Assessing scalability of an intervention: why, how and who?

Karen Zamboni ORCID logo; Joanna Schellenberg ORCID logo; Claudia Hanson ORCID logo; Ana PilarBetran; AlexandreDumont; (2019) Assessing scalability of an intervention: why, how and who? Health policy and planning, 34 (7). pp. 544-552. ISSN 0268-1080 DOI: 10.1093/heapol/czz068
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Public health interventions should be designed with scale in mind, and researchers and implementers must plan for scale-up at an early stage. Yet, there is limited awareness among researchers of the critical value of considering scalability and relatively limited empirical evidence on assessing scalability, despite emerging methodological guidance. We aimed to integrate scalability considerations in the design of a study to evaluate a multi-component intervention to reduce unnecessary caesarean sections in low- and middle-income countries. First, we reviewed and synthesized existing scale up frameworks to identify relevant dimensions and available scalability assessment tools. Based on these, we defined our scalability assessment process and adapted existing tools for our study. Here, we document our experience and the methodological challenges we encountered in integrating a scalability assessment in our study protocol. These include: achieving consensus on the purpose of a scalability assessment; and identifying the optimal timing of such an assessment, moving away from the concept of a one-off assessment at the start of a project. We also encountered tensions between the need to establish the proof of principle, and the need to design an innovation that would be fit-for-scale. Particularly for complex interventions, scaling up may warrant rigorous research to determine an efficient and effective scaling-up strategy. We call for researchers to better incorporate scalability considerations in pragmatic trials through greater integration of impact and process evaluation, more stringent definition and measurement of scale-up objectives and outcome evaluation plans that allow for comparison of effects at different stages of scale-up.



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