Surveillance Systems

The development of a real time surveillance system for Forces on duty areas is one of the 5 initiatives of the November 2002 Prague’s NATO meeting. The French Military Health Service has decided to implement a military demonstrator within Forces in operations in a tropical area. This military prototype has three main objectives : i) to study the feasability of real time surveillance system within Forces in operations ii) to evaluate the benefit of such a system and iii) to develop a interoperable system for NATO. This French real time system has been developped by a multidisciplinary team, with military people but also with civilian experts from Pasteur Institute and Mediterranean University of Marseille.

Objective

This paper describes the new real time surveillance system, which has been installed within the French Forces in French Guiana.

Real-time disease surveillance is critical for early detection of the covert release of a biological threat agent (BTA). Numerous software applications have been developed to detect emerging disease clusters resulting from either naturally occurring phenomena or from occult acts of bioterrorism. However, these do not focus adequately on the diagnosis of BTA infection in proportion to the potential risk to public health.

GUARDIAN is a real-time, scalable, extensible, automated, knowledge-based BTA detection and diagnosis system. GUARDIAN conducts real-time analysis of multiple pre-diagnostic parameters from records already being collected within an emergency department. The goal of this system is to move from simple trend anomaly detection to an infectious disease specific expert system in order to assist clinicians in detecting potential BTAs as quickly and effectively as possible. GUARDIAN improves the diagnostic process for BTA infection through the capture and automated application of associated clinical expertise. The automated application of this knowledge provides the focus and accuracy necessary for effective BTA infection diagnosis. The continuity of this process improves the efficiency by which diagnoses of BTA infections can be made.

Complex, highly parameterized data models are often used to detect syndromic outbreaks. Unfortunately, such models can pose greater maintenance challenges as parameter variations increase. As such, our work focuses on whether day-of-the-week (DoW) effects may (or may not) show little variation across hospitals.

Objective

This paper investigates the existence of the DoW effect across twenty-six hospitals within the Indiana Public Health Emergency Surveillance System. We will consider both the impact of each DoW and the impact of individual hospitals.

In a classical surveillance system one looks for disturbances in the number of cases, but in a spatio-temporal system, not only the number of cases observed but also where they are located is reported. What location is reported, and to which degree of accuracy it is reported are important. At one extreme les near-perfect information about each case, as with contact tracing; at the other extreme we have no information about location; viz. just that the patient exisits, or a temporal system. From maximum spatial precision to no spatial precision, one gains in speed of reporting and privacy; but one loses power to detect outbreaks. For example, in Ozonoff et al. we see that more than one address is better than just a single one. This general point is intuitively appealing, and can be demonstrated. 

Objective

This paper quantifies the effect of not providing full information about the location of patients when dealing with spatio-temporal systems in syndromic surveillance. The study investigates the loss of power to detect clusters when aggregation takes place. 

The New York State Department of Health (NYSDOH) currently applies EARS’s CuSum analyses to Medicaid Over the Counter and Prescription Medications data obtained from the Office of Medicaid Management's data warehouse. Daily drug category counts are compared with counts for a 7-day baseline period to generate C1, C2, and C3 signals for 62 counties and 8 Syndromic Surveillance Regions. Summary tables and graphs are posted on the NYSDOH Secure Health Commerce Network for access by state, regional, and county users.

The 7-day baseline CuSum method of analysis of syndromic surveillance databases can result in the generation of a large number of signals. Many signals are generated for counts that, upon manual review of 30-day or long-term trend graphs, are clearly within the range of normal daily variation and would not require follow up by public health authorities.

In order to prevent user desensitization to generated signals and minimize NYSDOH Syndromic Surveillance System end-user burden, supplemental measures that would indicate a daily count is higher than expected are currently being investigated.

Objective

To supplement CuSum analyses of syndromic surveillance databases within NYSDOH's Electronic Syndromic Surveillance System with other measures that would indicate a daily count is higher than expected in order to minimize the end-user burden of following up generated signals.

CDC is building a public health information grid to enable controlled distribution of data, services and applications for researchers, Federal authorities, local and state health departments nationwide, enabling efficient controlled sharing of data and analytical tools. Federated aggregate analysis of distributed data sources may detect clusters that might be invisible to smaller, isolated systems. Success of the public health grid is contingent upon the number of participating agencies and the quantity, quality, and utility of data and applications available for sharing. Grid protocols allow data owners to control data access, but requires a model to control the level of identifiability of depending upon the user’s permissions. Here, we describe a work currently in progress involving the design and implementation of an ambulatory syndromic surveillance data stream generator for the public health grid. The project is intended to broadly disseminate aggregate syndrome counts for general use by the public health community, to develop a model for sharing varying levels of identifiable data on cases depending upon the user, and to facilitate ongoing development of the grid.

Objective

To implement a syndromic surveillance system on CDC’s public health information grid, capable of securely distributing syndromic data streams ranging from aggregate case counts to individual case details, to appropriate personnel.

Syndromic surveillance had been implemented in Dongcheng District with a view to probing into the feasibility of establishing a syndromic surveillance system in major Chinese cities, sieving syndromic surveillance indicators applicable to the eruption of infectious respiratory tract and digestive tract diseases, and attempting the operating method of data collection in different locations such as hospital and drug stores in Dongcheng of Beijing China.

Objective

The project has fund donated by World Bank under joint management of WHO and Ministry of Health of P.R.China , The target was try to build up a syndromic surveillance system in Beijing.

The interest of medication sales data in Syndromic Surveillance is well recognized. In France, where a real-time computerized surveillance system of frequent communicable diseases based on Sentinel general practitioners (SGPs) provides since 1984 a gold standard to evaluate other indicators, it has been shown that medication sales provided early alerts for influenza. Gastroenteritis surveillance relies in France on the surveillance of acute diarrhea by the SGPs in the general population, since 1991. The main objective of this study is to validate, at a national level, new indicators based on medication sales data to facilitate the detection of gastroenteritis epidemics.

Objective

This study examines how medication sales data can detect gastroenteritis epidemics in France.

On October 24, 2005, Hurricane Wilma made landfall on the southwest coast of Florida as a category 3 storm. The storm moved toward the northeast and passed through Palm Beach and Broward Counties before entering the Atlantic Ocean. Hurricane force winds and rain caused extensive damage to electrical infrastructure and traffic lights, and temporarily displaced thousands of residents. Power outages in Broward County affected over 90% of its 1.8 million residents, with some outages lasting >2 weeks. Boil water notices were declared for much of the county. Acute care hospitals remained open during this time, although services provided by health care providers in other settings were interrupted due to structural damage and power outages.

Objective

We used the syndromic surveillance system ESSENCE to describe the morbidity after Hurricane Wilma in Broward County, Florida.

The University of Washington has been working since 2000 with partners in Washington State to advance bioterrorism (BT) detection and preparedness. This project collects data on patients presenting with influenza-like illnesses and other potentially BT-related syndromes at emergency departments and primary care clinics (Kitsap, Clallam, and Jefferson counties) using a secure automated informatics approach. Local health jurisdiction epidemiologists use a web-based interface to view de-identified data and use a version of CDC’s EARS disease detection algorithms to watch for variances in patterns of diagnoses, volume, time and space as part of the public health real-time disease surveillance system. This processed hospital data is also made available back to the officials and administrators at the reporting hospital.

Objective

To understand GIS issues in a rural-tourban setting and demonstrate limitations of ZIPcode-only approaches compared to census tract and block approaches.