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The Global Appraisal of Individual Needs (GAIN) Evaluators Handbook: Practical Guidelines for Using GAIN Data To Support Local and Cross-Site Program Evaluation.

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Presentation on theme: "The Global Appraisal of Individual Needs (GAIN) Evaluators Handbook: Practical Guidelines for Using GAIN Data To Support Local and Cross-Site Program Evaluation."— Presentation transcript:

1 The Global Appraisal of Individual Needs (GAIN) Evaluators Handbook: Practical Guidelines for Using GAIN Data To Support Local and Cross-Site Program Evaluation and Development Michael Dennis, Melissa Ives, & Rodney Funk Chestnut Health Systems, Bloomington, IL Joint Meeting on Adolescent Treatment Effectiveness, April 25-27, 2007, Washington, DC

2 Objectives 1. Identify common questions used in evaluations and available GAIN tools and reports. 2. Understand how to respond to these questions using GAIN data, tools and reports. 3. Identify and answer questions that will help you use GAIN to support program evaluation and development. 4. Get your input on what would be most useful to have in the GAIN Evaluators Handbook

3 Questions 1. We will encourage you to ask questions as we go if something is not clear 2. We are handing out note cards to get more detailed questions to answer at the end. If you put your or address on them (or sign up) we will send you copies of our answers in writing. 3. Are there anything you are specifically here for that you want us to be sure and cover?

4 Common Questions in Local Program Evaluation and Clinical Research 1. Who is being served? 2. What services are they receiving? 3. To what extent are services being targeted at the those in need? 4. To what extent are services being delivered as expected? (Performance/Fidelity) 5. Which is most effective of several services delivered? 6. What does it cost, cost effectiveness? Source: Dennis, Fetterman & Sechrest (1994)

5 GAIN Scales & Variable File 1. Purpose 2. Type of Measure 3. Interpretative Cut Points 4. Description 5. Syntax 6. References 7. Items 8. Summarizing in a table

6 Purpose 1. Diagnosis based on APA 2. Treatment planning based on CARF, COA, JCAHO, NIDA principals and SAMHSA TIPs 3. Placement based on ASAM and statistical models 4. Covariates based on lifetime or past year measures 5. Change Scores based on past 90 days, month, week or current status or time since last event 6. Methods Measures 7. Economic Measures

7 Types of Measures 1. Scale: a set of symptoms or items that are inter- correlated (e.g.., dependence, depression) where we are interested in the pattern (i.e. Common variance, ONLY one where alpha makes sense) 2. Index: a set of items that may not be directly related but add up to predict (e.g., sources of stress, barriers to treatment, expenses) 3. Ratio Estimators: one measure divided by another (e.g.., percent of unprotected sex acts) 4. Status measures: a categorical status based on a single question or created across multiple (e.g.., vocational status, housing status) 5. Survival: Time to first event (e.g. time to first use)

8 Interpretative Cut-Points 1. Definition of low, moderate and high clinical significance bands to aid interpretation and decision making (scale name + g for group) 2. Useful for defining need at both the client and program level 3. Basis - DSM or other clinical standards where available (e.g.., clinical is 3+/7 dependence) - 50 th & 90 th percentile for common issues (e.g. days of alcohol use) - 1+ and median of 1+ for zero saturated (more than half) and right skewed variables 4. Reversed coded if up is low clinical significance

9 Descriptions 1. GAIN-I S&V excel file has text based descriptions, literal syntax (including older version if applicable), items, and references 2. GAIN main scales and indexes word file includes text to put in a journal article or report, including: - short definition - any subscales - source of measure - key reports/citations - alphas for adolescents and adults if applicable 3.The articles in the GAIN bibliography (many of which are included on the CD) have more details as well.

10 Possible Comparison Groups published data site over time subsites, staff, or clinics compare site to larger program (all sites) compare site to similar level of care, geography, demographic subgroup, or clinical subgroup match clinical subgroups from GAIN related presentations or papers formal matching or propensity scoring to make groups more statistically comparable formal randomized experiments path or mediation models to test whether it is actually the dosage or key ingredient driving the change

11 Major Predictors of Effective Programs that we have to be cognizant of.. 1. An explicit intervention protocol (typically manualized) that a priori evidence that it works when followed 2. Use of monitoring, feedback, supervision and quality assurance to ensure protocol adherence and project implementation 3. Use proactive case supervision at the individual level to ensure quality of care 4. Triage to focus on the higher severity subgroups of individuals

12 Impact of Intake Severity on Outcome Source: ATM Main Findings data set SPSM groupings Dot/Lines show Means 06 Wave 8 10 Substance Problem Scale (0-16 Past Month Symptoms) No problems (0-25%ile) 1-3 problems (25-50%ile) 4-8 problems (50-75%ile) 9+ problems (75-100%ile) OVERALL Intake Severity Correlated -.66 with amount of change

13 Different than Regression to the Mean Source: ATM Main Findings data set SPSM groupings Dot/Lines show Means 06 Wave 8 10 Substance Problem Scale (0-16 Past Month Symptoms) No problems (0-25%ile) 1-3 problems (25-50%ile) 4-8 problems (50-75%ile) 9+ problems (75-100%ile) OVERALL In its most basic form, the mean & variance are the same at both time points; no correlation between intake & amount of change

14 Different than Regression to the Mean Source: ATM Main Findings data set SPSM groupings Dot/Lines show Means 06 Wave 8 10 Substance Problem Scale (0-16 Past Month Symptoms) No problems (0-25%ile) 1-3 problems (25-50%ile) 4-8 problems (50-75%ile) 9+ problems (75-100%ile) OVERALL If it was regression around the mean combined with an mean effect it would; but still no change in variance or correlation between intake & amount of change

15 Example of Multi-dimensional HIV Subgroups A. Low Risk B. Mod. Risk Low W/T C. Mod. Risk High W/T D. Very High Risk Total Cohen's Effect Size d Unprotected Sex Acts (f=.14) Days of Victimization (f=.22) Days of Needle Use (f=1.19) Source: Lloyd et al 2007

16 Key things to Test and Monitor Assumptions about population characteristics and needs (using site profiles) Comparability of comparison groups (using site profiles) Simple performance measures and early outcomes for monitoring implementation Measure of competence, fidelity and implementation Variability in outcomes by subgroup

17 Melissa Ives Melissa will now demonstrate how to use some of the data and tools we provide to do these things.

18 GAIN Evaluators Handbook: Resources for answering 'Who is being served?' Melissa L. Ives, MSW Research Associate Chestnut Health Systems Lighthouse Institute GAIN Coordinating Center Joint Meeting on Adolescent Treatment Effectiveness, April 25-27, 2007, Washington, DC

19 Introduction and goals The first of the 5 key questions is – Who is being served? Two goals of this portion of the presentation: – Identify tools that are already available from the GCC. – Explore the use of one key tool for examining characteristics of those being served. Always our goal: To answer your questions. – Be sure to write down any questions that are not answered during the presentation. – Answers to these questions will be used to enhance the Evaluators Handbook.

20 Overview It is always easier to use the right tool than to create a new one – especially if the tool is readily available. I used the AutoContent Wizard provided by PowerPoint to create these slides. The GCC currently provides several tools to support evaluators or analysts in answering the key questions.

21 TTL Report FUL Report Syntax & template files Evaluator Or Analyst Site Profiles Adult & Adolesce nt Norms GAIN-I / M90 data Electronic Encyclopedia (GI S&V) LI Analytic Training Series Memos Tools

22 Site Profiles Excel file containing information about the characteristics of clients being served. Aggregated by site within a program or study. Contents: – Title page – defining what groups are included (with grant numbers as acknowledgement) and what time period is covered. – Chart Options – Interactive tab to select desired site(s) included in graphs. – Table of Contents – list of graph – Single site charts – Two-group comparison charts – Data tables – Worksheets

23 Site Profiles Provided quarterly for CSAT Programs on the APSS website. Can be created as Profiles (based on a variable other than site). A version for Level of Care is provided on todays CD.

24 Example from ESD 113: Olympia, WA EAT site with additional GAIN data from 2 other locations. Interested in examining one of these locations in comparison with the rest of their own EAT site and with the whole EAT program. Used the SPSS syntax and template in Excel Open ESD Site ProfilesOpen ESD Site Profiles Open ESD PresentationOpen ESD Presentation

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27 Summary At this point you should: – Be aware of the existence of several tools to assist you in understanding who is being served. – Be able to find information about tools you want to use. – Be excited about how you can use these tools for your own analysis. – NOT be worried if you still have questions! WRITE any questions on your index card. For a direct reply after this meeting: – Write legibly and include your name and address.

28 Where to Get More Information Our website: – FTP Common Site: Evaluators Folder – ftp://data.chestnut.org ftp://data.chestnut.org Username: Common Password:public Send to: –

29 LI Analytic Training Series Presentations and Posters Information about CHS studies APSSGAIN Information

30 Data Sharing Agreements GAIN Instrument: Archive APSS Norms, Naming Conventions, & GAIN-I Scales and Variables file!!

31 Where to Get More Information Our website: – FTP Common Site: Evaluators Folder – ftp://data.chestnut.org ftp://data.chestnut.org Username: Common Password:public Send to: –

32 Examples of Analysis Using GAIN Data Rod Funk Chestnut Health Systems, Bloomington, IL

33 Acknowledgement: This presentation was developed under contract # from the Center for Substance Abuse Treatment (CSAT) of the Substance Abuse and Mental Health Services Administration (SAMHSA) and presents data from the Persistent Effects of Treatment Study (PETS, Contract No ) and the Cannabis Youth Treatment (CYT) Cooperative Agreement (Grant Nos. TI11317, TI11320, TI11321, TI11323, and TI11324) as well as the Assertive Continuing Care Study supported by funds and data from the National Institute on Alcoholism & Alcohol Abuse (RO1 AA 10368). The opinions are those of the authors and do not reflect official positions of the government.

34 Evaluating the Effects of Treatment Short Term Outcome Stability Difference between average of early (3-6) and latter (9-12) follow-up interviews Treatment Outcome Difference between intake and average of all short term follow-ups (3-12) Long Term Stability Difference between average of short term follow-ups (3-12) and long term follow-up (30) Source: Dennis et al, 2003, 2004 Month Z-Score

35 Change in Substance Frequency Scale in CYT Experiment 1: Incremental Arm Months from Intake Source: Dennis et al, CPDD, 2003 Treatment Outcome: -Use reduced (-34%) - No Sig. Dif. by condition Short Term Stability: - Outcomes stable (-1%) - No Sig. Dif. by condition Long Term Stability: - Use increases (+64%) - No Sig. Dif. by condition

36 Change in Substance Frequency Scale in CYT Experiment 2: Alternative Arm Months from Intake Source: Dennis et al, CPDD 2003 Treatment Outcome: - Use reduced (-35%) - No Sig. Dif. by condition Short Term Stability: -Further reductions (-6%) - Sig. Dif. by condition (+4% vs. –10% vs. –11%) Long Term Stability: - Outcomes stable (+20%) -No Sig. Dif. by condition

37 $1,559 $1,413 $1,984 $3,322 $1,197 $1,126 $- $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000 MET/CBT5 (6.8 weeks) MET/CBT12 (13.4 weeks) FSN (14.2 weeks w/family) MET/CBT5 (6.5 weeks) ACRA (12.8 weeks) MDFT(13.2 weeks w/family) $1,776 $3,495 NTIES Est (6.7 weeks) NTIES Est.(13.1 weeks) Average Cost Per Client-Episode of Care | Economic Cost | Director Estimate-----| Average Episode Cost ($US) of Treatment Source: French et al., 2002, 2003 Less than average for 6 weeks Less than average for 12 weeks

38 Cost Per Person in Recovery at 12 and 30 Months After Intake by CYT Condition Source: Dennis et al., 2004; 2005 $0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 CPPR at 12 months** $6,437 $10,405 $24,725 $27,109 $8,257 $14,222 CPPR at 30 months* $3,958 $7,377 $15,116 $6,611 $4,460 $11,775 MET/ CBT5MET/ CBT12FSNMMET/ CBT5ACRAMDFT Experiment 1 (n=299)Experiment 2 (n=297) Cost Per Person in Recovery (CPPR) * P<.0001, Cohens f= 1.42 and 1.77 at 12 months ** P<.0001, Cohens f= 0.76 and 0.94 at 30 months Stability of MET/CBT-5 findings mixed at 30 months MET/CBT-5, -12 and ACRA more cost effective at 12 months Integrated family therapy (MDFT) was more cost effective than adding it on top of treatment (FSN) at 30 months ACRA Effect Largely Sustained

39 Environmental Factors are also the Major Predictors of Relapse Recovery Environment Risk Social Risk Family Conflict Family Cohesion Social Support Substance Use Substance- Related Problems Baseline Source: Godley, Kahn et al (2005) Model Fit CFI=.97 to.99 by follow-up wave RMSEA=.04 to.06 by wave AOD use in the home, family problems, homelessness, fighting, victimization, self help group participation, structure activities Peer AOD use, fighting, illegal activity, treatment, recovery, vocational activity The effects of adolescent treatment are mediated by the extent to which they lead to actual changes in the recovery environment or peer group

40 Assertive Continuing Care (ACC) Hypotheses Assertive Continuin g Care General Continuin g Care Adherence Relative to UCC, ACC will increase General Continuing Care Adherence (GCCA) Early Abstinence GCCA (whether due to UCC or ACC) will be associated with higher rates of early abstinence Sustained Abstinence Early abstinence will be associated with higher rates of long term abstinence.

41 ACC Improved General Continuing Care Adherence (GCCA) Source: Godley et al 2002, % 10% 20% 30% 40%50%60%70%80% WeeklyTx Weekly 12 step meetings Regular urine tests Contact w/probation/school Follow up on referrals* ACC * p<.05 90% 100% Relapse prevention* Communication skills training* Problem solving component* Meet with parents 1-2x month* Weekly telephone contact* Referrals to other services* Discuss probation/school compliance* Adherence: Meets 7/12 criteria* UCC

42 ACC was associated with Reduced Relapse Days to First Marijuana Use p< Proportion Remaining Abstinent from Marijuana UCC Source; Godley et al 2002 ACC ACC almost doubled the time before relapse and reduce long term relapse

43 GCCA Improved Early (0-3 mon.) Abstinence Source: Godley et al 2002, % 36% 38% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Any AOD (OR=2.16*)Alcohol (OR=1.94*) Marijuana (OR=1.98*) Low (0-6/12) GCCA 43% 55% High (7-12/12) GCCA * p<.05 Regardless of condition

44 Early (0-3 mon.) Abstinence Improved Sustained (4-9 mon.) Abstinence 19% 22% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Any AOD (OR=11.16*)Alcohol (OR=5.47*) Marijuana (OR=11.15*) Early(0-3 mon.) Relapse 69% 59% 73% Early (0-3 mon.) Abstainer * p<.05 Source: Godley et al 2002, 2007

45 Victimization and Level of Care Interact to Predict Outcomes Source: Funk, et al., Intake6 MonthsIntake6 Months Marijuana Use (Days of 90) OP -HighOP - Low/ModResid-HighResid - Low/Mod. CHS Outpatient CHS Residential Traumatized groups have higher severity High trauma group does not respond to OP Both groups respond to residential treatment

46 How do CHS OPs high GVS outcomes compare with other OP programs on average? Source: CYT and ATM Outpatient Data Set, Dennis IntakeMon 1-3Mon 4-6Mon 7-9Mon Z-Score on Substance Frequency Scale (SFS) CYT Total (n=217; d=0.51) ATM Total (n=284; d=0.41) CHSOP (n=57; d=0.18) Other programs serve clients who have significantly higher severity And on average they have moderate effect sizes even with high GVS Green line is CHS OPs High GVS adolescents; they have some initial gains but substantial relapse

47 Which 5 OP programs did the best with high GVS adolescents? IntakeMon 1-3Mon 4-6Mon 7-9Mon Z-Score on Substance Frequency Scale (SFS) 7 Challenges (n=42; d=1.21) Tucson Drug Court (n=27; d=0.65) MET/CBT5a (n=34; d=0.62) MET/CBT5b (n=40; d=0.55) FSN/MET/CBT12 (n=34; d=0.53) CHSOP (n=57; d=0.18) The two best were used with much higher severity adolescents and TDC was not manualized Next we can check to see if they are any more similar in severity Source: CYT and ATM Outpatient Data Set, Dennis 2005 Currently CHS is doing an experiment comparing its regular OP with MET/CBT5

48 Methodological Issues to Be Aware of.. Site differences: Beware of demographic differences between sites, such as on gender and race. You can use cluster analysis to create homogeneous subgroups or propensity scores to create more equivalent groups. Floor & Ceiling Effects: Check distributions of outcome variables. If wanting to look at needle use, there is very little to begin with in the CSAT data which would make it difficult to look at change over time. Non-normal distributions: A lot of variables used for outcome analysis can be very zero saturated and therefore highly right skewed.

49 Methodological Issues Continued.. Co-Occurring Disorders: Beware that adolescents are more than likely presenting for more problems than just substance use, such as internal and external disorders. Controlled Environment: Be sure to check for days in controlled environment. You may need to adjust your outcomes, such as days of abstinence. You could subtract days in a controlled environment from your dependent variable, use it as another outcome variable or use it as a covariate in your analysis

50 References Dennis, M. (2005). State of the art of treating adolescent substance use disorders: Course, treatment system, and evidence based practices. Paper presented at the 2005 State Adolescent Coordinators (SAC) Grantee Orientation Meeting, Baltimore, MD. Dennis, M. L., Godley, S. H., Diamond, G., Tims, F. M., Babor, T., Donaldson, J., Liddle, H., et al. (2004). The Cannabis Youth Treatment (CYT) study: Main findings from two randomized trials. Journal of Substance Abuse Treatment, 27, 197–213. Dennis, M. L., et al. (2003).Cannabis Youth Treatment Experiment: 12 and 30 Month Findings. Presentation at College of problems of Drug Dependence, Bal Harbour, FL. French, M.T., Roebuck, M.C., Dennis, M.L., Diamond, G., Godley, S.H., Tims, F., Webb, C., & Herrell, J.M. (2002). The economic cost of outpatient marijuana treatment for adolescents: Findings from a multisite experiment. Addiction, 97, S84-S97. French, M. T., Roebuck, M. C., Dennis, M. L., Diamond, G., Godley, S. H., Liddle, H. A., and Tims, F. M. (2003). Outpatient marijuana treatment for adolescents Economic evaluation of a multisite field experiment. Evaluation Review,27(4) Funk, R. R., McDermeit, M., Godley, S. H., & Adams, L. (2003). Maltreatment issues by level of adolescent substance abuse treatment The extent of the problem at intake and relationship to early outcomes. Journal of Child Maltreatment, 8, Godley, M. D., Godley, S. H., Dennis, M. L., Funk, R., & Passetti, L. (2002). Preliminary outcomes from the assertive continuing care experiment for adolescents discharged from residential treatment. Journal of Substance Abuse Treatment, 23, Godley, M. D., Godley, S. H., Dennis, M. L., Funk, R. R., & Passetti, L. L. (2007). The effect of Assertive Continuing Care on continuing care linkage, adherence, and abstinence following residential treatment for adolescents with substance use disorders. Addiction, 102, Godley, M. D., Kahn, J. H., Dennis, M. L., Godley, S. H., & Funk, R. R. (2005). The stability and impact of environmental factors on substance use and problems after adolescent outpatient treatment. Psychology of Addictive Behaviors, 19,

51 Reduced Relapse: Marijuana Days to First Marijuana Use p< Proportion Remaining Abstinent ACC UCC Godley, CPDD Poster, 2003

52 Logistic Regression Example ACC main findings, Godley, et al)

53 Baseline PhaseContinuing Care PhaseFollow-up Phase SRI SFS SPS SFS LOS Final model only showing paths significant at p <.05; RMSEA =.03 (90% CI = 0 to.06); CFI =.99; TLI =.98. Note: Relationships between exogenous variables were estimated but are not shown. The percentage of variance explained for each endogenous variable is indicated by the bold arrows not associated with path lines. SPS GCCA SRI SFS SPS RERI ACC.42 Garner, Godley, Godley, Funk, & Dennis (in press). Psychology of Addictive Behaviors


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