J Clin Transl Endocrinol

ObjectivesSurveys for U.S. diabetes surveillance do not reliably distinguish between type 1 and type 2 diabetes, potentially obscuring trends in type 1 among adults. To validate survey-based algorithms for distinguishing diabetes type, we linked survey data collected from adult patients with diabetes to a gold standard diabetes type.Research design and methodsWe collected data through a telephone survey of 771 adults with diabetes receiving care in a large healthcare system in North Carolina. We tested 34 survey classification algorithms utilizing information on respondents\u2019 report of physician-diagnosed diabetes type, age at onset, diabetes drug use, and body mass index. Algorithms were evaluated by calculating type 1 and type 2 sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to a gold standard diagnosis of diabetes type determined through analysis of EHR data and endocrinologist review of selected cases.ResultsAlgorithms based on self-reported type outperformed those based solely on other data elements. The top-performing algorithm classified as type 1 all respondents who reported type 1 and were prescribed insulin, as \u201cother diabetes type\u201d all respondents who reported \u201cother,\u201d and as type 2 the remaining respondents (type 1 sensitivity 91.6%, type 1 specificity 98.9%, type 1 PPV 82.5%, type 1 NPV 99.5%). This algorithm performed well in most demographic subpopulations.ConclusionsThe major federal health surveys should consider including self-reported diabetes type if they do not already, as the gains in the accuracy of typing are substantial compared to classifications based on other data elements. This study provides much-needed guidance on the accuracy of survey-based diabetes typing algorithms.U01 DP006327/DP/NCCDPHP CDC HHS/United StatesU01DP006327/ACL/ACL HHS/United StatesUL1 TR002489/TR/NCATS NIH HHS/United States2020-07-03T00:00:00Z32695611PMC7365930865