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Pediatr Diabetes. 2014 Dec;15(8):573-84. doi: 10.1111/pedi.12152. Epub 2014 Jun 9.

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.

Author information

1
Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.

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.

KEYWORDS:

administrative data; case ascertainment; childhood diabetes; electronic health record; type classification

PMID:
24913103
PMCID:
PMC4229415
DOI:
10.1111/pedi.12152
[Indexed for MEDLINE]
Free PMC Article

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