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Michael L. Dennis, Ph.D. Chestnut Health Systems, Bloomington, IL

Using a cluster analysis based case-mix solution to facilitate the evaluation and development of adolescent substance abuse treatment programs. Michael L. Dennis, Ph.D. Chestnut Health Systems, Bloomington, IL. Objectives.

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Michael L. Dennis, Ph.D. Chestnut Health Systems, Bloomington, IL

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  1. Using a cluster analysis based case-mix solution to facilitate the evaluation and development of adolescent substance abuse treatment programs. Michael L. Dennis, Ph.D. Chestnut Health Systems, Bloomington, IL

  2. Objectives • Identification of Clients with similar presenting pathology based on a cluster analysis of the GAIN’s core psychiatric and behavior scales. • Demonstration of how the “case-mix” of these subgroups impacts program averages. • Illustration of how psychiatric case mix groups can be used to aid program evaluation and planning within or across program evaluation.

  3. Global Appraisal of Individual Needs (GAIN) • A standardized bio-psycho-social that integrates clinical and research assessment for diagnosis, placement, treatment planning, process measures, outcome monitoring, and economic evaluation. • Core sections include cognitive assessment, background/access, substance use, physical health, risk behaviors, mental health, environment, legal, vocational, staff ratings • Over 100 scales/indices, with alpha over .9 on main scales and over .7 on subscales • Test retest data suggest reliability of items/scales over .7 • Self reported use consistent with urine, salvia, and collateral reports (Kappa of .81 or more) • Predicts blind diagnosis of co-occurring psychiatric disorders including ADHD (kappa = 1.00), Mood Disorders (kappa = 0.85), Conduct Disorder or Oppositional Defiant Disorder (kappa = 0.82), Adjustment Disorder (kappa = 0.69), and No other diagnosis (kappa = 0.91)

  4. Factor Structure and Cluster Analysis based on 2968 Clients from 61 Treatment Units Farmington, CT Chicago, IL New York, NY Peoria, IL Philadelphia, PA Oakland, CA Bloomington, IL Baltimore, MD Cantonsville, MD Maryville, IL Los Angeles, CA Shiprock, NM Phoenix/Tempe, AZ Tucson, AZ St. Petersburg, FL Miami, FL Adolescent Outpatient/IOP Adolescent Inpatient/Therapeutic Community Adult Outpatient/IOP/OP Methadone Treatment Adult Inpatient/Therapeutic Community

  5. Internal Mental Distress Crime and Violence Behavioral Complexity Hypothesized Structure of the GAIN’s Psychopathology Measures * Main scales have alpha over .85, subscales over .7

  6. rs .64 .55 SIIY .80 .51 .71 SA Problems SAIY .78 .88 SDIY .54 ri .74 SSI .67 .73 .60 DSI .82 .27 .52 Internal HSTI .77 .88 .78 ASI .68 .47 .23 TSI General re Severity .60 HII .71 .62 .51 .83 .91 IAI External .68 .46 CDI .50 .39 rv .54 GCTI .62 .63 .25 PCI .79 .62 Crime/Violence .79 ICI .74 .55 DCI Confirmatory Factor Analysis (CFA) Comparative Fit Index: .974 Root Mean Square Error of Approximation: 0.079 Invariant vs Variant Across Age and Level of Care Comparative Fit Index: .97 vs .98 Parsimony Ratio: .80 vs .70 CFI x PR: .78 vs .68 Root Mean Square Error of Approximation: .04 vs .04

  7. Creating Cluster Code Types • The overall severity and four core dimensions were used to create 7 code types with Ward’s minimum distance cluster analysis. • Total and four dimensional scores triaged into low, medium and high based on +/- .5 standard deviations from the mean • Code types labeled most common group as: • High, medium or low overall severity on total score • Labeled in order from highest to lowest severity dimension • Lines // used to separate those in high/ medium/ low severity on each each of four dimensions • Sample size • Discriminate Function Analysis for Classifying New Cases (Kappa =.82)

  8. Code Type (A,B,C..) High to Low Severity order Hi / Med / Low range divided by // 7 Cluster Code Types High G., CV, BC ID, SP// (N=214) Low A. 8% //CV, ID, BC, SP (N=545) High F. ID, BC, SP, CV// 19% (N=336) 12% Low B. SP/ID/ CV, BC (N=370) High E. 13% CV, BC, SP/ ID/ (N=429) 15% Med. C. Med. D. /BC, CV/ID, SP (N=467) SP/ BC, ID/ CV 16% (N=471) 17%

  9. General Severity by Code Type

  10. Substance Problem (SP) by Code Type

  11. Internal Distress (ID) by Code Type

  12. Behavior Complexity (BC) by Code Type

  13. Behavior Complexity (CV) by Code Type

  14. Case Mix by Age and Level of Care

  15. ATM Adolescent Treatment Model Program Sites New York, NY Baltimore, MD Oakland, CA Bloomington, IL Cantonsville, MD Shiprock, NM Tempe, AZ Los Angeles, CA Tucson, AZ 1999 1998 Miami, FL Sponsored By: Center for Substance Abuse Treatment (CSAT), Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services (DHHS)

  16. ATM involved the full range of Code Types

  17. Evaluating Cluster Code Types • Severity should go up with level of care (LOC) – one of the most commonly used case mix variables. • The cluster code type should do better than LOC in terms of: • Maximizing individual differences between cluster subgroups • Minimizing individual indifference by LOC within cluster subgroups • The cluster code types should help to predict differential response patterns to treatment

  18. PCM Index Score (Weighted Average) Case Mix Severity Goes up With Level of Care G-High CV,BC,ID,SP// 100% 90% F-High ID,BC,SP/CV/-- 80% E-High 70% CV,BC,SP/ID/ 60% D-Mod SP/BC,ID/CV 50% C-Mod 40% BC/CV,ID/SP 30% B-Low /SP,ID/CV,BC 20% A-Low 10% //CV,ID,BC,SP 0% PCM Index Score Early Intervention OP/IOP LTR STR

  19. Individual Differences explained by LOC quantified with Cohen’s effect size f Level of Care Is Related to “Average” Severity 4.0 OP (n=553) 3.0 LTR (n=373) 2.0 STR (n=573) 1.0 Z-score 0.0 -1.0 -2.0 -3.0 -4.0 Total Score (f=0.4) ID. Internal (f=0.29) Distress Complexity (f=0.28) SP. Substance Problem (f=0.26) (f=0.14) CV. BC Behavior Crime/Violence

  20. +561% +310% +338% +85% +750% Cohen’s effect size f increased by 85% to 750% However Cluster Subgroups are More Distinct From Each Other A-Low //CV,ID,BC,SP 4.0 (n=208) 3.0 B-Low /SP,ID/CV,BC 2.0 (n=101) C-Mod 1.0 BC/CV,ID/SP Z-score (n=286) 0.0 D-Mod -1.0 SP/BC,ID/CV (n=252) -2.0 E-High -3.0 CV,BC,SP/ID/ (n=281) -4.0 F-High ID,BC,SP/CV/-- Total Score (f=1.75) (n=180) ID. Internal (f=1.19) Distress Complexity (f=1.85) SP. Substance Problem (f=0.48) BC Behavior (f=1.19) Violence CV.Crime G-High CV,BC,ID,SP// (n=191)

  21. Once we account for subgroup, LOC differences are gone and Cohen’s effect size f goes down A-Low //CV,ID,BC,SP 4.0 OP (n=114) 3.0 LTR (n=59) 2.0 STR (n=35) 1.0 Z-score 0.0 -1.0 -2.0 -3.0 -4.0 Total Score (f=0.05) ID. Internal (f=0.11) Distress Complexity (f=0.16) SP. Substance (f=0.04) Problem BC Behavior (f=0.04) CV. Crime/Violence

  22. B-Low /SP,ID/CV,BC 4.0 OP (n=38) 3.0 LTR (n=23) 2.0 STR (n=40) 1.0 Z-score 0.0 -1.0 -2.0 -3.0 -4.0 Total Score (f=0.08) ID. Internal (f=0.06) Distress Complexity (f=0.02) SP. Substance (f=0.12) Problem BC Behavior (f=0.09) CV. Crime/Violence

  23. C-Mod BC/CV,ID/SP 4.0 OP (n=138) 3.0 LTR (n=82) 2.0 STR (n=66) 1.0 Z-score 0.0 -1.0 -2.0 -3.0 -4.0 Total Score (f=0.18) ID. Internal (f=0.22) Distress Complexity (f=0.13) SP. Substance (f=0.13) Problem BC Behavior (f=0.09) CV. Crime/Violence

  24. D-Mod SP/BC,ID/CV 4.0 OP (n=78) 3.0 LTR (n=57) 2.0 STR (n=117) 1.0 Z-score 0.0 -1.0 -2.0 -3.0 -4.0 Total Score (f=0.17) ID. Internal Distress (f=0.14) Complexity (f=0.1) SP. Substance Problem (f=0.18) BC Behavior (f=0.1) CV. Crime/Violence

  25. E-High CV,BC,SP/ID/ 4.0 OP (n=103) 3.0 LTR (n=50) 2.0 STR (n=128) 1.0 Z-score 0.0 -1.0 -2.0 -3.0 -4.0 Total Score (f=0.13) ID. Internal (f=0.14) Distress Complexity (f=0.08) SP. Substance (f=0.22) Problem BC Behavior (f=0.08) CV. Crime/Violence

  26. F-High ID,BC,SP/CV/ 4.0 OP (n=43) 3.0 LTR (n=44) 2.0 STR (n=93) 1.0 Z-score 0.0 -1.0 -2.0 -3.0 -4.0 Total Score (f=0.06) ID. Internal (f=0.05) Distress Complexity (f=0.06) SP. Substance Problem (f=0.18) (f=0.08) CV. BC Behavior Crime/Violence

  27. G-High CV,BC,ID,SP// 4.0 OP (n=39) 3.0 LTR (n=58) 2.0 STR (n=94) 1.0 Z-score 0.0 -1.0 -2.0 -3.0 -4.0 Total Score (f=0.15) Complexity (f=0.13) SP. Substance Problem (f=0.28) (f=0.06) Distress (f=0.1) CV. ID. Internal BC Behavior Crime/Violence

  28. Cluster Subgroups Significantly Reduces the Individual Differences Associated with Level of Care A-Low //CV,ID,BC,SP 100.0% (n=208) 80.0% B-Low /SP,ID/CV,BC 60.0% (n=101) 40.0% C-Mod BC/CV,ID/SP 20.0% (n=286) Change in LOC Effect Size f 0.0% D-Mod SP/BC,ID/CV -20.0% (n=252) -40.0% -60.0% E-High CV,BC,SP/ID/ (n=281) -80.0% -100.0% F-High ID,BC,SP/CV/-- Total Score (n=180) ID. Internal Distress Complexity SP. Substance Problem BC Behavior CV. Crime/Violence G-High CV,BC,ID,SP// (n=191)

  29. Differentiates initial severity, and differences in response Outpatient by Cluster Types

  30. Can identify subgroups (E, B) that are a higher risk of relapse or having other problems Long Term Residential by Cluster Types

  31. Short Term Residential by Cluster Types Different levels of care/programs may do well (A,F,G) or have problems (B,C,D, E) with different subgroups

  32. However this is still quasi-experimental and the adjustments are often imperfect For a Given Subtype, it can identify when a particular level of care (or program) appears to do better. C-Mod BC/CV,ID/SP by LOC

  33. Conclusions • Clustering people based on presenting problems appears to work better than level of care for describing initial case mix but is also correlated with it. • Clinical subtype clusters can help to identify subgroups for which a program works well and/or where continuing care or other services may be needed. • Within a clinical subtype, comparisons of level of care (programs, services etc) could be used to guide placement decisions and/or identify promising areas for experimentation.

  34. Contact Information Michael L. Dennis, Ph.D. Lighthouse Institute, Chestnut Health Systems 720 West Chestnut, Bloomington, IL 61701 Phone: (309) 827-6026, Fax: (309) 829-4661 E-Mail: mdennis@chestnut.org A copy of these slides will be posted at: www.chestnut.org/li/posters

  35. Errata The following additional slide was presented by the discussant, Dr. Mark Fishman, to show how case mix varied at the program level even within level of care.

  36. Early Intervention at the low end Also demonstrates that Level of Care is only a rough proxy of case mix STR/LTR dominates high end Case Mix by Level of Care/ATM program

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