Syndromic Surveillance

According to the Center for Disease Control (CDC), binge drinking causes over half of the 88,000 excessive alcohol use deaths and costs approximately $149 billion dollars annually in the United States. Additionally, excessive alcohol use can increase the risk of many other health problems, including injuries and cancer, placing a large burden on public health. In Franklin County, Ohio, The Ohio State University (OSU) football games are an occasion of binge drinking for the student body and Columbus population alike. The purpose of this study is to determine if the binge drinking population is significantly different during football games.

Objective

Identify any relationship between alcohol-related emergency department visits in Franklin County, Ohio and Ohio State University football games.

In France, the surveillance of GE is performed by several complementary systems including specific and syndromic surveillance systems.

The GP’s emergency associations “SOS Médecins” are part of the French syndromic surveillance system SurSaUD since 2006. SOS Médecins functions as a liberal medical regulation. In 9 years, the network has become almost exhaustive and contribute to the surveillance of seasonal and non-seasonal health events at different geographical scales, in the fields of infectious diseases and environmental health. GE is one of the 50 indicators daily followed by the by the French Institute for Public Health Surveillance (InVS) syndromic surveillance unit.

Objective

To illustrate the complementarity and added value of the GP’s emergency network “SOS Médecins” through an example of an epidemic of gastroenteritis (GE).

In South Korea, the NNDSS is organized at three levels: local, provincial, and central. At the local level, physicians report the cased to the Public Health Center (PHC) and PHC conduct control measures. At the provincial level, the PHC reports the cases to the Department of Health (DOH) of the province and DOH obliged to report the cases to the Korea Centers for Disease Control and Prevention (KCDC) and feedback of the surveillance data to PHC and physicians. At the central level, the Disease Web Statistics System (http://is.cdc.go.kr/ dstat/index.jsp) provides real-time data on 54 national notifiable infectious diseases.

Although there are variations according to the disease and surveillance step, the KNNDSS generally functions well in terms of timeliness and Yu et al. reported that 89.7% of mumps, one of the most incident contagious disease in South Korea, reported in 15 days after the physician’s notification. To improve the timeliness of feedback at the provincial level, we explored the KNNDSS data and suggested an evidence based duration for publication of the weekly reports, in consideration of reducing the publication schedule.

Objective

This study will explore the timeliness of the Korean National Notifiable Disease Surveillance System (KNNDSS) at provincial Level, and suggest a reasonable duration for publication the weekly reports to improve timely feedback of infectious disease surveillance data to physicians and community.

Timely monitoring and prediction of the trajectory of seasonal influenza epidemics allows hospitals and medical centers to prepare for, and provide better service to, patients with influenza. The CDC’s ILINet system collects data on influenza-like illnesses from over 3,300 health care providers, and uses this data to produce accurate indicators of current influenza epidemic severity. However, ILINet indicators are typically reported at a lag of 1-2 weeks. Another source of severity data, Google Flu Trends, is calculated by aggregating Google searches for certain influenza related terms. Google Flu Trends data is provided in near-real time, but is a less direct measurement of severity than ILINet indicators, and is likely to suffer from bias. We create a hierarchical model to estimate epidemic severity for the 2014 - 2015 epidemic season which incorporates current and historical data from both ILINet and Google Flu Trends, allowing our model to benefit both from the recency of Google Flu Trends data and the accuracy of ILINet data.

Objective

To use multiple data sources of influenza epidemic severity to inform a model which can estimate and forecast severity for the current influenza epidemic season by accounting for the bias from each source.

Georgia Department of Public Health (DPH) epidemiologists have responded to multiple emergent outbreaks with diverse surveillance needs. During the 2009 H1N1 influenza response, it was necessary to electronically integrate multiple reporting sources and view population-level data, while during the 2014–2015 West African Ebola epidemic, it was necessary to easily collect and view individual level data from travelers to facilitate early detection of potential imported Ebola disease. DPH in-house information technology (IT) staff work closely with epidemiologists to understand and accommodate surveillance needs. Through this collaboration, IT created a robust electronic surveillance and outbreak management system (OMS) to accommodate routine reporting of notifiable diseases and outbreak investigations, and surveillance during emergent events.

Objective

To describe how flexible surveillance systems can be rapidly adapted and deployed, and increase the efficiency and accuracy of surveillance, during responses to outbreaks and all hazard emergent events.

Zika virus disease and Zika virus congenital infection are nationally notifiable conditions that became prominent recently as a growing number of travel-associated infections have been identified in the United States. The Centers for Disease Control and Prevention (CDC) have dedicated significant time and effort on determining and addressing the risks and impact of Zika on pregnant women and their babies who are most vulnerable to the disease. CDC relies on two sources of information, reported voluntarily by healthcare providers, to monitor Zika virus disease: ArboNET and the newly established U.S. Zika Pregnancy Registry. A study by IMS Health compared U.S.trends of the Zika virus disease in general and pregnant women with Zika virus disease in particular observed in an IMS healthcare claims database and the CDC ArboNET and the newly established U.S. ZikaPregnancy Registry.

Objective

Demonstrate the value of consolidated claims data from community healthcare providers in Zika Virus Disease surveillance at local level.

CPC provides the 24/7/365 poison hotline for the entire state of North Carolina and currently handles approximately 80,000 calls per year. CPC consultation services that assist callers with poison exposure, diagnosis, optimal patient management, therapy, and patient disposition guidance remain indispensable to the public and health care providers. Poison control center data have been used for years in syndromic surveillance practice as a reliable data source for early event detection. This information has been useful for a variety of public health issues, including environmental exposures, foodborne diseases, overdoses, medication errors, drug identification, drug abuse trends and other information needs. The North Carolina Department of Health and Human Services started formal integration of CPC information into surveillance activities in 2004. CPC call data are uploaded in real time (hourly), 24/7/365, to the NC DETECT state database.

Objective

To describe Carolinas Poison Control Center (CPC) calls data collected in the NC DETECT syndromic surveillance system.

In spite of the noted benefits of syndromic surveillance, and more than a decade after it started gaining support, the primary use for syndromic surveillance appears to be largely for seasonal and jurisdictional disease monitoring, event response and situational awareness as opposed to its intended purpose of early event detection. Research assessing the user characteristics and standards applied at local public health agencies (LPHA’s) for syndromic surveillance are scarce, and in national surveys epidemiologists frequently tend to utilize their own syndromic surveillance systems as opposed to a national system such as Biosense. While the National Syndromic Surveillance Program (NSSP) has addressed many operational concerns from stakeholders, and is in the process of providing access to the cloud based Biosense platform-along with ESSENCE as a key tool, there is still a paucity of research that exists as to what can be done to improve the utilization of syndromic surveillance systems for its primary purpose of early event detection.

Objective

A mixed methods study is being conducted on the statewide Early Notification of Community Based Epidemics (ESSENCE) system in Missouri to identify factors that can improve the timeliness and identification of outbreaks. This research will provide stakeholders with guidance on how best to implement and improve ESSENCE usage statewide, and by sharing this research input can be solicited on the utility of the applied framework as well as future implications from this body of work.

The 9th IOIG took place in Reunion Island from July 31 to August 9, 2015. This sport event gathered approximatively 1 640 athletes, 2 000 volunteers and several thousand spectators from seven islands:Comoros, Madagascar, Maldives, Mauritius, Mayotte, Seychelles and Reunion.In response to the import risk of infectious diseases from these countries where some of them are endemics, the syndromic surveillance system, which captures 100% of all Emergency Department visits, was enhanced in order to detect any health event.

Objective

To describe how syndromic surveillance was enhanced to detect health events during the 9th Indian Ocean Island Games (IOIG) in Reunion Island.

As a participant in the National Syndromic Surveillance Program (NSSP), the Massachusetts Department of Public Health (MDPH) has worked closely with our statewide Health Information Exchange (HIE) and National Syndromic Surveillance Program (NSSP) technical staff to collect and transmit emergency department (ED) data from eligible hospitals (EHs) to the NSSP. Our goal is to ensure complete and accurate data using a multi-step process beginning with pre-production data and continuing after EHs are sending live data to production.

Objective

To develop a detailed data validation strategy for facilities sending emergency department data to the Massachusetts Syndromic Surveillance program and to evaluate the validation strategy by comparing data quality metrics before and after implementation of the strategy.