Challenges of Harnessing theInformatics Landscape to Promote     Health Behavior Change               David B. Abrams, PhD...
Population Impact: The                                                   Example of Tobacco                               ...
Revisit Goal of                                                      Population ImpactImpact = Reach x EfficacyEfficiency:...
Back in 2005…• Internet adoption in US: from 15% in 1995 to 75% in 2006     – More than 70 million adults go online each d...
5+ Years Later: Where                                                      Are We Now?Crounse commentary (2007): “Even tho...
Assumptions1. The promise of informatics and technology to change   public health can be realized using traditional scient...
Assumption 1:                                                   Traditional Science              The Individual Effectiven...
A New Definition of                                                              Translational Research              T1   ...
Assumption 2:           Single-level interventionsOutsidethe skin Underthe skin
Assumption 3:Multi-level integration
Source: Lazer et al. (2009). Life in the network: the coming age of computational socialscience. Science. 323(5915): 721–7...
Iterative Continuous                                    ImprovementDynamic model of research for multi-level impact: Theor...
Example: Multiphase Optimization        Strategy (MOST)• Collins, Murphy, Strecher. The  multiphase optimization strategy ...
From Gene Chip Arrays                                                              To Population Arrays                   ...
Illustrative Examples from                                 the Schroeder Institute1. The iQUITT Study - Internet (Graham, ...
Assumptions1. The promise of informatics and technology to change   public health can be realized using traditional scient...
Internet and Telephone Treatment for Smoking Cessation              Amanda L. Graham, PhD (PI)                 National Ca...
Initial Evaluation of                                          QuitNet• Observational study in December 2002• Total # surv...
Initial Evaluation of                                       QuitNet                                 Least conservativeADHE...
2005 participants                                                                          Recruited online               ...
Control Condition Static site designed  by research team “look and feel” of  QuitNet Extracted content  from QuitNet N...
Recruitment Approach         “Active User         Interception          Sampling”     Google, AOL, MSN,          Yahoo!   ...
Informed Consent  3 explicit steps:Do you give informedconsent?Contact information“Digital signature”
Recruitment      Results1. Denominator,   denominator,   wherefore art   thou denominator2. Generalizability
Research Questions1. Informed Consent: For low-risk, population-based studies   focused on dissemination and implementatio...
30 day abstinence
Population ImpactImpact = Reach x EfficacyEfficiency: Continuous optimization of quality ofevidence-based intervention    ...
Population Impact
IMPACT:                                Secondary Analyses• Of funnels and tunnels and rabbit holes…• From community newspa...
IMPACT:Utilization & Outcomes
User Engagement &                                                           OutcomesPilot study 2002:• Use of any social s...
Engagement:             Social Networks & CessationNEXT STUDY
Sequential MultipleAssignment Randomized         Trial (SMART)
Assumptions1. The promise of informatics and technology to change   public health can be realized using traditional scient...
Am J Public Health. 2010 Jul;100(7):1282-9.J Med Internet Res. 2011 Dec 19;13(4):e119.
QuitNet By the                                                    Numbers     • Website overview 2007        – 1.17 millio...
QuitNet Scope• One of the 1st examples of large-scale, web-based therapeutic social network• > 750,00 members – approx. 30...
QuitNet Data                                           ApplicationsA: Longitudinal Social Network Analysis   – 5+ years of...
Source: http://instagr.am/p/nm695/
Example: Facebook• 65 M users/month (US  alone)   – Covers over 50% of     people aged 15-24• Age:   – 45% of the populati...
Why Online Networks?• For Interventions:   – Faster intervention development   – Better diffusion and dissemination• For E...
Network Impact
Network Impact
“Impact 2.0”• Traditional View:     Impact = Reach X Efficacy• Network View:     Impact = (Initial Reach X R) X Effectiven...
Network Impact
“Impact 2.0+”      Impact = (Initial Reach X R) X Effectiveness                                + ExternalitiesSource: Chri...
Bringing the“mountain toMohammed”
Example: Facebook                 R01• Nate Cobb, PI (2012 – 2015)• Planned >12,000 participants  in factorial design• Out...
Diffusion Model
Assumptions1. The promise of informatics and technology to change   public health can be realized using traditional scient...
Ecological Momentary Tobacco Control          Thomas R. Kirchner, PhD (PI)National Institute on Drug Abuse / DC Department...
Real-time Exposure
Ecological Momentary       “Surveillance”        IVR        MMS        SMS        Email        GPS
Amazon Mechanical            Turk
Amazon Mechanical            Turk
Socio-economic            POST VariationAverage pack price: Newport M = $7.75 block-group white M = $7.29 block-group non-...
Real-time ExposureJan 6 – Jan 9, 2012:  M = 2.3 touches, 6 outlets  M Newport $7.13 LCC $3.53
Relapse DynamicsSOURCE: Kirchner et al. Relapse dynamics during smoking cessation: Recurrentabstinence violation effects a...
SOURCE: Shiyko MP, Lanza ST,Tan X, Li R, Shiffman S. Using theTime-Varying Effect Model(TVEM) to Examine DynamicAssociatio...
Simulation Modeling
Summary &Conclusions
Solutions & Future                                                            DirectionsCrounse commentary (2007):     “al...
Iterative Continuous                                    ImprovementDynamic model of research for multi-level impact: Theor...
Assumptions1. The promise of informatics and technology to change   public health can be realized using traditional scient...
Promises Promises…Bio + behavioral + social + population - based sciences MAYfinally make the dream of efficient populatio...
• “Today, the hurricane and earthquake do not pose the  greatest danger.• It is the unanticipated effects of our own actio...
Embrace Complexity•   The world is complex, contextual, dynamic, multi-causal (causal    loops), multi-level, multiply det...
WE NEED EVIDENCE IN T2-T4 THAT…       IS MORE                    IS LESSContextual              Isolated, de-contextualize...
www.re-aim.org                                                EXTENDED CONSORT DIAGRAM  RE-AIM Issue                      ...
The Challenge: If we have it all,                          then will they really come?• Impact = Efficacy x Reach /cost + ...
Enddabrams@legacyforhealth.org
Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change
Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change
Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change
Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change
Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change
Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change
Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change
Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change
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Challenges of Harnessing the Informatics Landscape to Promote Health Behavior Change

  1. 1. Challenges of Harnessing theInformatics Landscape to Promote Health Behavior Change David B. Abrams, PhDExecutive Director, The Schroeder Institute for Tobacco Research and Policy Studies The Johns Hopkins Bloomberg School of Public Health Georgetown University Medical Center KEYNOTE PRESENTED AT THE AMERICAN ACADEMY OF HEALTH BEHAVIOR AUSTIN, TEXAS MARCH 19, 2012
  2. 2. Population Impact: The Example of Tobacco FDA actSource: Mendez, Warner. Tobacco control. Nicotine & Tobacco Research., August 11, 2010.
  3. 3. Revisit Goal of Population ImpactImpact = Reach x EfficacyEfficiency: Continuous optimization of quality ofevidence-based intervention  delivery at scale, cost-effectivelyRE-AIM: multi-level integrationSOURCES: (1) Abrams et al. (1996). Integrating individual and public health perspectivesfor treatment of tobacco: A combined stepped care matching model. Annals of BehMed,18,290-304. (2) Glasgow, Green, Klesges, Abrams et al. (2006). External validity: weneed to do more. Ann Behav Med,31(2),105-108.
  4. 4. Back in 2005…• Internet adoption in US: from 15% in 1995 to 75% in 2006 – More than 70 million adults go online each day• ~ 80% of Internet users have searched online for health information at some point in their lives (Pew, 2005)BUT…• In spite of a surge of technologic capability, research and evaluation methodologies have not kept pace with rapid evolution & proliferation of communication technologies• Nor has the dissemination of effective eHealth interventions achieved the level of penetration one might have hoped, given the number of people who now access the InternetSource: Atienza, Hesse, Abrams, Rimer, et al. Critical Issues in eHealth Research. Am JPrev Med. 2007 May; 32(5 Suppl): S71–S74.
  5. 5. 5+ Years Later: Where Are We Now?Crounse commentary (2007): “Even though robust communication and collaborationsolutions exist to speed scientific discovery and thedelivery of care, all too often our methodology fallsback on that which we know and have always donebefore… But we must not dig in our heels, resistchange, and continue to conduct business as we havealways done before just because it suits our comfortlevel. Others around the world will not indulge in ortolerate that luxury.”Source: Crounse B. The newspaper, the wristwatch, and the clinician. Am J Prev Med.2007 May;32(5 Suppl):S134.
  6. 6. Assumptions1. The promise of informatics and technology to change public health can be realized using traditional scientific theories and methods (with perhaps only some fine tuning)2. Single level interventions delivered at scale (mass customization) can change health behavior at the population level and make a timely impact.3. Integration across platforms in real time can overcome barriers to reach, engagement, and efficient delivery of behavior change interventions and their seamless integration into delivery systems and policy
  7. 7. Assumption 1: Traditional Science The Individual Effectiveness to Population Impact ChasmSource: Abrams, D (1999). Transdisciplinary paradigms for tobacco research. Nicotine& Tobacco Research, 1, S15.
  8. 8. A New Definition of Translational Research T1 T2 T3 T4 Potential Application Efficacy Effectiveness Population-BasedBasic Science Potential Evidence- Clinical Care Health ofDiscovery Clinical Based or Community Application Guidelines Intervention or Population Basic Theoretical Efficacy Applied Public HealthKnowledge Knowledge Knowledge Knowledge Knowledge Types • Phase 3 trials • Phase 4 clinical trials • Phase 1, 2 trials •T3 type studies in community of • Systematic reviews • Implementation • Observational • Population / outcome studiesResearch • Health services studies • Communication • Cost-benefits, policy impact • Observational studies • Dissemination • Studies beyond clinical care • Diffusion • Systematic reviews Sources: 1) Szilagyi P. 2010: From Research to Dissemination Implementation: http://www.research-practice.org/presentations.aspx. 2) Khoury M, et al. Gen Med, 2007;9:665-674. 3) Glasgow et al., RE-AIM.
  9. 9. Assumption 2: Single-level interventionsOutsidethe skin Underthe skin
  10. 10. Assumption 3:Multi-level integration
  11. 11. Source: Lazer et al. (2009). Life in the network: the coming age of computational socialscience. Science. 323(5915): 721–723.
  12. 12. Iterative Continuous ImprovementDynamic model of research for multi-level impact: Theory to mechanisms to practice to policy loop
  13. 13. Example: Multiphase Optimization Strategy (MOST)• Collins, Murphy, Strecher. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med. 2007 May;32(5 Suppl):S112-8. PMCID: PMC2062525.• Collins et al. The Multiphase Optimization Strategy for Engineering Effective Tobacco Use Interventions. Ann Behav Med. 2011 Apr;41(2):208-26. PMCID: PMC3053423.
  14. 14. From Gene Chip Arrays To Population Arrays Multi-level tailoring at: • biological level • individual level • proximal socio-behavioral level • community level • population level GENOMICS TO POPULOMICSSource: Murray et al. (2006). Eight Americas: Investigating Mortality Disparities acrossRaces, Counties, and Race-Counties in the United States. PLoS Medicine: Vol 3,15139, e260.
  15. 15. Illustrative Examples from the Schroeder Institute1. The iQUITT Study - Internet (Graham, PI)2. Facebook (Cobb, PI)3. POSSE (Kirchner, PI)4. Adaptive designs in clinical trials (Niaura)
  16. 16. Assumptions1. The promise of informatics and technology to change public health can be realized using traditional scientific theories and methods (with perhaps only some fine tuning)2. Single level interventions delivered at scale (mass customization) can change health behavior at the population level and make a timely impact.3. Integration across platforms in real time can overcome barriers to reach, engagement, and efficient delivery of behavior change interventions and their seamless integration into delivery systems and policy
  17. 17. Internet and Telephone Treatment for Smoking Cessation Amanda L. Graham, PhD (PI) National Cancer Institute 5 R01 CA104836 2004 – 2010
  18. 18. Initial Evaluation of QuitNet• Observational study in December 2002• Total # surveyed = 1,501• Responders: 25.6% (N=385)
  19. 19. Initial Evaluation of QuitNet Least conservativeADHERENCE SAMPLE (N=223): 30.0% – Respondents only • Used site ≥ 2x (N=336): 13.1% • Used site >1x (N=488): 9.8% • Excluding bounced (N=892): 8.0%INTENTION TO TREAT (N=1,024): 7.0% – Counts all non-responders as smokers Most conservative
  20. 20. 2005 participants Recruited online Randomized to “real world” Internet or phone treatments ~ 70% follow-up rates 3-18 monthsSource: Graham AL, Bock BC, Cobb NK, Niaura R, Abrams DB. Characteristics of smokers reachedand recruited to an internet smoking cessation trial: a case of denominators. Nicotine Tob Res. 2006Dec;8 Suppl 1:S43-8.
  21. 21. Control Condition Static site designed by research team “look and feel” of QuitNet Extracted content from QuitNet No interactive features No online community
  22. 22. Recruitment Approach “Active User Interception Sampling” Google, AOL, MSN, Yahoo!  Quit smoking  Stop smoking  Quitting smoking  Stopping smoking
  23. 23. Informed Consent 3 explicit steps:Do you give informedconsent?Contact information“Digital signature”
  24. 24. Recruitment Results1. Denominator, denominator, wherefore art thou denominator2. Generalizability
  25. 25. Research Questions1. Informed Consent: For low-risk, population-based studies focused on dissemination and implementation research (i.e., evaluating interventions as they are used in the “real world”), what is the appropriate and optimal level of informed consent? How might informed consent be a barrier that actually limits the reach and understanding of the target population in fundamental ways?2. Control/Comparison Group: What is the appropriate control condition or comparison condition? Is one needed at all? How can we move away from traditional RCTs and consider SMART/adaptive designs, practical & comparative efficacy trials, and other approaches?
  26. 26. 30 day abstinence
  27. 27. Population ImpactImpact = Reach x EfficacyEfficiency: Continuous optimization of quality ofevidence-based intervention  delivery at scale, cost-effectivelyRE-AIM: multi-level integrationSOURCES: (1) Abrams et al. (1996). Integrating individual and public health perspectivesfor treatment of tobacco: A combined stepped care matching model. Annals of BehMed,18,290-304. (2) Glasgow, Green, Klesges, Abrams et al. (2006). External validity: weneed to do more. Ann Behav Med,31(2),105-108.
  28. 28. Population Impact
  29. 29. IMPACT: Secondary Analyses• Of funnels and tunnels and rabbit holes…• From community newspaper to Internet tx seekers…• From 10+ million to 99,900 to 2,005…• Who do we have here, who is NOT here, and how much implementation dissemination, generalizability and scalability do we REALLY have here?• Oh (nearest and dearest) denominator wherefore art thou?
  30. 30. IMPACT:Utilization & Outcomes
  31. 31. User Engagement & OutcomesPilot study 2002:• Use of any social support and  2-month continuous abstinence: OR = 4.03 • Intensity of website use and  2-month continuous abstinence: OR = 6.07 iQUITT Study 2011: Compared to no treatment:• 3+ logins were 1.9x more likely to quit (p < .05)• 3+ calls were 2.4x more likely to quit (p < .01)NOTE: to date we can’t explain the growth of the static minimal Internet comparison(control) group
  32. 32. Engagement: Social Networks & CessationNEXT STUDY
  33. 33. Sequential MultipleAssignment Randomized Trial (SMART)
  34. 34. Assumptions1. The promise of informatics and technology to change public health can be realized using traditional scientific theories and methods (with perhaps only some fine tuning)2. Single level interventions delivered at scale (mass customization) can change health behavior at the population level and make a timely impact.3. Integration across platforms in real time can overcome barriers to reach, engagement, and efficient delivery of behavior change interventions and their seamless integration into delivery systems and policy
  35. 35. Am J Public Health. 2010 Jul;100(7):1282-9.J Med Internet Res. 2011 Dec 19;13(4):e119.
  36. 36. QuitNet By the Numbers • Website overview 2007 – 1.17 million unique visitors to the web site – 76.45 million “page views” – 123,927 unique registered users – 160,000 active users • Internal communications 2007 – 1.36 million internal email (“Qmail”) messages – 815,070 forum posts, ~ equal numbers in “Clubs”37
  37. 37. QuitNet Scope• One of the 1st examples of large-scale, web-based therapeutic social network• > 750,00 members – approx. 30-50K are active in any given month• Growth rates of up to 22,000 members in a month.
  38. 38. QuitNet Data ApplicationsA: Longitudinal Social Network Analysis – 5+ years of detailed network dataB: Content Analysis – 10+ years of forum postings, chat logs, private message history, blog posts, personal profiles and testimonials.C: Agent Based Modeling – Recreation of QuitNet as a dynamic, synthetic network that can be manipulated.
  39. 39. Source: http://instagr.am/p/nm695/
  40. 40. Example: Facebook• 65 M users/month (US alone) – Covers over 50% of people aged 15-24• Age: – 45% of the population is over 25 – Over 35 population doubling every 2 months• Gender: – Women are fastest growing segment
  41. 41. Why Online Networks?• For Interventions: – Faster intervention development – Better diffusion and dissemination• For Evaluation: – Faster recruitment – Fewer barriers to enrollment – Fewer barriers to follow-up – Broader conceptualization of impact
  42. 42. Network Impact
  43. 43. Network Impact
  44. 44. “Impact 2.0”• Traditional View: Impact = Reach X Efficacy• Network View: Impact = (Initial Reach X R) X EffectivenessWhere R is the reproductive ratio or viral spread of an intervention or behavior.
  45. 45. Network Impact
  46. 46. “Impact 2.0+” Impact = (Initial Reach X R) X Effectiveness + ExternalitiesSource: Christakis NA. Social networks and collateral health effects. BMJ 2004, Jul24;329(7459):184-5329.
  47. 47. Bringing the“mountain toMohammed”
  48. 48. Example: Facebook R01• Nate Cobb, PI (2012 – 2015)• Planned >12,000 participants in factorial design• Outcome is R - diffusion of the application from one member to another. Not effect!• Answers question of what drives diffusion and spread?• Entire process is automated from enrollment to tracking of diffusion.
  49. 49. Diffusion Model
  50. 50. Assumptions1. The promise of informatics and technology to change public health can be realized using traditional scientific theories and methods (with perhaps only some fine tuning)2. Single level interventions delivered at scale (mass customization) can change health behavior at the population level and make a timely impact.3. Integration across platforms in real time can overcome barriers to reach, engagement, and efficient delivery of behavior change interventions and their seamless integration into delivery systems and policy
  51. 51. Ecological Momentary Tobacco Control Thomas R. Kirchner, PhD (PI)National Institute on Drug Abuse / DC Department of Health RC1 DA028710 / CDC CPPW Contract 2009 – 2012
  52. 52. Real-time Exposure
  53. 53. Ecological Momentary “Surveillance” IVR MMS SMS Email GPS
  54. 54. Amazon Mechanical Turk
  55. 55. Amazon Mechanical Turk
  56. 56. Socio-economic POST VariationAverage pack price: Newport M = $7.75 block-group white M = $7.29 block-group non-white p = 0.004Low pack price: All cigarette brands M = $6.73Average pack price: LCC M = $3.71Low cost LCCs more prevalent innon-white block-groups (2 = 4.31, p=0.04).
  57. 57. Real-time ExposureJan 6 – Jan 9, 2012: M = 2.3 touches, 6 outlets M Newport $7.13 LCC $3.53
  58. 58. Relapse DynamicsSOURCE: Kirchner et al. Relapse dynamics during smoking cessation: Recurrentabstinence violation effects and lapse-relapse progression. J Abn Psych; 2012: 121(1).
  59. 59. SOURCE: Shiyko MP, Lanza ST,Tan X, Li R, Shiffman S. Using theTime-Varying Effect Model(TVEM) to Examine DynamicAssociations between NegativeAffect and Self Confidence onSmoking Urges: Differencesbetween Successful Quitters andRelapsers. Prev Sci. 2012 Jan 14.[Epub ahead of print].
  60. 60. Simulation Modeling
  61. 61. Summary &Conclusions
  62. 62. Solutions & Future DirectionsCrounse commentary (2007): “all too often our methodology falls back on that which we know and have always done before....But we must...not dig in our heels, resist change and continue to conduct business as we’ve always done so before just because it suits our comfort level. Others around the world will not indulge in or tolerate that luxury”Source: Crounse B. The newspaper, the wristwatch, and the clinician. Am J Prev Med.2007 May;32(5 Suppl):S134.
  63. 63. Iterative Continuous ImprovementDynamic model of research for multi-level impact: Theory to mechanisms to practice to policy loop
  64. 64. Assumptions1. The promise of informatics and technology to change public health can be realized using traditional scientific theories and methods (with perhaps only some fine tuning)2. Single level interventions delivered at scale (mass customization) can change health behavior at the population level and make a timely impact.3. Integration across platforms in real time can overcome barriers to reach, engagement, and efficient delivery of behavior change interventions and their seamless integration into delivery systems and policy
  65. 65. Promises Promises…Bio + behavioral + social + population - based sciences MAYfinally make the dream of efficient population behavior changea reality if and only if:• Rapid innovation across: platforms, modes, capacity in near or in real time, will overcome prior barriers to: – reach – engagement – utilization of efficient tailored behavior change interventions – and their seamless proximal and distal integration into contexts (i.e. traditional and new -- social media, Internet, community, low SES subgroups, health and public health delivery systems and aligned policy at scale)
  66. 66. • “Today, the hurricane and earthquake do not pose the greatest danger.• It is the unanticipated effects of our own actions, effects created by our inability to understand the complex systems we have created and in which we are embedded.• Creating a healthy, sustainable future requires a fundamental shift in the way we generate, learn from, and act on evidence about the delayed and distal effects of our technologies, policies, and institutions.”Source: Sterman JD. Learning from evidence in a complex world. Am J Public Health.2006 Mar;96(3):505-14. Epub 2006 Jan 31.
  67. 67. Embrace Complexity• The world is complex, contextual, dynamic, multi-causal (causal loops), multi-level, multiply determined… – For every complex problem there is a simple solution….and it is usually wrong• Research designs, methods and measures should take this into account and capitalize on advances in computer sciences, technology, informatics, imaging, knowledge management, networking and communications• Vertical integration: cells to society across varying time units (seconds to centuries)• Solid basic behavioral and social and population science is needed as a firm foundation to build systems within systems models• Aligned incentives at every level of the system can change populations
  68. 68. WE NEED EVIDENCE IN T2-T4 THAT… IS MORE IS LESSContextual Isolated, de-contextualizedPractical, efficient Abstract, intensiveRobust, generalizable Singular (Setting, staff, population)Comparative AcademicComprehensive Single outcomeRepresentative From ideal settings 75
  69. 69. www.re-aim.org EXTENDED CONSORT DIAGRAM RE-AIM Issue Content Critical Considerations Total number potential settings Settings Eligible Excluded by Investigator n and % n, %, and reasons ADOPTION Setting and Agents Setting and Agents Other Characteristics Who Participate Who Decline n and % n and % n, %, and reasons Of Adopters vs Non Total Potential Participants, n Excluded by Investigator REACH Individuals Eligible N, %, and reasons n and % Individuals Enroll Individuals Characteristics Not Contacted/ N and % Decline Other Of Enrolles vs. N, %, and reasons N and % Decliners Extent Tx Delivered Component A = XX% Extent Tx IMPLEMENTATION By Different Agents Component B = YY% Delivered as as in Protocol Etc. Intended Complete Tx Drop out of TX Characteristics N,%, and Reasons; EFFICACY (n and % and And Amount of change of Drop-outs vs Amount of Change (By Condition) (By Condition) Completers MAINTENANCE Present at Follow-up Lost to Follow-up Characteristics (n and %) and Amount N, %, and Reasons of Drop-outs vs. a) Individual of Change or Relapse Amount of change or (By Condition) Relapse (By Condition) Completers Level Characteristics b) Setting of Settings that Settings in which Program is Settings in which Level Continued And/or Modified after Program not Continue vs Research is Over Maintained Do Not (n, %, and reasons) (n, %, and reasons) *At each step, record qualitative and quantitative information and factors affecting each RE-AIM dimension and step in flowchart
  70. 70. The Challenge: If we have it all, then will they really come?• Impact = Efficacy x Reach /cost + externalities Not nearly as much as we could be!
  71. 71. Enddabrams@legacyforhealth.org

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