title: Validating ICD coding algorithms for diabetes mellitus from administrative data.
abstract: OBJECTIVE
  To assess validity of diabetes International Classification of Disease (ICD) 9 and 10 coding algorithms from administrative data using physicians' charts as the 'gold standard' across time periods and geographic regions.
  
  
  METHODS
  From 48 urban and 16 rural general practitioners' clinics in Alberta and British Columbia, Canada, we randomly selected 50patient charts/clinic for those who visited the clinic in either 2001 or 2004. Reviewed chart data were linked with inpatient discharge abstract and physician claims administrative data. We identified patients with diabetes in the administrative databases using ICD-9 code 250.xx and ICD-10 codes E10.x-E14.x.
  
  
  RESULTS
  The prevalence of diabetes was 8.1% among clinic charts. The coding algorithm of "2 physician claims within 2 years or 1 hospitalization with the relevant diabetes ICD codes" had higher validity than other 7 algorithms assessed (sensitivity 92.3%, specificity 96.9%, positive predictive value 77.2%, and negative predictive value 99.3%). After adjustment for age, sex, and comorbid conditions, sensitivity and positive predictive values were not significantly different between time periods and regions.
  
  
  CONCLUSIONS
  Diabetes could be accurately identified in administrative data using the following case definition "2 physician claims within 2 years or 1 hospital discharge abstract record with diagnosis codes 250.xx or E10.x-E14.x".
authors.affiliation: Department of Community Health Sciences, University of Calgary, Canada. guchen@ucalgary.ca
authors.forename: Guanmin
authors.initials: G
authors.lastname: Chen
authors.forename: Nadia
authors.initials: N
authors.lastname: Khan
authors.forename: Robin
authors.initials: R
authors.lastname: Walker
authors.forename: Hude
authors.initials: H
authors.lastname: Quan
mesh_terms: D000328:Adult; D000465:Algorithms; D003920:Diabetes Mellitus; D005260:Female; D006801:Humans; D038801:International Classification of Diseases / Q000145:classification* / Q000706:statistics & numerical data*; D008297:Male; D008499:Medical Records / Q000145:classification* / Q000706:statistics & numerical data*; D008875:Middle Aged