title: Accuracy of syndrome definitions based on diagnoses in physician claims.
abstract: BACKGROUND
  Community clinics offer potential for timelier outbreak detection and monitoring than emergency departments. However, the accuracy of syndrome definitions used in surveillance has never been evaluated in community settings. This study's objective was to assess the accuracy of syndrome definitions based on diagnostic codes in physician claims for identifying 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory including influenza-like illness) in community clinics.
  
  
  METHODS
  We selected a random sample of 3,600 community-based primary care physicians who practiced in the fee-for-service system in the province of Quebec, Canada in 2005-2007. We randomly selected 10 visits per physician from their claims, stratifying on syndrome type and presence, diagnosis, and month. Double-blinded chart reviews were conducted by telephone with consenting physicians to obtain information on patient diagnoses for each sampled visit. The sensitivity, specificity, and positive predictive value (PPV) of physician claims were estimated by comparison to chart review.
  
  
  RESULTS
  1,098 (30.5%) physicians completed the chart review. A chart entry on the date of the corresponding claim was found for 10,529 (95.9%) visits. The sensitivity of syndrome definitions based on diagnostic codes in physician claims was low, ranging from 0.11 (fever) to 0.44 (respiratory), the specificity was high, and the PPV was moderate to high, ranging from 0.59 (fever) to 0.85 (respiratory). We found that rarely used diagnostic codes had a higher probability of being false-positives, and that more commonly used diagnostic codes had a higher PPV.
  
  
  CONCLUSIONS
  Future research should identify physician, patient, and encounter characteristics associated with the accuracy of diagnostic codes in physician claims. This would enable public health to improve syndromic surveillance, either by focusing on physician claims whose diagnostic code is more likely to be accurate, or by using all physician claims and weighing each according to the likelihood that its diagnostic code is accurate.
authors.affiliation: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada. genevieve.cadieux@mail.mcgill.ca
authors.forename: Geneviève
authors.initials: G
authors.lastname: Cadieux
authors.forename: David L
authors.initials: DL
authors.lastname: Buckeridge
authors.forename: André
authors.initials: A
authors.lastname: Jacques
authors.forename: Michael
authors.initials: M
authors.lastname: Libman
authors.forename: Nandini
authors.initials: N
authors.lastname: Dendukuri
authors.forename: Robyn
authors.initials: R
authors.lastname: Tamblyn
mesh_terms: D059019:Clinical Coding / Q000379:methods; D003153:Community Health Services / Q000706:statistics & numerical data; D003625:Data Collection; D019937:Diagnostic Techniques and Procedures / Q000592:standards*; D005076:Exanthema / Q000145:classification / Q000175:diagnosis; D005260:Female; D005334:Fever / Q000145:classification / Q000175:diagnosis; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D009422:Nervous System Diseases / Q000145:classification / Q000175:diagnosis; D009819:Office Visits / Q000706:statistics & numerical data; D011159:Population Surveillance / Q000379:methods*; D011792:Quebec; D012042:Registries; D012141:Respiratory Tract Infections / Q000145:classification / Q000175:diagnosis; D012680:Sensitivity and Specificity