An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study.
To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data.



Presumptive diabetes cases <20 years of age from 2 large independent health care systems were identified as those having ≥1 of the 5 indicators in the past 3.5 years, including elevated HbA1c, elevated blood glucose, diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified.



We developed a stepwise surveillance approach using billing code-based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately ≥90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with "other" type were also correctly classified. This stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods.



EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed.
Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
Victor W
VW
Zhong
Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.
Jihad S
JS
Obeid
Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.
Jean B
JB
Craig
North Carolina TraCS Institute, University of North Carolina, Chapel Hill, NC, USA.
Emily R
ER
Pfaff
Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
Joan
J
Thomas
Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Lindsay M
LM
Jaacks
Department of Biostatistical Sciences, School of Medicine, Wake Forest University, Winston-Salem, NC, USA.
Daniel P
DP
Beavers
Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, NC, USA.
Timothy S
TS
Carey
Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
Jean M
JM
Lawrence
Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, CO, USA.
Dana
D
Dabelea
Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, CO, USA.
Richard F
RF
Hamman
Division of Pediatric Endocrinology, Medical University of South Carolina, Charleston, SC, USA.
Deborah A
DA
Bowlby
Department of Washington, University of Washington, Seattle, WA, USA.
Catherine
C
Pihoker
Centers for Disease Control and Prevention, Division of Diabetes Translation, Atlanta, GA, USA.
Sharon H
SH
Saydah
Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA ejmayer_davis@unc.edu.|Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
Elizabeth J
EJ
Mayer-Davis
D000293:Adolescent; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D059019:Clinical Coding; D003922:Diabetes Mellitus, Type 1 / Q000145:classification; D003924:Diabetes Mellitus, Type 2 / Q000145:classification; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D011159:Population Surveillance / Q000379:methods*; D012680:Sensitivity and Specificity; D055815:Young Adult
ascertainment and classification; automated algorithm; childhood diabetes; electronic health records; surveillance