Surveillance Systems

The syndromic surveillance system “2SE FAG” has been installed within the French Armed Forces in French Guiana (3000 people) in October 2004 [1-2]. During the conception and the deployment of such a system, ergonomic issues were highlighted and training of stakeholders as well [3]. Daily exchanges with users have already permitted to enhance the system. An standardized and quantified evaluation among the users had to be done after 18 months of functioning. The objectives of this work were to evaluate the knowledge, the attitude and the practice of the stakeholders of the system.

Objective

This paper describes an evaluation survey made within the users of a real time surveillance system in French Guiana.

One of the first county-wide syndromic surveillance systems in the nation, the Syndromic Tracking and Reporting System (STARS) has been in operation since 11/01/2001, and now covers Hillsborough, Pinellas and Collier counties. STARS uses hospital emergency department visit data to detect aberrations of non-specific syndromes and serves as an earlier warning system for public health threats. Patient’s syndrome is collected upon arrival, separately from routine collection of clinical and administrative data; but in some hospitals the process is being streamlined with routine data collection. Aberration detection is done twice daily using the statistical system EARS developed by the CDC. Upon flagging of an aberration, follow-up investigation is conducted to verify cases, and identify source of exposure following a sequence of decision procedure. After several years of operation and some instituted enhancements, a systematic evaluation was called to (1) assess if STARS has met the operation specifications and (2) characterize system efficacy and effectiveness.

Objective

To evaluate STARS with respect to quality of syndrome diagnoses, timeliness and completeness of data collection and processing, performance of aberration detection methods, and aberration investigation.

Karachi is the largest metropolitan, principal port city and commercial hub of Pakistan. Although there is a national database for registering vital evens such as births and deaths but like in any other developing country the coverage of the system is sub optimal with many of birth and death not recorded. Undercounting of these events leads to inaccurate estimates of vital indicators for informed decision and planning for health at local and national level. In these settings demographic surveillance systems have the potential to supplement data from the vital registration systems and provide avenues for research by virtue of having a well-defined cohort and continuous surveillance. The Department of Paediatrics and Child Health of Aga Khan University Karachi, Pakistan maintains health and demographic surveillance system at four peri-urban and one urban community in Karachi with focus on maternal and child health. This also provide platform for many epidemiological and interventional studies as well as vaccination programs using a well-established identification procedures providing linkage to health and socio-economic data. In 2010 the surveillance system was reorganized to follow INDEPTH methodology and guidelines.

Objective

The purpose of Karachi Health and Demographic Surveillance System (HDSS) is to generate longitudinal information on health and demographics of a geographically defined population of low socioeconomic status and provide platform for larger projects in efforts for diseases control.

Syndromic Surveillance utilizes health-related symptom data to monitor disease outbreaks. Its’ potential for prompt detection of disease outbreaks and strengthening of rapid public health response is anticipated. As a result, syndromic surveillance is widely employed by many local and regional health care agencies across the country in both routine monitoring of disease outbreaks as well as in special national events. However, the efficacy and effectiveness of syndromic surveillance are yet to be substantiated. In Florida many localized Syndromic Surveillance have been deployed by county health departments with little oversight or coordination of any state and federal agencies. Furthermore, many aspects including the design, operation, and funding characteristics of these systems are not well known and information and practice are not shared, hindering the potential for regional networks with shared data source, networked platform, expanded geographic coverage. This survey aims to establish an inventory of Syndromic Surveillance in the State of Florida and helps identify issues common among these systems.

Objective

To gather inventory information on syndromic surveillance deployment and utilization in the State of Florida; To identify issues in developing, operating, and sustaining local systems; To assess needs for system evaluation in order to establish efficacy and effectiveness of syndromic/disease surveillance in the state.

Syndromic surveillance is the surveillance of healthrelated data that precedes diagnosis to detect a disease outbreak or other health related event that warrants a public health response. Though syndromic surveillance is typically utilized to detect infectious disease outbreaks, its utility to detect bioterrorism events is increasingly being explored by public health agencies. Many agencies believe that syndromic surveillance holds great promise in enhancing our ability to detect both planned and unplanned outbreaks of disease and have made significant investments to develop syndromic surveillance capabilities.

For instance, the Centers for Disease Control and Prevention has invested in Biosense and the Department of Defense has invested in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) which it has deployed in partnership with the Department of Veterans Affairs. The Department of Homeland Security has invested heavily in the National Bio-surveillance Integration System which integrates a broad spectrum of bio-surveillance information including data from Biosense and ESSENCE. The University of Pittsburgh has also developed a prominent tool and is considered a thought leader in this space.

Despite the significant investments in the area of syndromic surveillance, the technology is young and the relatively small field remains fragmented. As a result, there is limited public information that addresses the field as a whole.

Objective

The objective of this assessment is to research, develop and maintain a national syndromic surveillance registry that describes each system’s configuration. By collecting current information on the leading systems we will gain a greater understanding of the syndromic surveillance landscape and capabilities.

Recognizing the threat of pandemic influenza and new or emerging disease such as SARS, the U.S. Department of Health and Human Services has recommended that schools work in partnership with their local health departments “to develop a surveillance system that would alert the local health department to substantial increases in absenteeism among students.”3 Tarrant County’s pilot project system meets that need and transcends absenteeism data; it seeks to quantify ILI in schools and lets school nurses view daily maps of changing disease patterns, access flu prevention resources, and receive and respond to action items suggested by TCPH. While the focus is on seasonal flu, best practices for mitigating seasonal flu also apply to pandemic flu. Because the system uses open source software4 , it’s affordable and replicable for other public health agencies seeking to strengthen their school partnerships as well as their local or regional biosurveillance capabilities.

Objective

This oral presentation will share key findings and next steps following the first year of a pilot project in which Tarrant County, Texas schools used a Web-based system to share their daily health data with Tarrant County Public Health (TCPH) epidemiologists, who can use ESSENCE1 to analyze the data. The projectís ongoing goal is to reduce the magnitude of flu outbreaks by focusing on school-aged children and youth, where infectious diseases often emerge first and spread rapidly.2

Syndromic surveillance systems have long been an important part of the public health arena. The long standing goal of early detection of disease outbreak has gained new urgency and requires a broader spectrum in the era of potential bioterrorism. A number of programs have used syndromic surveillance to broadly monitor community health. Outpatient chief complaints as well as positive laboratory tests have been used to monitor the occurrence of natural diseases. 

Limitations of the systems currently attempted include overbroad syndromic categories, labor intensive syndrome recognition training and time intensive manual data entry. Optimal use of laboratory data has been impeded by some of the same issues as well as a too often narrow focus and significant limitations on real time reporting. Given the likelihood of blunt and/or penetrating trauma being a manifestation of terrorist activity, the continuous inclusion of common traumatic and medical emergency conditions is a valuable tool for surveillance.

Objective

This paper describes the use of a multiple collective community health care database to monitor the occurrence of natural and manmade illness and injuries.

The North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS) serves public health users across North Carolina at the local, regional and state levels, providing syndromic surveillance capabilities.  At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health.  NC BEIPS currently receives daily data from the North Carolina Emergency Department Database (NCEDD), Carolina Poison Control Center (CPC), Prehospital Medical Information System (PreMIS) and the Piedmont Wildlife Center (PWC). Future data sources will include the North Carolina State University College of Veterinary Medicine Laboratories.  The PWC is a non-profit organization dedicated to wildlife rehabilitation, education, and scientific study of health and disease in wildlife populations.  PWC admits approximately 3,000 animals annually, including mammals, birds, and reptiles, the majority of which are from 21 counties in central North Carolina.  

Objective

This poster will illustrate how a novel data source, wildlife health center data, is being incorporated and used in a syndromic surveillance system.

Current state-of-the-art outbreak detection methods [1-3] combine spatial, temporal, and other covariate information from multiple data streams to detect emerging clusters of disease.  However, these approaches use fixed methods and models for analysis, and cannot improve their performance over time.   Here we consider two methods for overcoming this limitation, learning a prior over outbreak regions and learning outbreak models from user feedback, using the recently proposed multivariate Bayesian scan statistic (MBSS) framework [1]. Given a set of outbreak types {Ok}, set of space-time regions S, and the multivariate dataset D, MBSS computes the posterior probability Pr(H1(S, Ok) | D) of each outbreak type in each region, using Bayes’ Theorem to combine the prior probabilities Pr(H1(S, Ok)) and the data likelihoods Pr(D | H1(S, Ok)). Each outbreak type can have a different prior distribution over regions, as well as a different model for its effects on the multiple streams.  The set of outbreak types, as well as the region priors and outbreak models for each type, can be learned incrementally from labeled data or user feedback.

Objective

We argue that the incorporation of machine learning algorithms is a natural next step in the evolution and improvement of disease surveillance systems. We consider how learning can be incorporated into one recently proposed multivariate detection method, and demonstrate that learning can enable systems to substantially improve detection performance over time.