Evaluation of Syndromic Surveillance

Despite the global emergence of syndromic surveillance systems and theories in recent years, the practical performance of this surveillance method under real circumstances had been rarely evaluated, especially in resource-limited areas. Since April 2012, a syndromic surveillance system named 'ISSC' has been established among health care facilities, pharmacies and primary schools in two rural counties (County A & B) of Jiangxi Province, China.

Objective

Our aim was to explore the practical performance of ISSC system through investigating the characteristics and verifying results of alert signals raised during real-time surveillance periods.

The time series of syphilis cases has been studied at the country and state level at the yearly basis, and it has been found that syphilis has a periodicity of approximately 10 years. However, to inform prevention efforts, it is important to understand the short term dynamics of disease activity.

Objective

(i) To forecast syphilis cases per state in the US to support early containment of outbreaks. (ii) For each state, to determine which states are most correlated, to find "bellwether" states to inform surveillance efforts. (iii) To determine a small collection of states whose syphilis incidence patterns are most closely correlated with all the states.

The HEDSS system was implemented in 2004 to monitor disease activity [1]. Twenty of 32 emergency departments (ED) and 1 urgent care clinic provide data. Chief complaints are routinely categorized into 8 syndromes. Although previous studies have shown that ED syndomic surveillance is not useful for early detection of GI outbreaks [2], it has demonstrated utility in monitoring trends in seasonal norovirus activity[3]. An evaluation to assess the utility of HEDSS to characterize endemic and out-break levels of GI illness has not been previously conducted in Connecticut.

Objective

To evaluate the utility of the Connecticut Hospital Emergency Department Syndromic Surveillance System (HEDSS) to monitor gastrointestinal (GI) illness in the community.

During responses, an electronic medical record (EMR) allows federal emergency response staff to view and evaluate near real-time clinical encounter data. Analysis of EMR patient data can enhance situational awareness and provide decision advantage for headquarters' staff during both domestic and international events. The EMR was utilized by field medical personnel during the response to the Haiti earthquake.

Objective

To describe some uses of EMR data for surveillance and situational awareness during disaster response.

Public health surveillance (also called field epidemiology) as defined by Centres for Disease Control and Prevention (CDC) is the ongoing systematic, collection, analysis, and interpretation of outcome specific data essential to the planning , implementation and evaluation of public health practises closely integrated with the timely dissemination of this data to those who need to know(1). This modern concept of surveillance includes 3 main features; the systematic collection of all relevant data, orderly consolidation and evaluation of this data and the prompt dissemination of the results to those who need to know (2). The IDSR is a strategy of the WHO Afro region adopted by the member states in 1998 as a regional strategy for strengthening weak national surveillance systems in the African region (3, 4). The DSNOs under the supervision of the Medical Officers of Health (MOHs) are responsible for surveillance activities within their Local Government catchment area. Therefore their role is very crucial to the success of the IDSR strategy. 

Objective

To evaluate the immediate impact of training on Integrated Disease Surveillance and Response (IDSR) on the knowledge of Disease Surveillance and Notification Officers (DSNOs) and the demographic characteristics associated with the change in knowledge.

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.

Public health surveillance systems are constantly facing challenges of epidemics and shortage in the health care workforce. These challenges are more pronounced in developing countries, which bear the greatest burden of disease and where new pathogens are more likely to emerge, old ones to reemerge and drug-resistant strains to propagate. In August 2008, a mobile phone based surveillance system was piloted in 6 of the 23 districts in the state of AP in India. Health workers in 3832 hospitals and health centers used mobile phones to send reports to and receive information from the nationwide Integrated Disease Surveillance Project (IDSP). Like in many other states, the IDSP in AP is facing many operational constraints like lack of human resource, irregular supply of logistics, hard to reach health facilities, poor coordination with various health programs and poor linkages with non-state stakeholders. The mobile phone based surveillance system was an attempt to tackle some of the barriers to improving the IDSP by capitalizing on the exponential growth in numbers as well as reach of mobile phones in the state. Promising results from the pilot of the system led AP state to extend it to about 16,000 reporting units in all 23 districts. This study evaluates how the system has affected the efficiency and effectiveness of IDSP in the state.

Objective

To assess the impact of use of mobile phones use on the efficiency and effectiveness of the Integrated Disease Surveillance Project (IDSP) in the state of Andhra Pradesh (AP)

The Extended Syndromic Surveillance Ontology (ESSO) is an open source terminological ontology designed to facilitate the text mining of clinical reports in English [1,2]. At the core of ESSO are 279 clinical concepts (for example, fever, confusion, headache, hallucination, fatigue) grouped into eight syndrome categories (rash, hemorrhagic, botulism, neurological, constitutional, influenza-like-illness, respiratory, and gastrointestinal). In addition to syndrome groupings, each concept is linked to synonyms, variant spellings and UMLS Concept Unique Identifiers. ESSO builds on the Syndromic Surveillance Ontology [3], a resource developed by a working group of eighteen researchers representing ten syndromic surveillance systems in North America. ESSO encodes almost three times as many clinical concepts as the Syndromic Surveillance Ontology, and incorporates eight syndrome categories, in contrast to the Syndromic Surveillance Ontology's four (influenza-like-illness, constitutional, respiratory and gastrointestinal). The new clinical concepts and syndrome groupings in ESSO were developed by a board-certified infectious disease physician (author JD) in conjunction with an informaticist (author MC).

Objective

In order to evaluate and audit these new syndrome definitions, we initiated a survey of syndromic surveillance practitioners. We present the results of an online survey designed to evaluate syndrome definitions encoded in the Extended Syndromic Surveillance Ontology.

In response to the terrorist attack of September 11, 2001, the NH Department of Health and Human Services (NH DHHS) engaged state and external partners in the design of an early warning surveillance system to support bioterrorism and emergency preparedness. Initially, NH DHHS began collecting four syndrome counts from thirteen hospital Emergency Departments (ED) by fax. Automation began in 2002, when an over the counter (OTC) syndromic surveillance pilot system was implemented by Scientific Technologies Corporation (STC). In 2003-2004 this system, the Syndromic Tracking and Encounter Management System (STEMS), was expanded to include school absentee and occupational health reports. Over time, an internal Death Data application was automated to query vital record deaths, and in 2005 a real-time ED surveillance pilot, the Automated Hospital ED Data System (AHEDD), was developed by STC to replace manual ED surveillance. Over the past decade NH continued to expand the original concept with innovative approaches to identify undetected or under reported disease outbreaks.

Objective

To illustrate development of syndromic surveillance in NH, share innovation experience with the public health community, and contribute to the syndromic surveillance body of knowledge in the new public health Information Technology landscape.

A devastating cholera outbreak began in Haiti in 2010. Sequencing of Vibrio cholerae isolates showed that the epidemic was likely the result of the introduction of cholera from a distant geographic source. The same strain of cholera was detected in other countries within 100 days. The unique instigation and geographic spread of this epidemic highlight the need for improvements in timely global outbreak surveillance. Novel information sources have been shown to provide early information about public health events and disease epidemiology. Particularly, volume of Internet metrics such as web searches or micro-blogs have been shown to be a good corollary for public health events. In this study, we evaluate geographic trends in online social media following an infectious disease outbreak to determine whether this may enable prediction of secondary outbreak locations.

Objective

To evaluate the association between and develop a risk model relating geographic trends of social media and spread of an infectious disease outbreak.