title: Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.
abstract: BACKGROUND
  Fine-grained influenza surveillance data are lacking in the US, hampering our ability to monitor disease spread at a local scale. Here we evaluate the performances of high-volume electronic medical claims data to assess local and regional influenza activity.
  
  
  METHODS
  We used electronic medical claims data compiled by IMS Health in 480 US locations to create weekly regional influenza-like-illness (ILI) time series during 2003-2010. IMS Health captured 62% of US outpatient visits in 2009. We studied the performances of IMS-ILI indicators against reference influenza surveillance datasets, including CDC-ILI outpatient and laboratory-confirmed influenza data. We estimated correlation in weekly incidences, peak timing and seasonal intensity across datasets, stratified by 10 regions and four age groups (<5, 5-29, 30-59, and 60+ years). To test IMS-Health performances at the city level, we compared IMS-ILI indicators to syndromic surveillance data for New York City. We also used control data on laboratory-confirmed Respiratory Syncytial Virus (RSV) activity to test the specificity of IMS-ILI for influenza surveillance.
  
  
  RESULTS
  Regional IMS-ILI indicators were highly synchronous with CDC's reference influenza surveillance data (Pearson correlation coefficients rho≥0.89; range across regions, 0.80-0.97, P<0.001). Seasonal intensity estimates were weakly correlated across datasets in all age data (rho≤0.52), moderately correlated among adults (rho≥0.64) and uncorrelated among school-age children. IMS-ILI indicators were more correlated with reference influenza data than control RSV indicators (rho = 0.93 with influenza v. rho = 0.33 with RSV, P<0.05). City-level IMS-ILI indicators were highly consistent with reference syndromic data (rho≥0.86).
  
  
  CONCLUSIONS
  Medical claims-based ILI indicators accurately capture weekly fluctuations in influenza activity in all US regions during inter-pandemic and pandemic seasons, and can be broken down by age groups and fine geographical areas. Medical claims data provide more reliable and fine-grained indicators of influenza activity than other high-volume electronic algorithms and should be used to augment existing influenza surveillance systems.
authors: Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.
authors: Cécile
authors: C
authors: Viboud
authors: Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America; School of Medecine, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors: Vivek
authors: V
authors: Charu
authors: New York City Department of Health and Mental Hygiene, New York, New York, United States of America.
authors: Donald
authors: D
authors: Olson
authors: Department of Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.
authors: Sébastien
authors: S
authors: Ballesteros
authors: Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.
authors: Julia
authors: J
authors: Gog
authors: IMS Health, Plymouth Meeting, Pennsylvania, United States of America.
authors: Farid
authors: F
authors: Khan
authors: Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America; Department of Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.
authors: Bryan
authors: B
authors: Grenfell
authors: Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America; School of Public Health and Health Services, George Washington University, Washington, D.C., United States of America.
authors: Lone
authors: L
authors: Simonsen
mesh_terms: D002947:Cities; D016208:Databases, Factual; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D007251:Influenza, Human / Q000453:epidemiology*; D011159:Population Surveillance; D018357:Respiratory Syncytial Virus Infections / Q000453:epidemiology; D012621:Seasons; D062206:Spatial Analysis; D014481:United States / Q000453:epidemiology