Thaddäus Tönnies1, Giuseppina Imperatore2, Annika Hoyer3, Sharon H Saydah2, Ralph B D'Agostino4, Jasmin Divers4, Scott Isom4, Dana Dabelea5, Jean M Lawrence6, Elizabeth J Mayer-Davis7, Catherine Pihoker8, Lawrence Dolan9, Ralph Brinks3. 1. Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at the Heinrich Heine University, Düsseldorf, Germany. Electronic address: thaddaeus.toennies@ddz.de. 2. Division of Diabetes Translation, Centers for Disease Control and Prevention (CDC), National Center for Chronic Disease Prevention and Health Promotion, Atlanta, GA. 3. Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at the Heinrich Heine University, Düsseldorf, Germany. 4. Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC. 5. Department of Epidemiology, Colorado School of Public Health, University of Colorado, Denver. 6. Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena. 7. Departments of Nutrition and Medicine, Gillings School of Global Public Health and School of Medicine, University of North Carolina, Chapel Hill. 8. Department of Pediatrics, University of Washington, Seattle. 9. Division of Endocrinology, Children's Hospital Medical Center, Cincinnati, OH.
Abstract
PURPOSE: Most surveillance efforts in childhood diabetes have focused on incidence, whereas prevalence is rarely reported. This study aimed to assess whether a mathematical illness-death model accurately estimated future prevalence from baseline prevalence and incidence rates in children. METHODS: SEARCH for Diabetes in Youth is an ongoing population-based surveillance study of prevalence and incidence of diabetes and its complications among youth in the United States. We used age-, sex-, and race/ethnicity-specific SEARCH estimates of the prevalence of type I and type II diabetes in 2001 and incidence from 2002 to 2008. These data were used in a partial differential equation to estimate prevalence in 2009 with 95% bootstrap confidence intervals. Model-based prevalence was compared with the observed prevalence in 2009. RESULTS: Most confidence intervals for the difference between estimated and observed prevalence included zero, indicating no evidence for a difference between the two methods. The width of confidence intervals indicated high precision for the estimated prevalence when considering all races/ethnicities. In strata with few cases, precision was reduced. CONCLUSIONS: Future prevalence of type I and type II diabetes in youth may be accurately estimated from baseline prevalence and incidence. Diabetes surveillance could benefit from potential cost savings of this method.
PURPOSE: Most surveillance efforts in childhood diabetes have focused on incidence, whereas prevalence is rarely reported. This study aimed to assess whether a mathematical illness-death model accurately estimated future prevalence from baseline prevalence and incidence rates in children. METHODS: SEARCH for Diabetes in Youth is an ongoing population-based surveillance study of prevalence and incidence of diabetes and its complications among youth in the United States. We used age-, sex-, and race/ethnicity-specific SEARCH estimates of the prevalence of type I and type II diabetes in 2001 and incidence from 2002 to 2008. These data were used in a partial differential equation to estimate prevalence in 2009 with 95% bootstrap confidence intervals. Model-based prevalence was compared with the observed prevalence in 2009. RESULTS: Most confidence intervals for the difference between estimated and observed prevalence included zero, indicating no evidence for a difference between the two methods. The width of confidence intervals indicated high precision for the estimated prevalence when considering all races/ethnicities. In strata with few cases, precision was reduced. CONCLUSIONS: Future prevalence of type I and type II diabetes in youth may be accurately estimated from baseline prevalence and incidence. Diabetes surveillance could benefit from potential cost savings of this method.
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: Edward W Gregg; Yiling J Cheng; Sharon Saydah; Catherine Cowie; Sanford Garfield; Linda Geiss; Lawrence Barker Journal: Diabetes Care Date: 2012-06 Impact factor: 19.112
Authors: Richard F Hamman; Ronny A Bell; Dana Dabelea; Ralph B D'Agostino; Lawrence Dolan; Giuseppina Imperatore; Jean M Lawrence; Barbara Linder; Santica M Marcovina; Elizabeth J Mayer-Davis; Catherine Pihoker; Beatriz L Rodriguez; Sharon Saydah Journal: Diabetes Care Date: 2014-12 Impact factor: 19.112