title: Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions.
abstract: BACKGROUND
  Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, and billing characteristics associated with the positive predictive value (PPV) of case definitions for 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory (including influenza-like illness)).
  
  
  METHODS
  The study sample comprised 4,330 syndrome-positive visits from the claims of 1,098 randomly-selected physicians working in Quebec, Canada in 2005-2007. For each visit, physician-facilitated chart review was used to assess whether the same syndrome was present in the medical chart (gold standard). We used multivariate logistic regression analyses to estimate the association between claim-chart agreement about the presence of a syndrome and physician, patient, encounter, and billing characteristics.
  
  
  RESULTS
  The likelihood of the medical chart agreeing with the physician claim about the presence of a syndrome was higher when the treating physician had billed many visits for the same syndrome recently (ORper 10 visit, 1.05; 95% CI, 1.01-1.08), had a lower workload (ORper 10 claims, 0.93; 95% CI, 0.90-0.97), and when the patient was younger (ORper 5 years of age, 0.96; 95% CI, 0.94-0.97), and less socially deprived (ORmost versus least deprived, 0.76; 95% CI, 0.60-0.95).
  
  
  CONCLUSIONS
  Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce false-positive alerts by surveillance systems, either by focusing on the data most likely to be accurate, or by adjusting the observed data for known biases in diagnosis reporting and performing surveillance using the adjusted values.
authors.affiliation: Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, QC, H3A 1A2, 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: D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008485:Medical Audit; D009819:Office Visits; D010350:Patient Credit and Collection; D011159:Population Surveillance / Q000379:methods*; D011792:Quebec; D013577:Syndrome