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National Institutes of Health and National Institute of Diabetes Digestive Diseases and Kidney and the Helmsley Charitable Trust.

Feature Article / Carbohydrate Counting in Adolescents

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 57,767 children aged <20 years as of December 31, 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 July 1, 2008 and December 31, 2011, presumptive cases were identified and validated by chart review. More refined algorithms were evaluated by type (type 1 versus type 2), age (<10 versus ≥10 years) and race/ethnicity (non-Hispanic white versus “other”). Sensitivity, specificity and positive predictive value were calculated and compared. Results The best algorithm for ascertainment of diabetes cases overall 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 type 2 youth 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.

The SEARCH for Diabetes in Youth Study prospectively identified youth aged <20 years with physician-diagnosed diabetes. Annual type 1 diabetes (T1D) incidence per 100,000 person-years (95% CI) overall, by age-group, and by sex were calculated for at-risk non-Hispanic white (NHW) youth from 2002 through 2009. Joinpoint and Poisson regression models were used to test for temporal trends. The age- and sex-adjusted incidence of T1D increased from 24.4/100,000 (95% CI 23.9–24.8) in 2002 to 27.4/100,000 (26.9–27.9) in 2009 (P for trend = 0.0008). The relative annual increase in T1D incidence was 2.72% (1.18–4.28) per year; 2.84% (1.12–4.58) per year for males and 2.57% (0.68–4.51) per year for females. After adjustment for sex, significant increases were found for youth aged 5–9 years (P = 0.0023), 10–14 years (P = 0.0008), and 15–19 years (P = 0.004) but not among 0–4-year-olds (P = 0.1862). Mean age at diagnosis did not change. The SEARCH study demonstrated a significant increase in the incidence of T1D among NHW youth from 2002 through 2009 overall and in all but the youngest age-group. Continued surveillance of T1D in U.S. youth to identify future trends in T1D incidence and to plan for health care delivery is warranted.