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Syndromic Surveillance

BioSense 2.0 protects the health of the American people by providing timely insight into the health of communities, regions, and the nation by offering a variety of features to improve data collection, standardization, storage, analysis, and collaboration. BioSense 2.0 is the result of a partnership between the Centers for Disease Control and Prevention (CDC) and the public health community to track the health and well-being of communities across the country. In 2010, the BioSense Program began a redesign effort to improve features such as centralized data mining and addressing concerns that the system could not meet its original objective to provide early warning or detect local outbreaks.

Objective

To familiarize public health practitioners with the BioSense 2.0 application and its use in all hazard surveillance.

 

Submitted by Magou on

The evolution of novel influenza viruses in humans is a bio- logical phenomenon that can not be stopped. All existing data suggest that vaccination against the morbidity and mortality of the novel influenza viruses is our best line of defence. Unfortunately, vaccination requires that the infectious agent to be quickly identified and a safe vaccine in large quantities is produced and administered. As was witnessed with the 2009 H1N1 influenza pandemic, these steps took a frustratingly long period during which the novel influenza virus continued its unstoppable and rapid global spreading. In addition to the different vaccination strategies ( e.g. random mass vaccination, age structured vaccination), isolation and quarantining of infected individuals is another effective method used by the public health agencies to control the spreading of infectious diseases. Isolation is effective against any infectious disease, however it can be very hard to detect infectious individuals in the population when: 1. Symptoms are ambiguous or easily misdiagnosed ( e.g. 2009 H1N1 influenza outbreak shared many symptoms with many other influenza like illnesses) 2. When the symptoms emerge after the individual become infectious.

Objective

The purpose of our work is to develop a system for automatic contact tracing with the goal of identifying individuals who are most likely infected, even if we do not have direct diagnostic information on their health status. Control of the spread of respiratory pathogens (e.g. novel influenza viruses) in the population using vaccination is a challenging problem that requires quick identification of the infectious agent followed by large-scale production and administration of a vaccine. This takes a significant amount of time. A complementary approach to control transmission is contact tracing and quarantining, which are currently applied to sexually transmitted diseases (STDs). For STDs, identifying the contacts that might have led to disease transmission is relatively easy; however, for respiratory pathogens, the contacts that can lead to transmission include a huge number of face-to-face daily social interactions that are impossible to trace manually.

 



 

Submitted by Magou on

The MSSS, described elsewhere (1), has been in use since 2003 and records Emergency Department (ED) chief complaint data along with the patient’s age, gender and zip code in real time. There were 85/139 hospital EDs enrolled in MSSS as of June 2012, capturing 77% of the annual hospital ED visits in Michigan. The MSSS is used routinely during the influenza season for situational awareness and is monitored throughout the year for aberrations that may indicate an outbreak, emerging disease or act of bioterrorism. The system has also been used to identify heat-related illnesses during periods of extreme heat. Very young children, the elderly, and people with mental illness and chronic diseases are at the highest risk of preventable heatrelated illnesses including sunburn, heat exhaustion, heat stroke and/or death (2). During a heat wave in the summer of 2012, data was reviewed on an ad hoc basis to monitor potential increases in heat-related ED visits.

Objective

The purpose of this work was to conduct an enhanced analysis of heat illness during a heat wave using Michigan’s Emergency Department Syndromic Surveillance System (MSSS) that could be provided to Public Health and Preparedness Stakeholders for situational awareness.

 

Submitted by Magou on

Scarlet fever is a bacterial infection caused by group A streptococcus (GAS). The clinical symptoms are usually mild. Before October, 2007, case-based surveillance of scarlet fever was conducted through notifiable infectious diseases in Taiwan, but was removed later from the list of notifiable disease because of improved medical care capacities. In 2011, Hong Kong had encountered an outbreak of scarlet fever (1,2). In response, Taiwan developed an integrated syndromic surveillance system using multiple data sources since July 2011.

Objective

To develop an integrated syndromic surveillance system for timely monitoring and early detection of unusual situations of scarlet fever in Taiwan, since Hong Kong, being so close geographically to Taiwan, had an outbreak of scarlet fever in June 2011.

 

Submitted by Magou on

Syndromic surveillance systems offer richer understanding of population health. However, because of their complexity, they are less used at small public health agencies, such as many local health departments (LHDs). The evolution of these systems has included modifying user interfaces for more efficient and effective use at the local level. The North Carolina Preparedness and Emergency Response Research Center previously evaluated use of syndromic surveillance information at LHDs in North Carolina. Since this time, both the NC DETECT system and distribution of syndromic surveillance information by the state public health agency have changed. This work describes use following these changes.

Objective

Our objective was to describe changes in use following syndromic surveillance system modifications and assess the effectiveness of these modifications.

 



 

Submitted by Magou on

The negative effect of air pollution on human health is well documented illustrating increased risk of respiratory, cardiac and other health conditions. Currently, during air pollution episodes Public Health England (PHE) syndromic surveillance systems provide a near real-time analysis of the health impact of poor air quality. In England, syndromic surveillance has previously been used on an ad hoc basis to monitor health impact; this has usually happened during widespread national air pollution episodes where the air pollution index has reached "High"™ or "Very High"™ levels on the UK Daily Air Quality Index (DAQI). We now aim to undertake a more systematic approach to understanding the utility of syndromic surveillance for monitoring the health impact of air pollution. This would improve our understanding of the sensitivity and specificity of syndromic surveillance systems for contributing to the public health response to acute air pollution incidents; form a baseline for future interventions; assess whether syndromic surveillance systems provide a useful tool for public health alerting; enable us to explore which pollutants drive changes in health-care seeking behaviour; and add to the knowledge base.

Objective:

To explore the utility of syndromic surveillance systems for detecting and monitoring the impact of air pollution incidents on health-care seeking behaviour in England between 2012 and 2017.

Submitted by elamb on

Effective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems. However, the quality of data in these systems can be poor or “unfit for use.” Therefore measuring and monitoring data quality is an essential activity for clinical and public health professionals as well as researchers. Current methods for examining data quality largely rely on manual queries and processes conducted by epidemiologists. Better, automated tools for examining data quality are desired by the surveillance community.

Objective:

To extend an open source analytics and visualization platform for measuring the quality of electronic health data transmitted to syndromic surveillance systems.

Submitted by elamb on

Public Health England's syndromic surveillance service monitor presentations for gastrointestinal illness to detect increases in health care seeking behaviour driven by infectious gastrointestinal disease. We use regression models to create baselines for expected activity and then identify any periods of signficant increases. The introduction of a rotavirus vaccine in England during July 2013 (Bawa, Z. et al. 2015) led to a reduction in incidence of the disease, requiring a readjustment of baselines.

Objective:

To adjust modelled baselines used for syndromic surveillance to account for public health interventions. Specifically to account for a change in the seasonality of diarrhoea and vomiting indicators following the introduction of a rotavirus vaccine in England.

Submitted by elamb on

Syndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.

Objective:

To investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.

Submitted by elamb on

Overdose deaths involving opioids (i.e., opioid pain relievers and illicit opioids such as heroin) accounted for at least 63% (N = 33,091) of overdose deaths in 2015. Overdose deaths related to illicit opioids, heroin and illicitly-manufactured fentanyl, have rapidly increased since 2010. For instance, heroin overdose deaths quadrupled from 3,036 in 2010 to 12,989 in 2015. Unfortunately, timely response to emerging trends is inhibited by time lags for national data on both overdose mortality via vital statistics (8-12 months) and morbidity via hospital discharge data (over 2 years). Emergency department (ED) syndromic data can be leveraged to respond more quickly to emerging drug overdose trends as well as identify drug overdose outbreaks. CDC’s NSSP BioSense Platform collects near real-time ED data on approximately two-thirds of ED visits in the US. NSSP’s data analysis and visualization tool, Electronic Surveillance System for the Notification of Community-based Epidemics (ESSENCE), allows for tailored syndrome queries and can monitor ED visits related to heroin overdose at the local, state, regional, and national levels quicker than hospital discharge data.

Objective:

This paper analyzes emergency department syndromic data in the Centers for Disease Control and Prevention's (CDC) National Syndromic Surveillance Program’s (NSSP) BioSense Platform to understand trends in suspected heroin overdose.

Submitted by elamb on
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