Syndromic Surveillance

In the past, the media has served a source of data for syndromic surveillance of infectious disease, whether it is outbreaks of disease in animals or humans resulting in illness or death.  More often than not, the reverse is true; data based on analyses of   syndromic surveillance often flows from hospital to local health departments and federal governmental agencies such as the CDC to the media which then relays it to the public. In both instances, the media may serve as a purveyor of vital information.  But, sometimes the media reports are less than ideal; the public may become fearful and panic at the news of a potential outbreak of an emerging infectious disease such as bird flu for which there is a high fatality case rate and no proven available vaccine, or curative therapy. Moreover, supplies of vaccine may be limited, and news of a shortage of antiviral medications such as Tamiflu may lead to stockpiling similar to what occurred with Cipro during the anthrax  ‘scare.’  

Objective:

This paper explores how the mass media covered bird flu outbreaks overseas in the Fall of 2005, and the nationís preparations for a possible bird flu pandemic, and how this period of intense media activity affected sales of antivirals in New City and New York State as monitored by syndromic surveillance techniques.

The revised International Health Regulations (IHR) have expanded traditional infectious disease notification to include surveillance diseases of international importance, including emerging infectious diseases.  However, there are no clearly established guidelines for how countries should conduct this surveillance, which types of syndromes should be reported, nor any means for enforcement.  The commonly established concept of syndromic surveillance in developed regions encompasses the use of pre-diagnostic information in a near real time fashion for further investigation for public health action.  Syndromic surveillance is widely used in North America and Europe, and is typically thought of as a highly complex, technology driven automated tool for early detection of outbreaks.  Nonetheless, applications of syndromic surveillance using technology appropriate for the setting are being used worldwide to augment traditional surveillance, and may enhance compliance with the revised IHR.

Objective:

To review applications of syndromic surveillance in developing countries

Most research in syndromic surveillance has emphasized early detection, but clinical diagnosis of the index case will tend to occur before detection by syndromic surveillance for certain types of outbreaks [1]. Syndromic surveillance may, however, still play an important role in rapidly characterizing the outbreak size because there will be additional non-diagnosed symptomatic cases in the medical system when the index case is identified. Other authors have shown that the temporal pattern of symptomatic cases could be used to project the total outbreak size, but their approach requires a priori knowledge of the incubation curve for the specific anthrax strain and exposure level [2]. In this paper, we focus on estimating the number of non-diagnosed symptomatic cases at the time of detection without making assumptions about the exposure level or disease course.

Objective 

Upon detection of an inhalational anthrax attack, a critical priority for the public health response would be to characterize the size and extent of the outbreak. Our objective is to assess the potential role of syn-dromic surveillance in estimating the outbreak size.

This paper describes a methodology for applying natural language parsing (NLP) technologies, originally developed for analyzing biomedical journal articles, to the monitoring of emergency department patient charts for infectious diseases of interest.

The paper outlined the major findings and statistical results of syndromic surveillance system in Taiwan since year 2000.

Since July 2004 the BioSense program at the Centers for Disease Control and Prevention (CDC) has received data from DoD military and VA outpatient clinics (not in real time). In January 2006 real-time hospital data (e.g. chief complaints and diagnoses) was added. Various diagnoses from all sources are binned into one or more of 11 syndrome categories.

Objective

This paper'­s objective is to compare syndromic categorization of newly acquired real-time civilian hospital data with existing BioSense data sources.

In order to be best prepared to identify health events using electronic disease surveillance systems, it is vital for users to participate in regular exercises that realistically simulate how events may present in their system following disease manifestation in the community. Furthermore, it is necessary that users exercise methods of communicating unusual occurrences to other intra and extra-jurisdictional investigators quickly and efficiently to determine first, if an event actually exits and if one does its characteristics. A simulation exercise held in the National Capital Region (NCR) in the spring of this year exercised a novel format for engaging users while testing the utility of an embedded event communication tool.

Objective

This is a description of an innovative design and format used to exercise public health preparedness in a tri-jurisdictional disease surveillance system in the spring of 2006.

Since we donít know when such a disaster may occur, we have to perform this syndromic surveillance routinely, and thus the system should be automatic. Namely, information is drawn from electronic medical records (EMR), and is statistical analyzed, aberrations are detected and then Results are reported by e-mail or HP. It is preferable that this system be fully automatic. Though many systems of this type have been developed in the US, they have not been well developed in Japan. So as to develop such a system, we made a prototype system and have been performing prospectively and evaluating the system.

While several authors have advocated wavelets for biosurveillance, there are few published wavelet method evaluations using real syndromic data. Goldenberg et al. performed an analysis using wavelet predictions as a way of detecting a simulated anthrax outbreak. The commercial RODS application uses averaged wavelet levels to normalize for longterm trends and negative singularities. In line with the implementation in and in contrast to, we introduce two preconditioning steps to account for the strong day-of-week effect and holidays, and then use all levels of the wavelets to predict or alarm.

Objective

Syndromic data are created by processes that operate on different time scales (daily, weekly, or even yearly) and can include events of different durations from a 1-2 day outbreak of foodborne illness to a more gradual, protracted flu season. The duration of an outbreak caused by a new pathogenic strain or a bioterrorist attack is indeterminate. Wavelets are well suited for detecting signals of uncertain duration because they decompose data at multiple time and frequency scales. This study evaluates the use of several wavelet-based algorithms for both time series forecasting and anomaly detection using real-world syndromic data from multiple data sources and geographic locations.

A comprehensive definition of a syndrome is composed of direct (911 calls, emergency departments, primary care providers, sensor, veterinary, agricultural and animal data) and indirect evidence (data from schools, drug stores, weather etc.). Syndromic surveillance will benefit from quickly integrating such data. There are three critical areas to address to build an effective syndromic surveillance system that is dynamic, organic and alert, capable of continuous growth, adaptability and vigilance: (1) timely collection of high quality data (2) timely integration and analysis of information (data in context) (3) applying innovative thinking and deriving deep insights from information analysis. In our view there is excessive emphasis on algorithms and applications to work on the collected data and insufficient emphasis on solving the integration challenges. Therefore, this paper is focused on information integration.

Objective

EII is the virtual consolidation of data from multiple systems into a unified, consistent and accurate representation. An analyst working in an EII environment can simultaneously view and analyze data from multiple data sources as if it were coming from one large local data warehouse. This paper posits that EII is a viable solution to implement a system covering large areas and disparate data sources for syndromic surveillance and discusses case studies from environments external to health.