Use of Real-World Data in Population Science to Improve the Prevention and Care of Diabetes-Related Outcomes.

Edward W Gregg, Elisabetta Patorno, Andrew J Karter, Roopa Mehta, Elbert S Huang, Martin White, Chirag J Patel, Allison T McElvaine, William T Cefalu, Joseph Selby, Matthew C Riddle, Kamlesh Khunti

Journal: Diabetes care 2023;46(7):1316-1326

PMID: 37339346

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Abstract

The past decade of population research for diabetes has seen a dramatic proliferation of the use of real-world data (RWD) and real-world evidence (RWE) generation from non-research settings, including both health and non-health sources, to influence decisions related to optimal diabetes care. A common attribute of these new data is that they were not collected for research purposes yet have the potential to enrich the information around the characteristics of individuals, risk factors, interventions, and health effects. This has expanded the role of subdisciplines like comparative effectiveness research and precision medicine, new quasi-experimental study designs, new research platforms like distributed data networks, and new analytic approaches for clinical prediction of prognosis or treatment response. The result of these developments is a greater potential to progress diabetes treatment and prevention through the increasing range of populations, interventions, outcomes, and settings that can be efficiently examined. However, this proliferation also carries an increased threat of bias and misleading findings. The level of evidence that may be derived from RWD is ultimately a function of the data quality and the rigorous application of study design and analysis. This report reviews the current landscape and applications of RWD in clinical effectiveness and population health research for diabetes and summarizes opportunities and best practices in the conduct, reporting, and dissemination of RWD to optimize its value and limit its drawbacks.

Address: 1School of Population Health, RRCSI University of Medicine and Health Sciences, Dublin, Ireland.; 2Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, U.K.; 3Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.; 4Division of Research, Kaiser Permanente, Oakland, CA.; 5Department of Health Systems and Population Health, University of Washington, Seattle, WA.; 6Metabolic Research Unit (UIEM), Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Medicas y Nutricion, Salvador Zubiran (INCMNSZ), Mexico City, Mexico.; 7Section of General Internal Medicine, Center for Chronic Disease Research and Policy (CDRP), The University of Chicago, Chicago, IL.; 8Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, U.K.; 9Department of Biomedical Informatics, Harvard Medical School, Boston, MA.; 10Research and Scientific Programs, American Diabetes Association, Arlington, VA.; 11Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD.; 12Patient-Centered Outcomes Institute, Washington, DC.; 13Division of Endocrinology, Diabetes, and Clinical Nutrition, Oregon Health & Science University, Portland, OR.; 14Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, U.K.

DOI: 10.2337/dc22-1438