel
of
Car
e
Residential (n=1,965)
OP/IOP (n=2,499)
Low
Mod. High ->
33
Total Disorder Screener Severity by Level of Care: Adults
Source: SAPISP 2009 Data and Dennis et al 2006
Residential Median= 8.5(59% at 10+)
Outpatient Median=4.5(29% at 10+)
10% of adult OP missed)
About 20% of OP are in the high severity range more typical of residential
GAIN SS Can Also be Used for Monitoring
109
11
910
8
32 2
0
4
8
12
16
20
Intake 3Mon
6Mon
9Mon
12Mon
15Mon
18Mon
21Mon
24Mon
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
Use of a short common screener can
Provide immediate clinical feedback that is a good approximation of diagnosis and be used to guide placement and treatment planning
Can be used repeatedly to track change
Support evaluation and planning at program or state level (e.g., needs, case mix, services needed)
Provide practice based evidence to guide future clinical decision
Be incorporated into health risk/ wellness assessments and/or school surveys
In practice we need a Continuum of Measurement (Common Measures)
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
Screener Quick C
omprehensive S
pecial
More E
xtensive / Longer/ E
xpensive
Longer assessments identify more areas to address in treatment planning
40%
69%
94%98%
22%
13%
3% 0%
22%
8%
1% 0%
9%8%
1% 1%3% 1% 1%7%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
GAIN SS GAIN Q(v2)
GAIN Q(v3 -Beta)
GAIN I
0 Reported
1 Prob.
2 Probs.
3 Probs.
4 Probs.
Source: Reclaiming Futures Portland, OR and Santa Cruz, CA sites (n=192)
Most substance users have multiple problems
37
5 min. 20 min 30 min 1-2 hr
How does this relate to the move towards Evidence Based Practice (EBP)?
EBP means introducing explicit intervention protocols – 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
Reliable and valid assessment is needed that can be used to – Immediately guide clinical judgments about
diagnosis/severity, placement, treatment planning, and the response to treatment at the individual level
– Drive longer term program evaluation, needs assessment, performance monitoring and program planning
– Allow evaluation of the same person or program over time– Allow comparisons with other people or interventions
Major Predictors of Bigger Effects Found in Multiple Meta Analyses
1. A strong intervention protocol based on prior evidence
2. Quality assurance to ensure protocol adherence and project implementation
3. Proactive case supervision of individual
4. Triage to focus on the highest severity subgroup
Impact of the numbers of these Favorable features on Recidivism in 509 Juvenile Justice Studies in Lipsey Meta Analysis
Source: Adapted from Lipsey, 1997, 2005
Average Practice
The more features, the lower
the recidivism
Evidenced Based Treatment (EBT) that Typically do Better than Usual Practice in Reducing Juvenile Recidivism (29% vs. 40%)
Aggression Replacement Training Reasoning & Rehabilitation Moral Reconation Therapy Thinking for a Change Interpersonal Social Problem Solving MET/CBT combinations and Other manualized CBT Multisystemic Therapy (MST) Functional Family Therapy (FFT) Multidimensional Family Therapy (MDFT) Adolescent Community Reinforcement Approach (ACRA) Assertive Continuing Care
Source: Adapted from Lipsey et al 2001, Waldron et al, 2001, Dennis et al, 2004
NOTE: There is generally little or no differences in mean effect size between these brand names
Implementation is Essential (Reduction in Recidivism from .50 Control Group Rate)
The effect of a well implemented weak program is
as big as a strong program implemented poorly
The best is to have a strong
program implemented
well
Thus one should optimally pick the strongest intervention that one can
implement wellSource: Adapted from Lipsey, 1997, 2005
43
Percentage Change in Abstinence (6 mo-Intake) by level of Adolescent Community Reinforcement Approach (A-CRA) Quality Assurance
4%
24%36%
0%10%20%30%40%50%60%70%80%90%
100%
Training Only Training,Coaching,
Monitoring
Clinical TrialOnsite Protocol
Monitors
% P
oint
Cha
nge
in A
bsti
nenc
e
Source: CSAT 2008 SA Dataset subset to 6 Month Follow up (n=1,961)
Effects associated with intensity of quality
assurance and monitoring (OR=13.5)
Source: 2008 CSAT AAFT Summary Analytic Dataset
553/771=72%unmet need
218/224=97% to targeted
771/982=79% in need
Importance of Targeting on Performance measures
Size of the Problem
Extent to which services are currently being targeted
Extent to which services are not reaching those in most need
Treatment Received in the first 3 months
Mental Health Need at Intake
No/Low Mod/High Total
Any Treatment 6 218 224
No Treatment 205 553 758
Total 211 771 982
Mental Health Problem (at intake) vs. Any MH Treatment by 3 months
79%
97%
72%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
% of Clients WithMod/High Need
(n=771/982)*
% w Need but No ServiceAfter 3 months
(n=553/771)
% of Services Going toThose in Need
(n=218/224)
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
36%
52%
90%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
% of Clients WithMod/High Need
(n=349/980)*
% w Need but NoService After 3 months
(n=315/349)
% of Services Going toThose in Need (n=34/66)
Opportunity to redirect
existing funds through better
targeting
Source: 2008 CSAT AAFT Summary Analytic Dataset
The 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
Designed to support secondary analyses and comparisons across individuals and programs
The GAIN is NOT an electronic health record (EHR), but a component that can interface with and support EHRs.
More in BZ, CA, CN, JP, MX
ID
ILMO
ND
VI
ME
OK
PR
SD
AR
KS
MS
MT
NM
WVIN
AL
AK
IA
MN
NJNV
RI
SC
UT
HI
LA
DENE
TN
PA
VT
VADC
MI
COKY
GA
OH
OR
MD
AZ
TX
NY
NH
WI
CA
NC
CT
FL
MA
WA
WY
No of GAIN Sites
None (Yet)
1 to 14
15 to 30
31 to 165
The GAIN was developed in collaboration with and is used by a wide range of systems in the US..
State or Regional System
GAIN-Short Screener
GAIN-Quick
GAIN-Full
3/10 49
50
Backbone Funded by CSAT to Support Grant Programs: Grantees Using the GAIN from 9/2007 to 6/2010
AK
ALAR
AZ
CACO
CT
DCDE
FL
GA
HI
IA
ID
ILIN
KSKY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PARI
SC
SD
TN
TX
UTVA
VTWA
WI
WV
WY
PR VI
AAFTARTATDCBIRTJTDCEARMARKEATFDCJDCOJJDPORPRCFSACSCANSCYTCEYORP
Individual Grantee(s) State & Individual Grantee(s)
Some numbers as of June 2010
22 states, 12 Federal, 6 Canadian provinces, 6 other countries, and 3 foundations mandate or strongly encourage its use
1,501 Licensed GAIN administrative units from 49 states (all by ND) and 7 countries
3,270 users in 396 Agencies using GAIN ABS
60,380 intake assessments (largest in field)
22,045 (88% w 1+ follow-up) from 278 CSAT grantees
3500 variables (including 103 scales and indices)
4 dozen researchers have published 179 GAIN-related research publications to date
51
52
Expected Factor Structure of Psychopathology and Psychopathy
Source: Dennis, Chan, and Funk (2006)
Screener items were selected using the Rasch (1p IRT) Measurement Model
-1.89 -.8 -.32 +.28 +.71Items around key
decision pointSource: Riley et al 2007 53
Co-occurring Mental Health Problems are Common, but the Type of Problems also Changes with Age
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 .
Internalizing Disorders go up
with age
Externalizing Disorders go down
with age (but do NOT go away)
Any Illegal Activity can be better predicted by using Intake Severity on Crime/Violence and Substance Problem Scales
58%46%
36%53%
33%26%44%
27%20%
0%
20%
40%
60%
An
y I
leg
al
Ac
tiv
ity
(mo
nth
s1
-6)
High Mod Low LowMod
High
Crime/Violence Scale (Intake)
Substance Problem Scale
(Intake)
Source: CSAT 2008 V5 dataset Adolescents aged 12-17 with 3 and/or 6 month follow-up (N=9006)
Intake Crime/ Violence Severity
Predicts Recidivism
Intake Substance Problem Severity
Predicts Recidivism
Knowing both is a better predictor(high –high group is 5.5 times more
likely than low low)
While there is risk, most (42-80%) actually do not commit
additional crime
Key Challenges to Quality Care Addressed by GAIN Logic Model1. 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
5. Lack of Infrastructure
Direct Services
Training and quality assurance on administration, clinical interpretation, data management, follow-up and project coordination
Webservices, software support, and data management
Evaluation and data available for secondary analysis
Technical assistance and back up to local trainer, clinicians, and evaluators
Development
Clinical Product Development
Software Development
Collaboration with IT/EHR vendors (e.g.WITS, NetSmart)
Over 48 internal & external scientists and students
Workgroups focused on specific subgroup, problem, or treatment approach
Labor supply (e.g., consultant pool, college courses)
Outcome: Implementation with Fidelity
62
2009 CSAT Adolescent Treatment Data Set Grantees
AK
ALAR
AZ
CACO
CT
DCDE
FL
GA
HI
IA
ID
ILIN
KSKY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PARI
SC
SD
TN
TX
UTVA
VTWA
WI
WV
WY
PR VI
AAFTARTATDCBIRTJTDCEARMARKEATFDCJDCOJJDPORPRCFSACSCANSCYTCEYORP
63
2009 CSAT Data Set by Age
Source: CSAT 2009 Summary Analytic Data Set (n=22,045)
18 Years or Older (18+)
12.7%, (n=2,793)
Under 15 Years Old (<15) 16.1%,
(n=3,547)
15-17 Years Old
71.2%, (n=15,705)
64
Diagnosis Time Period Matters
57%48%
18%
30%
32%
18%
13%19%
63%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Lifetime Past Year Past Month
No Use
Use
Abuse
Dependence
Source: CSAT 2009 Summary Analytic Data Set (n=21,659)
65
Definition of Substance Use Severity Matters
80%
54%
24%
93%
34%
72%
57%
48%
26%
5%0% 10
%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Past Year Substance Diagnosis
3 or More Years of Use
Weekly Use
Any Past Year Dependence
Any Withdrawal Symptoms in the Past Week
Severe Withdrawal (11+ Symptoms)
Can Give 1+ Reasons to Quit*
Client Believes Need ANY Treatment
Acknowledges Having an AOD Problem
Any Prior Substance Abuse Treatment
Source: CSAT 2009 Summary Analytic Data Set (n=21,816) *(n=11,066)
66
Multiple Clinical Problems are the NORM!
20%
41%
80%
48%
33%
63%
11%
24%
14%
34%
27%0% 10
%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Alcohol
Cannabis
Other drug disorder
Depression
Anxiety
Trauma
ADHD
CD
Suicide
Victimization
Violence/ illegal activity
Source: CSAT 2009 Summary Analytic Data Set (n=20,826)
67
The Number of Clinical Problems is related to Level of Care (over lapping but different mix)
41% 45%53%
65%
80%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
OP IOP CC-OP LTR STR
None
One
Two
Three
Four
Five to Twelve
Source: CSAT 2009 Summary Analytic Data Set (n=21,332)
Significantly more likely to
have 5+ problems (OR=5.8)
68
46%
71%
15%0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Low (0) Moderate (1-3) High (4-15)
None
One
Two
Three
Four
Five to Twelve
The Number of Major Clinical Problemsis highly related to Victimization
Source: CSAT 2009 Summary Analytic Data Set (n=21,784)
Significantly more likely to have 5+
problems (OR=13.9)
But this is the issue staff least
like to ask about!
Overcoming Staff Reluctance with General Victimization Scale
40%
31%
6%10%
1%8%9%
26%
29%7%
57%32%
19%11%
35%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Ever attacked w/ gun, knife, other weapon
Ever hurt by striking/beating
Abused emotionally
Ever forced sex acts against your will/anyone
Age of 1st abuse < 18
Any with more than one person involved
Any several times or for long time
Was person family member/trusted one
Were you afraid for your life/injury
People you told not believe you/help you
Result in oral, vaginal, anal sex
Currently worried someone attack
Currently worried someone beat/hurt
Currently worried someone abuse emotionally
Currently worried someone force sex acts
Source: CSAT 2009 Summary Analytic Data Set (n=19,318) 69
70
B1. Intoxication/Withdrawal Treatment Plan Needs
39%
22%
17%
1%
1%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Any Detox or withdrawal services
Ambulatory Detox (Risk/Mild)
Non-opioid Meds
Opiate Meds
Monitoring withdrawal and AOD medscompliance
Source: CSAT 2009 Summary Analytic Data Set (n=17,392)
71
B2. Biomedical Treatment Plan Needs
60%
33%
29%
17%
6%
1%
1%
78%
3%
4%
11%
16%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Tobacco cessation
Accom. for medical conditions
Discuss compliance w/ prescribed meds
Compliance with meds for PH probs
Discuss ER/hospitalization history
Currently treated for med problem
Tetanus shot
Eating disorder
Treatment of infectious diseases
Accommodations current pregnancy
Reduce sexual behavior risk
Reduce needle use/risk
Source: CSAT 2009 Summary Analytic Data Set (n=17,392)
72
B3. Psychological Treatment Plan Needs
59%
23%
22%
31%
18%
13%
12%
41%
74%
1%
4%
4%
8%
16%
17%
68%
72%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Any Co-occuring
Consq of behavior control problems
Refer to anger management
Suicidal risk intervention
Problems reading and writing
Compliance with psych meds
Currently treated for psych problem
Self-mutilation
Monitor self-mutilation
Cognitive impairment
Discuss lifetime mh hosp. history
Coordination with justice system
Consq of interpersonal illegal acts
Consq of drug-related illegal acts
Discuss lifetime arrest history
Consq of other illegal acts
Civil court proceedings
Source: CSAT 2009 Summary Analytic Data Set (n=18,733)
73
B4.Readiness Treatment Plan Needs
81%
16%
9%
3%
79%
73%
63%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Any Treatment Readiness Issues
Wrap-around or casemanagement services
Any pressure to be in treatment
Required to go to treatment
Reviw expectations for length oftreatment
Review dissatisfaction w/treatment
Partner to understandtreatment process
Source: CSAT 2009 Summary Analytic Data Set (n=9,169)
74
B5. Relapse Potential Treatment Plan Needs
67%
2%
84%
30%
28%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
High Relapse Potential
Recovery coach or mentor
Continuing Care aftercontrolled environment
Significant time in controlledenvironment
Discuss substance abusetreatment history
Source: CSAT 2009 Summary Analytic Data Set (n=21,239)
75
B6. Environment Treatment Plan Needs
63%
32%
29%
26%
32%
47%
54%
56%
70%
85%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Attended school in past 90 days
Coping with psycho-socialstressors
Child maltreatment
Recent school problems
Dissatisfaction withenvironment
Family fighting in the home
Vocational or governmentassistance
Substance use in the home
Employed in past 90 days
Housing situation
Source: CSAT 2009 Summary Analytic Data Set (n=14,952)
76
Individual Strengths
73%
44%
33%
49%
59%
59%
67%
73%
75%
0% 10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Listening, caring or communicating withothers
Doing well at sports, exercise, physicalactivity
Doing well at with your family
Problem solving and figuring things out
Doing well at school or training
Working or playing with computers
Doing well at music, dancing, acting,other performing art
Drawing, painting, design or other artactivities
Doing well at work
Source: CSAT 2009 Summary Analytic Data Set (n=14,952)
77
Social Support
77%
53%
57%
71%
71%
77%
79%
85%
0% 10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Family members/close partners
Someone to talk to about emotions
Legal hobby or activity
Someone to help cope with problems
People at work/school: get assignments
People at work/school: Day to day things
Friends/colleagues from othercompanies/schools
Professional counselor/health provider
Source: CSAT 2009 Summary Analytic Data Set (n=14,952)
78
Mentors in the Recovery Environment
52%
75%
25%
18%
58%
41%
30%
16%
63%
46%
29%
16%
0% 10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
None involved in shouting, arguing orfighting most weeks
None involved in illegal activity
Know any in treatment
Know any in recoveryNone involved in shouting, arguing or
fighting most weeksNone involved in illegal activity
Know any in treatment
Know any in recoveryNone involved in shouting, arguing or
fighting most weeksNone involved in illegal activity
Know any in treatment
Know any in recovery
Source: CSAT 2009 Summary Analytic Data Set (n=14,952)
Hom
eS
choo
l or
Wor
kS
ocia
l P
eers
Critical gap in
connection to recovery community
79
NOMS: Early Treatment Outcomes
56%
66%
76%
84%
72%
58%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Initiation within 14 days
Evidenced Based Practice
Engagement for at least 6weeks
Any Continuing Care (91-180 days)
Substance Use-Abstinent/Reduced 50% at 3 Months
12 month cost within bandsfor initial type of treatment
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups (n=11,668)
80
NOMS: Post Treatment Outcome (6-12 mo)
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups
41%
90%
71%
12%
89%
80%
66%
17%
44%
99%
76%
68%
47%
44%
0% 10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Use
Abuse/Dependence Sx*
Physical Health
Mental Health
Nights of Psychiatric Inpatient
Illegal Activity
Arrests
Housed in Community**
Family/Home Problems
Vocational Problems
Social Support/Engagement
Recovery Environment Risk
Quarterly Cost to Society
In Work/School**
Reduced 50%or NoProblemNo Problem
*This variable measures the last 30 days. All others measure the past 90 days
**The blue bar represents an increase of 50% or no problem
81
But Need to Control for the lack of Problems at Intake
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups
98%
79%
13%
33%37%
52%
78%
61%
11%37%
42%19%
5%
2%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Use
Abuse/Dependence Sx*
Physical Health
Mental Health
Nights of Psychiatric Inpatient
Illegal Activity
Arrests
Housed in Community
Family/Home Problems
Vocational Problems
Social Support/Engagement
Recovery Environment Risk
Quarterly Cost to Society
In Work/School
* Variable measures the last 30 days. All others measure the past 90 days.
82
Change in Number of Positive NOMS Outcomes (Last Follow up – Intake)
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups (n=18,770)
8%6%8%
14%
12%
29%
11%
13%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Total
Five or More
Four
Three
Two
One
None
Negative one
Less than negative one
78% Improved in 1 or more areas (65% in 3 or more)
83
Acknowledgments and Contact Information
Available at www.chestnut.org/li/posters. This presentation was supported by analytic runs provided by Chestnut Health Systems for the
Substance Abuse and Mental Health Services Administration's (SAMHSA's) Center for Substance Abuse Treatment (CSAT) under Contracts 207-98-7047, 277-00-6500, 270-2003-00006 and 270-
2007-00004C using data provided by the following 152 grantees: TI11317 TI11321 TI11323 TI11324 TI11422 TI11423 TI11424 TI11432 TI11433 TI11871 TI11874 TI11888 TI11892 TI11894
TI13190TI13305 TI13308 TI13313 TI13322 TI13323 TI13344 TI13345 TI13354 TI13356 TI13601 TI14090 TI14188 TI14189 TI14196 TI14252 TI14261 TI14267 TI14271 TI14272 TI14283 TI14311 TI14315 TI14376 TI15413 TI15415 TI15421 TI15433 TI15438 TI15446 TI15447 TI15458 TI15461 TI15466 TI15467 TI15469 TI15475 TI15478 TI15479 TI15481 TI15483 TI15485 TI15486 TI15489 TI15511 TI15514 TI15524 TI15524 TI15527 TI15545 TI15562 TI15577 TI15584 TI15586 TI15670 TI15671 TI15672 TI15674 TI15677 TI15678 TI15682 TI15686 TI16386 TI16400 TI16414 TI16904 TI16928 TI16939 TI16961 TI16984 TI16992 TI17046 TI17070 TI17071 TI17334 TI17433 TI17434 TI17446 TI17475 TI17476 TI17484 TI17486 TI17490 TI17517 TI17523 TI17535 TI17547 TI17589 TI17604 TI17605 TI17638 TI17646 TI17648 TI17673 TI17702 TI17719 TI17724 TI17728 TI17742 TI17744 TI17751 TI17755 TI17761 TI17763 TI17765 TI17769 TI17775 TI17779 TI17786 TI17788 TI17812 TI17817 TI17825 TI17830 TI17831 TI17864 TI18406 TI18587 TI18671 TI18723 TI19313 TI19323 TI655374. Any opinions about this data are those of the authors and do not reflect official
positions of the government or individual grantees. Comments or questions can be addressed to Michael Dennis, Chestnut Health Systems, 448 Wylie Drive, Normal, IL 61761. Phone 1-309-451-
7801; E-mail: [email protected]. More information on the GAIN is available at www.chestnut.org/li/gain or by e-mailing [email protected] .