Aberration Detection

The Early Aberration Reporting System was developed at the Centers for Disease Control and Prevention to help assist local and state health officials to focus limited resources on appropriate activities of public health surveillance. Outbreaks of

infectious diseases are indicated in multiple spatial and temporal data sources, such as emergency department visits, drug store sales, and ambulatory clinic visits. Based on this premise, we provided correlated data sets and investigated disease clusters.

Objective

We present a pilot study of simulation of correlated outbreak signals for early aberration reporting and evaluating detection methods.

To recognize outbreaks so that early interventions can be applied, BioSense uses a modification of the EARS C2 method, stratifying days used to calculate the expected value by weekend vs weekday, and including a rate-based method that accounts for total visits. These modifications produce lower residuals (observed minus expected counts), but their effect on sensitivity has not been studied.

Objective

To evaluate several variations of a commonlyused control chart method for detecting injected signals in 2 BioSense System datasets.

By capturing the spatio-temporal organization of the data using a graph, GraphScan avoids the challenges associated with trying to “fit” incoming data into moving windows of predefined shapes and sizes. Whereas the popular space-time permutation scan statistic [1] attempts to find clusters within spacetime volumes of predefined shape, GraphScan employs no such preconceptions about the form of the clusters.  Instead, clusters are allowed to “evolve” freely to better reflect the structural properties of the data.  Moreover, GraphScan is capable of tracking possible causal relationships between spatio-temporal events.

Objective

This paper proposes an efficient and flexible algorithm applicable to spatio-temporal aberration detection in public health data.

The City of Atlanta, volunteer organizations, and the faith community operate several homeless shelters throughout the city. Services available at these shelters vary, ranging from day services, such as meals, mail collection, and medical clinics, to overnight shelter accommodations. In addition to the medical clinics available at these facilities, the Atlanta homeless population also utilizes emergency departments in Fulton County for their health care needs.

Objective

This paper describes a cluster of Streptococcus pneumoniae infections identified through emergency department syndromic surveillance.

Traditionally Emergency Department syndromic surveillance methods have relied on ICD-9 codes and chief complaints. The implementation of electronic medical record keeping has made much more information available than can potentially be used for surveillance. For example, information such as vital signs, review of systems and physical exam data are being stored discreetly. These data have the potential to detect specific diseases or outbreaks in a community earlier that the traditionally used ICD-9 and chief complaint.

Objective

This paper describes the integration of novel data sets from an Emergency Department Electronic Medical Record into a syndromic surveillance application.

After the SARS outbreak in 2003, Beijing established Fever Clinics in major hospitals for the early detection of potential respiratory disease outbreaks. The data collection in Fever Clinics contains the basic patient information, body temperature, cough, and breath condition, as well as a primary diagnosis. Since the symptoms and diagnosis are mainly recorded in free text format, it is very difficult to use for data analysis. Because of the problems in data processing, the data collection has decreased.

Objective

This paper describes the methodology in the development of an Integrated Surveillance System for Beijing, China.

The Public Health Agency of Canada is currently utilizing a syndromic surveillance prototype called the Canadian Early Warning System (CEWS). This system monitors several live data feeds, including emergency room chief complaint records from all seven local hospitals, Telehealth (24/7 nurse hotline) calls, and over-the-counter drug sales from a number of the large chain drug stores. Data trends are analysed for aberrations as early indicators of outbreak events. Collaborators on this Winnipeg, Manitoba-based pilot include the Winnipeg Regional Health Authority and IBM Business Solutions. Algorithms currently in CEWS include the 3, 5 and 7-day moving averages, CUSUM and the CDC’s EARS. We seek to investigate the performance of these algorithms in view of the fact that their detection ability may be dependent upon data source and/or the type of outbreak event.

Objective

To determine the sensitivity, specificity and days to detection of several commonly used algorithms in syndromic surveillance systems.

We developed, implemented and evaluated Meningitis and Encephalitis (M/E) syndrome case definitions based on electronic Emergency Department (ED) chief complaint data; and assessed their ability to detect aberrations that correspond with M/E outbreaks.

The H5N1 avian influenza virus is now considered endemic in poultry in some parts of the world and the continued exposure in humans suggests that the risk of the virus evolving into a more transmissible agent in humans − a step towards worldwide pandemic – remains high. Universities, with large assembly of students and student movements determined by the class schedules and travel routes between classes, in addition to the faculty and staff located in close proximity, are extremely susceptible environments to the spread of pandemic events. Moreover, large universities in the U.S. often have a good proportion of international students, who commute to/from their home country within their study period. Therefore, a good surveillance system to detect disease outbreaks is essential to support a system that is robust to this high impact low probability disruptive event.

Objective

This paper describes a framework for an aberration detection method − change-point analysis for mean and variance − adapted for Poisson-distributed data, for syndromic surveillance in an academic environment.

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