CATEGORY/THEME: Dissemination and Implementation Science
Background: Evidence-based interventions (EBIs) are often adapted to fit different populations or different service settings. Researchers and community stakeholders often raise concern with EBI effectiveness with new populations or in different settings. Does every such EBI adaptation need to be re-evaluated, or can we “borrow strength” from prior effectiveness trials? We introduce a new concept called “scaling out” in which an EBI is efficiently adapted to a new delivery system or population.
Methods: We define population fixed scaling out as implementation where an EBI is delivered through a different delivery system to the same or very similar population where it has previously been tested. System fixed scaling out extends the reach of an existing intervention to a different population within a similar service system. Following the Exploration, Preparation, Implementation, Sustainment (EPIS) framework, Dynamic Sustainability Framework, and modern mediation analysis, we describe methodology to “borrow strength” from results of existing studies in relation to the proposed ecological context, the health delivery system, target population, and characteristics of the EBI itself.
Findings: Existing frameworks involving external validity and cultural adaptation are applied to scaling out. We present research designs for scaling out that assess similarity with previous studies so that evidence-based inferences regarding effectiveness can be made with a higher degree of confidence. We propose combining new data with evidence from previous trials. To reduce time and scope of evaluation, we can first test for equivalence in the core elements of the mediation model leading to proximal targets. A second strategy compares uptake of the EBI in the new versus old context. We illustrate these methods with examples where prevention programs are now being adapted for primary care.
Discussion: Under a range of conditions, there is reason to expect EBIs adapted to new settings will produce similar impact as they had in previous studies. However, there has historically been great burden and time lag to demonstrate effectiveness with a new population or in a new setting. The proposed scaling-out approach would dramatically reduce the time that EBIs can be evaluated as adapted for new settings or populations.