TY - JOUR
T1 - "Scaling-out" evidence-based interventions to new populations or new health care delivery systems
AU - Aarons, Gregory A.
AU - Sklar, Marisa
AU - Mustanski, Brian
AU - Benbow, Nanette
AU - Brown, C. Hendricks
N1 - Funding Information:
This project was supported by the US National Institutes of Health (NIH) and National Institute on Drug Abuse (NIDA) grants P30DA027828 (PI: CHB), R01DA038466 (PI: GAA), National Institute of Mental Health (NIMH) grants R01MH072961 and R01MH092950 (PI: GAA), NIMH and NIDA grant R01DA035145 (BM), Agency for Healthcare Research and Quality grant F32HS024192 (PI: MS) and National Institute on Minority Health and Health Disparities (NIMHD) grant U01MD011281 (PI: BM). We also acknowledge the NIH supported Third Coast Center for AIDS Research for creating a supportive environment for HIV/AIDS research (P30AI117943).
Funding Information:
GAA is an Associate Editor and CHB is on the Editorial Board of Implementation Science. All decisions on this paper were made by another editor. NB receives salary support from a subcontract from the University of Chicago that is supported by Gilead, the maker of PrEP, which is mentioned in this paper. The authors declare that they have no other competing interests.
Publisher Copyright:
© 2017 The Author(s).
PY - 2017/9/6
Y1 - 2017/9/6
N2 - 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.
AB - 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.
KW - Delivery system fixed
KW - Effectiveness
KW - Evidence-based intervention
KW - External validity
KW - Implementation science
KW - Intervention adaptation
KW - Mediational equivalence
KW - Multilevel mediation modeling
KW - Population fixed
KW - Scaling-out
KW - Scaling-up
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UR - http://www.scopus.com/inward/citedby.url?scp=85028950846&partnerID=8YFLogxK
U2 - 10.1186/s13012-017-0640-6
DO - 10.1186/s13012-017-0640-6
M3 - Article
C2 - 28877746
AN - SCOPUS:85028950846
SN - 1748-5908
VL - 12
JO - Implementation Science
JF - Implementation Science
IS - 1
M1 - 111
ER -