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
Published Date:Jun 09 2014
Source:Pediatr Diabetes. 15(8):573-584.
Diabetes Mellitus, Type 1
Diabetes Mellitus, Type 2
Electronic Health Record
Electronic Health Records
Pubmed Central ID:PMC4229415
Funding:1U18DP002709/DP/NCCDPHP CDC HHS/United States
DP-05069/DP/NCCDPHP CDC HHS/United States
DP-10001/DP/NCCDPHP CDC HHS/United States
M01 RR000069/RR/NCRR NIH HHS/United States
P30 DK017047/DK/NIDDK NIH HHS/United States
P30 DK057516/DK/NIDDK NIH HHS/United States
P30 DK57516/DK/NIDDK NIH HHS/United States
U01 DP000244/DP/NCCDPHP CDC HHS/United States
U01 DP000245/DP/NCCDPHP CDC HHS/United States
U01 DP000246/DP/NCCDPHP CDC HHS/United States
U01 DP000247/DP/NCCDPHP CDC HHS/United States
U01 DP000248/DP/NCCDPHP CDC HHS/United States
U01 DP000250/DP/NCCDPHP CDC HHS/United States
U01 DP000254/DP/NCCDPHP CDC HHS/United States
U18DP002710-01/DP/NCCDPHP CDC HHS/United States
U18DP002714/DP/NCCDPHP CDC HHS/United States
UL1 RR025014/RR/NCRR NIH HHS/United States
UL1 RR026314/RR/NCRR NIH HHS/United States
UL1 RR029882/RR/NCRR NIH HHS/United States
UL1 TR000077/TR/NCATS NIH HHS/United States
UL1 TR000154/TR/NCATS NIH HHS/United States
UL1 TR000423/TR/NCATS NIH HHS/United States
UL1 TR00423/TR/NCATS NIH HHS/United States
UL1RR029882/RR/NCRR NIH HHS/United States
UL1TR000083/TR/NCATS NIH HHS/United States
The performance of automated algorithms for childhood diabetes case ascertainment and type classification may differ by demographic characteristics.
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.
57,767 children aged <20 years as of December 31, 2011 seen at University of North Carolina Health Care System in 2011 were included.
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.
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.
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.
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