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      Near Real-Time Monitoring of Emergency Department Syndromic Surveillance Data During the 2013 Super Bowl and Mardi Gras Events in New Orleans, LA

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          Objective To demonstrate the value of syndromic surveillance as a tool to provide situational awareness during high profile events such as the Super Bowl and Mardi Gras in New Orleans, LA. Introduction The Louisiana Office of Public Health (OPH) conducts emergency department (ED) syndromic surveillance using the Louisiana Early Event Detection System (LEEDS). LEEDS automatically processes electronic chief complaint and diagnosis data to identify ED visits indicative of specific syndromes. The Infectious Disease Epidemiology section (IDEpi) of OPH uses LEEDS to monitor infectious disease and injury syndromes during natural or man-made disasters and high profile events. Past events monitored include hurricanes Katrina, Rita and Isaac, the Gulf Coast oil spill, yearly Mardi Gras festivities, marsh fires and chemical leaks. LEEDS has proven to be an invaluable tool in providing all-hazards public health situational awareness during these types of events by enabling near real-time monitoring of infectious disease and injury syndromes. Methods IDEpi monitored LEEDS for infectious disease and injury syndromes during the 2013 Super Bowl and Mardi Gras activities in New Orleans. The Super Bowl took place on February 3, Mardi Gras day fell on February 12, and the period of surveillance was January 7 through February 28, 2013. Data was collected and analyzed daily from all EDs (n=11) in the greater New Orleans area. IDEpi monitored six infectious disease syndromes: fever, gastrointestinal complaints (GI), influenza-like illness (ILI), lower respiratory tract infections (LRTI), upper respiratory tract infections (URTI), and skin and soft tissue infections (SSTI); and five injury syndromes: alcohol use, drug abuse, personal injuries (lacerations, falls, etc.), violence, and motor vehicle accidents (MVA). The Early Aberration Reporting System (EARS) C2 method and Change Point Analysis (CPA) were used to monitor for aberrations in daily percentages of ED visits associated with each syndrome. C2 has been shown to effectively detect sudden major changes while CPA has been shown to detect more subtle changes in time-series data (1). Daily data were exported from LEEDS and analyzed in R statistics package. Results EARS C2 method was used to detect aberrations in percentage of ED visits attributed to each syndrome. Alarms were generated for fever (on January 20 and February 12), URTI (February 15), alcohol (February 2), drug abuse (February 10), personal injuries (January 20), violence (January 21), and MVA (February 23) (Figures 1 and 2). The syndrome aberrations that generated alerts were not sustained and therefore did not warrant investigation. CPA was used to detect changes in mean percentages of ED visits over the period. No change points were detected. Conclusions Monitoring syndromic surveillance data and utilizing C2 and CPA aberration detection methods provided OPH essential situational awareness during the Super Bowl and Mardi Gras events in New Orleans. This type of situational awareness is necessary to indicate if and when mobilization of public health resources and messaging may have been needed to prevent additional injury and illness. OPH will continue to utilize LEEDS as a valuable tool to provide all-hazards public health situational awareness during natural or man-made disasters and high profile events. Figure 1 ED visits related to infectious disease syndromes – Greater New Orleans Area, January 7 – February 28, 2013 Figure 2 ED visits related to injury syndromes – Greater New Orleans Area, January 7 – February 28, 2013

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          Application of change point analysis to daily influenza-like illness emergency department visits

          Background 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 (, 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.

            Author and article information

            Online J Public Health Inform
            Online J Public Health Inform
            Online Journal of Public Health Informatics
            University of Illinois at Chicago Library
            29 April 2014
            : 6
            : 1
            Louisiana Office of Public Health - Infectious Disease Epidemiology, New Orleans, LA, USA
            Author notes
            [* ]Jenna Iberg Johnson, E-mail: jenna.ibergjohnson@
            ISDS Annual Conference Proceedings 2013. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (, permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
            ISDS 2013 Conference Abstracts


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