ISDS Conference

The syndromic surveillance system in Scotland was implemented in response to Gleneagles hosting the G8 summit in July 2005. Part of this surveillance system used data from NHS24, a nurse led telephone help line that is the means of access to out of hours general practice services for the Scottish population. This data was processed by the ERS system and reports generated for 10 syndromes considered relevant to possible bio-terrorism or disease outbreaks. These syndromes are; colds and flu, difficulty breathing, fever, diarrhoea, coughs, double vision, eye problems, rash, lumps and vomiting. Following the G8 summit the ERS has been updated weekly using data pre-catagorised into syndromes at NHS24 (known as protocolled data). The proportion of calls processed by the protocol at NHS24 over this time has however fallen to around 40%. This change has given the impetus to create a free text searching algorithm which can classify all calls received by NHS 24 into one of the 10 syndromes or “other”. This therefore allows all calls to be analysed by the ERS.

Objective

Public Health consultants at Health Protection Scotland (HPS) monitor routine data from the NHS24 telephone helpline to provide information on possible epidemics of flu or other infectious diseases in Scotland. Within this paper the exception reporting system run at HPS is described and the adaptations made to the classification system as a response to the change of data recording patterns at NHS24 are described.

In North Carolina, select hospital emergency departments have been submitting data since 2003 for use in syndromic surveillance. These data are collected, stored, and parsed into syndrome categories by the North Carolina Emergency Department Database. The fever with rash illness syndrome is designed to capture smallpox cases. This syndrome was created as a combination of the separate fever and rash syndromes proposed by the consensus recommendations of the CDC’s Working Group on Syndrome Groups.

Objective 

This paper describes the construction of a syndromic surveillance case definition and a test for its ability to capture the appropriate syndromic cases.

The City of Atlanta, volunteer organizations, and the faith community operate several homeless shelters throughout the city. Services available at these shelters vary, ranging from day services, such as meals, mail collection, and medical clinics, to overnight shelter accommodations. In addition to the medical clinics available at these facilities, the Atlanta homeless population also utilizes emergency departments in Fulton County for their health care needs.

Objective

This paper describes a cluster of Streptococcus pneumoniae infections identified through emergency department syndromic surveillance.

Versatile, user-friendly visualization tools are required to organize the wealth of information available to users of large, regional surveillance systems into a coherent view of population health status. Communications components must allow multiple users of the same system to share information about the health of their populations in an organized fashion and facilitate communications among jurisdictions.

The Johns Hopkins University Applied Physics Laboratory has developed a communications tool to be used within the regional disease surveillance system in the National Capital Region. This abstract describes this new communications component that is designed to encourage and facilitate communication between multiple jurisdictions using a common surveillance system.

Objective

The objective is to create a capability within an existing regional disease surveillance system that allows event information to be shared easily, thoroughly, and in a timely manner, while gathering the knowledge needed to improve the entire system in the future. The functionality of this communication component must balance the utility of immediate situational awareness with the long term benefits of capturing critical information, such as system usage patterns and user response behavior, which can be used to develop future system enhancements. 

Current veterinary surveillance systems may be ineffective for timely detection of outbreaks involving non-targeted disease. Earlier detection could enable quicker intervention that might prevent the spread of disease and limit lost revenue. Data sources, similar to those used for early outbreak surveillance in humans, may provide for earlier outbreak detection in animals. Veterinary diagnostic laboratories are a source of data that might be valuable to such efforts.

Objective

To study the value of data from veterinary diagnostic laboratories as an initial step in developing an early outbreak surveillance system for animals.

In November 2002 a NATO summit meeting issued an initiative calling for member states to begin development of an interoperable disease surveillance system that had the ability to give early warning in the event of an attack on armed forces using weapons of mass destruction. In response, the French military have developed the “Projet de Surveillance Spatiale des épidémies au Sein des Forces Armées en Guyane” (2SE FAG), a prototype real-time syndromic surveillance system based on fever case reporting which has been in operation among armed forces personnel in French Guiana since October 2004. Between January and June 2006, French Guiana experienced the largest epidemic of dengue fever in its history. During that year, 2255 confirmed cases and many thousands more suspected cases were recorded among the civilian population. 2SE FAG issued an alert based on a rise in fever cases among armed forces personnel in week 2 of 2006, 5 weeks before a rise was noticed among the civilian population. Limited evaluations of the system have taken place in the past; this study represents a final evaluation of the system before its possible expansion.

Objective

The objective of this study was the evaluation of the syndromic surveillance system 2SE FAG which operates among armed forces personnel in French Guiana using the “Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks,” published by CDC.

Rhode Island implemented the Real-time Outbreak and Disease Surveillance (RODS) system, developed in 1999 by the University of Pittsburgh’s Center for Biomedical Informatics. This system is based on real-time information from hospital emergency departments that is transmitted and analyzed electronically for the purpose of early detection of and situational awareness for public health emergencies. Through this system, chief complaint is reported in real-time. Diagnoses, coded in the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), are reported to the RI RODS system as they become available. Three hospitals are currently participating in a pilot implementation of the RI RODS system.

Preliminary work by a CDC Working Group (CDCWG) developed recommendations for syndrome definitions for use in syndromic surveillance programs. Ten syndromes, based on ICD-9-CM diagnosis codes, identified diseases associated with critical bioterrorism-associated agents or indicative of naturally occurring infectious disease outbreaks. As a component of the evaluation of the RI RODS system, we evaluated the RI RODS chief complaint classifier (CoCo) using ICD-9-CM codes and the CDCWG work as the gold standard.

Objective

This paper presents findings related to the evaluation of the CoCo used in the pilot implementation of a syndromic surveillance system in Rhode Island.

Efforts have been made to standardize and prioritize the description and evaluation of syndromic surveillance systems. Systematic information on the performance of existing systems can be used to assess and compare the value of these systems, and inform decisions regarding their use. 

The Michigan’s Emergency Department Syndromic Surveillance System (MSSS) is an implementation of an early version of the Real-time Outbreak and Disease Surveillance system developed by the University of Pittsburgh, which collects patient chief complaint data from emergent care facilities in real time. At the Michigan Department of Community Health the system has been in use since 2003. Alterations to the system and recruitment of data contributors have been ongoing. The primary stated purpose of the MSSS is earlier detection of outbreaks of severe illness, enabling a more rapid public health response and intervention to reduce the impact of public health threats.

Objective

This work describes key characteristics of MSSS and reports on its evaluation.

Immediately following September 11, 2001, the District of Columbia Department of Health began a syndromic surveillance program based on emergency room (ER) visits. ER logs are faxed on a daily basis to the health department, where health department staff code them on the basis of chief complaint, recording the number of patients in each of the following syndromic categories: death, sepsis, rash, respiratory complaints, gastrointestinal complaints, unspecified infection, neurological, or other complaints. These data are analyzed daily and when a syndromic category shows an unusually high occurrence, a patient chart review is initiated to determine if the irregularity is a real threat. 

A time series analysis of the data from this system has shown that with the application of a variety of detection algorithms, the syndromic surveillance data does well in identifying the onset of the flu season. In addition, simulation studies using the same data have shown that over a range of simulated outbreak types, the univariate and multivariate CUSUM algorithms performed more effectively than other algorithms. The multivariate CUSUM was preferred to the univariate CUSUM for some but not all outbreak types.

Objective

This paper evaluates an ER syndromic surveillance system based on simulation studies and comparisons with other surveillance systems.

CDC’s BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Spatial approaches depend strongly on having reliable estimated values for counts among the geographic sub-regions. If estimates are poor, algorithms will find irrelevant clusters, and clusters of importance may be missed. While many studies have focused on improved computation time and more general cluster shapes, our effort focused on finding anomalies that are correct according to available BioSense data history.

Objective

We applied spatial scan statistics to data from CDC’s BioSense system and examined the effect of the spatial prediction method on determination of anomalous disease clusters. The objectives were to decide on a reliable spatial estimation method for one BioSense data source and to establish criteria for making this decision using other sources.