Abstract
BACKGROUND: The performance of automated algorithms for childhood diabetes case ascertainment and type classification may differ by demographic characteristics. OBJECTIVE: This study evaluated the potential of administrative and electronic health record (EHR) data from a large academic care delivery system to conduct diabetes case ascertainment in youth according to type, age, and race/ethnicity. SUBJECTS: Of 57 767 children aged <20 yr as of 31 December 2011 seen at University of North Carolina Health Care System in 2011 were included. METHODS: Using an initial algorithm including billing data, patient problem lists, laboratory test results, and diabetes related medications between 1 July 2008 and 31 December 2011, presumptive cases were identified and validated by chart review. More refined algorithms were evaluated by type (type 1 vs. type 2), age (<10 vs. ≥10 yr) and race/ethnicity (non-Hispanic White vs. 'other'). Sensitivity, specificity, and positive predictive value were calculated and compared. RESULTS: The best algorithm for ascertainment of overall diabetes cases was billing data. The best type 1 algorithm was the ratio of the number of type 1 billing codes to the sum of type 1 and type 2 billing codes ≥0.5. A useful algorithm to ascertain youth with type 2 diabetes with 'other' race/ethnicity was identified. Considerable age and racial/ethnic differences were present in type-non-specific and type 2 algorithms. CONCLUSIONS: Administrative and EHR data may be used to identify cases of childhood diabetes (any type), and to identify type 1 cases. The performance of type 2 case ascertainment algorithms differed substantially by race/ethnicity.