Demonstrating the Use of High-Volume Electronic Medical Claims Data to Monitor Local and Regional Influenza Activity in the US

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.

Human mobility and the spatial transmission of influenza in the United States

“…Our analysis builds on earlier work indicating that city-level medical insurance claims of influenza-like-illnesses (ILI) are useful and specific indicators of influenza virus activity across the US [21]. We extend previous analyses of the autumn wave of the 2009 A/H1N1 pandemic [19] to study spatial spread among 310 distinct geo-referenced locations across continental US during the 2002/03 through 2009/10 influenza seasons, covering both mild and severe epidemics as well as the 2009 pandemic (Table 1, supporting S1 Text, Table A1).…”

Socio-environmental and measurement factors drive variation in influenza-like illness

“…Using aggregated U.S. medical claims for influenza-like illness (ILI) from the 2001-2002 through 2008-2009 flu seasons [2], we developed a Bayesian hierarchical modeling framework to estimate the importance of both ecological and social determinants and measurement-related factors on observed county-level variation of influenza disease burden across the United States. Integrated Nested Laplace Approximation (INLA) techniques for Bayesian inference were used to render our questions computationally tractable due to the high spatial resolution of our data (Figure 1) and the multiplicity of models in our analysis [3].…”

Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study (Preprint)

“…[29,30] In addition, it has been shown that the influenza activity changes detected retrospectively with EHR-based ILI indicators are highly correlated with the influenza surveillance data. [31,32] However, few HBD-based models have been developed to monitor influenza. [7,33] Santillana et al proposed a model using HBD and a machine learning algorithm (Support Vector Machine) with good performances at the regional scale.…”