Developing priorities to achieve health equity through diabetes translation research: a concept mapping study

INTRODUCTION: The goal of diabetes translation research is to advance research into practice and ensure equitable benefit from scientific evidence. This study uses concept mapping to inform and refine future directions of diabetes translation research with the goal of achieving health equity in diabetes prevention and control. RESEARCH DESIGN AND METHODS: This study used concept mapping and input from a national network of diabetes researchers and public health practitioners. Concept mapping is a mixed-method, participant-based process. First, participants generated statements by responding to a focus prompt (“To eliminate disparities and achieve health equity in the prevention and treatment of diabetes, research should…”). Participants then sorted statements by conceptual similarity and rated each statement on importance and feasibility (Likert scale of 1–5). A cluster map was created using multidimensional scaling and hierarchical cluster analysis; statements were plotted by average importance and feasibility. RESULTS: Ten clusters were identified containing between 6 and 12 statements from 95 total generated statements. The ranges of average importance and feasibility ratings for clusters were fairly high and narrow (3.62–4.09; 3.10–3.93, respectively). Clusters with the most statements in the “go-zone” quadrant (above average importance/feasibility) were community and partner engagement (n=7), dissemination and implementation principles (n=4), and enrichment and capacity building (n=4). Clusters with the most statements in the “innovative-targets” quadrant (above average importance, below average feasibility) included next generation interventions (n=6), policy approaches (n=4), and interventions for specific populations (n=4). CONCLUSIONS: This study created a framework of 10 priority areas to guide current and future efforts in diabetes translation research to achieve health equity. Themes rated as highly important and feasible provide the basis to evaluate current research support. Future efforts should explore how to best support innovative-targets, those rated highly important but less feasible.