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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.


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.

PROVIDER: S-EPMC4229415 | BioStudies |

REPOSITORIES: biostudies

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