Syndromic Surveillance

A review of the development of veterinary syndromic surveillance in 2011 indicated that the field was incipient, but fast growing. Many countries are starting to explore different sources of data for syndromic surveillance. Some of the data streams evaluated share similarities with those used in public health syndromic surveillance, such as clinical records and laboratory data. However, many unique animal data sources have arisen, such as abattoir and carcass collection data. We suggest there are three main challenges in the current development of animal syndromic surveillance: The lack of standards in disease classification; The development of statistical methods appropriate to deal with animal data; The creation of ready-to-use tools that employ these statistical methods.

Objective

To summarize the challenges in the development of syndromic surveillance tools in veterinary medicine, and describe the development of an R package to address some of the current gaps.

Air pollution is well documented to cause adverse health effects in the population. Epidemiological/toxicological studies have demonstrated that air pollution is associated with various adverse health outcomes, ranging from mortality to subclinical respiratory symptoms. Classical epidemiological studies of the health effects of air pollution are typically retrospective. In order to assess the effectiveness of any public health messages or interventions in a timely manner there is a need to be able to systematically detect any health effects occurring in real-time. The UK syndromic surveillance systems are coordinated by Public Health England (PHE) and are used to monitor infectious diseases in real-time. This study is the first in the UK to explore whether syndromic surveillance systems can detect public health impacts associated with air pollution events.

Objective: This study examined whether the current UK real-time syndromic surveillance systems can detect public health impacts associated with air pollution events such as fires and ambient air pollution episodes.

Co-financed by the European Commission through the Executive Agency for Health and Consumers, the European Triple-S project (Syndromic Surveillance Survey, Assessment towards Guidelines for Europe) was launched in 2010 for a 3-year period and includes 24 organizations in 13 countries. Numerous European countries have created SyS systems. These systems analyze and report their SyS findings to local, regional or national public-health authorities in accordance with their national priorities. But the country outputs are not systematically reported and compared at the EU level, hindering a global overview and interpretation of the health situations observed in different regions or countries in Europe. The Triple-S project has thus proposed a strategy for coordinating the comparison and interpretation of SyS information across Europe to produce a Europe-wide epidemiological picture of a given health event in a timely manner, and thereby support coordinated public-health action.

Objective

To present a proposal for coordinating syndromic surveillance (SyS) systems operated by European countries and for comparing findings from these systems.

Syndromic surveillance has great advantages in promoting the early detection of infectious disease outbreak and enabling the real-time tracking of on-going epidemics. However, establishing a syndromic surveillance system required huge investment in money, information system, manpower and capacity building activities, which remains a big challenge in resource-limited areas. Funded by European Union's 7th Framework Programme, a syndromic surveillance system named 'ISSC' was prepared to be built and incorporated with the existing case report system in rural Jiangxi Province of China.

Objective

Before the start of ISSC project, a pilot investigation was carried out among the candidate surveillance units (health care facility, pharmacy and primary school) and related stakeholders to assess their capacity and potential needs with regard to the implementation of ISSC system, so as to design customized capacity building and training strategies.

The CDC's BioSense 2.0 system is designed with a user-centered approach, where the needs and requests of the users are part of its continued development. User requirements were gathered extensively to help design BioSense 2.0 and users continue to submit feedback which is used to make improvements to the system. However, in order to ensure that these needs are gathered in a formal and ongoing way, the BioSense 2.0 Governance Group, comprised primarily of state and local public health representatives, was established to advise the CDC on the development of BioSense 2.0. The Governance Group (GG) understands that to make recommendations having direct relevance and utility to the community, they must engage public health jurisdictions which use BioSense 2.0. To that end, the GG has conducted three surveys of the BioSense 2.0 community. The survey results will help inform the group's prioritized recommendations to the CDC.

Objective

In this presentation we discuss the findings and lessons learned from these surveys.

National telephone health advice service data have been investigated as a source for syndromic surveillance of influenza-like illness and gastroenteritis . Providing a high level of coverage, the system might serve as an early outbreak detection tool. We have previously found that telephone triage service data of acute gastroenteritis was superior to web queries as well as over-the-counter pharmacy sales of anti-diarrhea medication to detect large water- and foodborne outbreaks of gastrointestinal illness in Sweden during the years 2007–2011 (4). However, information is limited regarding the usefulness, characteristics, and signal properties of local telephone triage data for monitoring and identifying outbreaks at the community level.

Objective

Our aim was to use telephone triage data to develop a model for community-level syndromic surveillance that can detect local outbreaks of acute gastroenteritis (AGE) and influenza-like illness (ILI) and allow targeted local disease control information.

The Houston Department of Health Department of Health and Human Services (HDHHS) monitors emergency departments (ED) chief complaints across the Houston metropolitan area, Harris County, and the surrounding jurisdictions by Real-time Outbreak Disease Surveillance (RODS). The influenza-like illnesses (ILI) data is collected by sentinel surveillance provider network of 12 physicians and RODS, an electronic syndromic surveillance database consisting of about 30 EDs in metropolitan Houston. Previous research indicates that there is a relationship between new HIV diagnoses and neighborhood poverty. However, there is limited research on health disparity to investigate the association between influenza-like illnesses (ILI) and social determinants of health (SDH), such as poverty.

Objective

To investigate the association between social determinants of health and influenza-like illnesses in Houston/Harris County and to identify neighborhoods for targeted surveillance or interventions.

The Florida Department of Health (FDOH) electronically receives both urgent care center (UCC) data and hospital emergency department (ED) data from health care facilities in 43 of its 67 counties through its Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL). Each submitted record is assigned to one of eleven ESSENCE Syndrome categories based on parsing of chief complaint data. The UCC data come from 22 urgent care centers located in Central Florida, and the ED data come from 161 hospitals located across the state. Traditionally, the data from these two sources are grouped and viewed together. To date, limited investigation has been carried out on the validity of grouping data from UCCS and EDs in ESSENCE-FL. This project will investigate and describe the differences between the data received from these two sources and provide best practices for grouping and analyzing these data sources.

Objective

To identify best practices for grouping emergency department and urgent care data in a syndromic surveillance system.

As technology advances, the implementation of statistically and computationally intensive methods to detect unusual clusters of illness becomes increasingly feasible at the state and local level [2]. Bayesian methods allow for the incorporation of prior knowledge directly into the model, which could potentially improve estimation of expected counts and enhance outbreak detection. This method is one of eight being formally evaluated as part of a grant from the Alfred P. Sloan Foundation.

Objective

To adapt a previously described Bayesian model-based surveillance technique for cluster detection [1] to NYC Emergency Department (ED) visits.