Emergency Department (ED)

On January 2, 2014 the cyclone Bejisa struck Reunion Island. This storm of Category 3 (Saffir–Simpson scale) disturbed electricity supply and drinking water systems. Floods, roof destructions and the threat of landslide led to the evacuation of residents to emergency shleters. In this context, the regional office of French Institute for Public Health Surveillance in Indian Ocean set up an epidemiological surveillance in order to assess the impact in the aftermath of the cyclone.

Objective

To assess the health impact of cyclone Bejisa from data of emergency departments (EDs) and emergency medical service (EMS)

In November 2011, Washington State voters passed Initiative 1183 (I-1183) which closed state-owned and contracted liquor stores and opened the market for “hard liquor” sales in the private sector. The change in law was implemented on June 1, 2012. Increases in alcohol-related ED visits were postulated as one potential impact if there was increased alcohol use or excessive consumption associated with the change in law.

Objective

To determine whether there were changes in alcohol-related emergency department (ED) visits in Washington State associated with statewide alcohol system deregulation.

Centers for Disease Control and Prevention’s (CDC) BioSense system receives near real-time health care utilization data from number of sources, including DoD and VA outpatient facilities, and nonfederal hospital EDs in the US to support all-hazards surveillance and situational awareness. However, the BioSense system lacks some critical functions such as creating ad hoc definition of syndrome or ad hoc query tool development. This limits CDC Emergency Operations Center’s (EOC) ability to monitor new health events such as MERS - a viral respiratory illness first reported in Saudi Arabia in 2012. In May 2014, CDC confirmed two unlinked imported cases of MERS in the US - one in Indiana, the other in Florida. Upon report of a MERS case in Indiana, staff initiated joint efforts with EOC and several affected jurisdictions to enhance the surveillance of MERS irrespective of jurisdictions’ preferred surveillance system.

Objective

To identify and monitor Middle East Respiratory Syndrome (MERS) like syndromes cases in the syndromic surveillance system.

Typical approaches to monitoring ED data classify cases into pre-defined syndromes and then monitor syndrome counts for anomalies. However, syndromes cannot be created to identify every possible cluster of cases of relevance to public health. To address this limitation, NC DETECT’s approach clusters cases by arrival times and monitors the textual chief complaint data associated with each identified cluster for relevant similarities [1]. This approach is time consuming and limited in its ability to detect emerging outbreaks that are dispersed across time. A new method is needed to automatically identify clusters of interest that would not be detected by existing syndromes. Clusters may be based on symptoms, events, place names, arrival time, or hospital location. The NC DPH dataset describes 198,511 de-identified ED visits over one year at 3 North Carolina hospitals. The data include chief complaint, altered date and time of arrival, hospital A/B/C, and age group. About 40 simulated outbreaks were injected into the data set by the NC DETECT team. For example, an inject cluster might consist of 4 patients who report getting sick after eating at a particular restaurant.

Objective

We apply a novel semantic scan statistic approach to solve a problem posed by the NC DETECT team, North Carolina Division of Public Health (NC DPH) and UNC Department of Emergency Medicine Carolina Center for Health Informatics, and facilitated by the ISDS Technical Conventions Committee. This use case identifies a need for methodology that detects emerging, potentially novel outbreaks in free-text emergency department (ED) chief complaint data.

 

Along with commensurate funding, an increased emphasis on syndromic surveillance systems occurred post September 11, 2001 and the subsequent anthrax attacks. Since then, many syndromic surveillance systems have evolved and have ever-increasing functionality and visualization tools. As outbreak detection using these systems has demonstrated an equivocal track record, epidemiologists have sought out other interesting and unique uses for these systems. Over the numerous years of the International Society for Disease Surveillance (ISDS) conference, many of these studies have been presented, however, there has been a dearth of discussion related to how these systems should be used on a routine basis. As the initial goal of these systems was to provide a near real-time disease surveillance tool, the question of how to most effectively conduct this type of routine surveillance is paramount.

Objective

To discuss how various emergency department based syndromic surveillance systems from across the country and world are being used and to develop best practices for moving forward.

 

GFT is a surveillance tool that gathers data on local internet searches to estimate the emergence of influenza-like illness in a given geographic location in real time.3 Previously, GFT has been proven to strongly correlate with influenza incidence at the national and regional level.2,3 GFT has shown promise as an easily accessed tool to enhance influenza surveillance and forecasting; however, further geographic validation of city-level data is needed. 1,2,6

Objective

To test if Google Flu Trends (GFT) is predictive of the volume of influenza and pneumonia emergency department (ED) visits across multiple United States cities.

 

In the summer of 2013, the New Jersey Department of Health (NJDOH) began planning for Super Bowl XLVIII to be held on February 2, 2014, in Met Life Stadium, located in the Meadowlands of Bergen County. Surveillance and epidemiology staff in the Communicable Disease Service (CDS) provided expertise in planning for disease surveillance activities leading up to, during, and after the game. A principal component of NJDOH’s Super Bowl surveillance activities included the utilization of an existing online syndromic surveillance system, EpiCenter. EpiCenter is a system developed by Health Monitoring Systems, Inc. (HMS) that incorporates statistical management and analytical techniques to process health-related data in real time. As of February, 2014, 75 of New Jersey’s 81 acute care and satellite emergency departments (EDs) were connected to this system. CDS staff primarily used EpiCenter to monitor ED visits for unusual activity and disease outbreaks during this event. In addition, NJDOH and HMS implemented enhanced reports and expanded monitoring of visit complaints.

Objective

To describe the surveillance planning and activities for a largescale event (Super Bowl XLVIII) using New Jersey’s syndromic surveillance system (EpiCenter).

 

The Florida Department of Health electronically receives hospital emergency department (ED) data from 180 EDs located in 54 of its 67 counties through its Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL). Florida EDs have begun to offer self-registration options to patients, which include ED self check-in kiosks, and pre-visit registration smartphone applications and websites. ESSENCE-FL receives ED data from multiple hospitals that use these patient self-registration methods. To date, limited investigation has been carried out to determine the impact of these self-registration methods on the data submitted to ESSENCE-FL. This project investigates and describes how SS data are affected by these options and provides possible best practices for identifying and analyzing these data.

Objective

To assess the effect of patient self-registration methods in hospital emergency departments on data in a syndromic surveillance (SS) system and provide suggestions for analysis of these data.