title: Estimating prevalence of CKD stages 3-5 using health system data.
abstract: BACKGROUND
  The feasibility of using health system data to estimate prevalence of chronic kidney disease (CKD) stages 3-5 was explored.
  
  
  METHODS
  Cohort study.
  
  
  METHODS
  A 5% national random sample of patients from the Veterans Affairs (VA) health care system, enrollees in a managed care plan in Michigan (M-CARE), and participants from the 2005-2006 National Health and Nutrition Examination Survey (NHANES).
  
  
  METHODS
  Observed CKD prevalence estimates in the health system population were calculated as patients with an available outpatient serum creatinine measurement with estimated glomerular filtration rate <60 mL/min/1.73 m(2), among those with at least one outpatient visit during the year.
  
  
  METHODS
  A logistic regression model was fitted using data from the 2005-2006 NHANES to predict CKD prevalence in those untested for serum creatinine in the health system population, adjusted for demographics and comorbid conditions. Model results then were combined with the observed prevalence in tested patients to derive an overall predicted prevalence of CKD within the health systems.
  
  
  RESULTS
  Patients in the VA system were older, had more comorbid conditions, and were more likely to be tested for serum creatinine than those in the M-CARE system. Observed prevalences of CKD stages 3-5 were 15.6% and 0.9% in the VA and M-CARE systems, respectively. Using data from NHANES, the overall predicted prevalences of CKD were 20.4% and 1.6% in the VA and M-CARE systems, respectively.
  
  
  CONCLUSIONS
  Health system data quality was limited by missing data for laboratory results and race. A single estimated glomerular filtration rate value was used to define CKD, rather than persistence over 3 months.
  
  
  CONCLUSIONS
  Estimation of CKD prevalence within health care systems is feasible, but discrepancies between observed and predicted prevalences suggest that this approach is dependent on data availability and quality of information for comorbid conditions, as well as the frequency of testing for CKD in the health care system.
authors.affiliation: Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109-2029, USA.
authors.forename: Vahakn B
authors.initials: VB
authors.lastname: Shahinian
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Hedgeman
authors.forename: Brenda W
authors.initials: BW
authors.lastname: Gillespie
authors.forename: Eric W
authors.initials: EW
authors.lastname: Young
authors.forename: Bruce
authors.initials: B
authors.lastname: Robinson
authors.forename: Chi-Yuan
authors.initials: CY
authors.lastname: Hsu
authors.forename: Laura C
authors.initials: LC
authors.lastname: Plantinga
authors.forename: Nilka Ríos
authors.initials: NR
authors.lastname: Burrows
authors.forename: Paul
authors.initials: P
authors.lastname: Eggers
authors.forename: Sharon
authors.initials: S
authors.lastname: Saydah
authors.forename: Neil R
authors.initials: NR
authors.lastname: Powe
authors.forename: Rajiv
authors.initials: R
authors.lastname: Saran
mesh_terms: D000328:Adult; D000368:Aged; D015331:Cohort Studies; D003404:Creatinine / Q000097:blood; D003695:Delivery of Health Care; D005240:Feasibility Studies; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007256:Information Systems; D016015:Logistic Models; D008297:Male; D008329:Managed Care Programs; D008824:Michigan / Q000453:epidemiology; D008875:Middle Aged; D009749:Nutrition Surveys; D015995:Prevalence; D051436:Renal Insufficiency, Chronic / Q000145:classification / Q000453:epidemiology*; D014481:United States / Q000453:epidemiology; D014493:United States Department of Veterans Affairs; D055815:Young Adult
chemical_list: D003404:Creatinine