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dc.contributor.authorKass-Hout, Taha Aen_US
dc.contributor.authorXu, Zhihengen_US
dc.contributor.authorMcMurray, Paulen_US
dc.contributor.authorPark, Soyounen_US
dc.contributor.authorBuckeridge, David Len_US
dc.contributor.authorBrownstein, John Sen_US
dc.contributor.authorFinelli, Lynen_US
dc.contributor.authorGroseclose, Samuel Len_US
dc.date.accessioned2014-03-11T02:48:54Z
dc.date.issued2012en_US
dc.identifier.citationKass-Hout, Taha A, Zhiheng Xu, Paul McMurray, Soyoun Park, David L Buckeridge, John S Brownstein, Lyn Finelli, and Samuel L Groseclose. 2012. “Application of change point analysis to daily influenza-like illness emergency department visits.” Journal of the American Medical Informatics Association : JAMIA 19 (6): 1075-1081. doi:10.1136/amiajnl-2011-000793. http://dx.doi.org/10.1136/amiajnl-2011-000793.en
dc.identifier.issn1067-5027en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11879071
dc.description.abstractBackground: The utility of healthcare utilization data from US emergency departments (EDs) for rapid monitoring of changes in influenza-like illness (ILI) activity was highlighted during the recent influenza A (H1N1) pandemic. Monitoring has tended to rely on detection algorithms, such as the Early Aberration Reporting System (EARS), which are limited in their ability to detect subtle changes and identify disease trends. Objective: To evaluate a complementary approach, change point analysis (CPA), for detecting changes in the incidence of ED visits due to ILI. Methodology and principal findings Data collected through the Distribute project (isdsdistribute.org), which aggregates data on ED visits for ILI from over 50 syndromic surveillance systems operated by state or local public health departments were used. The performance was compared of the cumulative sum (CUSUM) CPA method in combination with EARS and the performance of three CPA methods (CUSUM, structural change model and Bayesian) in detecting change points in daily time-series data from four contiguous US states participating in the Distribute network. Simulation data were generated to assess the impact of autocorrelation inherent in these time-series data on CPA performance. The CUSUM CPA method was robust in detecting change points with respect to autocorrelation in time-series data (coverage rates at 90% when −0.2≤ρ≤0.2 and 80% when −0.5≤ρ≤0.5). During the 2008–9 season, 21 change points were detected and ILI trends increased significantly after 12 of these change points and decreased nine times. In the 2009–10 flu season, we detected 11 change points and ILI trends increased significantly after two of these change points and decreased nine times. Using CPA combined with EARS to analyze automatically daily ED-based ILI data, a significant increase was detected of 3% in ILI on April 27, 2009, followed by multiple anomalies in the ensuing days, suggesting the onset of the H1N1 pandemic in the four contiguous states. Conclusions and significance As a complementary approach to EARS and other aberration detection methods, the CPA method can be used as a tool to detect subtle changes in time-series data more effectively and determine the moving direction (ie, up, down, or stable) in ILI trends between change points. The combined use of EARS and CPA might greatly improve the accuracy of outbreak detection in syndromic surveillance systems.en
dc.language.isoen_USen
dc.publisherBMJ Groupen
dc.relation.isversionofdoi:10.1136/amiajnl-2011-000793en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3534458/pdf/en
dash.licenseLAAen_US
dc.subjectSurveillanceen
dc.subjectpublic healthen
dc.subjectdiseaseen
dc.subjectoutbreaksen
dc.subjectalgorithmen
dc.subjectchange point analysisen
dc.subjectSimulation of complex systems (at all levels: molecules to work groups to organizations)en
dc.subjectmonitoring the health of populationsen
dc.subjectdetecting disease outbreaks and biological threatsen
dc.titleApplication of change point analysis to daily influenza-like illness emergency department visitsen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalJournal of the American Medical Informatics Association : JAMIAen
dash.depositing.authorBrownstein, John Sen_US
dc.date.available2014-03-11T02:48:54Z
dc.identifier.doi10.1136/amiajnl-2011-000793*
dash.contributor.affiliatedBrownstein, John


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