Syndromic Surveillance

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.

To highlight the key role of Emergency Department syn-dromic surveillance in linking acute care and public health, thus enabling collaborative detection, monitoring and management of a local food borne outbreak.

This paper describes a simple technique for utilizing linked health information in syndromic surveillance. Using knowledge of which patient encounters resulted in laboratory test requests and prescriptions may improve sensitivity and specificity of detection algorithms.

To evaluate four algorithms with varying baseline periods and adjustment for day of week for anomaly detection in syndromic surveillance data.

The use of spatially-based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health datasets, by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of blurring patient locations on detection of spatial clustering as measured by the SaTScan purely-spatial Bernoulli scanning statistic.

Syndromic surveillance has traditionally been used by public health to supplement mandatory disease reporting. The use of chief complaints as a data source is common for early event detection. Though some public health syndromic surveillance systems allow individual hospitals to view their own data through a web interface, many ICPs have the experience and knowledge-base to conduct their own surveillance and analysis internally. Additionally, they often have interests specific to their hospital which may motivate them to conduct additional syndromic surveillance projects themselves. Lastly, in many cases, ICPs are better able to investigate problems with chief complaint syndrome categorization and aberrations within their own facility before notification of public health staff. A good understanding of the foundation of syndromic surveillance by hospital ICPs can be extremely beneficial when paired with public health to investigate possible cases and outbreaks. ICPs at Greenville Hospital System (GHS), composed of 1110 beds, a level I trauma center with an average of 85,000 visits per year plus three smaller outlying emergency rooms, has had interest in syndromic surveillance for many years and collected data manually for trend analysis using Microsoft Excel to monitor chief complaint data since August 2003.

Objective

Demonstrate the use and benefit to hospital-based infection control practitioners (ICP) of chief complaint data for syndromic surveillance in partnership with public health to assist with traditional public health disease investigations.

To apply syndromic techniques in assessing whether the false-positive rate (FP rate) of a rapid oral HIV test, routinely used for screening in New York Cityís STD clinics, deviated from the manufacturerís claim; results of which have important implications for assessing clinical test performance.