Electronic Health Record-based Surveillance of Diabetes by Type in Young Adults in Pennsylvania

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1 Currently there are 4.6 million young adults (18 to 44 years of age) with diabetes in the US and the incidence 2 and prevalence are increasing in this age group. However, due to limitations of traditional surveillance 3 strategies, it remains unknown whether these increases are in type 1 or type 2 diabetes. Electronic health 4 record (EHR)-based surveillance is a relatively simple, sustainable, and timely alternative to more traditional 5 methods. Recognizing the attributes of EHR-based surveillance, the Centers for Disease Control and 6 Prevention (CDC) has funded efforts to develop, evaluate, and deploy EHR-based surveillance of diabetes, 7 including the SEARCH for Diabetes in Youth (SEARCH) study and the Diabetes in Young Adults (DiYA) Study. 8 However, the geographic coverage of these studies has been limited. The geographical gaps in these studies 9 are problematic, as there are known geographic disparities in diabetes prevalence and incidence. Moreover, 10 little is known about how the methods applied in these studies will perform in other regions of the country, in 11 rural communities, and in other health systems. The proposed study will use more than two decades of EHR 12 data and administrative claims data to develop and implement EHR-based surveillance of type 1 and type 2 13 diabetes among young adults in a large region of Pennsylvania, the state with the 5th highest prevalence of 14 diabetes in this age group. This information is essential to informing public health strategies, assessing disease 15 burden, and prioritizing type-specific health services. We will use EHR data from Geisinger, a health system 16 serving a large and diverse region of Pennsylvania, to expand the geography of existing surveillance of 17 diabetes subtypes in young adults to the Middle Atlantic, an area without prior EHR-based diabetes 18 surveillance estimates. This region includes a combination of rural and urban communities, enabling us to 19 evaluate differences in the performance of EHR-based algorithms for case ascertainment by community type. 20 In the first phase of the study, we will evaluate the validity; simplicity; and consistency of EHR-based 21 algorithms for identifying diabetes subtypes. This work will build on previously developed algorithms from the 22 SEARCH and DiYA studies. We will use manual review of clinician notes as the gold standard to determine the 23 positive predictive value, sensitivity, and specificity of these algorithms. We propose to use an innovative, 24 efficient, and rigorous validation approach that incorporates natural language processing of clinician notes. We 25 will use a secondary data source, administrative claims data, to assess data completeness and our ability to 26 distinguish between incident and prevalent cases. In the second phase, we will use the best performing 27 algorithms to report on the annual incidence and prevalence of diabetes, by type, in young adults, between 28 2014 and 2024 in 38 Pennsylvania counties. All analyses will be stratified by age, sex, race/ethnicity, insurance 29 status, and community type (rural/urban). Finally, we will coordinate with CDC and other sites to conduct joint 30 analyses of aggregated data, greatly expanding the population under study.