Anne F. Rositch

Role of Local Evidence in Transferring Evidence-Based Interventions to Low- and Middle-Income Country Settings: Application to Global Cancer Prevention and Control

PURPOSE Although the global burden of cancer falls increasingly on low- and middle-income countries (LMICs), much of the evidence for cancer prevention and control comes from high-income countries and may not be directly applicable to LMIC settings. In this paper, we focus on the following question: When the majority of the evidence supporting an evidence-based intervention or implementation strategy comes from high-income countries, what local, contextual evidence is needed when transferring and adapting an intervention or strategy to a specific LMIC setting? METHODS We draw on an existing framework (the Population, Intervention, Environment, Transfer-T process model) for assessing transferability of interventions between distinct settings and apply the model to two case studies as learning examples involving implementation of tobacco use treatment guidelines and self sampling for human papillomavirus DNA in cervical cancer screening. RESULTS These two case studies illustrate how researchers, policymakers, practitioners, and consumers may approach the need for local evidence from different perspectives and with different priorities. As uses and expectations around local evidence may be different for different groups, aligning these priorities through multistakeholder engagement in which all parties participate in defining the questions and cocreating the solutions is critical, along with promoting standardized reporting of contextual factors. CONCLUSION Local, contextual evidence can be important for both researchers and practitioners, and its absence may hinder translation of research and implementation efforts across different settings. However, it is essential for researchers, practitioners, and other stakeholders to be able to clearly articulate the type of data needed and why it is important. In particular, where resources are limited, evidence generation should be prioritized to address real needs and gaps in knowledge.