Foodborne Illness

Shigella remains highly infectious in the United States and rapid detection of Shigella outbreaks is crucial for disease control and timely public health actions. The New York State Department of Health (NYSDOH) implemented a Communicable Disease Electronic Surveillance System (CDESS) for local health departments (LHDs) to collect clinical and laboratory testing information and supplement epidemiologic information for the patients from New York State, excluding New York City, with infectious diseases. The CDESS includes reported cases that are involved in outbreaks and which constituted the base for identifying any outbreak. The selection of a fitted outbreak detection method would play a critical role in enhancing disease surveillance.

Objective

To explore the possibility of using statistical methods to detect Shigella outbreaks, assess the effectiveness of the methods to signal real outbreaks, provide manageable information for follow-up activities and avoid unnecessary surveillance work.

The Decrease Epidemiological Threats with Environmental Controls and Testing (DETECT) program consisted of a four month initiative to provide a two-day in-depth training modified from the National Environmental Health Association (NEHA) Epi-Ready training. In each Public Health Area, Central Office Epidemiology (EPI), Environmental, and Laboratory staff presented FBO information to county and area Public Health staff. To train and equip field staff in the management of foodborne outbreak processes, to establish a Foodborne Outbreak (FBO) Team in each Public Health Area, to establish defined processes and protocols for FBO for improved response.

Argus is an event-based, multi-lingual surveillance system which captures and analyzes information from publicly available Internet media. Argus produces reports that summarize and contextualize indications and warning (I&W) of emerging threats, and makes these reports available to the system's users. The significance of the Escherichia coli (EHEC) outbreak analyzed here lies primarily in the fact that it raised epidemiological questions and public health infrastructure concerns that have yet to be resolved, and required the development of new resources for detecting and responding to newly-emerging epidemics.

Objective

To demonstrate how event-based biosurveillance, using direct and indirect I&W of disease, provides early warning and situational awareness of the emergence of infectious diseases that have the potential to cause social disruption and negatively impact public health infrastructure, trade, and the economy. Specifically, tracking of I&W during the 2011 enterohaemorrhagic EHEC O104:H4 outbreak in Germany and Europe was selected to illustrate this methodology.

Syndromic surveillance of livestock animals at points of concentration, such as livestock markets, has the potential to provide early detection of endemic, zoonotic, transboundary, environmental, and newly emerging animal diseases and to identify animal health trends. In the United States, inspectors at livestock auction markets routinely observe animals for clinical signs of disease, but do not usually document the number of cattle or clinical signs observed. The purpose of this pilot program was to demonstrate the benefit and feasibility of utilizing inspectors at livestock markets to record the total number of animals observed and the number displaying body system-associated clinical signs/syndromes (BSAS). This project is a Federal and State partnership between the United States Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) Veterinary Services (VS) and the Texas Animal Health Commission (TAHC). The livestock market syndromic surveillance pilot project is part of a broader effort in VS to develop and monitor non-traditional animal health surveillance data streams. These data streams include clinical sign information from private veterinary practitioners, veterinary diagnostic laboratory test requests, and livestock slaughter facility condemnations.

Objective

To describe the design and implementation of a syndromic surveillance program in selected cattle markets in Texas, USA.

The monitoring of whole or partial carcass condemnations can constitute a valuable indirect indicator of herd health (1). Nevertheless, systematic collection and use of such data for epidemiological surveillance is scarce within the European Union (2).

Objective

We evaluate Swiss abattoir data for integration in a national syndromic surveillance system for production animals. More specifically, we identify gaps in the current federal meat inspection database and provide suggestions for its improvement.

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.

A retrospective chart review was performed to examine chief complaint, syndrome classification and discharge diagnoses of laboratory confirmed Salmonella Enteritidis phage type (PT) 13 cases who visited the Emergency Departments (ED) of two local hospitals during a province-wide outbreak in Ontario in the fall of 2005. This assessment was used to assess the sensitivity of the ED syn-dromic surveillance system to detect a local foodborne outbreak, and to modify syndrome classification.

To construct and validate a prediction algorithm that detects early increases in laboratory reports of enteric illnesses on the basis of calls to a poison control center reporting suspected foodborne illnesses.

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.