Syndromic Surveillance

Emphasis has been placed on the improvement of existing surveillance systems and developing innovative new surveillance systems around the world after the events of 9/11 in 2001, severe acute respiratory syndrome (SARS) in 2003. Investments have not only been made in traditional public health surveillance systems but also novel approaches such as syndromic surveillance systems. It is important to have timely, relevant evaluations of these systems to understand their usefulness. While most of the published syndromic surveillance systems evaluations looked at technical attributes of the system i.e. accuracy [1]. Other aspects such as utility, acceptability and feasibility[2] as given in the generic Centers for Disease Control and Prevention evaluation framework[3] were not always explicitly addressed. Moreover, most of syndromic surveillance systems are established in developed countries or areas that already have other types of advanced surveillance systems. There are few public reports of the development and implementation of a syndromic surveillance system in rural China.

Objective

To identify the different acceptability groups of village doctors of an integrated syndromic surveillance system (ISS) and to explore factors influencing acceptability from village doctors' perspective before ISS launched.

In light of recent communicable disease outbreaks, the ability of Florida Department of HealthÕs (FDOH) syndromic surveillance system, ESSENCE-FL, to identify emergent disease outbreaks using reportable disease data and algorithms originally designed for emergency department chief complaint data was examined. Preliminary work on this analysis presented last year was recently updated and expanded to include additional diseases, further levels of locale, and detector algorithm comparisons. Cases are entered into Merlin, the Bureau of EpidemiologyÕs secure web-based reporting and epidemiologic analysis system, by all 67 county health departments and the de-identified case data are sent hourly to ESSENCE-FL. These data are then available for ad hoc queries, allowing users to observe unusual changes in disease activity and assist in timely identification of infectious disease outbreaks. Based on system algorithms, weekly case tallies are assigned an increasing intensity awareness status from normal to alert and are monitored by county and state epidemiologists to guide timely disease control efforts, but may not by themselves be definitive actionable information.

Objective

To determine if there is an association between known outbreak activity and ESSENCE generated alerts. 

In 2012, an outbreak of Mycobacterium chelonae infections in tattoo recipients in Rochester, NY was found to be associated with premixed tattoo ink contaminated before distribution.1 In May 2012, a case of M. chelonae was reported in a New York City (NYC) resident who received a tattoo with ink alleged to have been diluted with tap water. When a second case of M. chelonae in a tattoo recipient was reported in March 2013, an investigation was initiated. M. chelonae is not reportable in NYC other than in clusters reported by providers or laboratories. To determine if there were additional tattoo-associated M. chelonae infections, we searched for cases using NYC ED syndromic surveillance.

Objective

To investigate tattoo-associated skin infections due to Mycobacterium chelonae using Emergency Department (ED) syndromic surveillance.

In Reunion Island, alcohol is the most experienced psychoactive substance [1]. Alcohol consumption is characterized by a massive ingestion of hard liquor and an early experimentation. Health consequences are significant: a high annual incidence of fetal alcohol syndromes [2] and a higher premature mortality than in France mainland [1]. Reunion island is one the French regions most affected by addictive behaviors related to alcohol. However, existing data are insufficient concerning the current health impact and associated factors.

Objective

Describe the emergency departments' visits for alcohol intoxication (AI) in Reunion Island and factors associated with their variations.

The 'Grand Raid de la Reunion' is one of the hardest ultra trails in the world (5,350 competitors in 2012). This one stage race takes place in Reunion Island, a French overseas department in the Indian Ocean. Ultra trails and ultra marathons are intense long-distance running races pushing back human physical abilities' limits. In general terms, studies about these races highlight different severity levels' injuries, from asymptomatic to critical condition [1-4]. No study has yet used syndromic surveillance to study the impact of such sporting events on ED visits. Using a syndromic surveillance approach to monitor sport-related visits could allow an early public health response.

Objective

To estimate the health impact of the 'Grand Raid de la Reunion' (GRR) ultra trail in 2012 on the emergency departments (ED) of Reunion Island.

Symptoms of influenza-like illness (ILI) were reported daily by health workers via the integrated syndromic surveillance system in rural China (ISSC)(1).

Objective

To analyze the growth and variation of ILI reports using individual growth curve model.

Adoption of electronic medical records is on the rise, due to the Health Information Technology for Economic and Clinical Health Act and meaningful use incentives. Simultaneously, numerous HIE initiatives provide data sharing flexibility to streamline clinical care. Due to the consolidated data availability in centralized HIE models, conducting syndromic surveillance using locally developed systems, such as GUARDIAN, is becoming feasible. During the past year, Chicago has embarked on a city-wide HIE deployment campaign. Perhaps the most unique aspect of this endeavor is that the data warehouse for the HIE is intricately tied to the GUARDIAN syndromic surveillance system.

Objective

The objective is to describe the technical process, challenges, and lessons learned in scaling up from a local to regional syndromic surveillance system using the MetroChicago Health Information Exchange (HIE) and Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification (GUARDIAN) collaborative initiative.