ArticlePloS one2014
Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed, 71 citations in OpenAlex.
- Complementing conventional infectious disease surveillance with national health insurance claims data in the Republic of Korea.Scientific reports · 2019Trial
- Beyond influenza-like-illness: data-driven syndromic surveillance for fine-scale monitoring of seasonal respiratory viruses.medRxiv : the preprint server for health sciences · 2026Article
- Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.medRxiv : the preprint server for health sciences · 2025Article
- Evaluating the generalizability of commercial healthcare claims data.American journal of epidemiology · 2025Article
- A Deep Learning Framework for Using Search Engine Data to Predict Influenza-Like Illness and Distinguish Epidemic and Nonepidemic Seasons: Multifeature Time Series Analysis.Journal of medical Internet research · 2025Article
- Neighborhood Disparities in Influenza and Influenza like Illness Hospitalization During Seasonal and Pandemic Influenza.Open forum infectious diseases · 2025Article
- Nowcasting reported covid-19 hospitalizations using de-identified, aggregated medical insurance claims data.PLoS computational biology · 2025Article
- Influenza surveillance systems using traditional and alternative sources of data: A scoping review.Influenza and other respiratory viruses · 2022Article
- Influenza and Pneumonia Mortality Across the 30 Biggest U.S. Cities: Assessment of Overall Trends and Racial Inequities.Journal of racial and ethnic health disparities · 2022Article
- Epidemic tracking and forecasting: Lessons learned from a tumultuous year.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- National Prevalence of Influenza Diagnoses and Vaccination Rates Among Patients Presenting to United States Physician Offices and Hospital Outpatient Departments, 2009 to 2016.Open forum infectious diseases · 2021Article
- Toward the use of neural networks for influenza prediction at multiple spatial resolutions.Science advances · 2021Article
- Health inequities in influenza transmission and surveillance.PLoS computational biology · 2021Article
- An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.Science advances · 2021Article
- Forecasting influenza activity using machine-learned mobility map.Nature communications · 2021Article
- Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.PloS one · 2021Article
- Real-time estimation of disease activity in emerging outbreaks using internet search information.PLoS computational biology · 2020Article
- SARS-CoV-2: Virology, epidemiology, immunology and vaccine development.Biologicals : journal of the International Association of Biological Standardization · 2020Article
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Authors and funding
8 authors at 7 institutions in 2 countries.
Funding
Abstract
introductionFine-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. MATERIAL AND
methodsWe 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.
resultsRegional 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).
conclusionMedical 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.
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