title: A systematic review of validated methods for identifying uveitis using administrative or claims data.
abstract: OBJECTIVE
  To review algorithms used to identify uveitis in administrative and claims databases.
  
  
  METHODS
  We searched the MEDLINE database via PubMed from 1991 to September 2012 using vocabulary and key terms related to uveitis. We also searched the reference lists of included studies. Two investigators independently assessed studies against pre-determined inclusion criteria. The same two investigators independently extracted data regarding participant and algorithm characteristics and assessed a study's methodological rigor using a pre-defined approach.
  
  
  RESULTS
  Seven studies met inclusion criteria. Variability exists among algorithms employed in these studies for finding cases of uveitis and related conditions as well as in use and implementation of validation methods. Of the seven included studies, three involved case validation. One used a narrow algorithm in addition to text mining of electronic medical records to identify incident cases and found a positive predictive value of 52.1%. The other two, which used broader uveitis definitions and included both incident and prevalent cases, found positive predictive values of 24.8% and 52.6%.
  
  
  CONCLUSIONS
  Further research, with case as well as individual code validation, is needed to determine appropriate uveitis algorithms for purposes of active surveillance in administrative data. Decisions about which algorithm to use will depend on the desired balance of sensitivity and specificity.
authors: Vanderbilt Vaccine Research Program, Vanderbilt University Medical Center North, 1161 21st Avenue, CCC 5326 Nashville, TN, 37232-0012, USA. Electronic address: elizabeth.williams@vanderbilt.edu.
authors: S Elizabeth
authors: SE
authors: Williams
authors: Department of Epidemiology, University of Iowa College of Public Health, S437 CPHB University of Iowa, 105 River Street, Iowa City, IA 52242, USA. Electronic address: ryan-carnahan@uiowa.edu.
authors: Ryan
authors: R
authors: Carnahan
authors: Vanderbilt Evidence-based Practice Center, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Suite 600, 2525 West End Avenue, Nashville, TN 37203-1738, USA. Electronic address: melissa.mcpheeters@vanderbilt.edu.
authors: Melissa L
authors: ML
authors: McPheeters
mesh_terms: D000465:Algorithms; D016208:Databases, Factual / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D006801:Humans; D015994:Incidence; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D038801:International Classification of Diseases / Q000706:statistics & numerical data*; D014605:Uveitis / Q000453:epidemiology*
keywords: AMD; Administrative database; FDA; HMO; IBD; ICD; ICD-9; PPV; Positive predictive value; US Food and Drug Administration; Uveitis; VA; VAISN; VEGF; acute macular degeneration; health maintenance organization; inflammatory bowel disease; international Classification of Diseases; positive predictive value; vascular endothelial growth factor; veterans integrated service network; veterans’ administration