Use of administrative and electronic health record data for development of automated algorithms for childhood diabetes case ascertainment and type classification: the SEARCH for Diabetes in Youth Study

Ongoing surveillance of childhood diabetes in the U.S. is needed to understand the trends in incidence and prevalence, and to anticipate health care delivery needs. The SEARCH for Diabetes in Youth Study (SEARCH) (1) documented an increase in the prevalence of type 1 and type 2 diabetes from 2001 to 2009 (2, 3). From 2010 to 2050, the number of youth with type 1 and type 2 diabetes is projected to increase by another 23% and 49%, respectively, even assuming no change in incidence since 2002 (4). Surveillance of childhood diabetes is challenging. First, we are unable to employ existing national surveillance systems such as the National Health and Nutrition Examination Survey (NHANES) because childhood diabetes is uncommon; NHANES (1999-2002) yielded only18 self-reported cases of diabetes among youth aged 12 to 19 years (5). Second, ascertainment of childhood diabetes cases is often costly in terms of time and financial resources. Currently the SEARCH study conducts validation of potential cases by manual review of medical records which is expensive, although the resulting case ascertainment is estimated to be very complete (i.e., >90% for both prevalent and incident cases) based on capture-recapture analyses (6). Third, a useful childhood diabetes surveillance system should be able to discriminate between types of diabetes in different age, and racial/ethnic groups as the distribution of childhood diabetes varies by type, age and race/ethnicity (7, 8); and both etiology and treatment differ by diabetes type (7, 9-11). The increasing utilization of computerized medical information systems may provide timely data for diabetes surveillance with substantially reduced cost relative to traditional approaches (12-14). Approaches to identify diabetes cases and classify type have been explored using administrative data (15-22), and electronic health record (EHR) data (23-26). Among all childhood diabetes algorithms in the literature, only two explored type-specific algorithms (21, 23). None of these studies evaluated algorithm performance according to age and race/ethnicity. In the U.S., it is not known whether administrative and EHR data from a large academic care delivery system can be used to accurately differentiate between childhood type 1 and type 2 diabetes (i.e., through use of type-sensitive algorithms) or whether such data can only identify cases without regard to type (i.e., through use of type-insensitive algorithms). It is also not known whether the performance of automated algorithms differs by age and race/ethnicity. Our objective was to identify algorithms with high performance, as demonstrated by high sensitivity, specificity, and positive predictive value (PPV), with a goal to efficiently identify diabetes cases and classify type in youth, overall and by age and race/ethnicity in a large academic care delivery system caring for patients with all payment sources, utilizing administrative and EHR data from the University of North Carolina Health Care System (UNCHCS).