Syndromic Surveillance

The Houston Department of Health Department of Health and Human Services (HDHHS) monitors emergency departments (ED) chief complaints across the Houston metropolitan area, Harris County, and the surrounding jurisdictions by Real-time Outbreak Disease Surveillance (RODS). The influenza-like illnesses (ILI) data is collected by sentinel surveillance provider network of 12 physicians and RODS, an electronic syndromic surveillance database consisting of about 30 EDs in metropolitan Houston. Previous research indicates that there is a relationship between new HIV diagnoses and neighborhood poverty. However, there is limited research on health disparity to investigate the association between influenza-like illnesses (ILI) and social determinants of health (SDH), such as poverty.

Objective

To investigate the association between social determinants of health and influenza-like illnesses in Houston/Harris County and to identify neighborhoods for targeted surveillance or interventions.

The Florida Department of Health (FDOH) electronically receives both urgent care center (UCC) data and hospital emergency department (ED) data from health care facilities in 43 of its 67 counties through its Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL). Each submitted record is assigned to one of eleven ESSENCE Syndrome categories based on parsing of chief complaint data. The UCC data come from 22 urgent care centers located in Central Florida, and the ED data come from 161 hospitals located across the state. Traditionally, the data from these two sources are grouped and viewed together. To date, limited investigation has been carried out on the validity of grouping data from UCCS and EDs in ESSENCE-FL. This project will investigate and describe the differences between the data received from these two sources and provide best practices for grouping and analyzing these data sources.

Objective

To identify best practices for grouping emergency department and urgent care data in a syndromic surveillance system.

As technology advances, the implementation of statistically and computationally intensive methods to detect unusual clusters of illness becomes increasingly feasible at the state and local level [2]. Bayesian methods allow for the incorporation of prior knowledge directly into the model, which could potentially improve estimation of expected counts and enhance outbreak detection. This method is one of eight being formally evaluated as part of a grant from the Alfred P. Sloan Foundation.

Objective

To adapt a previously described Bayesian model-based surveillance technique for cluster detection [1] to NYC Emergency Department (ED) visits.

The success of syndromic surveillance depends on the ability of the surveillance community to quickly and accurately recognize anomalous data. Current methods of anomaly detection focus on sets of syndromic categories and rely on a priori knowledge to map chief complaints to these general syndromic categories. As a result, the mapping scheme may miss key terms and phrases that have not previously been used. Furthermore, analysts do not have a good way of being alerted to these new terms in order to determine if they should be added to the syndromic mapping schema. We use a dynamic dictionary of terms to side-step the downfalls of a priori knowledge in this rapidly evolving field by alerting the analyst to rare and brand new words used in the chief complaint field.

Objective

To automate the detection of very unusual emergency department chief complaints based on a comparison between a trained dictionary of terms and the unstructured chief complaint field.

This paper evaluates the expended and enhanced surveillance system specifically syndromic surveillance, which was implemented during the International Cricket Council Cricket World Cup West Indies 2007 (ICC CWC WI 2007) in Western Jamaica.

The interpretation of aberrations detected by syndromic surveillance is critical for success, but poses challenges for local health departments who must conduct appropriate follow-up and confirm outbreaks. This paper describes the response of the Boston Public Health Commission (BPHC) to a cluster of emergency department (ED) visits in children detected by syndromic surveillance.

We explored the utility of tracking emergency department (ED) visits for febrile illness as a proxy for influenza surveillance, from both a local and a regional perspective.

To evaluate whether a rise or shift in severe morbidity and mortality by influenza can be detected with syndromic surveillance.

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.

Syndromic surveillance may be suited for detection of emerging respiratory disease elevations that could pass undiagnosed. The syndromes under surveillance should then retrospectively reflect known respiratory pathogen activity. To validate this for respiratory syndromes we analyzed dutch medical registration data from 1999-2003 (national hospital discharge diagnoses and causes of death). We assume that syndromes with a good reflection of pathogen activity have the potential ability to reflect unexpected respiratory pathogen activity in prospective surveillance.

Objective

As a validation for syndromic surveillance we studied whether respiratory syndromes indeed reflect the activity of respiratory pathogens. Therefore we retrospectively estimated the temporal trend of two respiratory syndromes by the seasonal dynamics of common respiratory pathogens.