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Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network.
Hirsch, Annemarie G; Conderino, Sarah; Crume, Tessa L; Liese, Angela D; Bellatorre, Anna; Bendik, Stefanie; Divers, Jasmin; Anthopolos, Rebecca; Dixon, Brian E; Guo, Yi; Imperatore, Giuseppina; Lee, David C; Reynolds, Kristi; Rosenman, Marc; Shao, Hui; Utidjian, Levon; Thorpe, Lorna E.
Afiliação
  • Hirsch AG; Department of Population Health Sciences, Geisinger, Danville, Pennsylvania, USA aghirsch@geisinger.edu.
  • Conderino S; Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA.
  • Crume TL; Department of Epidemiology, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD), University of Colorado - Anschutz Medical Campus, Aurora, Colorado, USA.
  • Liese AD; Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, South Carolina, USA.
  • Bellatorre A; Department of Epidemiology, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD), University of Colorado - Anschutz Medical Campus, Aurora, Colorado, USA.
  • Bendik S; Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA.
  • Divers J; Department of Foundations of Medicine, New York University Long Island School of Medicine, Mineola, New York, USA.
  • Anthopolos R; Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA.
  • Dixon BE; Department of Epidemiology, Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana, USA.
  • Guo Y; Center for Biomedical Informatics, Regenstrief Institute Inc, Indianapolis, Indiana, USA.
  • Imperatore G; Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.
  • Lee DC; Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Reynolds K; Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA.
  • Rosenman M; Departmnt of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
  • Shao H; Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, and Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
  • Utidjian L; Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
  • Thorpe LE; Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, Florida, USA.
BMJ Open ; 14(1): e073791, 2024 01 17.
Article em En | MEDLINE | ID: mdl-38233060
ABSTRACT

INTRODUCTION:

Traditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks to advance diabetes surveillance efforts in youth and young adults through the use of large-volume electronic health record (EHR) data. The network has two primary aims, namely (1) to refine and validate EHR-based computable phenotype algorithms for accurate identification of type 1 and type 2 diabetes among youth and young adults and (2) to estimate the incidence and prevalence of type 1 and type 2 diabetes among youth and young adults and trends therein. The network aims to augment diabetes surveillance capacity in the USA and assess performance of EHR-based surveillance. This paper describes the DiCAYA Network and how these aims will be achieved. METHODS AND

ANALYSIS:

The DiCAYA Network is spread across eight geographically diverse US-based centres and a coordinating centre. Three centres conduct diabetes surveillance in youth aged 0-17 years only (component A), three centres conduct surveillance in young adults aged 18-44 years only (component B) and two centres conduct surveillance in components A and B. The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. Prevalence and incidence rates will be presented as unadjusted estimates and as race/ethnicity, sex and age-adjusted estimates using Poisson regression. ETHICS AND DISSEMINATION The DiCAYA Network is well positioned to advance diabetes surveillance methods. The network will disseminate EHR-based surveillance methodology that can be broadly adopted and will report diabetes prevalence and incidence for key demographic subgroups of youth and young adults in a large set of regions across the USA.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Diabetes Mellitus Tipo 2 Tipo de estudo: Incidence_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Adolescent / Adult / Child / Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Diabetes Mellitus Tipo 2 Tipo de estudo: Incidence_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Adolescent / Adult / Child / Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article