Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Jun 30, 2025
Date Accepted: Sep 24, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Type 2 Diabetes Prevalence Estimates using EHR Data with Similar Phenotyping Algorithms: A Test of Equivalence using Two One-sided t-Test
ABSTRACT
Background:
Timely surveillance of diabetes mellitus remains a challenge for public health. In this study, researchers compared 2022 Type 2 Diabetes (T2D) prevalence estimates using EHR data and computable phenotypes (CPs) as defined and applied by two initiatives, one a research consortium, and the other a public health surveillance network.
Objective:
The purpose of this study is to compare two computable phenotypes (CP) methods for estimating prevalence of type 2 diabetes using EHR data.
Methods:
Diagnostic, laboratory, and medication data for young adults aged 18-44 years were extracted from multiple EHR systems serving the eleven counties constituting the Indianapolis metropolitan area. Similar but distinct CPs from each network were used to estimate prevalence. The Two One-Sided T-Test (TOST) was used to compare the estimated prevalences. TOST results at the overall level, and stratified by sex, age, and race/ethnicity were examined.
Results:
Overall prevalence estimates for 2022 were 4.1% for CP1 and 2.4% for CP2. Although prevalence estimates from CP1 were consistently and slightly higher than those of CP2, absolute differences were generally less than 2.0 percentage points which did not result in a statistical difference between values. Therefore, we conclude the two computable phenotypes largely produce equivalent estimates of T2D prevalence. However, prevalence estimates for Hispanic patients were significantly different.
Conclusions:
The two computable phenotypes showed equivalent T2D prevalence estimates. Although the CPs can be considered statistically equivalent, the data driving each CP may impact accuracy and completeness. One CP was driven primarily by clinical diagnostic codes, whereas the other considered laboratory data and medications along with T2D clinical diagnosis. Results have implications for the improvement of CPs for public health surveillance. Clinical Trial: Not applicable
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