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 Study1. Issue 8 (9th June 2014)

• Record Type: Journal Article
• Authors: Zhong, Victor W.; Pfaff, Emily R.; Beavers, Daniel P.; Thomas, Joan; Jaacks, Lindsay M.; Bowlby, Deborah A.; Carey, Timothy S.; Lawrence, Jean M.; Dabelea, Dana; Hamman, Richard F.; Pihoker, Catherine; Saydah, Sharon H.; Mayer‐Davis, Elizabeth J.; For the Search for Diabetes in Youth Study Group
• Is Part Of: Pediatric diabetes. Volume 15:Issue 8(2014:Dec.)
• Page Start: 573
• Page End: 584
• Publication Date: 2014-06-09
• Subjects: Diabetes in children -- Periodicals; 616.462
• DOI: 10.1111/pedi.12152
• Languages: English
• ISSNs: 1399-543X

Abstract

<abstract abstract-type="main" id="pedi12152-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="pedi12152-sec-0001" sec-type="section"> <title>Background</title> <p id="pedi12152-para-0001">The performance of automated algorithms for childhood diabetes case ascertainment and type classification may differ by demographic characteristics.</p> </sec> <sec id="pedi12152-sec-0002" sec-type="section"> <title>Objective</title> <p id="pedi12152-para-0002">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.</p> </sec> <sec id="pedi12152-sec-0003" sec-type="section"> <title>Subjects</title> <p id="pedi12152-para-0003">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.</p> </sec> <sec id="pedi12152-sec-0004" sec-type="section"> <title>Methods</title> <p id="pedi12152-para-0004">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.</p> </sec> <sec id="pedi12152-sec-0005" sec-type="section"> <title>Results</title> <p id="pedi12152-para-0005">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.</p> </sec> <sec id="pedi12152-sec-0006" sec-type="section"> <title>Conclusions</title> <p id="pedi12152-para-0006">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.</p> </sec> </abstract>