Construction of an Implementation Science for Scaling Out Interventions
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The Johns Hopkins Bloomberg School of Public Health Center for Implementation Research ...

The Johns Hopkins Bloomberg School of Public Health Center for Implementation Research
The Johns Hopkins Center for AIDS Research
& the Dean’s Office invite you to
The Center for Implementation Research Implementation Science Speaker Series

Construction of an Implementation Science for Scaling Out Interventions

Wednesday, May 7, 2014
12:15pm – 1:15pm
W1020 Becton Dickinson – 615 N. Wolfe Street

C Hendricks Brown, Ph. D.
Director, Center for Prevention Implementation Methodology (Ce-PIM)
Director, Prevention Science and Methodology Group (PSMG)
Professor, Department of Psychiatry and Behavioral Sciences and Department of Preventive Medicine
Northwestern University, Feinberg School of Medicine

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Construction of an Implementation Science for Scaling Out Interventions Presentation Transcript

  • 1. Construction of an Implementation Science for Scaling Out Interventions C Hendricks Brown Department of Psychiatry and Behavioral Sciences Department of Preventive Medicine Institute for Public Health and Medicine Center for Engineering and Health Director, Center for Prevention Implementation Methodology (Ce-PIM) Director, Prevention Science and Methodology Group (PSMG) Hendricks.brown@northwestern.edu
  • 2. Acknowledgments Funded by National Institute on Drug Abuse (NIDA) and Office of Behavioral and Social Science Research (OBSSR) P30 DA027828 Substance Abuse and Mental Health Services Administration (SAMHSA) NIMH Hopkins PRC & Training Sheppard Kellam, Phil Leaf, Nick Ialongo
  • 3. Co-Authors • Patti Chamberlain, OSLC • Lisa Saldana, OSLC • Courteney Padgett, OSLC • Gracelyn Cruden, Northwestern • Wei Wang, USF Carlos Gallo, Northwestern Car Hai, USC Hopkins Pevention Research Center May 2014
  • 4. Outline 1. Standard Research Pipeline involving Implementation CAL-OH Head-to-Head Randomized Implementation Trial 2. Adding Scaling-Out to our Research Pipeline Definition and Illustrations 3. Constructing Implementation Science for Scaling Out – Off-Label Prevention Implementation Evaluation Design Options Automatizing Fidelity Ratings 4. Conclusions Hopkins Pevention Research Center May 2014
  • 5. Objectives •Illustrate Use of Team Science and Practice Networks •Make Connections to Major Themes in this Hopkins Implementation Meeting Simulation Modeling Technology: Continuous Evaluation of Evolving Interventions and Automated Fidelity Assessment •Illustrate elements required for “Scaling-Out” Alternative Research/Evaluation Designs Hopkins Pevention Research Center May 2014
  • 6. Acknowledgments: Team Science and Practice in Implementation Construction of an implementation Science Implementation of Implementation Science Hopkins Pevention Research Center May 2014
  • 7. 2014 Ce-PIM Team Science and Practice
  • 8. Hopkins Pevention Research Center May 2014
  • 9. Hopkins Pevention Research Center May 2014
  • 10. Hopkins Pevention Research Center May 2014
  • 11. We are making progress in Implementation Science Example: A Head-to-Head Randomized Implementation Trial of Two Alternative Implementation Strategies for a Single Evidence- Based Intervention Hopkins Pevention Research Center May 2014
  • 12. For Implementation, the Program Delivery System, rather than the Clinical/Preventive Intervention, is in the Foreground Clinical/Preventive Intervention Multilevel, Program Delivery System I Landsverk J, Brown CH, Chamberlain P, Palinkas L, Horwitz SM, Ogihara M. Design and Analysis in Dissemination Research 2012. Clinical/Preventive Intervention Multilevel, Program Delivery System II Different Same Hopkins Pevention Research Center May 2014
  • 13. CAL-OH Head-to-Head Randomized Implementation Trial • Single evidence-based intervention: − Multidimensional Treatment Foster Care (MTFC) • 2 alternative strategies of implementing this same program Standard implementation (Stnd) Community Development Team (CDT) • Randomize 51 Counties to implementation strategy* • Evaluate implementation success using Stages of Implementation Completion (Saldana IS 2014) − Implementatoin Should Be Faster and go Farther w CDT − More (# families served), − Better (fidelity) *Chamberlain P, et al. (2008). Engaging and Recruiting Counties in an Experiment on Implementing Evidence–Based Practice in California. Administration and Policy in Mental Health and Mental Health Services Research, 35(4): 250-260. Hopkins Pevention Research Center May 2014
  • 14. Randomize 51 Counties in CA and OH to Implementation Strategy and Time (Cohort) Randomized Roll-Out Design* 40 CA Counties 26 Wait LIsted CDT Stnd Wait Listed 13 Wait LIsted COHORT 1 COHORT 2 COHORT 3 COHORT 4 *Brown, et al. 2009 Ann Rev PH 11 OH Counties Hopkins Pevention Research Center May 2014
  • 15. Summary of Findings (Brown et al., under review) • Mixed Results • Evidence that − CDT increased numbers of families served − CDT counties completed implementation more thoroughly • No evidence that − CDT affected rate of adoption − CDT changed speed of implementation 0 5 10 15 20 25 30 051015202530 Figure 4. Comparison of Placement Quantiles for CDT and IND Counties CDT IND Number Served Quantiles for CDT versus STD (EQQ Plot) Hopkins Pevention Research Center May 2014
  • 16. Remarks about CAL-OH Randomized Implementation Trial •Able to Maintain Randomize County Design even during statewide depression •Equipoise may really exist •Expensive, Judiciously Choose Head- to-Head Implementation Trials Hopkins Pevention Research Center May 2014
  • 17. But …. Implementation requires more scientific and methodologic frameworks to be developed Hopkins Pevention Research Center May 2014
  • 18. Implementation Exploration Adoption / Preparation Implementation Sustainment Effectiveness Studies Efficacy Studies Preintervention Making a Program Work Does a Program Work? Could a Program Work? Traditional Translation Pipeline RealWorldRelevance Standard Research Pipeline for Implementation Local knowledge Generalized knowledge IOM 2009 Landsverk, Brown et al. 2012 Aarons et al., 2011 Intervention Hopkins Pevention Research Center May 2014
  • 19. A 3-Dimensional View of Implementation Vertical Scaling Adoption Increased coverage, range, sustainability of services (Ilott et al., IS 2013, Elias et al., School Psychol Rev 2003) Scaling-Up (Large-Scale Implementation) Horizontal Scaling Implement Same Intervention across Different Settings Scaling-Out Depth Qual Improvement/Local Evaluation versus Generalizable Knowledge Landsverk et al., 2012, Brown et al., 2014, Cheung & Duan 2013 Hopkins Pevention Research Center May 2014
  • 20. Implications of Moving through the Pipeline • Determine “Evidence-Based” Programs Blueprints (www.blupeprintsprograms.com) Evidence from experimental design Clear findings of positive impact Multisite replication Programs deemed ready for widespread use • Research focus on Implementation, Leaving questions of Effectiveness Behind • Consider the Program as Fixed, • Inihibit or Permit limited adaptation or modification • Guidance on Where to Use? Hopkins Pevention Research Center May 2014
  • 21. Little Focus on • How Context Affects a Fixed Program • How Programs may need to be revised to fit in different contexts • Improving Programs as they are Sustained (Chambers et al., Imp Sci 2013) Hopkins Pevention Research Center May 2014
  • 22. Resolution: standard / high Figure 1. Program drift and voltage drop. Illustrating the concepts of 'program drift,’ in which the expected effect of an intervention is presumed to decrease over time as practitioners adapt the delivery of the intervention (A), and 'voltage drop,’ in which the effect of an intervention is presumed to decrease as testing moves from Efficacy to Effectiveness to Dissemination and Implementation (D&I) research stages (B). Chambers et al. Implementation Science 2013 8:117   doi:10.1186/1748-5908-8-117 Download authors' original image Chambers et al., 2013 Maximal benefit of any intervention can only be realized through ongoing development, evaluation and refinement in diverse populations and systems Hopkins Pevention Research Center May 2014
  • 23. Dynamic Sustainability Framework (Chambers et al., IS 2013) “Ultimate aim : Quality Improvement of Interventions, not Quality Assurance of Interventions” Hopkins Pevention Research Center May 2014
  • 24. Scaling Out Is an evidence-based program, shown to be effective in one or two settings, also effective in diverse settings? Hopkins Pevention Research Center May 2014
  • 25. Scaling Out refers to the transportation of an existing, evidence-based intervention (or decision system) into a new service delivery system and broader ecological system. • Moving SISTA, an HIV Prevention Program, and Strong African American Families, an adolescent drug and HIV prevention program from CBOs into African American churches. • Embedding Familias Unidas, an Hispanic adolescent drug abuse and HIV prevention program initially delivered through schools, into Family and Adolescent Medicine. • Moving QUIT, a brief intervention for primary care into drug treatment programs. • Moving Communities that Care, a community-based decision support system, and Strong African American Families from rural into urban settings. • Moving the Good Behavior Game from urban into rural and pediatric care systems. • Integrating Prevention Programs from communities into Juvenile Justice • Moving a peer-to-peer suicide prevention program from schools into the Air Force Hopkins Pevention Research Center May 2014
  • 26. Working Definition of Scaling-Out (Ce-PIM Workgroup on Scaling Out, unpub, 2014) “Scaling out is the deliberate use of strategies to implement and sustain evidence-based interventions through or across settings to promote the greatest public health impact. Scaling out involves both practice and research perspectives valuing local knowledge and expertise through collaborations of service settings, policy-makers, consumers, and researchers. Scaling out can involve promoting fidelity and appropriate adaptations that may improve care, outcomes and public health for local contexts and populations.” Hopkins Pevention Research Center May 2014
  • 27. ChihMing Scaling Out Perspective on Implementation Research Local knowledge Generalized knowledge Scale Up Scale Out Across Diverse Contexts Explore Adopt/Prepare Im plem ent Sustain Hopkins Pevention Research Center May 2014
  • 28. Two Research/Practice Options involving Scaling Out Static Dynamic Hopkins Pevention Research Center May 2014
  • 29. Static Model 1. Conduct years of efficacy and effectiveness randomized trials to demonstrate that you could get a program to work within a limited context. 2. Have someone else replicate the trial on a similar population and settting 3. Have this program identified as an Evidence-Based Program 4. Promote the Widescale Use of this Program in diverse settings 5. Believe it works wherever the program is delivered. 6. Resist Adapting the Intervention, “Strict Adherence to the Model” Hopkins Pevention Research Center May 2014
  • 30. Dynamic Model – Planned Adaptation (Aarons et al., 2012) 1. Conduct years of efficacy and effectiveness randomized trials to demonstrate that you could get a program to work within a limited context. 2. Have someone else replicate the trial on a similar population and settting 3. Identify this as Evidence-Based Program 4. Plan and Allow for Planned Adaptions in Program, Organization, and Surrounding Ecologic System 5. Evaluate both effectiveness and implementation in diverse settings Hopkins Pevention Research Center May 2014
  • 31. ChihMing Scaling Out Implementation Evaluation Local knowledge Generalized knowledge Scale Up Scale Out Across Diverse Contexts Explore Adopt/Prepare Im plem ent Sustain Hopkins Pevention Research Center May 2014
  • 32. Implementation Exploration Adoption / Preparation Implementation Sustainment Effectiveness Studies Efficacy Studies Preintervention Making a Program Work Does a Program Work? Could a Program Work? Traditional Translation Pipeline RealWorldRelevanceStandard Research Pipeline for Effectiveness and Implementation Local knowledge Generalized knowledge IOM 2009 Landsverk, Brown et al. 2012 Aarons et al., 2011 Intervention Hopkins Pevention Research Center May 2014
  • 33. Implementation Exploration Adoption / Preparation Implementation Sustainment Effectiveness Studies Efficacy Studies Preintervention Making a Program Work Does a Program Work? Could a Program Work? Traditional Translation Pipeline RealWorldRelevanceStandard Research Pipeline for Effectiveness and Implementation Local knowledge Generalized knowledge IOM 2009 Landsverk, Brown et al. 2012 Aarons et al., 2011 Intervention Hopkins Pevention Research Center May 2014
  • 34. Dynamic Model: Local Adaptation • Start with existing program • Adapt program and service delivery context…. Without Evaluation, this is like Off-Label Prescription Use Unlike FDA We don’t have specification of conditions for its use 1/5 of prescriptions are off-label , ¾ had little scientific evidence (Radley et al., JAMA Intern Med 2006) Need a Scientific and Evaluation Approach for Off-Label Prevention Implementation Hopkins Pevention Research Center May 2014
  • 35. Two Approaches in the Literature •ADAPT-ITT •Dynamic Adaptation Process Hopkins Pevention Research Center May 2014
  • 36. ADAPT-ITT Wingood & DiClemente JAIDS 2008 Assess risk in target population, address cultural context staying with core intervention elements Evaluation: 1)Pilot test intervention w/ 20 participants, stakeholders and agency staff 2)Pilot randomized trial w/ 3-month outcome data . . . . . Conduct randomized effectiveness trial Hopkins Pevention Research Center May 2014
  • 37. Dynamic Adaptation Process Aarons et al. Imp Sci, 2012 Allows both program adaptation and organizational adaptations in a planned way Distinguishes core elements and allowable adaptations Has been used for a small evaluation Hopkins Pevention Research Center May 2014
  • 38. Perspectives on Scaling Out (Ce-PIM Workgroup 2014) • Ecological Context Community Epidemiologic / Cultural / Linguistic / Health Disparities Policy • Service Delivery System Organizational Culture and Climate Systems Engineering Economic Analysis Informatics Technical Assistance for Implementation • Program Core Elements Program Adaptation • Measurement, Research Design, and Analysis Implementation Stages Fidelity Assessment • Networks, Partnerships,and Interacttions Hopkins Pevention Research Center May 2014
  • 39. What Should We Test while Scaling Out? Was program implemented Is program effective Hopkins Pevention Research Center May 2014
  • 40. How do you if Scaling Out Works as Intended? Minimum Needed to Evaluate •If you use an “Evidence-Based” Program / Principles- Still require: participant engagement Attend, Satisfaction program fidelity Ratings Hybrid Designs -- Type III ( Curran et al.,2012) primarily to evaluate implementation, secondarily effectiveness Hopkins Pevention Research Center May 2014
  • 41. Clinical/Prev. Effectiveness Research Implementation Research Hybrid Type I Hybrid Type I Hybrid Type II Hybrid Type II Hybrid Type III Hybrid Type III Hybrid Type I: test clinical/prevention intervention, observe/gather information on implementation Hybrid Type II: test clinical/prevention intervention, study implementation strategy Hybrid Type III: test implementation strategy, observe/gather information on clinical/prevention outcomes Types of Hybrids Hopkins Pevention Research Center May 2014
  • 42. Community ContextCommunity Context Effectiveness Trial Intervention Agency Intervention Agency Intervention Agent Intervention Fidelity Intervention Fidelity TargetTarget ParticipationParticipation Proximal Outcome Proximal Outcome Distal Outcome Distal Outcome Hopkins Pevention Research Center May 2014
  • 43. Community ContextCommunity Context Evaluation Guided by How a Program Should Work Intervention Agency Intervention Agency Intervention Agent Intervention Fidelity Intervention Fidelity TargetTarget ParticipationParticipation Proximal Outcome Proximal Outcome Distal Outcome Distal Outcome Assess Fidelity, Participation, and Feedback Loops Hopkins Pevention Research Center May 2014
  • 44. Simplest Evaluations: Quality Improvement Strategies •Statistical Control Charts Monitor a) the Key Hypothesized Change Factors b) Whether Feedback is Occurring Hopkins Pevention Research Center May 2014
  • 45. Number of Youth Suicide Deaths from 1988 to 2002 in County years deaths 1988 1990 1992 1994 1996 1998 2000 2002 0123456 Hopkins Pevention Research Center May 2014
  • 46. Attitudes Changed through QPR Training Wyman et al., 2008 Improvements from Training and Time Effect Size Null Low Med High Knowledge of Warning Signs and QPR behaviors 0.46 Attitudes about Suicide Prevention 0.89 Self-Evaluation of Suicide Prevention Knowledge 1.06 Knowledge of Clinical Resources 0.99 Efficacy to Perform Gatekeeper Role 1.22 Reluctance to engage with suicidal students 0.29 Hopkins Pevention Research Center May 2014
  • 47. 5 10 15 20 57911 Control Chart Self Efficacy for Gatekeeper Role Time Efficacy Benneyan et al., 2003 Hopkins Pevention Research Center May 2014
  • 48. Two More Elaborate Potential Research Strategies for Scaling Out •Continual Evaluation Designs (Mohr et al., 2013) •Simulation with Agent Based Modeling Hopkins Pevention Research Center May 2014
  • 49. Continuous Evaluation of Evolving Behavioral Intervention Technologies (CEEBIT) Mohr et al., AJPM 2013 Remove Inferior Interventions Hopkins Pevention Research Center May 2014
  • 50. Simulation Modeling as a Development Tool •Predict How a Program will Interact with its Context How many peer leaders are needed? Hopkins Pevention Research Center May 2014
  • 51. Technology: Computational Linguistics and Automated Signal Processing in Implementation Hopkins Pevention Research Center May 2014
  • 52. Social Informatics as a Way to Reduce Fidelity Assessment Burden It is more efficient to have lots of poor quality assessments than a small number of high quality assessments As long as you can adjust for bias! Brown, Mohr, Gallo et al. JAIDS 2013 Hopkins Pevention Research Center May 2014
  • 53. Two Illustrations •African Talking Drums: Long sequence of repeated or similar beats •Good Behavior Game: – Carlos Gallo: Assessing Teacher’s Emotional Tone through signal processing Hopkins Pevention Research Center May 2014
  • 54. Hopkins Pevention Research Center May 2014
  • 55. Yoruba sentence translated • Yoruba is a tonal language of Africa. • Tonality is used to distinguish meaning at the word level. Similar to the difference between “very” and “berry” except that the difference is based on the high or low tone. • This video has a sample of a Yoruba sentence translated into drums language. (Clip 2) Hopkins Pevention Research Center May 2014
  • 56. Talking Drums language • Can you hear the words “spoken” by this drum? (clip 1) This song was translated from a scale of 7 tones (do, re .. si) to a 3 tone scale (do, re, mi). While there is loss of information, There is enough to recognize the song, and even, recover lyrics Hopkins Pevention Research Center May 2014
  • 57. Good Behavior Game: Machine can code all teacher verbalizations while Observer or Coach can only do a small number 0.2 0.4 0.6 0.8 1.0 024 Benefit of Machine Codings to Observer Proportion of Neutral Statements RelativeEfficiency Modest Validity Data (N=20) More Validity Data (N=40) Hopkins Pevention Research Center May 2014
  • 58. Conclusions 1. Standard Research Pipeline Judiciously Select Head-to-Head Trials 2. Adding Scaling-Out to our Research Pipeline Fit of Program, Service, Ecologic Context Evaluation: Minimal Design Approach: Fidelity, Participation Use of Simulation Modeling 3. Social Informatics Support Efficient Fidelity Assessment Hopkins Pevention Research Center May 2014
  • 59. References Benneyan J, Lloyd R, Plsek P (2003). Statistical quality control as tool for research and healthcare improvement. Qual Safety Health Care, 12(6), 458-64. Brown, CH, Ten Have TR, Jo B, Dagne G, Wyman PA, Muthén BO, Gibbons RD. Adaptive Designs in Public Health. Annual Review Public Health, 30: 17.1-17.25, 2009. Brown CH, Sloboda Z, Faggiano F, Teasdale B, Keller F, Burkhart G, Vigna-Taglianti F, Howe G, Masyn K, Wang W, Muthén B, Stephens P, Grey S, Perrino T, and the Prevention Science and Methodology Group. Methods for Synthesizing Findings on Moderation Effects Across Multiple Randomized Trials. Prevention Science, 14(2): 144-156, 2013. Brown CH, Kellam SG, Kaupert S, Muthén BO, Wang W, Muthén L, Chamberlain P, PoVey C, Cady R, Valente T, Ogihara M, Prado G, Pantin H, Szapocznik J, Czaja S, McManus J. Partnerships for the Design, Conduct, and Analysis of Effectiveness, and Implementation Research: Experiences of the Prevention Science and Methodology Group. Administration and Policy in Mental Health, 39: 301-316, 2012. Brown CH, Mason WA, Brown EC (2014). Translating the Intervention Approach into an Appropriate Research Design -- The Next Generation Designs for Effectiveness and Implementation Research. In Z Sloboda and H Petras (Eds.), Advances in Prevention Science: Defining Prevention Science, Springer Publishing. Brown CH, Mohr DC, Gallo CG, Mader C, Palinkas LA, Wingood G, Prado G, Poduska J, Gibbons RD, Kellam SG, Pantin H, McManus J, Ogihara M, Valente T, Wulczyn F, Czaja S, Sutcliffe G, Villamar J, Jacombs C. A Computational Future for Preventing HIV in Minority Communities: How Advanced Technology Can Improve Implementation of Effective Programs. JAIDS 63: Supplement 1, S72-S84, 2013. Gallo C., Pantin H, Villamar J, Prado G, Tapia M, Ogihara M, Cruden G, Brown CH (Accepted). Blending Qualitative and Computational Linguistics Methods for Fidelity Assessment: Experience with the Familias Unidas Preventive Intervention. Accepted for publication in Admin Mental Health Policy. Hopkins Pevention Research Center May 2014
  • 60. Brown CH, Chamberlain P, Saldana L, Wang W, Cruden G, Padgett C.,(under review,) Evaluation of two implementation strategies in fifty-one counties in two states: Results of a cluster randomized implementation trial Chamberlain P, Brown CH, Saldana L, Reid J, Wang W, Marsenich L, Cosna T, Padgett C. Engaging and Recruiting Counties in an Experiment on Implementing Evidence–Based Practice in California. Administration and Policy in Mental Health and Mental Health Services Research, 35(4): 250-260, 2008. Chamberlain, P, Brown, CH, Saldana, L. Observational Measure of Implementation Progress: The Stages of Implementation Completion (SIC), Implementation Science, 6(116), 1-8, 2011. Cheung K and Duan N (2013). Design of Implementation Studies for Quality Improvement Programs: An Effectiveness/Cost Effectiveness Framework. AJPH epub. Glasgow RE, McKay HG, Piette JD, Reynolds KD (2001). The RE-AIM framework for evaluating interventions: what can it tell us about approaches to chronic illness management? Patient Ed and Counseling: 44: 119-127. Hawkins JD, Oesterle S, Brown EC, Abbott RD, Catalano RF (2014). Youth problem behaviors 8 years after implementing the Communities that Care Prevention System: A Community-Randomized Trial. JAMA Pediatr, 168(2) 122-129. IOM 2009 Preventing Mental, Emotional, and Behavioral Disorders Among Young People: Progress and Possibilities. Committee on Prevention of Mental Disorders and Substance Abuse Among Children, Youth, and Young Adults: Research Advances and Promising Interventions. Mary Ellen O’Connell, Thomas Boat, and Kenneth E. Warner, Editors. Board on Children, Youth, and Families, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press, 2009. Palinkas LA, Holloway IW, Rice E, Brown CH, Valente TW, Chamberlain P. Influence Network Linkages across Treatment Conditions in Randomized Controlled Trials. Accepted for publication in Prevention Science. Hopkins Pevention Research Center May 2014
  • 61. Landsverk J, Brown CH, Chamberlain P, Palinkas L, Horwitz SM, Ogihara M. Design and Analysis in Dissemination and Implementation Research. (2012). In R Brownson, G Colditz and E Proctor (Eds.), Dissemination and Implementation Research in Health: Translating Science to Practice, Oxford University Press Mohr DC, Cheung K, Schueller SM, Brown CH, Duan N Continuous Evaluation of Evolving Behavioral Intervention Technologies. American Journal of Preventive Medicine, 45(4): 517-523, 2013. Spoth R, Rohrbach L, Greenberg M, Robertson E, Leaf P, Brown CH, Fagan A, Catalano R, Pentz MA, Sloboda Z, Meyer A, Hawkins D. (2013). Addressing Challenges for the Next Generation of Type 2 Translation Research: The Translation Science to Population Impact (TSci2PI) Framework. Prevention Science. Prev Sci. 14(4): 319–351. Hopkins Pevention Research Center May 2014