Summary of SAMHSA behavioral health data findings related to Native Americans". 1/14/13 Michael L. Dennis, Chestnut Health Systems. Normal, IL Created

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)

2

Goals for the Presentation

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

3

3

The Quality Chasm In Substance Use Disorder Tx

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 co-occurring 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

4

Native Americans are disproportionately effected by Substance Use Disorders (SUD)

Source: SAMHSA, 2011 National Survey on Drug Use and Health (NSDUH; (p8 of SAMHSA 1/13 newsletter)

Native Americans (NA) have higher* than average rates of Substance Use Disorders (SUD)

* p<.001

5

Existing Data Sources on Substance Use Among Native Americans (NA)* by Age

Source (NA % of total) Pros & Cons

Native Americans (n)Under

18 18-25 26+ Total

2011 NSDUH Pop Est. (0.9%) raw n raw n with SUD

202,039363(52)

369,683412

(109)

1,709,700376(53)

2,281,4421,151(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

6 6

The NA Rates of SUD & Unmet Need vary by Age

Source: SAMHSA 2011 National Survey on Drug Use and Health subset to Native Americans (n=1151, population estimate=2,281,422). * p<.001

Higher rates* of need for young adults

Higher rates* of unmet need for adolescents and young adults

7 7

Source: SAMHSA 2011 National Survey on Drug Use and Health subset to Native Americans (n=1151, population estimate=2,281,422). * p<.001

Higher rates* of need for Males overall and for adolescent girls

Higher rates* of unmet need for adolescent girls than boys

The NA Rates of SUD & Unmet Need vary by Gender

8 8

In Spite of Longer Stays, NA Teens less likely to Complete Tx

Source: SAMHSA 2009 Treatment Episode Data Set – Discharges (TEDS-D) for Native Americans. P<.001

Completion rates are lower * for adolescents and young adults

Lengths of stay are longer *for young adults and adults

9

Native American/Alaskan/Hawaiian Clients by State(3,929 clients from 271 sites between 7/11-6/12)

AK

AL

ARAZ

CA CO

CT

DC

DE

FL

GA

HI

IA

ID

IL IN

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

VT

WA

WI

WV

WY

PRVI

None1 to 2526 to 100101 to 500500+

10

NA Demographic Characteristics

Mostly male, NA, multi-racial, and under 18

Source: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,929)

11

NA Pattern of Weekly Use (13+/90 days)

*Not a weekly measure; any in past 90 days


12

NA Substance Use Problems

*Count of 8 items


13

NA Substance Problem Recognition

* n=2,876


14

NA Co-Occurring Psychiatric Problems

* Count of Conduct Disorder, ADHD/ADD Major Depressive Disorder, Traumatic Stress Disorder, and Generalized Anxiety Disorder


15

NA Past Year Crime & Justice Involvement

*Dealing, manufacturing, prostitution, gambling (does not include simple possession or use)


16

NA Count of Major Clinical Problems at Intake

*Based on count of self reporting criteria to suggest alcohol, cannabis, or other drug disorder, depression, anxiety, trauma, suicide, ADHD, CD, victimization, violence/ illegal activitySource: GAIN-I 2010 SuperData subset to Native American/Hawaiian/Alaskan (n=3,811)

17

NA Severity of Victimization Scale

*Mean of 15 items


18

NA Count of Major Clinical Problems* at Intake by Severity of Victimization

*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


19

NA Quarterly Cost of Health Care Utilization

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.

20

Cost to Society in 2011 Dollars

*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.

Description Unit Unit Cost

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

21

NA Quarterly Health Care Utilization Cost

Source: GAIN-I 2010 SuperData subset to Native American/ Hawaiian/A laskan (n=3,668)

22

NA Cost of Crime in the Past Year

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.

23

Cost of Crime in 2011 Dollars*

*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.

Description Unit Unit Cost

Purposely damaged or destroyed property Times $5,095.64

Passed bad checks/forged a prescription/took money from employer 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 Times $11,294.29

Used a weapon, force, or strong-arm methods to get money or things from a person 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 Times $252,450.22

Been involved in the death or murder of another person (including accidents) Times $9,418,450.51

Intentionally set a building, car, or other property on fire Times $22,126.20

24

NA Cost of Crime in the Past Year

Source: GAIN-I 2010 SuperData subset to Native American/ Hawaiian/ Alaskan (n=3,595)

25

NA GAIN Quick (GAIN-Q) Version 3 Problem Profile

*Not used in the GQ Problem Count Across the 9 screeners on the Q3 85% of respondents have 3 or more that rate as moderate to high problems


26

NA Four Summary Indices

*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


27

NA Unmet Need for Medical Treatment by 3 Months

* p<.05

Gender*Age*

Unmet Need Higher for

Males

Unmet Need Higher for Adolescent and

Young Adults

SAMHSA 2011 GAIN SA Data Set subset to Native American/Hawaiian/Alaskan w/ 3m Follow up (n=735)

28

NA Unmet Need for Mental Health Treatment by 3 Months

* p<.05

Gender*Age*

Unmet Need Higher for Males

Unmet Need Higher for young adults and adolescents

SAMHSA 2011 GAIN SA Data Set subset to Native American/Hawaiian/Alaskan w/ 3m Follow up (n=1,202)

29

What can we do?

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

30

Regardless of Diagnosis or Where Patients Enter, High Quality Care Should be:

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

31

Structural Challenges to Delivery of Quality Care

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

32

Programs often LACK Evidenced Based Assessment to Identify and Practices to Treat:

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

33

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– CIDI, DISC, KSADS, PDI, SCAN

Screener Quick Com

prehensive SpecialM

ore Extensive / Longer/ Expensive

34

Longer Measures Assess and Identify More Problems

Source: CSAT 2010 AT Summary Analytic Data Set (n = 17,356)

35

Some Advantages of the GAIN System

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

36

Appendix

The following are more detailed slides supporting points above that might be useful to

have readily available.

37

Substance Use Disorders are Common, US Treatment Participation Rates Are Low

Source: SAMHSA 2010. National Survey On Drug Use And Health, 2010 [Computer file]

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

Much of the private funding is limited to 30 days or less and authorized day by day or week by week

38

Potential AOD Screening & Intervention SitesAdolescents (age 12-17)

Source: SAMHSA 2010. National Survey On Drug Use And Health, 2010 [Computer file]

39

Adolescent Rates of High (2+) Scores on Mental Health (MH) or Substance Abuse (SA) Screener by Setting in WA state

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)

StudentAssistancePrograms(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/

Problems could be easily identified & Comorbidity common

Under reporting of SA in mental

health & children’s

admin

40

Adolescent Client Validation of High Co-Occurring from GAIN Short Screener vs. Clinical Records by Setting in WA State

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

Yet the two-page measure closely approximated all found in the clinical record after the next 2 years


41

Where in the System are the Adolescents with Mental Health, Substance Abuse and Co-occurring?

41

School Assistance Programs (SAP) largest part of BH/MH system; 2nd largest of SA & Co-occurring systems

SAP+ SA Treatment Over half of system


42

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)

Low

Mod. High ->

42

Total Disorder Screener Severity by Level of Care: Adolescents

Source: SAPISP 2009 Data and Dennis et al 2006

Residential Median= 10.5

Outpatient Median=6.0

Few missed (1/2-3%)

About 30% of OP are in the high severity range more typical of residential

About 41% of Residential are below 10 (more likely typical OP)

43

Any Illegal Activity in the Next Twelve Months by Intake Severity on Crime/Violence and Substance Disorder Screeners

Source: CSAT 2010 Summary Analytic Dataset (n=20,982)

44

Predictive Power of Simple Screener: 12 Month Recidivism

Crime/ViolenceScreener

SubstanceDisorder Screener

12 MonthRecidivism

Rate

Odds Ratio

\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*

High (3-5) High (3-5) 61% 7.6*\a Odds of row (%/(1-%) over low/low odds across all groups with * p<.05

Source: CSAT 2010 Summary Analytic Dataset (n=20,932)

45

Major Predictors of Bigger Effects

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

46

46

Impact of the numbers of these Favorable features on Recidivism in 509 Juvenile Justice Studies in Lipsey Meta Analysis


Average Practice

The more features, the lower the recidivism

47

47

Cognitive Behavioral Therapy (CBT) Interventions 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

48

48

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 well


49

Economic Analysis of SAMHSA/CSAT Funded Treatment

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.

50

SAMHSA/CSAT’s Adult Clients by Level of Care

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 $19,748

Residential $19,443 $24,297 ($4,854)c

Total $17,035 $12,442 $4,592

\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

51

SAMHSA/CSAT’s Adolescents Clients by Level of Care

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

52

Adolescents Clients by Setting

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

Just Health Care Cost $11,122 $6,475 $4,648

A-CRA in Schools $13,614 $10,489 $3,125

Just Health Care Costs $10,100 $7,686 $2,413

\a Includes the cost of treatment\b Year after intake (including treatment) minus year before treatment

53

GAIN Treatment Planning/Placement Grid

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%20Manuals/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.

54

GAIN Treatment Planning/Placement Grid

Problem Recency/Severity

None Past Current (past 90 days)* Low-Mod | High Severity T

reatm

en

t His

tory

None P

ast Current

1. No problem

2. Past problem Consider monitoring and relapse prevention.

3. Low/Moderate problems; Not in treatmentConsider initial or low invasive treatment.

4. Severe problems;Not in treatment Consider a more intensive treatment or intervention strategies.0. Not Logical

Check under- standing of problem or lying and recode.

5. No current problems; Currently in treatmentReview for step down or discharge.

6. Low/Moderate problems; Currently in treatment Review need to continue or step up.

7. Severe problems; Currently in treatmentReview need for more intensive or assertive levels.

* Current for Dimension B1 = Past 7 days

55

GAIN Placement Cells by ASAM Dimension


56

GAIN Placement Cells by ASAM Dimension - Interpretation

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

57

ASAM Dimension Treatment Planning Needs

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

58

NA B1. Intoxication/Withdrawal – Common Treatment Planning Needs

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)


59

NA B2. Biomedical – Common Treatment Planning Needs

*n = 1,865

Most common are reduction of risky sexual behavior and tobacco cessation


60

NA B3. Psychological – Common Treatment Planning Needs

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


61

NA B4. Readiness – Common Treatment Planning Needs

*n=3,753

Most (>60%) are required and/or under pressure to attend treatment


62

NA B5. Relapse Potential – Common Treatment Planning Needs

Nearly 60% are not close to anyone in recovery


63

NA B6. Environment – Common Treatment Planning Needs

*n=1,854 **n=1,823 ***n=1,124 +n=1,824

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


64

Four Measures from the SAMHSA 2011Global Appraisal of Individual Needs (GAIN) Data Set

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

65

NA Any Substance Use Disorder at Intake vs. Any SUD Treatment by 3 Months

*Any past year AOD problems, use, abuse, or dependence** ‘Services’ is self-report of any days of SA treatment at 3 months


Services for drug use are well targeted with those in need receiving services and services not being

spread to those without need

66

NA Physical Health Problem at Intake vs. Any Medical Treatment by 3 Months

*Current Need on ASAM dimension B2 criteria (past 90 days)** ‘Services’ is self-report of any days of physical health treatment at 3 months

Need, unmet need, and untargeted services are all of approximately equal concern


67

NA Unmet Need for Medical Treatment by 3 Months

* p<.05

Gender*Age*

Unmet Need Higher for

Males

Unmet Need Higher for Adolescent and

Young Adults

SAMHSA 2011 GAIN SA Data Set subset to Native American/Hawaiian/Alaskan w/ 3m Follow up (n=735)

68

NA Mental Health Problem at Intake vs. Mental Health Treatment by 3 Months

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


69

NA Unmet Need for Mental Health Treatment by 3 Months

* p<.05

Gender*Age*

Unmet Need Higher for Males

Unmet Need Higher for young adults and adolescents


70

NA Risk Recovery Environment at Intake vs. Any Self-Help by 3 Months

*Current Need on ASAM dimension B6 criteria (past 90 days)** ‘Services’ is self-report of any days of self-help attendance at 3 months

Extremely high rate of recovery environment problems; Large unmet need


71

* p<.05

Age* Gender

NA Unmet Need for Any Self-Help at 3 Months


Unmet Need Higher for adolescents and young adults

72

Reducing Health Disparities

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

73

Some Lessons From IOM

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

74

74

Alcohol and Other Drug Abuse, Dependence and Problem Use Peaks at Age 20

Source: 2002 NSDUH and Dennis & Scott, 2007, Neumark et al., 2000

010

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+Other drug or heavy alcohol use in the past yearAlcohol or Drug Use (AOD) Abuse or Dependence in the past year

Age

Severity Category

Over 90% of use and problems start between the ages of 12-20

It takes decades before most recover or die

Per

cent

age

People with drug dependence die an average of 22.5 years sooner than those without a diagnosis

75

75

Yet Recovery is likely and better than averagecompared with other Mental Health Diagnoses

Source: Dennis, Coleman, Scott & Funk forthcoming; National Co morbidity Study Replication

15% 13%8%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%An

y AO

D

Alco

hol

Dru

g

Any

Exte

rnal

izin

g

Cond

uct

Opp

ositi

onal

Defi

ant

Inte

rmitt

ent

Expl

osiv

e

Atten

tion

Defi

cit

Any

Inte

rnal

izin

g

Anxi

ety

:

Moo

d :

Postt

raum

atic

Stre

ss

Lifetime Diagnosis

10% 10% 7%

Past Year Recovery (no past year symptoms)

66%

77%

83%

Recovery Rate (% Recovery / % Dependent)

25%

10% 10% 8% 8%

46%

31%

7%

20%

15% 8% 9%4%

18% 12% 11%3%4%

58%

89% 89%

45%50%

39%56% 48%

40%Median of 8 to 9 years in recovery

76

76

People Entering Publicly Funded Treatment Generally Use For DecadesP

erce

nt s

till

usi

ng

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

77

77

Per

cent

sti

ll u

sing

Years from first use to 1+ years of abstinence

under 15*

21+

15-20

Age of First Use

302520151050


100%90%80%70%60%50%40%30%20%10%0%

60% longer

The Younger They Start, The Longer They Use

* p<.05

78

78

Per

cent

sti

ll u

sing

Years from first use to 1+ years of abstinence

Years to first Treatment Admission*

302520151050


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 To Treatment, The Quicker They Get To Abstinence

* p<.05

79

79

Cannabis Youth Treatment Experiment: Cumulative Recovery Pattern at 30 months

Source: Dennis et al, forthcoming

37% Sustained Problems

5% Sustained Recovery

19% Intermittent, currently in

recovery

39% Intermittent, currently not in

recovery

The Majority of Adolescents Cycle in and out of Recovery

80

80

CSAT Adolescent Treatment Data Set: Recovery* by Level of Care

* Recovery defined as no past month use, abuse, or dependence symptoms while living in the community. Percentages in parentheses are the treatment outcome (intake to 12 month change) and the stability of the outcomes (3months to 12 month change) Source: CSAT Adolescent Treatment Outcome Data Set (n-9,276)

0%10%20%30%40%50%60%70%80%90%

100%

Pre-Intake Mon 1-3 Mon 4-6 Mon 7-9 Mon 10-12

Perc

ent i

n Pa

st M

onth

Rec

over

y* Outpatient (+79%, -1%)

Residential(+143%, +17%)

OP Cont. Care (+220%, +18%)

OP & Resid

Similar

OPCC better

OPCC includes approaches that

focus on community re-entry and more

than just use (e.g., ACC,

MDFT, MST)

81

2011 National Survey on Drug Use and Health (NSDUH)

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

82

2010 Treatment Episode Data Set(TEDS)

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

83

2011 SAMHSA Global Appraisal of Individual Needs (GAIN) datasets

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

84

Sites in the 2010 Expanded GAIN-I Data Set (1998-2010)

AK

AL

ARAZ

CA CO

CT

DC

DE

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

VT

WA

WI

WV

WY

PRVI

CSAT

IR

GU

85

Number of Native American/Alaskan/Hawaiian Clients in 2010 Expanded GAIN-I Data by State

AK

AL

ARAZ

CA CO

CT

DC

DE

FL

GA

HI

IA

ID

IL IN

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

VT

WA

WI

WV

WY

PRVI

None1 to 2526 to 100101 to 500500+

86

2012 All GAIN Data

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-

identified analytic files

87

GAIN ABS Account – Data Permission Status

AK

AL

ARAZ

CA CO

CT

DC

DE

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

PA

RI

SC

SD

TN

TX

UTVA

VT

WA

WI

WV

WY

PRVI

GUTX

CSAT Grantee AgenciesParticipating Independent AgenciesPending AgenciesRefused Agencies

88

All 2012 GAIN Data

Race Group Total % All % Native

Unique people (all race groups) 170426 100%

Any Native 7658 4% 100%

Alaskan Native 303 0% 4%

Native American 6393 4% 83%

Native Hawaiian 279 0% 4%

Pacific Islander 877 1% 11%

2 or more Native Groups 194 0% 3%

Native & Non-native race group 4977 3% 65%

89

Detailed Acknowledgements

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, 270-12-0397 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

Documents

Summary of SAMHSA behavioral health data findings related to Native Americans". 1/14/13 Michael L. Dennis, Chestnut Health Systems. Normal, IL Created