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

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 s...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Pediatric diabetes Ročník 15; číslo 8; s. 573 - 584
Hlavní autoři: 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.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Former Munksgaard John Wiley & Sons A/S 01.12.2014
Témata:
administrative data
Adolescent
Adult
Algorithms
case ascertainment
Child
Child, Preschool
childhood diabetes
Diabetes Mellitus, Type 1 - classification
Diabetes Mellitus, Type 1 - diagnosis
Diabetes Mellitus, Type 1 - epidemiology
Diabetes Mellitus, Type 2 - classification
Diabetes Mellitus, Type 2 - diagnosis
Diabetes Mellitus, Type 2 - epidemiology
electronic health record
Electronic Health Records - standards
Female
Humans
Infant
Infant, Newborn
Male
Mass Screening - methods
type classification
Young Adult
childhood diabetes
type classification
administrative data
case ascertainment
electronic health record
ISSN:1399-543X, 1399-5448, 1399-5448
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:National Center for Advancing Translational Sciences
Table S1. The performance of each data combination in ascertaining diabetes cases in total study population (N = 57 767) in the Carolina Data Warehouse for Health in 2011.
National Center for Research Resources
National Institutes of Health - No. UL1TR000083
National Institute of Diabetes and Digestive and Kidney Diseases
ark:/67375/WNG-MPK83D40-K
This study was presented in part as two posters at the 73rd Scientific Session of the American Diabetes Association, Chicago, IL, 21-25 June 2013.
Centers for Disease Control and Prevention - No. DP-05-069; No. DP-10-001
istex:5FBA221C2CA61C0C297015E47AE3C0BC2A5B8CDD
ArticleID:PEDI12152
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1399-543X
1399-5448
1399-5448
DOI:10.1111/pedi.12152