Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.

<h4>Introduction</h4>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.<h4>...

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Autores principales:	Cécile Viboud, Vivek Charu, Donald Olson, Sébastien Ballesteros, Julia Gog, Farid Khan, Bryan Grenfell, Lone Simonsen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0615fa324f6549b684ccb1109efab06f
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spelling	oai:doaj.org-article:0615fa324f6549b684ccb1109efab06f2021-11-25T06:06:49ZDemonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.1932-620310.1371/journal.pone.0102429https://doaj.org/article/0615fa324f6549b684ccb1109efab06f2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25072598/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Introduction</h4>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.<h4>Material and methods</h4>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.<h4>Results</h4>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).<h4>Conclusion</h4>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.Cécile ViboudVivek CharuDonald OlsonSébastien BallesterosJulia GogFarid KhanBryan GrenfellLone SimonsenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e102429 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cécile Viboud Vivek Charu Donald Olson Sébastien Ballesteros Julia Gog Farid Khan Bryan Grenfell Lone Simonsen Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.
description	<h4>Introduction</h4>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.<h4>Material and methods</h4>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.<h4>Results</h4>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).<h4>Conclusion</h4>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.
format	article
author	Cécile Viboud Vivek Charu Donald Olson Sébastien Ballesteros Julia Gog Farid Khan Bryan Grenfell Lone Simonsen
author_facet	Cécile Viboud Vivek Charu Donald Olson Sébastien Ballesteros Julia Gog Farid Khan Bryan Grenfell Lone Simonsen
author_sort	Cécile Viboud
title	Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.
title_short	Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.
title_full	Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.
title_fullStr	Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.
title_full_unstemmed	Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.
title_sort	demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the us.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/0615fa324f6549b684ccb1109efab06f
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Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.

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