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      “Scaling-out” evidence-based interventions to new populations or new health care delivery systems

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          Abstract

          Background

          Implementing treatments and interventions with demonstrated effectiveness is critical for improving patient health outcomes at a reduced cost. When an evidence-based intervention (EBI) is implemented with fidelity in a setting that is very similar to the setting wherein it was previously found to be effective, it is reasonable to anticipate similar benefits of that EBI. However, one goal of implementation science is to expand the use of EBIs as broadly as is feasible and appropriate in order to foster the greatest public health impact. When implementing an EBI in a novel setting, or targeting novel populations, one must consider whether there is sufficient justification that the EBI would have similar benefits to those found in earlier trials.

          Discussion

          In this paper, we introduce a new concept for implementation called “scaling-out” when EBIs are adapted either to new populations or new delivery systems, or both. Using existing external validity theories and multilevel mediation modeling, we provide a logical framework for determining what new empirical evidence is required for an intervention to retain its evidence-based standard in this new context. The motivating questions are whether scale-out can reasonably be expected to produce population-level effectiveness as found in previous studies, and what additional empirical evaluations would be necessary to test for this short of an entirely new effectiveness trial. We present evaluation options for assessing whether scaling-out results in the ultimate health outcome of interest.

          Conclusion

          In scaling to health or service delivery systems or population/community contexts that are different from the setting where the EBI was originally tested, there are situations where a shorter timeframe of translation is possible. We argue that implementation of an EBI in a moderately different setting or with a different population can sometimes “borrow strength” from evidence of impact in a prior effectiveness trial. The collection of additional empirical data is deemed necessary by the nature and degree of adaptations to the EBI and the context. Our argument in this paper is conceptual, and we propose formal empirical tests of mediational equivalence in a follow-up paper.

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          Most cited references66

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          Mediation Analysis: A Practitioner's Guide

          This article provides an overview of recent developments in mediation analysis, that is, analyses used to assess the relative magnitude of different pathways and mechanisms by which an exposure may affect an outcome. Traditional approaches to mediation in the biomedical and social sciences are described. Attention is given to the confounding assumptions required for a causal interpretation of direct and indirect effect estimates. Methods from the causal inference literature to conduct mediation in the presence of exposure-mediator interactions, binary outcomes, binary mediators, and case-control study designs are presented. Sensitivity analysis techniques for unmeasured confounding and measurement error are introduced. Discussion is given to extensions to time-to-event outcomes and multiple mediators. Further flexible modeling strategies arising from the precise counterfactual definitions of direct and indirect effects are also described. The focus throughout is on methodology that is easily implementable in practice across a broad range of potential applications.
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            Investigating Variation in Replicability

            Although replication is a central tenet of science, direct replications are rare in psychology. This research tested variation in the replicability of 13 classic and contemporary effects across 36 independent samples totaling 6,344 participants. In the aggregate, 10 effects replicated consistently. One effect – imagined contact reducing prejudice – showed weak support for replicability. And two effects – flag priming influencing conservatism and currency priming influencing system justification – did not replicate. We compared whether the conditions such as lab versus online or US versus international sample predicted effect magnitudes. By and large they did not. The results of this small sample of effects suggest that replicability is more dependent on the effect itself than on the sample and setting used to investigate the effect.
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              The ADAPT-ITT model: a novel method of adapting evidence-based HIV Interventions.

              The Institute of Medicine (IOM) recommends the use of HIV prevention interventions with proven efficacy to avert new infections. Given the time and cost associated with the development, implementation and evaluation of efficacious HIV interventions, adapting existing evidence-based interventions (EBIs) to be appropriate for a myriad of at-risk populations may facilitate the efficient development of new EBIs. Unfortunately, few models of theoretic frameworks exist to guide the adaptation of EBIs. Over the past few years, the authors have systematically developed a framework for adapting HIV-related EBIs, known as the "ADAPT-ITT" model. The ADAPT-ITT model consists of 8 sequential phases that inform HIV prevention providers and researchers of a prescriptive method for adapting EBIs. The current article summarizes key components of the ADAPT-ITT model and illustrates the use of the model in several case studies.
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                Author and article information

                Contributors
                gaarons@ucsd.edu
                marisa_sklar@brown.edu
                Brian@northwestern.edu
                nanette.benbow1@northwestern.edu
                Hendricks.brown@northwestern.edu
                Journal
                Implement Sci
                Implement Sci
                Implementation Science : IS
                BioMed Central (London )
                1748-5908
                6 September 2017
                6 September 2017
                2017
                : 12
                : 111
                Affiliations
                [1 ]Department of Psychiatry, University of California, San Diego, La Jolla, CA USA
                [2 ]Child and Adolescent Services Research Center, San Diego, CA USA
                [3 ]ISNI 0000 0004 1936 9094, GRID grid.40263.33, Department of Psychiatry and Human Behavior, , Brown University, ; Box G-A1, Providence, RI USA
                [4 ]ISNI 0000 0001 2299 3507, GRID grid.16753.36, Feinberg School of Medicine, , Northwestern University, ; Chicago, IL USA
                Article
                640
                10.1186/s13012-017-0640-6
                5588712
                28877746
                b9683cbb-257f-4b4e-834b-f0b75e9bbe51
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 14 February 2017
                : 18 August 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: P30DA027828
                Award ID: R01DA038466
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: R01MH072961
                Award ID: R01MH092950
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000133, Agency for Healthcare Research and Quality;
                Award ID: F32HS024192
                Award Recipient :
                Categories
                Debate
                Custom metadata
                © The Author(s) 2017

                Medicine
                scaling-out,scaling-up,delivery system fixed,population fixed,implementation science,evidence-based intervention,intervention adaptation,external validity,multilevel mediation modeling,effectiveness,mediational equivalence

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