Anna R Kahkoska1, Teeranan Pokaprakarn2, G Rumay Alexander3, Tessa L Crume4, Dana Dabelea4,5, Jasmin Divers6, Lawrence M Dolan7, Elizabeth T Jensen8, Jean M Lawrence9, Santica Marcovina10, Amy K Mottl11, Catherine Pihoker12, Sharon H Saydah13, Michael R Kosorok2,14, Elizabeth J Mayer-Davis1,11. 1. 1Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC. 2. 2Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC. 3. 3School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC. 4. 4Department of Epidemiology, Colorado School of Public Health, Aurora, CO. 5. 5Department of Pediatrics, School of Medicine, University of Colorado, Aurora, CO. 6. 6Division of Health Services Research, Department of Foundations of Medicine, NYU Long Island School of Medicine, Mineola, NY. 7. 7Division of Endocrinology, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH. 8. 8Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC. 9. 9Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA. 10. 10Department of Medicine, Northwest Lipid Metabolism and Diabetes Research Laboratories, University of Washington, Seattle, WA. 11. 11Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC. 12. 12Department of Pediatrics, University of Washington, Seattle, WA. 13. 13Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA. 14. 14Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Abstract
OBJECTIVE: To estimate difference in population-level glycemic control and the emergence of diabetes complications given a theoretical scenario in which non-White youth and young adults (YYA) with type 1 diabetes (T1D) receive and follow an equivalent distribution of diabetes treatment regimens as non-Hispanic White YYA. RESEARCH DESIGN AND METHODS: Longitudinal data from YYA diagnosed 2002-2005 in the SEARCH for Diabetes in Youth Study were analyzed. Based on self-reported race/ethnicity, YYA were classified as non-White race or Hispanic ethnicity (non-White subgroup) versus non-Hispanic White race (White subgroup). In the White versus non-White subgroups, the propensity score models estimated treatment regimens, including patterns of insulin modality, self-monitored glucose frequency, and continuous glucose monitoring use. An analysis based on policy evaluation techniques in reinforcement learning estimated the effect of each treatment regimen on mean hemoglobin A1c (HbA1c) and the prevalence of diabetes complications for non-White YYA. RESULTS: The study included 978 YYA. The sample was 47.5% female and 77.5% non-Hispanic White, with a mean age of 12.8 ± 2.4 years at diagnosis. The estimated population mean of longitudinal average HbA1c over visits was 9.2% and 8.2% for the non-White and White subgroup, respectively (difference of 0.9%). Within the non-White subgroup, mean HbA1c across visits was estimated to decrease by 0.33% (95% CI -0.45, -0.21) if these YYA received the distribution of diabetes treatment regimens of the White subgroup, explaining ∼35% of the estimated difference between the two subgroups. The non-White subgroup was also estimated to have a lower risk of developing diabetic retinopathy, diabetic kidney disease, and peripheral neuropathy with the White youth treatment regimen distribution (P < 0.05), although the low proportion of YYA who developed complications limited statistical power for risk estimations. CONCLUSIONS: Mathematically modeling an equalized distribution of T1D self-management tools and technology accounted for part of but not all disparities in glycemic control between non-White and White YYA, underscoring the complexity of race and ethnicity-based health inequity.
OBJECTIVE: To estimate difference in population-level glycemic control and the emergence of diabetes complications given a theoretical scenario in which non-White youth and young adults (YYA) with type 1 diabetes (T1D) receive and follow an equivalent distribution of diabetes treatment regimens as non-Hispanic White YYA. RESEARCH DESIGN AND METHODS: Longitudinal data from YYA diagnosed 2002-2005 in the SEARCH for Diabetes in Youth Study were analyzed. Based on self-reported race/ethnicity, YYA were classified as non-White race or Hispanic ethnicity (non-White subgroup) versus non-Hispanic White race (White subgroup). In the White versus non-White subgroups, the propensity score models estimated treatment regimens, including patterns of insulin modality, self-monitored glucose frequency, and continuous glucose monitoring use. An analysis based on policy evaluation techniques in reinforcement learning estimated the effect of each treatment regimen on mean hemoglobin A1c (HbA1c) and the prevalence of diabetes complications for non-White YYA. RESULTS: The study included 978 YYA. The sample was 47.5% female and 77.5% non-Hispanic White, with a mean age of 12.8 ± 2.4 years at diagnosis. The estimated population mean of longitudinal average HbA1c over visits was 9.2% and 8.2% for the non-White and White subgroup, respectively (difference of 0.9%). Within the non-White subgroup, mean HbA1c across visits was estimated to decrease by 0.33% (95% CI -0.45, -0.21) if these YYA received the distribution of diabetes treatment regimens of the White subgroup, explaining ∼35% of the estimated difference between the two subgroups. The non-White subgroup was also estimated to have a lower risk of developing diabetic retinopathy, diabetic kidney disease, and peripheral neuropathy with the White youth treatment regimen distribution (P < 0.05), although the low proportion of YYA who developed complications limited statistical power for risk estimations. CONCLUSIONS: Mathematically modeling an equalized distribution of T1D self-management tools and technology accounted for part of but not all disparities in glycemic control between non-White and White YYA, underscoring the complexity of race and ethnicity-based health inequity.
Authors: Angela D Liese; Ralph B D'Agostino; Richard F Hamman; Patrick D Kilgo; Jean M Lawrence; Lenna L Liu; Beth Loots; Barbara Linder; Santica Marcovina; Beatriz Rodriguez; Debra Standiford; Desmond E Williams Journal: Pediatrics Date: 2006-10 Impact factor: 7.124
Authors: Katherine A Sauder; Jeanette M Stafford; Elizabeth J Mayer-Davis; Elizabeth T Jensen; Sharon Saydah; Amy Mottl; Lawrence M Dolan; Richard F Hamman; Jean M Lawrence; Catherine Pihoker; Santica Marcovina; Ralph B D'Agostino; Dana Dabelea Journal: Lancet Child Adolesc Health Date: 2018-11-06
Authors: Katherine A Sauder; Jeanette M Stafford; Natalie S The; Elizabeth J Mayer-Davis; Joan Thomas; Jean M Lawrence; Grace Kim; Karen R Siegel; Elizabeth T Jensen; Amy S Shah; Ralph B D'Agostino; Dana Dabelea Journal: Pediatr Diabetes Date: 2020-08-23 Impact factor: 3.409
Authors: Daria Igudesman; Beth A Reboussin; Katherine J Souris; Catherine Pihoker; Lawrence Dolan; Jean M Lawrence; Sharon Saydah; Dana Dabelea; Santica Marcovina; Noémie Clouet-Foraison; Faisal S Malik; Elizabeth J Mayer-Davis Journal: J Diabetes Res Date: 2022-07-22 Impact factor: 4.061