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“Scaling-out” evidence-based interventions to new populations or new health care delivery systems
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5588712/ https://www.ncbi.nlm.nih.gov/pubmed/28877746 http://dx.doi.org/10.1186/s13012-017-0640-6 |
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author | Aarons, Gregory A. Sklar, Marisa Mustanski, Brian Benbow, Nanette Brown, C. Hendricks |
author_facet | Aarons, Gregory A. Sklar, Marisa Mustanski, Brian Benbow, Nanette Brown, C. Hendricks |
author_sort | Aarons, Gregory A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5588712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55887122017-09-14 “Scaling-out” evidence-based interventions to new populations or new health care delivery systems Aarons, Gregory A. Sklar, Marisa Mustanski, Brian Benbow, Nanette Brown, C. Hendricks Implement Sci Debate 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. BioMed Central 2017-09-06 /pmc/articles/PMC5588712/ /pubmed/28877746 http://dx.doi.org/10.1186/s13012-017-0640-6 Text en © 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. |
spellingShingle | Debate Aarons, Gregory A. Sklar, Marisa Mustanski, Brian Benbow, Nanette Brown, C. Hendricks “Scaling-out” evidence-based interventions to new populations or new health care delivery systems |
title | “Scaling-out” evidence-based interventions to new populations or new health care delivery systems |
title_full | “Scaling-out” evidence-based interventions to new populations or new health care delivery systems |
title_fullStr | “Scaling-out” evidence-based interventions to new populations or new health care delivery systems |
title_full_unstemmed | “Scaling-out” evidence-based interventions to new populations or new health care delivery systems |
title_short | “Scaling-out” evidence-based interventions to new populations or new health care delivery systems |
title_sort | “scaling-out” evidence-based interventions to new populations or new health care delivery systems |
topic | Debate |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5588712/ https://www.ncbi.nlm.nih.gov/pubmed/28877746 http://dx.doi.org/10.1186/s13012-017-0640-6 |
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