Mitra Mosslemi1,2, Hannah L Park2, Christine E McLaren3, Nathan D Wong1,2. 1. Heart Disease Prevention Program, Division of Cardiology. 2. Department of Epidemiology. 3. Department of Medicine, School of Medicine, University of California, Irvine, California, USA.
Abstract
In epidemiology studies, identification of diabetes type (type 1 vs. type 2) among study participants with diabetes is important; however, conventional diabetes type identification approaches that include age at diabetes diagnosis as an initial criterion introduces biases. Using data from the National Health and Nutrition Examination Survey, we have developed a novel algorithm which does not include age at diagnosis to identify participants with self-reported diagnosed diabetes as having type 1 vs. type 2 diabetes. METHODS: A total of 5457 National Health and Nutrition Examination Survey participants between cycles 1999-2000 and 2015-2016 reported that a health professional had diagnosed them as having diabetes at a time other than during pregnancy and had complete information on diabetes-related questions. After developing an algorithm based on information regarding the treatment(s) they received, we classified these participants as having type 1 or type 2 diabetes. RESULTS: The treatment-based algorithm yielded a 6-94% split for type 1 and type 2 diabetes, which is consistent with reports from the Centers for Disease Control and other resources. Moreover, the demographics and clinical characteristics of the assigned type 1 and type 2 cases were consistent with contemporary epidemiologic findings. CONCLUSION: Applying diabetes treatment information from the National Health and Nutrition Examination Survey, as formulated in our treatment-based algorithm, may better identify type 1 and type 2 diabetes cases and thus prevent the specific biases imposed by conventional approaches which include the age of diabetes diagnosis as an initial criterion for diabetes type classification.
In epidemiology studies, identification of diabetes type (type 1 vs. type 2) among study participants with diabetes is important; however, conventional diabetes type identification approaches that include age at diabetes diagnosis as an initial criterion introduces biases. Using data from the National Health and Nutrition Examination Survey, we have developed a novel algorithm which does not include age at diagnosis to identify participants with self-reported diagnosed diabetes as having type 1 vs. type 2 diabetes. METHODS: A total of 5457 National Health and Nutrition Examination Survey participants between cycles 1999-2000 and 2015-2016 reported that a health professional had diagnosed them as having diabetes at a time other than during pregnancy and had complete information on diabetes-related questions. After developing an algorithm based on information regarding the treatment(s) they received, we classified these participants as having type 1 or type 2 diabetes. RESULTS: The treatment-based algorithm yielded a 6-94% split for type 1 and type 2 diabetes, which is consistent with reports from the Centers for Disease Control and other resources. Moreover, the demographics and clinical characteristics of the assigned type 1 and type 2 cases were consistent with contemporary epidemiologic findings. CONCLUSION: Applying diabetes treatment information from the National Health and Nutrition Examination Survey, as formulated in our treatment-based algorithm, may better identify type 1 and type 2 diabetes cases and thus prevent the specific biases imposed by conventional approaches which include the age of diabetes diagnosis as an initial criterion for diabetes type classification.
Keywords:
National Health and Nutrition Examination Survey; diabetes epidemiology; diabetes type identification; endocrinology; type 1 diabetes; type 2 diabetes
Authors: David M Maahs; Nancy A West; Jean M Lawrence; Elizabeth J Mayer-Davis Journal: Endocrinol Metab Clin North Am Date: 2010-09 Impact factor: 4.741
Authors: Elizabeth J Mayer-Davis; Jean M Lawrence; Dana Dabelea; Jasmin Divers; Scott Isom; Lawrence Dolan; Giuseppina Imperatore; Barbara Linder; Santica Marcovina; David J Pettitt; Catherine Pihoker; Sharon Saydah; Lynne Wagenknecht Journal: N Engl J Med Date: 2017-04-13 Impact factor: 91.245
Authors: Kellee M Miller; Nicole C Foster; Roy W Beck; Richard M Bergenstal; Stephanie N DuBose; Linda A DiMeglio; David M Maahs; William V Tamborlane Journal: Diabetes Care Date: 2015-06 Impact factor: 19.112
Authors: Nathan D Wong; Christopher Patao; Kalina Wong; Shaista Malik; Stanley S Franklin; Uchenna Iloeje Journal: Diab Vasc Dis Res Date: 2013-08-22 Impact factor: 3.291