title: Validity of administrative data claim-based methods for identifying individuals with diabetes at a population level.
abstract: OBJECTIVE
  This study assessed the validity of a widely-accepted administrative data surveillance methodology for identifying individuals with diabetes relative to three laboratory data reference standard definitions for diabetes.
  
  
  METHODS
  We used a combination of linked regional data (hospital discharge abstracts and physician data) and laboratory data to test the validity of administrative data surveillance definitions for diabetes relative to a laboratory data reference standard. The administrative discharge data methodology includes two definitions for diabetes: a strict administrative data definition of one hospitalization code or two physician claims indicating diabetes; and a more liberal definition of one hospitalization code or a single physician claim. The laboratory data, meanwhile, produced three reference standard definitions based on glucose levels +/- HbA1c levels.
  
  
  RESULTS
  Sensitivities ranged from 68.4% to 86.9% for the administrative data definitions tested relative to the three laboratory data reference standards. Sensitivities were higher for the more liberal administrative data definition. Positive predictive values (PPV), meanwhile, ranged from 53.0% to 88.3%, with the liberal administrative data definition producing lower PPVs.
  
  
  CONCLUSIONS
  These findings demonstrate the trade-offs of sensitivity and PPV for selecting diabetes surveillance definitions. Centralized laboratory data may be of value to future surveillance initiatives that use combined data sources to optimize case detection.
authors.affiliation: Department of Community Health Sciences, University of Calgary, Calgary, AB.
authors.forename: Danielle A
authors.initials: DA
authors.lastname: Southern
authors.forename: Barbara
authors.initials: B
authors.lastname: Roberts
authors.forename: Alun
authors.initials: A
authors.lastname: Edwards
authors.forename: Stafford
authors.initials: S
authors.lastname: Dean
authors.forename: Peter
authors.initials: P
authors.lastname: Norton
authors.forename: Lawrence W
authors.initials: LW
authors.lastname: Svenson
authors.forename: Erik
authors.initials: E
authors.lastname: Larsen
authors.forename: Peter
authors.initials: P
authors.lastname: Sargious
authors.forename: David C W
authors.initials: DC
authors.lastname: Lau
authors.forename: William A
authors.initials: WA
authors.lastname: Ghali
mesh_terms: D000416:Alberta / Q000453:epidemiology; D000465:Algorithms; D001786:Blood Glucose; D016001:Confidence Intervals; D003625:Data Collection / Q000379:methods; D003920:Diabetes Mellitus / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology; D005951:Glucose Tolerance Test; D006442:Glycated Hemoglobin; D006801:Humans; D007345:Insurance Claim Review; D008498:Medical Record Linkage / Q000379:methods*; D044382:Population Groups; D011159:Population Surveillance; D015203:Reproducibility of Results
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin