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NEW METHODS FOR PARTICIPATORY COMMUNITY-BASED INTERVENTION RESEARCH – HOW CAN WE DEVELOP A SYSTEMATIC UNDERSTANDING OF CHANGE THAT MATTERS? Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health Division of Community Collaboration and Implementation Science Albert Einstein College of Medicine

Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

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NEW METHODS FOR PARTICIPATORY COMMUNITY-BASED INTERVENTION RESEARCH – HOW CAN WE DEVELOP A SYSTEMATIC UNDERSTANDING OF CHANGE THAT MATTERS?. Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health Division of Community Collaboration and Implementation Science - PowerPoint PPT Presentation

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Page 1: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

NEW METHODS FOR PARTICIPATORY COMMUNITY-BASED INTERVENTION

RESEARCH –

HOW CAN WE DEVELOP A SYSTEMATIC UNDERSTANDING OF

CHANGE THAT MATTERS?

Bruce D. Rapkin, PhDProfessor of Epidemiology and Population Health

Division of Community Collaboration and Implementation ScienceAlbert Einstein College of Medicine

Page 2: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Conclusions• Participatory models of intervention research are superior to

top down models.

• Scientific rigor does not equal the randomized controlled trial.

• Communities of shared interest must form around Learning Systems - with successive studies leading to refinement of key distinctions among interventions, types populations and settings

• Comprehensive dynamic trials are intended to support the learning system, by inventing and evolving interventions in place, drawing upon multiple sources of information gained during the conduct of an intervention.

Page 3: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Why do we need alternatives to the Randomized Clinical Trial model?

• The community argument• The business practices argument• The statistical argument• The scientific argument• The psychological argument

Page 4: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Community-Academic Relationships Imposed by the Medical Model

• Funders resist changing interventions promoted as national standards (despite absence of external validity)

• Communities must figure out how to fit themselves to the program – the program dictates the terms

• What communities know about prevention or engaging clients is only relevant if it pertains to the manual

• A tightly scripted protocol does not respond to collaborators’ circumstances

• Danger that lessons learned will be framed as “what the community did wrong to make the program fail”

• Unwillingness to consider limits of research theories and methods, local problems will remain unsolved

Page 5: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Is this any way to run a business?

• Businesses including clinics examine their practices continually to seek improvements

• Research protocols are designed to resist or restrict change over the course of a study, to ensure “standardization”

• Lessons learned must be “ignored” until the next study

• Valuing fidelity over quality impedes progress to optimal intervention approaches

Page 6: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

What is a “Treatment Effect”?

• The RCT is designed to determine an estimate of a population “treatment effect”

• Is the “treatment effect” a useful construct?– How is the effect determined by the initial

composition of the sample? – Is information beyond aggregate change error

variance or meaningful trajectories?– How does the control condition determine the

effect’? Is this ignorable?

Page 7: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

-10

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TIME 1 TIME 2 TIME 3

Page 8: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

-10

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TIME 1 TIME 2 TIME 3

Page 9: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Does a Successful RCT Mean that Faithful Replication of an

Intervention will Ensure Outcomes?• Not necessarily because…

– Original RCT findings do not generalize to a “universe” – too dependent on context

– Mechanics of interventions have different implications, depending on setting norms

– Even the meaning & impact of core elements may be transformed by local ecology

• We don’t know because of the lack of attention to external validity!

Page 10: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Desirable Features for Study Designs• Must take into account diversity inherent in the determinants

of health and risk behavior• Must recognize that different people can respond to the same

intervention in different ways, or in the same way for different reasons

• Must accommodate diversity and personal preferences• Must avoid ethical dilemmas associated with substandard

treatment of some participants• Must be responsive to evolving understanding of how to best

administer an intervention, and to local innovations and ideas• Must contribute to community capacity building and

empowerment at every step of the research process

Page 11: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

The Research Paradigm We Need…

• A Learning System • A Community Science = A “WIKI”• Who has input

– True integration of multiple methods and perspectives• Who makes decisions?

– The peer review process– The community review process

• Progress toward adequate intervention theory and practice can be quantified

Page 12: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

We have (some of) the building blocks

Page 13: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

But bridges are always built Somewhere -

Page 14: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Comprehensive Dynamic Trials Designs

• Comprehensive => use complete information from multiple sources to understand what is happening in a trial

• Dynamic => built-in mechanisms for feedback to respond to different needs and changing circumstances

• Trials => Systematic, replicable activities that yield high quality information useful for testing causal hypotheses

Page 15: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

The Multi-way Decision Matrix

What outcomes distinct are associated with different intervention approaches?

How do characteristics of target population affect outcomes?

How are outcomes affected by history, resources, and contexts?

Oipc|t

The conditionalprobability of an outcome, for this type ofintervention with this population inthis context, givenwhat is known atthe present time.

Page 16: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Three CDT Designs

• Community Empowerment to enable communities to create new interventions

• Quality Improvement to adapt existing manuals and procedures to new contexts

• Titration-Mastery to optimize algorithms for delivering a continuum of services

Rapkin & Trickett(2005)

Page 17: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

CDT Community Empowerment Design

• Closest to the “orthodox” model of CBPR– No pre-conceived “intervention”– No need for externally-imposed explanation of

the problem or theory of change• Common process of planning• Common criteria for evaluating

implementation across multiple settings and/or multiple “epochs”

Page 18: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

CDT Quality Improvement Design

• Starting point– An evidence-based intervention– A established standard of practice– An innovation ready for diffusion

• Alternative to the traditional “top-down” model of intervention dissemination

• Begin with a baseline intervention, then systematically evolve and optimize it

Page 19: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

CDT Titration toMastery Design

• Suited to practice settings committed to the client/patient/participant

• Does NOT ask about intervention effects?• Rather, asks what combination of

interventions will get closest to 100% positive outcome most efficiently?

• Begins with a tailoring algorithm to systematically apply a tool kit, which is then evolved and optimized

Page 20: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Ingredients of a Comprehensive Dynamic Trial

Just add community and stir…

Page 21: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

How does the CDT Feedback Loop Work?

Page 22: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

What Types of Data Are Needed?

• Outcome Indicators• Fidelity• Mechanistic Measures• Intervention Processes• Structural Impediments• Adverse Events• Propitious Events• Context Measures

Page 23: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

The Deliberation Process• Key stakeholders should be involved in

deliberation• Research systematically provides data to

stakeholders to make decisions about how to modify and optimize interventions

• Timing is based upon the study design• The nature and extent of changes should be

measurable, and expressed in terms of intervention components and procedures

• Deliberation process should be bounded by theory

Page 24: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Ethical Principles – Lounsbury et al.

• Transparency• Shared Authority• Specific Relevance• Rights of Research Participants– Self-Determination– Third-Party Rights– Employees’ Rights

• Privacy• Sound Business Practice• Shared Ownership

Page 25: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

A Model for Maximizing Partnership Success: Key Considerations for Planning, Development, and Self-Assessment – Weiss et al.

Environmental Factors

Composition, Structure and Functions

Characteristics of the Group Process

Intermediate Indicators of Partnership

Effectiveness

Development & Implementation of

Programs & Activities

Outcome Indicators of Partnership Effectiveness

Page 26: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

A CDT-QI Model to Disseminate an Evidenced-Based Approach to Promote

Breast Cancer Screening

The Bronx ACCESS ProjectThe Albert Einstein Cancer Center

Program Project Application Under Development

Page 27: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Exchange of Information Among ACCESS Projects & Research Cores

Lay Health Advisors

Core

Intervention Core

Statistical Analysis & Modeling

Core

Project 2. Community

Involvement in Intervention Adaptation

Data Acquisition

& Geospatial

Core

Project 1. Reach and Effectiveness of Adapted Strategies for BrCa Screening

Project 3. Academic Consultation to Build

Organizational Capacity, Competence

and Commitmenta b c d

i j k

f g

e h

m

o

n

l

Page 28: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

PerformanceScreening Outcomes

Intervention Components

Deliberation Processes

Program ImplementationC

ontexts

CommunityStakeholders

Research Staff

Support

Schema for the Bronx ACCESS PlusComprehensive Dynamic Trial

CBPR Repeated Over Multiple Cycles to Optimize Performance and Outcomes in Different Settings

AgencyLeadership

& Staff

Page 29: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Changing the Rules to Conduct Research in the Real World

• How to incorporate local input in an evidence based paradigm?– Solution: Fidelity gets a vote, but not a veto

• How to deal with cultural and risk specificity of mammography screening interventions?– Solution: disseminate a “suite” of theoretically

equivalent strategies as a tool kit• How to address agencies’ many priorities?

– Solution: Encompass these as “community targeted strategies” for outreach & retention

Page 30: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

How Do You Get Science Out of All That Data?

Page 31: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

In Any One CDT …• Analyses are intrinsic to intervention

• The program should get better as it goes along

• Experimental effects may be examined in context

• Particularly interested in accounting for diverse trajectories and patterns of responses

• Ability to steer toward optimal intervention components

• Case study of community problem solving

• Able to examine setting impacts, sustainability

Page 32: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

The Real Payoff – Science as a Community Process

Page 33: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

The Epistemology of CDT

• A Community Science = A “WIKI”• A learning system• Who has input

– True integration of multiple methods• Who makes decisions?

– the peer review process• Progress toward theory development can be

quantified• Theory can be (provisionally) completed

Page 34: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

The Multi-way Decision Matrix

What outcomes distinct are associated with different intervention approaches?

How do characteristics of target population affect outcomes?

How are outcomes affected by history, resources, and contexts?

Oipc|t

The conditionalprobability of an outcome, for this type ofintervention with this population inthis context, givenwhat is known atthe present time.

Page 35: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Wiring Up the Decision Matrix:Systems Dynamics? Neural Networks? Genetic

Algorithms?

1) An intervention

3) in different contexts

1

2

3

4

5

6

7

8

2) experienced by different people

4) may lead to different outcomes.

ARROWS indicate probabilistic pathways Oipc|t at time T

Tx

Page 36: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Scientific Enterprise Needed to Support this paradigm

• Evaluation for funding will consider soundness of researchers’ relationships with communities

• Multiple sources of data will gain importance• Emphasis on practice-based evidence• Case studies of planning, decision making and

community involvement will be highly important• Awareness that results depend on context, so a single

trial will not receive undue weight• Investigators will work in tandem to create service

suites and knowledge bases• Meta-analysis will grow more important, as a way of

integrating multiple types of studies

Page 37: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Where’s the Science?

• In community process.• In understanding how researchers’ roles

and activities impact CBPR.• In the evolution and refinement of

intervention implementation strategies, through dynamic exchange and reflection.

• It emerges out of the synthesis of CBPR findings

Page 38: Bruce D. Rapkin, PhD Professor of Epidemiology and Population Health

Conclusions• Participatory models of intervention research are superior to

top down models.

• Scientific rigor does not equal the randomized controlled trial.

• Communities of shared interest must form around Learning Systems - with successive studies leading to refinement of key distinctions among interventions, types populations and settings

• Comprehensive dynamic trials are intended to support the learning system, by inventing and evolving interventions in place, drawing upon multiple sources of information gained during the conduct of an intervention.