Emergency Department (ED)

In Connecticut, several syndromic surveillance systems have been established to detect and monitor potential public health threats: 1) the hospital admissions syndromic surveillance (HASS) system in 2001; and 2) the emergency department syndromic surveillance (EDSS) system in 2004. For the HASS, hospitals manually categorize unscheduled admissions into 11 syndrome categories and report these aggregate counts through an internet-based system daily to DPH; all 32 hospitals participate. For the EDSS, hospitals electronically report deidentified emergency department chief complaint data to DPH, and using a computerized algorithm, DPH categorizes this data into 8 syndrome categories; currently 17 hospitals participate. As part of pandemic influenza planning, there has been an increased focus on situational awareness at the state and national level; Connecticut would likely rely on these two systems for this purpose.

Objective

To evaluate the performance of the HASS and EDSS systems in reflecting seasonal influenza activity in Connecticut and, thus, their possible utility during a pandemic.

The 2005 Youth Risk Behavior Survey of 9th to 12th graders in Miami-Dade County public schools found that 69.7% of students tried alcohol, 28.3% tried marijuana, and 6.3% tried cocaine in their lifetime. Results also showed that Hispanics had a higher percentage of usage when compared to Blacks or Whites. The 2007 White House Office of National Drug Control Policy special report entitled “Hispanic Teens and Drugs” also concluded that Hispanics were at the highest risk for substance abuse. With the county’s 60% Hispanic population, this issue is of concern for the community. This is the first study to compare multiple sources of data to describe substance abuse among youth from areas such as healthcare utilization to criminal charges.

Visitors from areas outside Miami-Dade County have the potential to introduce diseases and/or strains of microorganisms circulating in their regions of residence. Immunocompromised and immunonaive travelers are at higher risk of contagion by locally transmitted pathogens. The first encounter with a local health care facility for many of these visitors is often an Emergency Departments (ED). Little is known about this group of patients with regard to socio-demographic and temporal patterns. This knowledge is essential to further characterize their syndromic patterns as well as to integrate this knowledge to the growing use of syndromic surveillance as an early-warning public health tool.

Objective

To describe socio-demographic and temporal patterns of patients who reside outside Miami-Dade and who visited EDs of hospitals located in this County during 2007.

During influenza season, the Boston Public Health Commission uses syndromic surveillance to monitor Emergency Department visits for chief complaints indicative of influenza-like illness (ILI). We created three syndrome definitions for ILI to capture variable presentations of disease, and compared the trends with Boston pneumonia and influenza mortality data, and onset dates for reported cases of influenza.

Objective

To evaluate the impact of different syndrome definitions for ILI by comparing weekly trends with other data sources during the 2005-2006 influenza season in Boston.

The Early Aberration Reporting System was developed at the Centers for Disease Control and Prevention to help assist local and state health officials to focus limited resources on appropriate activities of public health surveillance. Outbreaks of

infectious diseases are indicated in multiple spatial and temporal data sources, such as emergency department visits, drug store sales, and ambulatory clinic visits. Based on this premise, we provided correlated data sets and investigated disease clusters.

Objective

We present a pilot study of simulation of correlated outbreak signals for early aberration reporting and evaluating detection methods.

The use of syndromic surveillance systems to assist with the timely detection of unusual health events first occurred prior to the events of September 11, 2001. In the State of Michigan a pilot project with emergency departments began collecting syndromic data in 2004. Little research has been done in rural settings which have unique characteristics such as having one medical facility for a large geographic region. In addition to being rural, the community in which the following study was done is a resort com-munity where the population differs between the summer and winter months in number and composi-tion. Another unique factor in this study is that there is little published literature utilizing triage and dis-charge syndromic groups as a means for determining system sensitivity and specificity.

Objective

This paper describes the analysis of sensitivity and specificity of an ICD-9 based syndromic surveillance system in a rural emergency department located in Northern Lower Michigan.

Previously we developed an “Ngram” classifier for syndromic surveillance of emergency department (ED) chief complaints (CC) in Turkish for bioterrorism. The classifier is developed from a set of ED visits for which both the ICD diagnosis code and CC are available. A computer program calculates the associations of text fragments within the CC (e.g. 3 characters for a “3-gram”) with a syndromic group of ICD codes. The program then generates an algorithm which can be deployed to evaluate chief complaint data in real-time. However, the N-gram method differs from most other classifiers in that it assigns a probability that each visit falls within the syndrome rather than ruling the visit “in” or “out” of the syndrome. It is possible to dichotomize visits “in” or “out” using N-grams by choosing a cut-off sensitivity for the n-grams used, but this affects the specificity of the method. The effect of this trade-off is best measured by a receiveroperator curve.

Objective

Our objective was to determine the sensitivity and specificity of the Ngram CC classifier for individual ED visits. We also wish to compare these results to those obtained when we substituted anglicized characters for 6 problematic Turkish characters.

Although many syndromic surveillance (SS) systems have been developed and implemented, few have included response protocols to guide local health jurisdictions when alerts occur [1,2]. SS was first implemented in GA during the 2004 G-8 Summit. Six EDs in the Coastal Public Health District (PHD), 1 of 18 GA PHDs (Figure 1), conducted SS during that “national security special event.” Since that time, EDs in other PHDs have been actively recruited to participate in GA’s SS system. In GA, the PHD has the responsibility for monitoring SS data. Likewise, the PHD responds to alerts and initiates public health investigations and interventions; the state Division of Public Health (DPH) assists, if requested. To address these responsibilities, the Coastal PHD informally developed their own response practices.

Objective

To develop a template protocol to guide local response to syndromic surveillance alerts generated through analyses of emergency department (ED) visit data.

In 2007, the CDC BioSense System received data from 450 non-federal hospitals. Hospitals provide data to Biosense based on their capability and willingness to supply electronic data. As of July 2008, Biosense is receiving data from 550 hospitals. The National Hospital Ambulatory Medical Care Survey (NHAMCS) is an annual national probability sample survey of hospitals that collects data on U.S. emergency department (ED) visits.

Objective

To assess the representativeness of BioSense ED data by comparing it with the NHAMCS results.

Disease surveillance provides essential information for control and response planning1. Emergency Room (ER) syndromic surveillance data can help to identify changes in disease incidence and affected group thereby providing valuable additional time for public health interventions1. The current study explored the relationship between ER syndromic surveillance data and influenza notification to the Houston Department of Health and Human services (HDHHS).