This study aimed to develop a novel method that can accurately estimate the prevalence of T1D in children and adolescents using EHR data. T1D cases among individuals <= 18 years old were identified from the OneFlorida EHR (2018-2020) using a computable phenotype algorithm we have developed. The T1D prevalence was estimated at the zip code level, with numerator being the total T1D cases and the denominator being the number of residents in each zip code area extracted from the census data. The observed health system penetration rates (HSPR) in 20 were estimated as a ratio of total patients who visited the healthcare systems in the OneFlorida network (2018-2020) to the total number of residents in each zip code area in 2019. We applied a joint-point regression to determine the cutoff thresholded where the observed HSPR can produce accurate prevalence estimation. The cutpoint value of 72.0% for the observed HSPR was identified, and the trend line between prevalence and HSPR plateaued in areas with HSPR>72% (slope=0.002, figure) . The estimated T1D prevalence in 20 was 0.243%, which is similar to that of estimated by SEARCH (0.255%) . Our algorithm provides a critical tool for developing a EHR-based diabetes surveillance system in the US.


P.Li: None. T.Lyu: None. J.Bian: None. Y.Guo: None. E.Shenkman: None. W.T.Donahoo: None. J.Guo: None. H.Shao: Board Member; BRAVO4HEALTH, LLC.



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