Megha L Mehrotra1,2, Maya L Petersen3, Elvin H Geng1. 1. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA. 2. Currently, Department of Biostatistics, University of California, Berkeley, Berkeley, CA. 3. Department of Biostatistics, University of California, Berkeley, Berkeley, CA.
Abstract
BACKGROUND: Implementation science focuses on evaluating strategies for delivering evidence-based interventions to improve HIV prevention and treatment. The effectiveness of these implementation strategies is often context-dependent and reconciling the desire to produce generalizable knowledge in the face of these contextual interventions is a central challenge for implementation science researchers. METHODS: We provide an overview of the causal transportability theory and conceptualize context under this framework. We review how causal graphs can be used to illustrate the assumptions necessary to apply the results of a study to a new context, and we illustrate this approach using an example of a community adherence group intervention that aims to improve retention in HIV care. Finally, we discuss several key insights highlighted by the transportability theory that are relevant to implementation science researchers. RESULTS: By adopting causal transportability to consider how context may affect the success of an implementation strategy, researchers can formally diagnose when the results of a study are likely to generalize to a given setting. Moreover, selection diagrams can highlight what additional measurements would be needed in a target population to estimate the effect of an implementation strategy in that target population without having to repeat the initial study. CONCLUSIONS: Transportability translates intuition about context-dependent interventions and external validity into actionable and testable insight.
BACKGROUND: Implementation science focuses on evaluating strategies for delivering evidence-based interventions to improve HIV prevention and treatment. The effectiveness of these implementation strategies is often context-dependent and reconciling the desire to produce generalizable knowledge in the face of these contextual interventions is a central challenge for implementation science researchers. METHODS: We provide an overview of the causal transportability theory and conceptualize context under this framework. We review how causal graphs can be used to illustrate the assumptions necessary to apply the results of a study to a new context, and we illustrate this approach using an example of a community adherence group intervention that aims to improve retention in HIV care. Finally, we discuss several key insights highlighted by the transportability theory that are relevant to implementation science researchers. RESULTS: By adopting causal transportability to consider how context may affect the success of an implementation strategy, researchers can formally diagnose when the results of a study are likely to generalize to a given setting. Moreover, selection diagrams can highlight what additional measurements would be needed in a target population to estimate the effect of an implementation strategy in that target population without having to repeat the initial study. CONCLUSIONS: Transportability translates intuition about context-dependent interventions and external validity into actionable and testable insight.
Authors: Ivy Mannoh; Danielle Amundsen; Gnilane Turpin; Carrie E Lyons; Nikita Viswasam; Elizabeth Hahn; Sofia Ryan; Stefan Baral; Bhakti Hansoti Journal: AIDS Behav Date: 2021-11-19
Authors: Jan A C Hontelez; Caroline A Bulstra; Anna Yakusik; Erik Lamontagne; Till W Bärnighausen; Rifat Atun Journal: PLoS Med Date: 2021-11-09 Impact factor: 11.069