Pediatr Diabetes

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.

24913103

PMC4229415

Journal Article

CDC Public Access