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

机译:“缩放”基于证据的新人或新的医疗保健交付系统的干预措施

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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. 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. 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.
机译:实施治疗和效率的效率对于以降低成本改善患者健康结果至关重要。当基于证据的干预(EBI)在与先前被发现有效的设置非常相似的设置中,预期该EBI的类似益处是合理的。然而,实施科学的一个目标是扩大EBIS的使用,这是可行和适当的,以便培养最大的公共卫生影响。在新颖的环境中实施EBI或针对新颖的人群时,必须考虑eBI是否有足够的理由,即EBI对早期试验中发现的人具有类似的益处。在本文中,当EBIS适应新的人群或新的递送系统时,我们介绍了一个名为“缩放”的新概念,或者。使用现有的外部有效性理论和多级中介建模,我们提供了一个逻辑框架,用于确定干预所需的新实证证据,以在这一新上下文中保留其证据标准。激励问题是可以合理地预期扩展的,以产生以前研究中的人口水平效果,以及在这缺乏完全新的有效性试验的情况下测试额外的实证评估是必要的。我们提出了评估是否在兴趣的最终健康结果中进行缩放结果。在缩放到健康或服务交付系统或与eBI最初测试的环境不同的人口/社区环境中,存在可能的情况较短的翻译时间。我们认为,在适度不同的环境中实施EBI或以不同的人口在有时会从现有有效性审判的影响的证据中“借用实力”。通过对EBI的性质和适应程度和上下文,所必需的额外经验数据的集合被视为必要。我们在本文中的论点是概念性的,我们提出了在随访纸上的媒体等效性的正式实证测试。

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