Incidence Trends of New-Onset Diabetes in Children and Adolescents Before and During the COVID-19 Pandemic: Findings From Florida.

TitleIncidence Trends of New-Onset Diabetes in Children and Adolescents Before and During the COVID-19 Pandemic: Findings From Florida.
Publication TypeJournal Article
Year of Publication2022
AuthorsGuo Y, Bian J, Chen A, Wang F, Posgai AL, Schatz DA, Shenkman EA, Atkinson MA
JournalDiabetes
Volume71
Issue12
Pagination2702-2706
Date Published2022 Dec 01
ISSN1939-327X
KeywordsCohort Studies, COVID-19, Diabetes Mellitus, Type 2, Florida, Humans, Incidence, Longitudinal Studies, Pandemics
Abstract

This study examined the incidence trends of new-onset type 1 and type 2 diabetes in children and adolescents in Florida before and during the coronavirus disease 2019 (COVID-19) pandemic. In this observational descriptive cohort study, we used a validated computable phenotype to identify incident diabetes cases among individuals <18 years of age in the OneFlorida+ network of the national Patient-Centered Clinical Research Network between January 2017 and June 2021. We conducted an interrupted time series analysis based on the autoregressive integrated moving average model to compare changes in age-adjusted incidence rates of type 1 and type 2 diabetes before and after March 2020, when COVID-19 was declared a national health emergency in the U.S. The age-adjusted incidence rates of both type 1 and type 2 diabetes increased post-COVID-19 for children and adolescents. These results highlight the need for longitudinal cohort studies to examine how the pandemic might influence subsequent diabetes onset in young individuals.

DOI10.2337/db22-0549
Alternate JournalDiabetes
PubMed ID36094294
PubMed Central IDPMC9750945
Grant ListR01 CA246418 / CA / NCI NIH HHS / United States
R21 AG068717 / AG / NIA NIH HHS / United States
R21 CA245858 / CA / NCI NIH HHS / United States
R21 CA253394 / CA / NCI NIH HHS / United States
P01 AI042288 / GF / NIH HHS / United States
U18 DP006512 / DP / NCCDPHP CDC HHS / United States
Division: 
Institute of Artificial Intelligence for Digital Health
Category: 
Faculty Publication