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Surveillance Systems

The South Carolina Aberration Alerting Network (SCAAN) is a collaborative network of syndromic systems within South Carolina. Currently, SCAAN contains the following data sources: SC Hospital Emergency Department chief-complaint data, Poison Control Center call data, Over-the-Counter pharmaceutical sales surveillance, and CDC’s BioSense biosurveillance system. The Influenza-like Illness Network (ILINet) is a collaboration between the Centers for Disease Control, state health departments and health care providers. ILINet is one of several components of SC’s influenza surveillance.

 

Objective

This paper compares the SCAAN hospital-based fever–flu syndrome category with the South Carolina Outpatient ILINet provider surveillance system. This is the first comparison of South Carolina’s syndromic surveillance SCAAN data with ILINet data since SCAAN’s deployment.

Referenced File
Submitted by hparton on

The Syndromic Surveillance Program (SSP) of the Acute Disease Epidemiology Section of the Georgia Division of Public Health, provides electronic influenza- like- illness (ILI) data to the Center for Disease Control and Prevention’s Influenza-like Illness Surveillance Network Program that characterizes the burden of influenza in states on a weekly basis.

ILI is defined as a fever of 1001, plus a cough or sore throat. This definition is used to classify ILI by the SSP, as well as in diagnosis at the pediatric hospital system. During the 2009 H1N1 pandemic, the SSP was provided a daily data transfer to the Center for Disease Control and Prevention to heighten situational awareness of the burden of ILI in Georgia. Throughout the peak of the pandemic, data from the pediatric hospital system identified when the percentage of daily visits for ILI had substantively increased. The data includes patient chief complaint (CC) data from emergency department visits for two facilities at Facilities A and B. The data received by SSP does not include diagnosis data.

Patient emergency department discharge data (DD) for ‘FLU’ was provided to SSP retrospectively to compare with the CC data routinely collected and analyzed. The data was derived from the pediatric health system’s month end, internal, syndromic surveillance report based upon emergency department visits, and including physician’s diagnosis at the time of patient’s discharge. The case definition of ‘FLU’ from the pediatric health system facilities is acute onset of fever, with cough and/or sore throat in the absence of a known cause other than influenza.

 

Objective

The objective of this study is to describe the difference between patient CC, ILI data provided daily to the Georgia SSP during the 2009 H1N1 pandemic, and patient DD subsequently provided for comparison with the SSP from its participating pediatric hospital system, and its two affiliated emergency rooms.

Referenced File
Submitted by hparton on

The Public Health Surveillance (PHS) component (one of five monitoring and surveillance components deployed in the Cincinnati drinking water contamination warning system) functions to detect public health incidents resulting from exposure to toxic chemicals that produce a rapid onset of symptoms. Within the PHS component, four data streams were monitored: 911 calls, Emergency Medical Services (EMS) logs, Local Poison Control Center call data, as well as Emergency Department data (via EpiCenter). The focus of this paper centers on the 911 and EMS surveillance tools. The 911 data is dependent on information provided by the caller and the information entered by the dispatcher. EMS data, on the other hand, is recorded by a medical professional, and although not provided as rapidly as 911 data, provides more detailed information. The data included in 911 and EMS alerts, when utilized together, can provide timely and beneficial information during investigation of a possible drinking water contamination incident.

 

Objective

This paper describes the design, application and use of 911 and EMS data in a drinking water contamination warning system.

Referenced File
Submitted by hparton on

Health care information is a fundamental source of data for biosurveillance, configuring electronic health records to report relevant data to health departments is technically challenging, labor intensive, and often requires custom solutions for each installation. Public health agencies wishing to deliver alerts to clinicians also must engage in an endless array of one-off systems integrations. SMART provides a common platform supporting an "app store for biosurveillance"?

This presentation will highlight the work accomplished by the Surveillance System Quality Working Group convened by the European Centre for Disease Prevention and Control (ECDC) in their efforts to develop an electronic manual for monitoring data quality and evaluations for public health surveillance systems. The aim of this project was to support processes for monitoring data quality and evaluation of surveillance systems in EU countries so as to provide accurate and timely information for decision making.

Dr. James Buehler, Director of the Public Health Surveillance and Informatics Office (PHISPO), will be joining ISDS to present an overview of the CDC's vision for surveillance and informatics by discussing the updated PHISPO Strategic Plan for the coming four years. This presentation will highlight the CDC's public health surveillance priorities, objectives, and strategies for success.

Sponsored by the ISDS Public Health Practice Committee

The US 9-1-1 and emergency medical services (EMS) systems provide critical, time sensitive care to millions of patients every year. While EMS typically provides medical care to one patient at a time, data from EMS patient records and 9-1-1 calls can be used to enhance public health surveillance efforts.  

This webinar will give an overview of the EMS systems and the National Emergency Medical Services Information System (NEMSIS) and discuss how NEMSIS can be used in syndromic surveillance. 

Emphasis has been placed on the improvement of existing surveillance systems and developing innovative new surveillance systems around the world after the events of 9/11 in 2001, severe acute respiratory syndrome (SARS) in 2003. Investments have not only been made in traditional public health surveillance systems but also novel approaches such as syndromic surveillance systems. It is important to have timely, relevant evaluations of these systems to understand their usefulness. While most of the published syndromic surveillance systems evaluations looked at technical attributes of the system i.e. accuracy [1]. Other aspects such as utility, acceptability and feasibility[2] as given in the generic Centers for Disease Control and Prevention evaluation framework[3] were not always explicitly addressed. Moreover, most of syndromic surveillance systems are established in developed countries or areas that already have other types of advanced surveillance systems. There are few public reports of the development and implementation of a syndromic surveillance system in rural China.

Objective

To identify the different acceptability groups of village doctors of an integrated syndromic surveillance system (ISS) and to explore factors influencing acceptability from village doctors' perspective before ISS launched.

Submitted by elamb on
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