ISDS Conference

Although Electronic Surveillance System for the Early Notification of Community Based Epidemics (ESSENCE) provides tools to detect a significant alert regarding an unusual public health event, combining that information with other surveillance data, such as 911 calls, school absenteeism and poison control records, has proved to be more sensitive in detecting an outbreak. On Monday, June 16, Florida Poison Information Network, which takes after-hours and weekend calls for Miami-Dade County Health Department (MDCHD), contacted the Office of Epidemiology and Disease Control about five homeless persons that visited the same hospital simultaneously with gastrointestinal symptoms on Saturday, June 14. Poison control staff asked MDCHD to investigate further to determine whether it was an outbreak.

Objective

To illustrate how MDCHD utilized ESSENCE in order to track a gastrointestinal outbreak in a homeless shelter.

Emergency Department (ED) syndromic surveillance data for influenza-like illness (ILI) have been found to provide timely and representative information about current influenza activity in NYC. DOHMH monitors visits daily from 50 of 61 EDs, capturing about 94% of all ED visits in NYC. Since January 1, 2007, DOHMH has been receiving disposition data (e.g., hospitalized, discharged) from a subset of EDs. Currently, disposition data is received from 37 EDs (approximately 1/3 of all visits by the next day and >60% of all visits within 1 week).

More detailed hospitalization data, including date, demographics, and diagnosis on all NYC hospitalizations are routinely collected by the New York State Department of Health Statewide Planning and Research Cooperative System (SPARCS). SPARCS is subject to a 2-3 year reporting lag, thus limiting its timeliness and prospective use. However, SPARCS data from prior to January 1, 2007 can supplement the ED syndromic data to develop a model for ILI hospitalizations and calculate excess hospitalizations attributable to influenza that can be used in near realtime, particularly in the event of a pandemic.

Objective

To use ED syndromic surveillance data to monitor hospitalizations for ILI and calculate excess hospitalizations attributable to influenza.

Difficulties in timely acquisition and interpretation of accurate data on communicable diseases can impede outbreak detection and control. These limitations are of global importance: they contribute to avoidable morbidity, economic losses, and social disruption; and, in a globalized world, epidemics can spread rapidly to other susceptible populations.

SARS and the potential for an influenza pandemic highlighted the importance of global disease surveillance. Similarly, the World Health Organization’s newly implemented 2005 International Health Regulations require member countries to provide notification of emerging infectious diseases of potential global importance. The challenges arise when Ministries of Health (MoH) in resource-poor countries add these mandates to already over-burdened and under-funded surveillance systems. Appropriately adapted, electronic disease surveillance systems could provide the tools and approaches MOHs need to meet today’s surveillance challenges.

Objective

In this presentation we will discuss the concept of electronic disease surveillance in resource-poor settings, and the issues to be considered during system planning and implementation.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) serves public health users across NC at the local, regional and state levels, providing early event detection and situational awareness capabilities. At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health. NC DETECT receives 10 different data feeds daily including emergency department visits, emergency medical service runs, poison center calls, veterinary laboratory test results, and wildlife treatment.

In order to fulfill our users’ needs with NC DETECT’s limited staff, business intelligence tools are utilized for the acquisition and processing of our multiple, disparate data sources as well as reporting our findings to our numerous end users. Business intelligence can be described as a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.

Objective

We report here on how NC DETECT uses business intelligence tools to automate both data capture and reporting in order to run a comprehensive surveillance system with limited resources.

OBJECTIVE

A “whole-system facsimile” recreates a complex automated biosurveillance system running prospectively on real historical datasets. We systematized this approach to compare the performance of otherwise identical surveillance systems that used alternative statistical outbreak detection approaches, those used by CDC’s BioSense syndromic system or a popular scan statistics.

There has been much recent interest in using disease signatures to better recognize disease outbreaks. Conversely, the metrics used to describe these signatures can also be used to better characterize the outbreaks. Recent work at the New York City Department of Health has shown the ability to identify characteristic age-specific patterns during influenza outbreaks. One issue that remains is how to implement a search for such patterns using prospective outbreak detection tools such as SatScan.

A potential approach to this problem arises from another currently active research area: the simultaneous use of multiple datastreams. One form of this is to disaggregate a data stream with respect to a third variable such as age. Two drawbacks to this approach are that the categories used to make the streams have to be defined a priori and that relationships between the streams cannot be exploited. Furthermore, the resulting description is less rich as it describes outbreaks in a few non-overlapping age-specific streams. It would be desirable to look for age specific patterns with the age groupings implicitly defined.

Objective

This paper presents an implementation of a citywide SatScan analysis that uses age as a one-dimensional spatial variable. The resulting clusters identify age-specific clusters of respiratory and fever/flu syndromes in the New York City Emergency Department Data.

One of the common tasks faced by the U.S. Department of Agriculture (USDA) food safety analysts is to estimate the risk of observing positive outcomes of microbial tests of food samples collected at the slaughter and food processing establishments. Resulting risk estimates can be used, among other criteria, to drive allocation of FSIS investigative resources. The Activity From Demographics and Links (AFDL) algorithm is a computationally efficient method for estimating activity of unlabeled entities in a graph from patterns of connectivity of known active entities, and from their demographic profiles. It has been successfully used in social network analysis and intelligence applications. In order to test its utility in the food safety context, we treat a co-occurrence of the same strain of bacteria (in particular a specific serotype of Salmonella) in samples taken at different establishments at roughly the same time, as a link in the graph spanning all of the USDA controlled establishments. Now, given the historical patterns of linkage and the information about the distribution of the currently observed microbial positives (which make the corresponding establishments “active” in the AFDL terminology), we aim at predicting which of the remaining establishments are likely to also report positive results of tests. Even though such definition of a link produces uncertain data given that the co-occurrences of specific test results at different establishments may be purely coincidental and our analysis does not attempt to distinguish them from truly correlated instances, we expect that using this inherently noisy data in combination with demographic features of establishments, would lead to useful predictability of microbial events.

Objective

The objective of the research summarized in this paper is to evaluate utility of the AFDL in predicting likelihood of positive isolates obtained from microbial testing of food samples collected at the USDA controlled establishments.

Surveillance of individual data streams is a well-accepted approach to monitor community incidence of infectious diseases such as influenza, and to enable timely detection of outbreaks so that control measures can be applied. However the performance of alerts may be improved by simultaneously monitor a variety of data sources, or multiple streams (eg from different geographic locations) of the same type, rather than monitoring only aggregate data. Rates of influenza-like illness in subtropical settings typically show greater variability than in temperate regions.

Objective

This paper describes the use of time series models for simultaneous monitoring of multiple streams of influenza surveillance data.

In Connecticut (CT), several syndromic surveillance systems have been established by the Department of Public Health (DPH) to detect and monitor potential public health threats. The emergency department syndromic surveillance (EDSS) routinely categorizes chief complaint data into pre-defined syndrome categories, and also has the flexibility to define syndromes in real-time. Thus, DPH can use this system for situational awareness during public health events. Several recent events provided an opportunity to evaluate EDSS for this purpose: 1) two cases of cutaneous anthrax in CT in September 2007; 2) national and local media attention surrounding MRSA infections and published research in October 2007 and 3) the introduction of rotavirus vaccine through the Vaccines for Children Program in July 2006 following its licensing in February 2006.

Objective

To evaluate the performance of the CT EDSS system for situational awareness during specific public health events.

On June 22, 2007 increases in over-the-counter (OTC) electrolyte and child anti-fever medication sales were detected through routine OTC surveillance. Increases in emergency department (ED) data for gastrointestinal (GI) illness among <5 year olds were observed on June 23 and 24. Further analyses indicated clustering within one borough of NYC, with three EDs having most of the visits. Because NYC has had limited success in detecting spatial outbreaks using syndromic surveillance in the past, an investigation was not immediately initiated.

DOHMH was notified of a multi-state outbreak of S. wandsworth suspected to be associated with the snack food Veggie Booty® on June 26. Cases were predominantly young children and included 8 confirmed cases among NYC residents with onset dates from March 4 – May 19.

Objective

To determine whether increases in GI illness detected through OTC drug sales and ED syndromic surveillance were linked to a multi-state outbreak of S. wandsworth associated with the consumption of Veggie Booty® snack food.