DREAMS Translational Core - Methods and Data Integration (MDI)

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Project Summary/Abstract: Methods and Data Integration (MDI) Core Over the last decade, there have been substantive advances in analytic research methods and the introduction of novel sources and methods of data collection for translational research; these offer tremendous potential for improving the prevention and management of type 2 diabetes. As one of two translational research cores within the Diabetes Research for Equity through Advanced Multilevel Science Center for Diabetes Translational Research (DREAMS-CDTR), the Methods and Data Integration (MDI) Core promote the application of innovative methods and the use of novel data sources that facilitate two overarching priorities of the CDTR: a) design and optimized targeting and tailoring of diabetes-related interventions and public health policy to promote health equity and reduce inequalities, and b) to monitor the impact of innovations in care delivery, public health and social policy on existing health inequalities. Diabetes policy research increasingly relies on methodological expertise in five key areas: (a) natural experiments research; (b) implementation science; (c) health economics including comparative effectiveness and cost-effectiveness analysis; (d) simulation modeling; and (e) machine learning. The MDI core mission is to promote those methods and data to facilitate policy research in diabetes and health equity. The MDI Core will promote technological advances in data and research methods that can be applied to support interventions in the T2-T4 translational research continuum ranging from ‘bench-to-bedside’ research, clinical care and health services research, community-based intervention research, implementation sciences research, comparative effectiveness analysis, all the way to agent-based simulation models that influence policy, systems and environments. We will support a research environment that disseminates new statistical and analytic approaches, such as hierarchical models, causal modeling, machine learning and artificial intelligence techniques, and technological advances in data sources such as lab-based biomarkers, remotely collected health data, and population health data. These goals will be accomplished by 1) Mentoring junior investigators within our DREAMS-CDTR research center, 2.) Providing evidence-based consulting to CDTR, health system, and community partners, and 3.) Disseminating knowledge of novel research methods and data resources facilitating multilevel diabetes translational research and analysis of its impact on equity in diabetes care and prevention to research sites within our CTDR, among all NIDDK-funded CDTRs and the broader research and public health community.