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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.

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Presentation on theme: "Briefing for the SAMHSA Workgroup on Underage and Problem Drinking Michael Dennis, Ph.D. Chestnut Health Systems, Normal, IL Presentation for the Substance."— Presentation transcript:

1 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 , as well as several individual CSAT, NIAAA, NIDA and private foundation grants. The opinions are those of the author and do not reflect official positions of the consortium or government. Available on line at or by contacting Michael Dennis at 448 Wylie Drive, Normal, IL 61761, phone: (309) , Fax: (309) ,

2 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

3 There 41.4 Million Under Age or Problem Drinkers in the U.S 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 National Survey On Drug Use And Health, 2006 [Computer file]

4 Heavy and Problem Alcohol Use is More Common Among Males Source: SAMHSA National Survey On Drug Use And Health, 2006 [Computer file] Total Population 52% 48%

5 Underage, Heavy and Youth Problem Alcohol Use is More Common Among Caucasians Source: SAMHSA National Survey On Drug Use And Health, 2006 [Computer file] 60% 70% Total Population

6 Alcohol Use Severity is associated with more Co-occurring Cannabis Abuse or Dependence Source: SAMHSA National Survey On Drug Use And Health, 2006 [Computer file] 5% 1% Total Population Odd Ratio=34.8 Odd Ratio=17.7

7 Alcohol Use Severity is associated with Co-occurring Other Drug Abuse or Dependence Source: SAMHSA National Survey On Drug Use And Health, 2006 [Computer file] 3% 1% Total Population Odd Ratio=17.5 Odd Ratio=8.6

8 Alcohol Disorders are associated with Co-occurring Depression Source: SAMHSA 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

9 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 (28%/46% Any)= 61% Co-occurring (13%/16% SUD)= 81% Co-occurring

10 NOTE: Not asked about work if under age 15 in NSDUH Potential Screening/ Intervention Sites: Age 12 to 20 (38.1 million) Source: SAMHSA National Survey On Drug Use And Health, 2006 [Computer file] Key potential of Workplace (e.g., EAP, Wellness, HRA) and School (e.g., SAP, EI, Prevention) Programs

11 Potential Screening/ Intervention Sites: Age 21+ (207.9 million) Source: SAMHSA National Survey On Drug Use And Health, 2006 [Computer file] Key potential of Workplace Programs NOTE: Not asked about School if over age 18 in NSDUH

12 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

13 Higher Severity is Associated with Higher Annual Cost to Society Per Person Source: 2002 NSDUH $0 $231 $725 $406 $0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000 No Alcohol or Drug Use Light Alcohol Use Only Any Infrequent Drug Use Regular AOD Use Abuse Dependence Median (50 th percentile) $948 $1,613 $1,078 $1,309 $1,528 $3,058 Mean (95% CI) This includes people who are in recovery, elderly, or do not use because of health problems Higher Costs Adults & Adolescents

14 Severity of Past Year Substance Use/Disorders by Age Source: 2002 NSDUH and Dennis & Scott 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 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)

15 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

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

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

18 Age of First Use Predicts Symptoms of Dependence an Average of 22 years Later Source: Dennis, Babor, Roebuck & Donaldson (2002) and 1998 NHSDA Pop.=151,442,082Pop.=176,188,916Pop.=71,704,012Pop.=38,997,916 % with 1+ Past Year Symptoms Under Age 15 Aged Aged 18 or older Tobacco: OR=1.49* Alcohol: OR=2.74* * p<.05 Marijuana: OR=2.45* Other Drugs: OR=2.65*

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

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

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

22 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

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

24 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.

25 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

26 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 Washington State Results with GAIN Short Screener: Adolescent Problems could be easily identified & Comorbidity common

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

28 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 School Assistance Programs (SAP) largest part of BH/MH system SAP+ SA Treatment Over half of system

29 Washington State Results with GAIN Short Screener: Adults Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders Among DSHS Clients. Olympia, WA: Department of Social and Health Services. Retrieved from Problems could be easily identified & Comorbidity common

30 Washington State Validation of Co-occurring: GAIN Short Screener vs Clinical Records 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 Higher rate in clinical record in Mental Health and Children’s Administration. (Important of considering urine tests and other sources of information)

31 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 Substance Abuse Treatment is over half of treatment system for substance disorders, other mental disorders, and co-occurring

32 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

33 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

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

35 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

36 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 Comprehensive Special More Extensive / Longer/ Expensive

37 Longer assessments identify more areas to address in treatment planning 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

38 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

39 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

40 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

41 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

42 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 Source: Adapted from Lipsey, 1997, 2005

43 43 Percentage Change in Abstinence (6 mo-Intake) by level of Adolescent Community Reinforcement Approach (A-CRA) Quality Assurance 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)

44 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/LowMod/HighTotal Any Treatment No Treatment Total

45 Mental Health Problem (at intake) vs. Any MH Treatment by 3 months Source: 2008 CSAT AAFT Summary Analytic Dataset

46 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.

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

48 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.

49 More in BZ, CA, CN, JP, MX ID IL MO ND VI ME OK PR SD AR KS MS MT NM WV IN AL AK IA MN NJ NV RI SC UT HI LA DE NE TN PA VT VA DC MI CO KY GA OH OR MD AZ TX NY NH WI CA NC CT FL MA WA WY No of GAIN Sites None (Yet) 1 to to 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 50 Backbone Funded by CSAT to Support Grant Programs: Grantees Using the GAIN from 9/2007 to 6/2010 AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY 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 UT VA VT WA WI WV WY PR VI AAFT ART ATDC BIRT JTDC EARMARK EAT FDC JDC OJJDP ORP RCF SAC SCAN SCY TCE YORP Individual Grantee(s) State & Individual Grantee(s)

51 Some numbers as of June 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 52 Expected Factor Structure of Psychopathology and Psychopathy Source: Dennis, Chan, and Funk (2006)

53 Screener items were selected using the Rasch (1p IRT) Measurement Model Items around key decision point Source: Riley et al

54 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) Internalizing Disorders go up with age Externalizing Disorders go down with age (but do NOT go away)

55 Any Illegal Activity can be better predicted by using Intake Severity on Crime/Violence and Substance Problem Scales Source: CSAT 2008 V5 dataset Adolescents aged 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

56 Key Challenges to Quality Care Addressed by GAIN Logic Model 1.High turnover workforce with variable education background related to diagnosis, placement, treatment planning and referral to other services 2.Heterogeneous needs and severity characterized by multiple problems, chronic relapse, and multiple episodes of care over several years 3.Lack of access to or use of data at the program level to guide immediate clinical decisions, billing and program planning 4.Missing, bad or misrepresented data that needs to be minimized and incorporated into interpretations 5.Lack of Infrastructure that is needed to support implementation and fidelity

57 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

58 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

59 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

60 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

61 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 CSAT Adolescent Treatment Data Set Grantees AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY 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 UT VA VT WA WI WV WY PR VI AAFT ART ATDC BIRT JTDC EARMARK EAT FDC JDC OJJDP ORP RCF SAC SCAN SCY TCE YORP

63 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) Years Old 71.2%, (n=15,705)

64 64 Diagnosis Time Period Matters Source: CSAT 2009 Summary Analytic Data Set (n=21,659)

65 65 Definition of Substance Use Severity Matters Source: CSAT 2009 Summary Analytic Data Set (n=21,816) *(n=11,066)

66 66 Multiple Clinical Problems are the NORM! Source: CSAT 2009 Summary Analytic Data Set (n=20,826)

67 67 The Number of Clinical Problems is related to Level of Care (over lapping but different mix) Source: CSAT 2009 Summary Analytic Data Set (n=21,332) Significantly more likely to have 5+ problems (OR=5.8)

68 68 The Number of Major Clinical Problems is 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!

69 Overcoming Staff Reluctance with General Victimization Scale Source: CSAT 2009 Summary Analytic Data Set (n=19,318) 69

70 70 B1. Intoxication/Withdrawal Treatment Plan Needs Source: CSAT 2009 Summary Analytic Data Set (n=17,392)

71 71 B2. Biomedical Treatment Plan Needs Source: CSAT 2009 Summary Analytic Data Set (n=17,392)

72 72 B3. Psychological Treatment Plan Needs Source: CSAT 2009 Summary Analytic Data Set (n=18,733)

73 73 B4.Readiness Treatment Plan Needs Source: CSAT 2009 Summary Analytic Data Set (n=9,169)

74 74 B5. Relapse Potential Treatment Plan Needs Source: CSAT 2009 Summary Analytic Data Set (n=21,239)

75 75 B6. Environment Treatment Plan Needs Source: CSAT 2009 Summary Analytic Data Set (n=14,952)

76 76 Individual Strengths Source: CSAT 2009 Summary Analytic Data Set (n=14,952)

77 77 Social Support Source: CSAT 2009 Summary Analytic Data Set (n=14,952)

78 78 Mentors in the Recovery Environment Source: CSAT 2009 Summary Analytic Data Set (n=14,952) Home School or Work Social Peers Critical gap in connection to recovery community

79 79 NOMS: Early Treatment Outcomes Source: CSAT 2009 SA Data Set subset to 1+ Follow ups (n=11,668)

80 80 NOMS: Post Treatment Outcome (6-12 mo) Source: CSAT 2009 SA Data Set subset to 1+ Follow ups *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 81 But Need to Control for the lack of Problems at Intake Source: CSAT 2009 SA Data Set subset to 1+ Follow ups * Variable measures the last 30 days. All others measure the past 90 days.

82 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) 78% Improved in 1 or more areas (65% in 3 or more)

83 83 Acknowledgments and Contact Information Available at 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 , , and C 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 TI 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 Phone ; More information on the GAIN is available at or by ing


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