Transcript Document 212095

Summary of SAMHSA
behavioral health data findings related to
Native Americans".
1/14/13
Michael L. Dennis,
Chestnut Health Systems. Normal, IL
Created for: Rod Robinson, Director Substance
Abuse and Mental Health Services Administration’s
(SAMHSA) Office of Indian Alcohol and Substance
Abuse (OIASA)
Goals for the Presentation
2
1. Summarize the major data available for supporting
OIASA mission and what it shows about the quality
chasm in behavioral health
2. Use existing data to identify some of the key needs
3. Discuss some of the things we can do now to
address these needs
Note: Back two thirds of this document are an
appendix of other material to follow up with if desired
The Quality Chasm In Substance Use Disorder Tx
3
 In general, less than 1 in 11 adults and 1 in 20
adolescents with substance use disorder access
treatment
 Half of those who enter leave before the 90 days
recommended by research and less than 1 in 5
receive any kind of continuing care
 While research suggest that approximately 2/3rds
have them, less than 20% are identified with cooccurring mental health disorders
 While few programs have formal assessment of
HIV risk behaviors, trauma, and crime/violence,
research suggests each are common
 Over half relapse within 3 to 12 months
post discharge
3
Native Americans are disproportionately effected
by Substance Use Disorders (SUD)
4
Native Americans (NA) have
higher* than average rates of
Substance Use Disorders (SUD)
Source: SAMHSA, 2011 National Survey on Drug Use and
Health (NSDUH; (p8 of SAMHSA 1/13 newsletter)
* p<.001
Existing Data Sources on Substance Use
Among Native Americans (NA)* by Age
5
Native Americans (n)
Source (NA % of total)
Pros & Cons
2011 NSDUH Pop Est. (0.9%)
raw n
raw n with SUD
Under
18
18-25
26+
Total
202,039 369,683 1,709,700 2,281,442
363
412
376
1,151
(52)
(109)
(53)
(214)
2010 TEDS-Admission (3%)
5,142
9,772
39,732
54,646
2009 TEDS Discharge (3%)
4,760
8,886
36,247
49,893
2007-2010 SAMHSA (7%)
GAIN datasets
2,908
432
589
3,929
2008-2012 All Systems of Care
GAIN data (4.5%)
3,978
1,200
2,387
7,849
* Including Native Alaskans, Hawaiians and Pacific Islanders
The NA Rates of SUD & Unmet Need vary by Age
66
100%
35%
80%
30%
25%
60%
20%
40%
15%
10%
20%
5%
0%
Substance Use Disorder
Treatment
Unmet Need
<18
14%
0.6%
96%
18-25
28%
1.3%
95%
26+
12%
3.1%
74%
0%
Source: SAMHSA 2011 National Survey on Drug Use and Health subset to Native Americans (n=1151, population
estimate=2,281,422). * p<.001
% Unmet Need
Higher rates*
of need for
young adults
40%
% Past Year
Higher rates*
of unmet
need for
adolescents
and young
adults
The NA Rates of SUD & Unmet Need vary by Gender
77
40%
Higher rates*
of unmet need
for adolescent
girls than boys
100%
35%
30%
Higher rates*
of need for
Males overall
and for
adolescent
girls
% Past Year
25%
60%
20%
40%
15%
10%
20%
5%
0%
All
All Males
Females
Substance Use Disorder 18%
12%
Treatment
3.7%
1.4%
Unmet Need
79%
88%
0%
Boys
Girls
7%
0.3%
96%
21%
1.0%
95%
Source: SAMHSA 2011 National Survey on Drug Use and Health subset to Native Americans (n=1151, population
estimate=2,281,422). * p<.001
% Unmet Need
80%
In Spite of Longer Stays, NA Teens less likely to Complete Tx
88
100%
Completion
rates are
lower * for
adolescents
and young
adults
90%
80%
% of Discharges
Lengths of
stay are
longer *
for young
adults
and adults
70%
60%
50%
40%
30%
20%
10%
0%
Comp. or Trans.
45 + days
<18
58%
62%
18-25
56%
53%
Source: SAMHSA 2009 Treatment Episode Data Set – Discharges (TEDS-D) for Native Americans. P<.001
26+
64%
44%
Native American/Alaskan/Hawaiian Clients by State
(3,929 clients from 271 sites between 7/11-6/12)
9
NH
WA
MT
OR
MN
MA
ID
WY
WI
SD
NV
PA
CO
KS
OK
NM
RI
IA
IL
UT
AZ
NY
MI
NE
CA
ME
VT
ND
OH
IN
VA
DE
KY
NC
TN
AR
GA
MS
TX
NJ
DC
WV
MO
CT
MD
SC
None
1 to 25
26 to 100
101 to 500
500+
AL
LA
AK
FL
HI
PR
VI
NA Demographic Characteristics
10
Mostly male,
NA, multiracial, and
under 18
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,929)
NA Pattern of Weekly Use (13+/90 days)
11
*Not a weekly measure; any in past 90 days
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,894)
NA Substance Use Problems
12
*Count of 8 items
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,862)
NA Substance Problem Recognition
13
* n=2,876
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,911)
NA Co-Occurring Psychiatric Problems
14
* Count of Conduct Disorder, ADHD/ADD Major Depressive Disorder,
Traumatic Stress Disorder, and Generalized Anxiety Disorder
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,749)
NA Past Year Crime & Justice Involvement
15
*Dealing, manufacturing, prostitution, gambling (does not include simple
possession or use)
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,768)
NA Count of Major Clinical Problems at Intake
16
*Based on count of self reporting criteria to suggest alcohol, cannabis, or other
drug disorder, depression, anxiety, trauma, suicide, ADHD, CD, victimization,
violence/ illegal activity
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,811)
NA Severity of Victimization Scale
17
*Mean of 15 items
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,803)
NA Count of Major Clinical Problems*
at Intake by Severity of Victimization
18
*Based on count of self reporting criteria to suggest alcohol, cannabis, or other
drug disorder, depression, anxiety, trauma, suicide, ADHD, CD, victimization,
violence/ illegal activity
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,838)
NA Quarterly Cost of
Health Care Utilization
19
 Using the GAIN, we are able estimate the quarterly cost to
society of tangible services (e.g., hospital visits, emergency
room visits, etc.) in 2011 dollars for the 90 days before
intake.
 For the 3,929 clients served in 271 sites between 7/1/2011
and 6/30/2012, the average Quarterly Cost of Health Care
Utilization (HCU) per client:
– in the quarter before they entered treatment, was $3,417 and
totaled $12,535,510 across clients.
– in the year before they entered treatment, was $13,668 per
client and totaled $50,142,040 across clients.
Cost to Society in 2011 Dollars
20
Description
Unit
Inpatient hospital day
Days
$
2,202.87
Emergency room visit
Visits
$
6,477.04
Outpatient clinic/doctor’s office visit
Visits
$
68.58
Nights spent in hospital
Nights
$
2,202.87
Times gone to emergency room
Times
$
6,477.04
Times seen MD in office or clinic
Times
$
79.77
How many days in detox
Days
$
234.86
Times in ER for AOD use
Times
$
270.51
Nights in residential for AOD use
Nights
$
121.62
Days in Intensive outpatient program for AOD use
Days
$
94.36
Times did you go to regular outpatient program
Times
$
32.50
*Quarterly Health Care Utilization 2011 dollars w/ SA TX based on French, M.T., Popovici, I., &
Tapsell, L. (2008). The economic costs of substance abuse treatment: Updated estimates and cost
bands for program assessment and reimbursement. Journal of Substance Abuse Treatment, 35,
462-469.
Unit Cost
NA Quarterly Health Care Utilization Cost
21
Source: GAIN-I 2010 SuperData subset to Native American/ Hawaiian/A
laskan (n=3,668)
NA Cost of Crime in the Past Year
22
 Using the GAIN we are able estimate the cost to society
associated with economic losses due to criminal activity
(e.g., vandalism, forgery, theft, assault, arson, rape,
murder) in 2011 dollars for the year prior to intake.
 Of the 3,929 clients served in 271 sites between
7/1/2011 and 6/30/2012, the average Cost of crime per
client, in the year before they entered treatment, was
$308,148 and totaled $1,107,793,754 across clients.
Cost of Crime in 2011 Dollars*
23
Description
Unit
Unit Cost
Purposely damaged or destroyed property
Passed bad checks/forged a prescription/took money from
employer
Times
$5,095.64
Times
$5,745.70
Taken money/property (not from a store)
Times
$8,360.63
Broken into a house/building to steal
Times
$6,775.32
Taken a car that didn't belong to you
Used a weapon, force, or strong-arm methods to get money or
things from a person
Times
$11,294.29
Times
$44,361.43
Hurt someone badly enough they needed bandages or a doctor
Times
$112,208.95
Made someone have sex with you by force
Been involved in the death or murder of another person
(including accidents)
Times
$252,450.22
Times
$9,418,450.51
Intentionally set a building, car, or other property on fire
Times
$22,126.20
*Cost of Crime 2011 dollars w/ SA TX based on McCollister, K. E., French, M. T., & Fang, H. (2010).
The cost of crime to society: New crime-specific estimates for policy and program evaluation. Drug and
Alcohol Dependence, 108(2)(1-2), 98-109.
NA Cost of Crime in the Past Year
24
Source: GAIN-I 2010 SuperData subset to Native American/ Hawaiian/
Alaskan (n=3,595)
NA GAIN Quick (GAIN-Q) Version 3
Problem Profile
25
*Not used in the GQ Problem Count
Source: GAIN-I 2010 SuperData subset to Native
American/Hawaiian/Alaskan (n=3,188)
Across the 9 screeners on the Q3 85%
of respondents have 3 or more that
rate as moderate to high problems
NA Four Summary Indices
Problematic
Beneficial
26
*GSI groups are usually reversed (low satisfaction scores (0-2) are in the high
problem group); here low satisfaction scores are in the low group, and high
satisfaction scores are in the high group; n=1,823
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,167)
NA Unmet Need for
Medical Treatment by 3 Months
27
Age*
Unmet Need Higher
for Adolescent and
Young Adults
* p<.05
SAMHSA 2011 GAIN SA Data Set subset to Native American/
Hawaiian/Alaskan w/ 3m Follow up (n=735)
Gender*
Unmet Need
Higher for
Males
NA Unmet Need for
Mental Health Treatment by 3 Months
28
Age*
Unmet Need Higher for
young adults and adolescents
* p<.05
SAMHSA 2011 GAIN SA Data Set subset to Native American/
Hawaiian/Alaskan w/ 3m Follow up (n=1,202)
Gender*
Unmet Need Higher
for Males
What can we do?
29
 Implement low cost screening and assessment
 Target locations youth because SUD is an
adolescent on-set disorder and early intervention is
the most effective and produces the greatest long
term services (in lives and money)
 Target assessment and treatment to assume that
there will be “multi-morbidity”
 To reduce health care and crime costs, target the
smaller group of people producing most of the costs
 Identify and reduce health disparities by targeting
treatment to not only NA, but by age, gender, and
other subgroups within NA
Regardless of Diagnosis or Where Patients
Enter, High Quality Care Should be:
30
1. Safe – do no harm
2. Effective – based on scientific knowledge and average
practice (based on actual data)
3. Patient-centered – respectful and responsible to
individual preferences, needs, values and participation in
clinical decision making (vs. staff centered)
4. Timely - reducing waits and delays and when care is
most effective
5. Efficient - avoiding waste of time, energy and money
6. Equitable – providing effective care based on clinical
criteria that does not vary by gender, race, age,
geography or social economic status
Source: IOM 2005
Structural Challenges to Delivery of Quality Care
31
1. Heterogeneous needs and severity characterized by
multiple problems, chronic relapse, and multiple
episodes of care over several years
2. High turnover workforce with variable education
background related to diagnosis, placement, treatment
planning and referral to other services
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
adaptation to NA community and/or
implementation with fidelity
Programs often LACK Evidenced Based Assessment
to Identify and Practices to Treat:
32
 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 suicidal thoughts)
 Crime and violence (e.g., inter-personal violence, drug
related crime, property crime, violent crime)
 HIV risk behaviors (e.g. needle use, sexual risk,
victimization)
 Child maltreatment (e.g. physical, sexual, emotional)
 Recovery environment and risk from social peers
 Long Term Relapse /Recovery Management
In practice we need a Continuum of Measurement
(Common Measures)
33
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
– CIDI, DISC, KSADS, PDI, SCAN
Longer Measures Assess and Identify More Problems
34
Source: CSAT 2010 AT Summary Analytic Data Set (n = 17,356)
Some Advantages of the GAIN System
35
 Provides an integrated continuum of measurement
using a series of evidenced based tools designed to
support clinical decision making
 Established training, certification and workforce
development plan including in person & distance
learning approaches
 Extensive support for line administration, clinical
interpretation, supervision, data management and
interface with electronic health record systems
 Existing Electronic infrastructure
 Track record of building collaboration between clinical
systems of care, clinical researchers & Health IT
 Capitalize on SAMHSA’s 15 year investment
Appendix
36
The following are more detailed slides
supporting points above that might be useful to
have readily available.
Substance Use Disorders are Common,
US Treatment Participation Rates Are Low
Over 88% of adolescent and
young adult treatment and
over 50% of adult treatment is
publicly funded
Few Get Treatment:
1 in 20 adolescents,
1 in 18 young adults,
1 in 11 adults
25%
Much of the private
funding is limited to 30
days or less and
authorized day by day
or week by week
20.1%
20%
15%
10%
7.4%
7.0%
5%
0.4%
1.1%
0.6%
0%
12 to 17
37
18 to 25
Abuse or Dependence in past year
26 or older
Treatment in past year
Source: SAMHSA 2010. National Survey On Drug Use And Health, 2010 [Computer file]
Potential AOD Screening & Intervention Sites
Adolescents (age 12-17)
38
Source: SAMHSA 2010. National Survey On Drug Use And Health, 2010 [Computer file]
Adolescent Rates of High (2+) Scores on Mental Health (MH)
or Substance Abuse (SA) Screener by Setting in WA state
Under
61%
60%
75%
75%
46%
35%
Juvenile Justice
(n=2,024)
High on Mental Health
12%
11%
Student
Assistance
Programs
(n=8,777)
12%
12%
Substance Abuse
Treatment
(n=8,213)
Either
73%
62%
40%
37%
77%
67%
57%
47%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
86%
83%
Problems could be easily identified & Comorbidity common
reporting of39
SA in mental
health &
children’s
admin
Mental Health
Treatment
(10,937)
Children's
Administration
(n=239)
High on Substance
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/
High on Both
Adolescent Client Validation of High Co-Occurring from GAIN Short
Screener vs. Clinical Records by Setting in WA State
40
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%
Yet the two-page measure closely approximated all
found in the clinical record after the next 2 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/
Where in the System are the Adolescents with Mental Health,
Substance Abuse and Co-occurring?
41
SAP+ SA
Treatment
Over half of
system
School Assistance
Programs (SAP) largest
part of BH/MH system;
2nd largest of SA & Cooccurring systems
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/
Total Disorder Screener Severity by Level of Care: Adolescents
Total Disorder Screener for Adolescents
% within Level of Care
42
11%
Lo Mod. High ->
10%
w
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%
0 1 2 3 4 5
Few missed
(1/2-3%)
Outpatient
Median=6.0
Residential (n=1,965)
OP/IOP (n=2,499)
Residential
Median= 10.5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Total Disorder Sceener (TDScr) Score
About 41% of Residential are below 10
(more likely typical OP)
Source: SAPISP 2009 Data and Dennis et al 2006
About 30% of OP are in the high severity
range more typical of residential
42
Any Illegal Activity in the Next Twelve Months by Intake Severity
on Crime/Violence and Substance Disorder Screeners
43
Source: CSAT 2010 Summary Analytic Dataset (n=20,982)
Predictive Power of Simple Screener:
12 Month Recidivism
44
\a
Crime/
Substance
12 Month
Odds
Violence
Disorder
Recidivism
Ratio
Screener
Screener
Rate
\a
Low (0)
Low (0)
17%
1.0
Low (0)
Mod (1-2)
29%
2.0*
Low (0)
High (3-5)
30%
2.1*
Mod (1-2)
Low (0)
30%
2.1*
Mod (1-2)
Mod (1-2)
35%
2.6*
Mod (1-2)
High (3-5)
42%
3.5*
High (3-5)
Low (0)
41%
3.4*
High (3-5)
Mod (1-2)
55%
6.0*
Odds
of row
(%/(1-%) over
low/low
odds across61%
all groups with * p<.05
High
(3-5)
High
(3-5)
7.6*
Source: CSAT 2010 Summary Analytic Dataset (n=20,932)
Major Predictors of Bigger Effects
45
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
Source: Adapted from Lipsey, 1997, 2005
Impact of the numbers of these Favorable features on Recidivism
in 509 Juvenile Justice Studies in Lipsey Meta Analysis
46
Average
Practice
Source: Adapted from Lipsey, 1997, 2005
The more
features,
the lower
the
recidivism
Cognitive Behavioral Therapy (CBT) Interventions that
Typically do Better than Usual Practice in Reducing Juvenile
Recidivism (29% vs. 40%)
47











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
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
Implementation is Essential
(Reduction in Recidivism from .50 Control Group Rate)
48
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
Economic Analysis of SAMHSA/CSAT Funded Treatment
49
 As part of SAMHSA/CSAT contract no. 270-07-0191, data
were pooled from 22,045 clients from 148 local evaluations,
recruited between 1997 to 2009, and followed quarterly for 6 to
12 months (over 80% completion).
 In 2009 dollars, the 2,793 adults averaged $1,417 in costs to
taxpayers in the 90 days before intake ($5,669 in the year
before intake).
 In 2009 dollars, the 16,915 adolescents averaged $3,908 in
costs to taxpayers in the 90 days before intake ($15,633 in the
year before intake).
 This would be $1.4 million per 1,000 adults served and $3.9
million per 1,000 adolescents served.
 Within 12 months, the cost of treatment provided by CSAT
grantees was offset by reductions in other costs producing a
net benefit to taxpayers of $1,992 per adult and $4,592 per
adolescent.
SAMHSA/CSAT’s Adult Clients by Level of Care
50
Adult Level of Care
Year
before
intake
Year
after
Intakea
One
Year
Savingsb
Outpatient
$12,806
$9,241
$3,565
Intensive Outpatient
$15,263
$15,197
$ 66
Outpatient Continuing Care
$34,057 $14,310
Residential
$19,443 $24,297 ($4,854)c
Total
$17,035 $12,442
\a Includes the cost of treatment
\b Year after intake (including treatment) minus year before treatment
\c Cost of residential treatment is not offset yet at one year after intake
$19,748
$4,592
SAMHSA/CSAT’s Adolescents Clients by Level of Care
51
Adolescent Level of Care
Year
before
intake
Year
after
Intakea
One
Year
Savingsb
Outpatient
$10,993
$10,433
$560
Intensive Outpatient
$20,745
$15,064
$5,682
Outpatient Continuing Care
$34,323
$17,000
$17,323
Long Term Residential
$27,489
$26,656
$833
Short Term Residential
$25,255
$21,900
$3,355
Total
$15,633
$13,642
$1,992
\a Includes the cost of treatment
\b Year after intake (including treatment) minus year before treatment
Adolescents Clients by Setting
52
Adolescent Level of Care
Year
before
intake
Year
after
Intakea
One
Year
Savingsb
Average Outpatient
$10,993
$10,433
$560
A-CRA Outpatient
$17,255
$10,615
$6,640
$11,122
$6,475
$4,648
$13,614
$10,489
$3,125
$10,100
$7,686
$2,413
Just Health Care Cost
A-CRA in Schools
Just Health Care Costs
\a Includes the cost of treatment
\b Year after intake (including treatment) minus year before treatment
GAIN Treatment Planning/Placement Grid
53




The problem recency/severity axis allows you to classify the client’s problem according
to whether it is a current problem, a past problem, or there is no problem ; “Current
problem” is further broken down into low to moderate severity or high severity
problem
The treatment history axis allows you to classify whether the client is currently
receiving treatment for a problem, received treatment in the past, or never received
treatment
Problem severity and treatment history are determined using responses to GAIN
questions For more information on defining problem severity, see Chapter 6 of the
GAIN manual, available free for download at
http://www.gaincc.org/_data/files/Instruments%20and%20Reports/Instruments%20M
anuals/GAIN-I%20manual_combined_0512.pdf or email [email protected]
Together, the two axes allow for categorization of the client’s problem according to
whether they have a problem and whether they are receiving treatment for it already.
In general:
– More severe problems indicate the need for a higher level of care, particularly if
current or prior interventions have been unsuccessful
– Lower severity problems may be addressed with a lower-intensity interventions,
unless there is a prior history of intervention
– This applies to problems on any ASAM treatment planning dimension.
GAIN Treatment Planning/Placement Grid
54
Problem Recency/Severity
None
Current (past 90 days)*
Past
Low-Mod
1. No problem
None
Past
Current
Treatment History
0. Not Logical
Check understanding of
problem or lying
and recode.
2. Past problem
Consider
monitoring
and relapse
prevention.
5. No current
problems;
Currently in
treatment
Review for step
down or
discharge.
3. Low/Moderate
problems;
Not in treatment
Consider initial or
low invasive
treatment.
6. Low/Moderate
problems;
Currently in
treatment
Review need to
continue or step up.
* Current for Dimension B1 = Past 7 days
|
High Severity
4. Severe
problems;
Not in treatment
Consider a more
intensive treatment
or intervention
strategies.
7. Severe
problems;
Currently in
treatment
Review need
for more intensive
or assertive levels.
GAIN Placement Cells by ASAM
Dimension
55
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,812)
GAIN Placement Cells by ASAM
Dimension - Interpretation
56
 Some ASAM dimensions are of relatively low concern in this
predominantly adolescent population (Intoxication/Withdrawal and
Biomedical concerns)
 The most severe problems appear in the Environment (B6), Relapse
potential (B5), Treatment acceptance/resistance (B4), and
Psychological/behavioral dimensions
– Of these, Relapse potential shows a high level of current treatment
for these problems (treatment or medication in the past 90 days;
NOTE: this includes receiving a breathalyzer)
 The highest rate of no problems is for intoxication/withdrawal, however,
current problems are measured in the past 7 days for this dimension,
rather than the past 90 days used for the other dimensions
 The high number of untreated past problems and those with no problems
in treatment on the Biomedical dimension suggests this may be an area
of concern for this population
ASAM Dimension Treatment
Planning Needs
57
 For each ASAM dimension, there is a large number of
possible treatment planning recommendations
 These statements can be generated based on responses to
GAIN questions and are included as recommendations in the
GAIN Recommendation and Referral Summary Report (a
text-based narrative designed to be edited and shared with
specialists, clinical staff from other agencies, insurers, and
lay people)
 The following slides provide data on the most commonly
produced treatment planning needs generated from
responses to the GAIN by ASAM dimension
NA B1. Intoxication/Withdrawal –
Common Treatment Planning Needs
58
Few clients with
dimension B1 needs;
most common is
need for detox (high
withdrawal or
substance use in the
past two days or
daily use)
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,903)
NA B2. Biomedical –
Common Treatment Planning Needs
59
Most common are
reduction of risky
sexual behavior
and tobacco
cessation
*n = 1,865
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,839)
NA B3. Psychological –
Common Treatment Planning Needs
60
More than 70% of clients
need to coordinate
services with the justice
system and more then
50% have problems with
anger management and
behavior control
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,705)
NA B4. Readiness –
Common Treatment Planning Needs
61
Most (>60%) are
required and/or
under pressure to
attend treatment
*n=3,753
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=1,752)
NA B5. Relapse Potential –
Common Treatment Planning Needs
62
Nearly 60% are not
close to anyone in
recovery
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,795)
NA B6. Environment –
Common Treatment Planning Needs
63
Environmental
risk considers
people the client
spends time with
who are involved
in school,
training, illegal
activities,
arguing/fighting,
using substances
or treatment, or
are in recovery
*n=1,854
**n=1,823
***n=1,124
+n=1,824
Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,756)
Four Measures from the SAMHSA 2011
Global Appraisal of Individual Needs (GAIN) Data Set
64
Need for Service at Intake
(% of Need / All admissions)
Unmet Need 3 months after Intake
(% No targeted service / Clients with mod/high need)
Any Services Targeted at Need
(% targeted service / All admissions)
Untargeted Services
(% targeted services / Clients with low need)
* P <05 as marked
NA Any Substance Use Disorder at Intake
vs. Any SUD Treatment by 3 Months
65
Services for drug use are well targeted with those
in need receiving services and services not being
spread to those without need
*Any past year AOD problems, use, abuse, or dependence
** ‘Services’ is self-report of any days of SA treatment at 3 months
SAMHSA 2011 GAIN SA Data Set subset to Native American/Hawaiian/Alaskan
w/ 3m Follow up (n=1,261)
NA Physical Health Problem at Intake vs.
Any Medical Treatment by 3 Months
66
Need, unmet need, and untargeted services are all of
approximately equal concern
*Current Need on ASAM dimension B2 criteria (past 90 days)
** ‘Services’ is self-report of any days of physical health treatment at 3 months
SAMHSA 2011 GAIN SA Data Set subset to Native American/Hawaiian/Alaskan
w/ 3m Follow up (n=1,529)
NA Unmet Need for
Medical Treatment by 3 Months
67
Age*
Unmet Need Higher
for Adolescent and
Young Adults
Gender*
Unmet Need
Higher for
Males
* p<.05
SAMHSA 2011 GAIN SA Data Set subset to Native American/Hawaiian/Alaskan
w/ 3m Follow up (n=735)
NA Mental Health Problem at Intake vs.
Mental Health Treatment by 3 Months
68
High rate of co-occurring mental health
problems; Large unmet need
*Current Need on ASAM dimension B3 criteria (past 90 days)
** ‘Services’ is self-report of any days of mental health treatment at 3 months
SAMHSA 2011 GAIN SA Data Set subset to Native American/Hawaiian/Alaskan
w/ 3m Follow up (n=1,544)
NA Unmet Need for
Mental Health Treatment by 3 Months
69
Age*
Unmet Need Higher for
young adults and adolescents
Gender*
Unmet Need Higher
for Males
* p<.05
SAMHSA 2011 GAIN SA Data Set subset to Native American/Hawaiian/Alaskan
w/ 3m Follow up (n=1,202)
NA Risk Recovery Environment at Intake
vs. Any Self-Help by 3 Months
70
Extremely high rate of recovery
environment problems; Large unmet need
*Current Need on ASAM dimension B6 criteria (past 90 days)
** ‘Services’ is self-report of any days of self-help attendance at 3 months
SAMHSA 2011 GAIN SA Data Set subset to Native American/Hawaiian/Alaskan
w/ 3m Follow up (n=1,545)
NA Unmet Need for
Any Self-Help at 3 Months
71
Age*
Gender
Unmet Need Higher for
adolescents and young adults
* p<.05
SAMHSA 2011 GAIN SA Data Set subset to Native American/Hawaiian/Alaskan
w/ 3m Follow up (n=1,533)
Reducing Health Disparities
72
1. Standardized screening and assessment to
identify need
2. Clinical decision support systems to
recommend targeted services
3. Data based performance monitoring overall
and by subgroups/problems for which there
are health disparities
4. Increase patient centeredness of care
5. Use of motivational interviewing and problem
solving
6. Taking services to the person
Some Lessons From IOM
73
1. Health Disparities may involve differences in
need requiring the targeting of a subgroup
2. They may also involve the lack of efficacious
services among the subset in need
3. Health Disparities are often difficult to see
and may vary from what people expect
4. Health Disparities can be reduced and
eliminated
5. Matching patient and providers gender, race
etc does not necessarily eliminate health
disparities
Alcohol and Other Drug Abuse, Dependence and
Problem Use Peaks at Age 20
74
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
Other drug or
heavy alcohol use
in the past year
Alcohol or Drug Use
(AOD) Abuse or
Dependence in the
past year
50
40
20
10
0
65+
50-64
35-49
30-34
21-29
18-20
16-17
14-15
12-13
Percentage
30
Age
Source: 2002 NSDUH and Dennis & Scott, 2007, Neumark et al., 2000
74
Yet Recovery is likely and better than average
compared with other Mental Health Diagnoses
100%
90%
46% 40% 39%
31%
20%
4%
4%
12%
11%
Past Year Recovery (no past year symptoms)
Recovery Rate (% Recovery / % Dependent)
Source: Dennis, Coleman, Scott & Funk forthcoming; National Co morbidity Study Replication
3%
Posttraumatic
Stress
9%
7%
Mood :
8%
18%
Anxiety :
15%
8%
Any Internalizing
7%
8%
Attention Deficit
10%
10%
Intermittent
Explosive
10%
Lifetime Diagnosis
10%
8%
Drug
13%
Alcohol
0%
15%
Any AOD
10%
45%
56% 48%
25%
30%
20%
50%
Median of
8 to 9 years in
recovery
Oppositional
Defiant
40%
58%
Conduct
50%
66%
Any
Externalizing
60%
89%
77%
80%
70%
89%
83%
75
SUD Remission Rates are
BETTER than many other
DSM Diagnoses
75
People Entering Publicly Funded Treatment Generally Use For Decades
Percent still using
76
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
0
5
10
15
20
25
Years from first use to 1+ years of abstinence
Source: Dennis et al., 2005
30
76
The Younger They Start, The Longer They Use
Percent still using
77
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
77
The Sooner They Get To Treatment,
The Quicker They Get To Abstinence
Percent still using
78
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
* p<.05
0 to 9
30 years
78
Cannabis Youth Treatment Experiment: Cumulative
Recovery Pattern at 30 months
79
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
79
Percent in Past Month Recovery*
CSAT Adolescent Treatment Data Set:
Recovery* by Level of Care
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
OPCC includes
approaches that
focus on
community reentry and more
than just use
(e.g., ACC,
MDFT, MST)
80
Outpatient (+79%, -1%)
Residential(+143%, +17%)
OP Cont. Care (+220%, +18%)
OPCC
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)
80
2011 National Survey on
Drug Use and Health (NSDUH)
81
 Pros:
– Nationally representative sample of U.S. household
population
– Can be used to examine need based on substance use
disorders, treatment, unmet need and health
disparities
– Includes data on 1151 (0.9% of total) who are Native
Americans (0.6%) or Hawaiian/ Pacific Islander (0.4%)
who represent a population of 2,281,422.
 Cons:
– Less than 20% (214 NA interviews) meet criteria for
alcohol or illicit use disorders in the past year
– No clinical decision support
2010 Treatment Episode Data Set
(TEDS)
82
 Pros:
– Close to a census of public treatment admissions
– Has discharge data on subgroup of 26 states
– Can be used to examine need based on substance use
severity, treatment participation, unmet need and
health disparities
– Includes data on 54,646 (3% of total) clients who are
Alaska Native (0.2%), American Indian (2.3%), or
Hawaiian/ Pacific Islander (0.5%).
 Cons:
– Only 2-3 page intake record and 2-3 follow-up
variables
– No psychometrics or clinical decision support
2011 SAMHSA Global Appraisal of Individual
Needs (GAIN) datasets
83

Pros:
– Data 58,624 patients from 298 CSAT adolescent and justice
grantees and other major systems of care from 1997-2011
– Includes comprehensive standardized clinical assessments at
intake and over 80% follow-up at 3 to 12 months post intake
on CSAT grantees only
– Includes workforce development program, health technology,
clinical decision support, & psychometrics
– Maps onto DSM, ASAM, multiple clinical standards,
epidemiological and economic measures
– Includes data on 3,929 (7% of total) who are Native Alaskan
(0.6%), American (6%), Hawaiian (0.02%), or Mixed (5%)
 Cons:
– Ad hoc sites selection
– Disproportionately youth
Sites in the 2010 Expanded GAIN-I
Data Set (1998-2010)
84
NH
WA
MT
ME
VT
ND
MN
OR
ID
MA
SD
MI
NV
UT
PA
IA
NE
CA
RI
CO
KS
OH
IN
IL
WV
MO
VA
DE
KY
NC
AZ
OK
NM
SC
MS
TX
AK
MD
TN
AR
GU
CT
NJ
DC
WY
NY
WI
GA
CSAT
IR
AL
LA
FL
HI
PR
VI
Number of Native American/Alaskan/Hawaiian Clients
in 2010 Expanded GAIN-I Data by State
85
NH
WA
MT
OR
MN
MA
ID
WY
WI
SD
NV
PA
CO
KS
OK
NM
RI
IA
IL
UT
AZ
NY
MI
NE
CA
ME
VT
ND
OH
IN
VA
DE
KY
NC
TN
AR
GA
MS
TX
NJ
DC
WV
MO
CT
MD
SC
None
1 to 25
26 to 100
101 to 500
500+
AL
LA
AK
FL
HI
PR
VI
2012 All GAIN Data
86

Pros:
– Data 170,426 patients from 502 agencies (all above
plus more non-CSAT grantees) from 2008-2012
– Growing by over 15,000 per quarter
– Similar pros to above , but more cases and more
diverse
– Currently includes data on 7,849 (4.5% of total) clients
who are Native Alaskan (0.2%), American (3.8%),
Hawaiian (0.2%), Pacific Islander (0.5%); with most
being Mixed (3%)
 Cons:
– Ad hoc sites selection
– Data has not all been cleaned or combined into de-
GAIN ABS Account – Data Permission
Status
87
NH
WA
MT
MN
OR
MA
ID
WY
WI
SD
NV
UT
PA
IL
CO
OK
NM
IN
OH
WV
MO
CT
NJ
DC
VA
DE
KY
NC
TN
AR
MD
SC
MS
GU
TX
AK
RI
IA
KS
AZ
NY
MI
NE
CA
ME
VT
ND
TX
CSAT Grantee Agencies
Participating Independent
Agencies
Pending Agencies
Refused Agencies
GA
AL
LA
FL
HI
PR
VI
All 2012 GAIN Data
88
Race Group
Unique people (all race groups)
Total
% All
% Native
170426
100%
7658
4%
100%
303
0%
4%
Native American
6393
4%
83%
Native Hawaiian
279
0%
4%
Pacific Islander
877
1%
11%
194
0%
3%
4977
3%
65%
Any Native
Alaskan Native
2 or more Native Groups
Native & Non-native race group
Detailed Acknowledgements
89




Any opinions about this data are those of the authors and do not reflect official positions of the
government or individual grantees.
Please include the following acknowledgement and disclaimer if you use these data:
This presentation was supported by analytic runs using data provided by 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, 270-07-0191, 270-120397 using data provided by the following 230 grantees: TI11317 TI11321 TI11323 TI11324 TI11422
TI11423 TI11424 TI11432 TI11433 TI11871 TI11874 TI11888 TI11892 TI11894 TI13190 TI13305 TI13308 TI13313
TI13322 TI13323 TI13344 TI13345 TI13354 TI13356 TI13601 TI14090 TI14188 TI14189 TI14196 TI14252 TI14261
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 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 TI15527 TI15545 TI15562 TI15577 TI15584 TI15586
TI15670 TI15671 TI15672 TI15674 TI15677 TI15678 TI15682 TI15686 TI16386 TI16400 TI16414 TI16904 TI16915
TI16928 TI16939 TI16961 TI16984 TI16992 TI17046 TI17070 TI17071 TI17334 TI17433 TI17434 TI17446 TI17475
TI17476 TI17484 TI17486 TI17490 TI17517 TI17523 TI17534 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 TI17821 TI17825 TI17830 TI17831 TI17847
TI17864 TI18406 TI18587 TI18671 TI18723 TI18735 TI18849 TI19313 TI19323 TI19911 TI19942 20084 20085 20086
TI20017 TI20759 TI20781 TI20798 TI20806 TI20827 TI20828 TI20847 TI20848 TI20849 TI20852 TI20865 TI20870
TI20910 TI20921 TI20924 TI20938 TI20941 TI20946 TI21551 TI21580 TI21585 TI21597 TI21624 TI21632 TI21639
TI21682 TI21688 TI21705 TI21714 TI21748 TI21774 TI21788 TI21815 TI21874 TI21883 TI21890 TI21892 TI21948
TI22424 TI22425 TI22443 TI22513 TI22544 TI22695 TI22874 TI22907 TI23037 TI23056 TI23064 TI23096 TI23101
TI23174 TI23186 TI23188 TI23195 TI23196 TI23197 TI23200 TI23202 TI23204 TI23224 TI23228 TI23244 TI23247
TI23265 TI23270 TI23278 TI23279 TI23296 TI23298 TI23304 TI23310 TI23312 TI23316 TI23322 TI23323 TI23325
TI23336 TI23345 TI23346 TI23348 655373 655374
The authors thank these grantees and their study clients for agreeing to share
their data