Syndromic Surveillance

During March-May 2013, 14 overdose deaths occurred in RI that were caused by acetyl fentanyl, a novel synthetic opioid about five times more potent than heroin1. Ten of these deaths were clustered in March, causing a significant increase over baseline of monthly illicit drug overdose deaths in RI1. Overdose deaths are well described in RI by forensic toxicology testing results. However, the overall number of ED visits associated with this event was unknown. We used RODS data retrospectively to characterize overdose related ED visits in RI and to analyze trends.

Objective

Determine if the Rhode Island (RI) Real-time Outbreak and Disease Surveillance (RODS) system (a syndromic surveillance system) identified an increase in overdoses during a known cluster of illicit drug overdose deaths in RI and characterize emergency department (ED) overdose visits during the 15 month period prior to and including the known cluster.

Monitoring trends of respiratory illnesses via syndromic surveillance in SC is performed on a daily basis. SC Syndromic Surveillance primarily utilizes emergency department data, and provides situational awareness regarding broad syndrome categories among hospitals in the state. Respiratory illnesses represent a significant public health burden, causing the second highest number of outbreaks reported in SC. Since syndromic surveillance can potentially serve as an earlier indicator of outbreaks,1 it is beneficial to assess seasonality of respiratory illnesses to identify illness clusters early to mobilize a rapid response.

Objective

To assess the temporal patterns of respiratory illnesses in South Carolina (SC) using syndromic surveillance emergency department (ED) data.

In early May of 2013, two chemical spills occurred within high schools in Atlantic county. These incidents, occurring within a week of each other, highlighted the need to strengthen statewide syndromic surveillance of illnesses caused by such exposures. In response to these spills, a new 'chemical exposure' classifier was created in EpiCenter, New JerseyÕs syndromic surveillance system, to track future events by monitoring registration chief complaint data taken from emergency department visits. The primary objective behind creation of the new classifier is to provide local epidemiologists with prompt notification once EpiCenter detects an abnormal numbers of chemical exposure cases.

Objective

To describe the development of a new chemical exposure classifier in New Jersey's syndromic surveillance system (EpiCenter).

The field of syndromic surveillance has received increased attention over the past decade as an expansion of traditional disease detection methods. There is, however, little or no consensus, regarding a standard definition encompassing the full scope of the term 'syndromic surveillance'. Several researchers have proposed at least 36 alternative names to differentiate various forms of syndromic surveillance but none has taken hold (including early warning, health indicator surveillance, enhanced surveillance, among others). Katz et al presented a redefining of syndromic surveillance as two overarching categories of 'syndrome based'“ versus 'syndrome non-specific'“ surveillance1. In addition, the Meaningful Use Stage 2 standard for syndromic surveillance includes both pre-diagnostic and diagnostic data elements, further broadening the scope of this surveillance method.

Objective

To provide a forum for stakeholders from various sectors of syndromic surveillance research and practice to discuss and establish a more accurate and comprehensive yet succinct definition of syndromic surveillance, based on lessons learned and innovations in public health surveillance practice.

Despite the global emergence of syndromic surveillance systems and theories in recent years, the practical performance of this surveillance method under real circumstances had been rarely evaluated, especially in resource-limited areas. Since April 2012, a syndromic surveillance system named 'ISSC' has been established among health care facilities, pharmacies and primary schools in two rural counties (County A & B) of Jiangxi Province, China.

Objective

Our aim was to explore the practical performance of ISSC system through investigating the characteristics and verifying results of alert signals raised during real-time surveillance periods.

In November of 2011, the local Public Health unit responsible for the Edmonton area (population 1.2mil) was alerted to an individual meeting the case definition for measles in the ED. A key part of the management strategy was to identify contacts to the index case, perform a risk assessment and, if applicable, inform them of the risk. Given the transmission characteristics, the risk for this group was defined as those present within the geographic area/environment of the index case within a specified time period. Public Health utilized the established manual lookup of hospital records and piloted an automated data query through the syndromic surveillance system, ARTSSN. This served as opportunity to validate the ability to generate a contact list, based on risk geography and time, of the ARTSSN system, and to compare the timeliness of each result.

Objective

Following a clinical case of measles presenting to an urban emergency department (ED), the local health authority sought to identify all patients that might be at risk for disease. This list of contacts was generated through a manual search of hospital records and through a piloted automated data query of the health authority's syndromic surveillance system, Alberta Real Time Syndromic Surveillance Net (ARTSSN). The purpose of this pilot study was to: 1) compare the completeness of the two lookup methods and, 2) describe the time requirements needed for each method.

BioSense 2.0 protects the health of the American people by providing timely insight into the health of communities, regions, and the nation by offering a variety of features to improve data collection, standardization, storage, analysis, and collaboration. BioSense 2.0 is the result of a partnership between the Centers for Disease Control and Prevention (CDC) and the public health community to track the health and well-being of communities across the country. As part of the redesign effort, new fat pipe system architecture has recently been implemented to improve the features and capabilities of the system.

Objective

The objective of this presentation is to provide an overview of the technical architecture of BioSense 2.0.

In 2010, as rules for the Centers for Medicaid and Medicare Electronic Heatlh Record (EHR) Incentive Programs (Meaningful Use)(1), were finalized, ISDS became aware of a trend towards new EHR systems capturing or sending emergency department (ED) chief complaint (CC) data as structured variables without including the free-text. This perceived shift in technology was occurring in the absence of consensus-based technical requirements for syndromic surveillance and survey data on the value of free-text CC to public health practice. On 1/31/11, ISDS, in collaboration with CDC BioSense, recommended a core set of data for public health syndromic surveillance (PHSS) to support public health's participation in Meaningful Use.

Objective

This study was conducted to better support a requirement for ED CC as free-text, by investigating the relationship between the unstructured, free-text form of CC data and its usefulness in public health practice. To better inform health IT standardization practices, specifically related to Meaningful Use, by describing how US public health agencies use unstructured, free-text EHR data to monitor, assess, investigate and manage issues of public health interest.

Syndromic surveillance uses syndrome (a specific collection of clinical symptoms) data that are monitored as indicators of a potential disease outbreak. Advanced surveillance systems have been implemented globally for early detection of infectious disease outbreaks and bioterrorist attacks. However, such systems are often confronted with the challenges such as (i) incorporate situation specific characteristics such as covariate information for certain diseases; (ii) accommodate the spatial and temporal dynamics of the disease; and (iii) provide analysis and visualization tools to help detect unexpected patterns. New methods that improve the overall detection capabilities of these systems while also minimizing the number of false positives can have a broad social impact.

The HEDSS system was implemented in 2004 to monitor disease activity [1]. Twenty of 32 emergency departments (ED) and 1 urgent care clinic provide data. Chief complaints are routinely categorized into 8 syndromes. Although previous studies have shown that ED syndomic surveillance is not useful for early detection of GI outbreaks [2], it has demonstrated utility in monitoring trends in seasonal norovirus activity[3]. An evaluation to assess the utility of HEDSS to characterize endemic and out-break levels of GI illness has not been previously conducted in Connecticut.

Objective

To evaluate the utility of the Connecticut Hospital Emergency Department Syndromic Surveillance System (HEDSS) to monitor gastrointestinal (GI) illness in the community.