The Adaptome

Abstract

In the past few decades, prevention scientists have developed and tested a range of interventions with demonstrated benefits on child and adolescent cognitive, affective, and behavioral health. These evidence-based interventions offer promise of population-level benefit if accompanied by findings of implementation science to facilitate adoption, widespread implementation, and sustainment. Though there have been notable examples of successful efforts to scale up interventions, more work is needed to optimize benefit. Although the traditional pathway from intervention development and testing to implementation has served the research community well—allowing for a systematic advance of evidence-based interventions that appear ready for implementation—progress has been limited by maintaining the hypothesis that evidence generation must be complete prior to implementation. This sets up the challenging dichotomy between fidelity and adaptation and limits the science of adaptation to findings from randomized trials of adapted interventions. The field can do better. This paper argues for the development of strategies to advance the science of adaptation in the context of implementation that would more comprehensively describe the needed fit between interventions and their settings, and embrace opportunities for ongoing learning about optimal intervention delivery over time. Efforts to build the resulting adaptome (pronounced “adapt-ohm”) will include the construction of a common data platform to house systematically captured information about variations in delivery of evidence-based interventions across multiple populations and contexts, and provide feedback to intervention developers, as well as the implementation research and practice communities. Finally, the article identifies next steps to jumpstart adaptome data platform development. In the past few decades, prevention scientists have developed and tested a range of interventions with demonstrated benefits on child and adolescent cognitive, affective, and behavioral health. These evidence-based interventions offer promise of population-level benefit if accompanied by findings of implementation science to facilitate adoption, widespread implementation, and sustainment. Though there have been notable examples of successful efforts to scale up interventions, more work is needed to optimize benefit. Although the traditional pathway from intervention development and testing to implementation has served the research community well—allowing for a systematic advance of evidence-based interventions that appear ready for implementation—progress has been limited by maintaining the hypothesis that evidence generation must be complete prior to implementation. This sets up the challenging dichotomy between fidelity and adaptation and limits the science of adaptation to findings from randomized trials of adapted interventions. The field can do better. This paper argues for the development of strategies to advance the science of adaptation in the context of implementation that would more comprehensively describe the needed fit between interventions and their settings, and embrace opportunities for ongoing learning about optimal intervention delivery over time. Efforts to build the resulting adaptome (pronounced “adapt-ohm”) will include the construction of a common data platform to house systematically captured information about variations in delivery of evidence-based interventions across multiple populations and contexts, and provide feedback to intervention developers, as well as the implementation research and practice communities. Finally, the article identifies next steps to jumpstart adaptome data platform development. Since the landmark IOM report on prevention in 1994,1Mrazek P.J. Haggerty R.J. Reducing Risks for Mental Disorders: Frontiers for Preventive Intervention Research. National Academies Press, Washington, DC1994Google Scholar a tremendous amount of progress has been made in building the evidence base for effective approaches to health promotion and disease prevention to improve child and adolescent behavioral health. Many evidence-based practices have been developed, tested, and chronicled in repositories such as the National Registry of Effective Programs and Practices (www.nrepp.samhsa.gov), reviewed in the Community Guide (www.thecommunityguide.org), and supported through targeted funding in federal, state, and local healthcare and education systems.2Chambers D.A. Ringeisen H. Hickman E.E. Federal, state, and foundation initiatives around evidence-based practices for child and adolescent mental health.Child Adolesc Psychiatr Clin N Am. 2005; 14: 307-327https://doi.org/10.1016/j.chc.2004.04.006Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar Although many of these intervention repositories include materials, tools, and resources, information about how such interventions have been utilized and adapted in real-world settings is scarce. In the 2010 Patient Protection and Affordable Care Act, prevention was prioritized amid large-scale health system reform, and calls to fully fund prevention services within insurance plans suggest greater access to prevention and health promotion than in the past. As a follow-up to the 1994 report,1Mrazek P.J. Haggerty R.J. Reducing Risks for Mental Disorders: Frontiers for Preventive Intervention Research. National Academies Press, Washington, DC1994Google Scholar the National Academy of Medicine (NAM) convened an expert panel3O’Connell M.E. Boat T. Warner K.E. Preventing Mental, Emotional, and Behavioral Disorders Among Young People: Progress and Possibilities. National Academies Press, Washington, DC2009Google Scholar to develop recommendations that would push prevention science, policy, and practice into a new phase to concentrate on implementing evidence-based interventions that had been developed in the decade and elapsed since the original release. Within the intervening 15 years, the field benefited from a number of landmark studies on implementation, particularly those that tested models of community-level decision making on the integration of effective prevention interventions within education, social service, and health-oriented agencies. Hawkins et al. (2009)4Hawkins J.D. Oesterle S. Brown E.C. et al.Results of a type 2 translational research trial to prevent adolescent drug use and delinquency: a test of Communities That Care.Arch Pediatr Adolesc Med. 2009; 163: 789-798https://doi.org/10.1001/archpediatrics.2009.141Crossref PubMed Scopus (208) Google Scholar developed and tested the Communities that Care intervention, which mapped community-level needs for prevention and health promotion to a variety of evidence-based interventions, offering support for implementation of interventions that met local needs. Spoth and colleagues’ (2011)5Spoth R. Guyll M. Redmond C. Greenberg M. Feinberg M. Six-year sustainability of evidence-based intervention implementation quality by community-university partnerships: the PROSPER study.Am J Community Psychol. 2011; 48: 412-425https://doi.org/10.1007/s10464-011-9430-5Crossref PubMed Scopus (50) Google Scholar PROmoting School-community-university Partnerships to Enhance Resilience (PROSPER) made similar connections through state extension agencies, resulting in a systematic approach to the implementation of an evidence-based system of care. Additional studies also examined the implementation and scale-up of individual-level prevention interventions, such as the scale-up of The Incredible Years® by Webster-Stratton et al.6Webster‐Stratton C. McCoy K.P. Bringing The Incredible Years® Programs to Scale.New Dir Child Adolesc Dev. 2015; 2015: 81-95https://doi.org/10.1002/cad.20115Crossref PubMed Scopus (12) Google Scholar and the spread of the Good Behavior Game by Kellam and colleagues.7Kellam S.G. Mackenzie A.C. Brown C.H. et al.The Good Behavior Game and the future of prevention and treatment.Addict Sci Clin Pract. 2011; 6: 73-84PubMed Google Scholar Despite this notable progress, the 2009 NAM report3O’Connell M.E. Boat T. Warner K.E. Preventing Mental, Emotional, and Behavioral Disorders Among Young People: Progress and Possibilities. National Academies Press, Washington, DC2009Google Scholar called for increased action on the implementation and sustainability of evidence-based prevention and health promotion interventions for children and adolescents, and growth in the capacity of the research community to conduct the next generation of research at the intersection of implementation and prevention sciences. Although progress has been made in advancing the science of implementation, too often the scientific community follows a linear, static, and simplified model of translating research into practice—one that often overlooks the complexity of pathways that better characterize research-to-practice processes.8Westfall J.M. Mold J. Fagnan L. Practice-based research—“Blue Highways” on the NIH roadmap.JAMA. 2007; 297: 403-406https://doi.org/10.1001/jama.297.4.403Crossref PubMed Scopus (881) Google Scholar The implications of this traditional model of intervention development (i.e., the optimal path from research to practice proceeds linearly from intervention development to efficacy to effectiveness to implementation) are that the field reifies a set of assumptions that limit what is learned from implementing evidence-based approaches to prevention, and limit the degree to which the field seeks to enhance the fit between evidence-based interventions and delivery settings.9Chambers D.A. Glasgow R.E. Stange K.C. The dynamic sustainability framework: addressing the paradox of sustainment amid ongoing change.Implement Sci. 2013; 8: 117https://doi.org/10.1186/1748-5908-8-117Crossref PubMed Scopus (743) Google Scholar Below, the authors spotlight three key assumptions that have not served prevention and implementation sciences well, and suggest these as evidence for the alternative concept and associated data platform proposed herein. The first assumption is that deviation from a manualized intervention (i.e., program drift) will result in lower impact on patient- or client-level outcomes.9Chambers D.A. Glasgow R.E. Stange K.C. The dynamic sustainability framework: addressing the paradox of sustainment amid ongoing change.Implement Sci. 2013; 8: 117https://doi.org/10.1186/1748-5908-8-117Crossref PubMed Scopus (743) Google Scholar This arises from an assumption that an evidence-based prevention intervention is sufficiently understood and codified after efficacy and effectiveness trials that it has reached its optimal level of impact. Following this assumption, each component of the intervention is deemed necessary for all recipients, and it is feared that adjustment in what is delivered and how it is delivered will have negative consequences. Although the field recognizes this is over-simplistic, and that it may be uncertainty rather than adverse effects that the field is trying to prevent, the designation of manualized interventions as evidence-based practices ultimately encourages a rigid view of fidelity. This designation decreases opportunities to learn from evidence-based intervention adaptations that result in improvements beyond what is expected. There has been recent discussion of identifying “exceptional responders,” individuals who have far superior outcomes than the average who receives a specific intervention.10U.S. DHHS, NIH. NIH exceptional responders to cancer therapy study launched. News release. www.nih.gov/news-events/news-releases/nih-exceptional-responders-cancer-therapy-study-launched. Published September 24, 2014. Accessed May 5, 2016.Google Scholar Perhaps an equivalent drive toward identifying “exceptional adaptations” might usefully refine the conceptualization of evidence-based practices. The second assumption is that once evidence is established for a given intervention, the field has as much knowledge as it needs about that intervention and can safely proceed to implementation. The problem, however, is that individuals who participate in trials are a small (and likely not representative) sample of the full population of individuals who could benefit from the intervention. For example, even if the field has a total of 30,000 participants across 30 prevention trials, full implementation of the intervention might reach hundreds of thousands or even millions of people, who may respond quite differently than the 30,000 participants given the methodologic limitations inherent in intervention trials, including selection bias and over-reliance on convenience versus random sampling strategies. The field should assume that the evidence base is ever evolving, and that exposure to the intervention following implementation can refine one’s understanding of how well the intervention works. In addition, the field needs to anticipate that new, better, and more cost-effective interventions will be developed and should replace existing evidence-based practices. For example, with the long time lag between development of an evidence-based intervention and its implementation, modes of intervention delivery will change and need to be accounted for over time. et al. and in health and the for Psychol. 2015; PubMed Scopus Google Scholar this with to and the that interventions may be are ready to be up interventions delivered or versus and interventions delivered versus or and et of intervention for behavioral intervention 2015; PubMed Scopus Google Scholar trials of intervention rather than interventions. can better for the of prevention interventions if that adaptations will need to and for as of and implementation. 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As healthcare have a more for implementation of prevention interventions, and a range of community and settings are to provide there is better time to advance understanding of adaptations to and delivery of evidence-based interventions. of the evidence on interventions is and to a system all evidence practice is far more in the than ever Though of the steps to use these data will the adaptome is proposed as of the drive for a learning healthcare adaptation and learning are and research evidence will be the to of this article was supported by of the on and Behavioral The authors would to C. Brown and for their on an of the The findings and in this report are those of the authors and do not the of the National by the authors of this