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Evaluation of Syndromic Surveillance

The Tennessee Department of Health (TDH) Foodborne Disease Program conducts routine surveillance for foodborne illnesses and enteric disease outbreaks and participates in statewide enhanced surveillance as part of the Foodborne Disease Center for Outbreak Response Enhancement (FoodCORE) and the Foodborne Diseases Active Surveillance Network (FoodNet) supported by the Centers for Disease Control and Prevention (CDC). TDH uses the CDC NEDSS Base System (NBS) application for routine disease surveillance. However, NBS serves multiple disease programs within TDH and modifications to the system for the rapidly changing data demands, grant requirements, and outbreak needs of the foodborne program, may not be a priority for the system as a whole. In 2014, the TDH Foodborne Disease Program began using the Research Electronic Data Capture (REDCap) application as a solution to changing surveillance needs. FoodCORE, FoodNet, and routine surveillance data elements are entered into REDCap to supplement NBS, depending on program specific needs and system capability.

Objective: The objective of this study is to evaluate the use of a supplementary data management application to meet surveillance demands for foodborne disease in Tennessee and to highlight successes, challenges, and opportunities identified through this process.

Submitted by elamb on

Communicable disease reporting from providers can be a time-consuming process that results in delayed or incomplete reporting of infectious diseases, limiting public health's ability to respond quickly to prevent or control disease. The recent development of an HL7 standard for automated Electronic initial case reports (eICR) represents an important advancement for public health surveillance. The Illinois Department of Public Health (IDPH) participated in a pilot with the Public Health Informatics Institute and an Illinois-based provider group to accept eICR reports for Gonorrhea and Chlamydia.

Objective: Comparison of content in eCR and ELR cases reporting Review technical challenges and strategies for data management

Submitted by elamb on

After MERS outbreak in 2015, the provincial government and infectious disease control center (GIDCC) initiated an emergency department (ED) based Gyeonggi-do provincial acute febrile illness (AFI) surveillance network (GAFINet) to monitor for a subsequent outbreak of emerging or imported infectious diseases since September 2016. Following pilot operation from September to December 2016, the operation was run for one year from June 2017 to May 2018. GAFINet Initiative involves ten hospitals, consisted of four university-affiliated hospitals and six provincial medical centers in Gyeonggi-do province. These hospitals participated in this network voluntarily.

Objective: The purpose of this study is to describe and evaluate the results of the GAFINET(Gyeonggi Acute Febrile Illness Surveillance Network) operated for one year.

Submitted by elamb on

Timely and accurate measurement of overdose morbidity using emergency department (ED) data is necessary to inform an effective public health response given the dynamic nature of opioid overdose epidemic in the United States. However, from jurisdiction to jurisdiction, differing sources and types of ED data vary in their quality and comprehensiveness. Many jurisdictions collect timely emergency department data through syndromic surveillance (SyS) systems, while others may have access to more complete, but slower emergency department discharge datasets. State and local epidemiologists must make decisions regarding which datasets to use and how to best operationalize, interpret, and present overdose morbidity using ED data. These choices may affect the number, timeliness, and accuracy of the cases identified.

Objective: Epidemiologists will understand the differences between syndromic and discharge emergency department data sources, the strengths and limitations of each data source, and how each of these different emergency department data sources can be best applied to inform a public health response to the opioid overdose epidemic.

Submitted by elamb on

Increasingly public health decision-makers are using syndromic surveillance for real-time reassurance and situational awareness in addition to early warning1. Decision-makers using intelligence, including syndromic data, need to understand what the systems are capable of detecting, what they cannot detect and specifically how much reassurance should be inferred when syndromic systems report nothing detected. In this study we quantify the detection capabilities of syndromic surveillance systems used by Public Health England (PHE). The key measures for detection capabilities are specificity and sensitivity (although timeliness is also very important for surveillance systems)2. However, measuring the specificity and sensitivity of syndromic surveillance systems is not straight forward. Firstly, syndromic systems are usually multi-purpose and may be better at identifying certain types of public health threat than others. Secondly, whilst it is easy to quantify statistical aberration detection algorithms, surveillance systems involve other stages, including data collection and human decision-making, which also affect detection capabilities. Here, we have taken a systems thinking approach to understand potential barriers to detection, and summarize what we know about detection capabilities of syndromic surveillance systems in England.

Objective: To communicate the detection capabilities of syndromic surveillance systems to public health decision makers.

Submitted by elamb on

It has been postulated that school absenteeism, a non-traditional surveillance data source, may allow for early detection of disease outbreaks, particularly among school-aged children who may not seek emergency medical attention. Although a New York City-based study showed moderate utility of school absenteeism in biosurveillance, no study to date has been reported on school absenteeism in Los Angeles County, which contains the second largest school district in the US.

 

Objective

To evaluate the utility of school absenteeism surveillance data in Los Angeles County during the 2009–2010 influenza season.

Referenced File
Submitted by hparton on

Salt Lake Valley Health Department uses syndromic surveillance to monitor influenza-like illness (ILI) activity as part of a comprehensive influenza surveillance program that includes pathogen-specific surveillance, sentinel surveillance, school absenteeism and pneumonia, and influenza mortality. During the 2009 spring and fall waves of novel H1N1 influenza, sentinel surveillance became increasingly burdensome for both community clinics and Salt Lake Valley Health Department, and an accurate, more efficient method for ILI surveillance was needed. One study found that syndromic surveillance performed, as well as a sentinel provider system in detecting an influenza outbreak and syndromic surveillance is currently used to monitor regional ILI in the United States.

 

Objective

The objective of this study is to compare the performance of syndromic surveillance with the United States Outpatient Influenza-like Illness Surveillance Network (ILINet), for the

detection of ILI during the fall 2009 wave of H1N1 influenza in Salt Lake County.

Referenced File
Submitted by hparton on

Influenza-like illness (ILI) data is collected by an Influenza Sentinel Provider Surveillance Network at the state (Iowa, USA) level. Historically, the Iowa Department of Public Health has maintained 19 different influenza sentinel surveillance sites. Because participation is voluntary, locations of the sentinel providers may not reflect optimal geographic placement. This study analyzes two different geographic placement algorithms - a maximal coverage model (MCM) and a K-median model. The MCM operates as follows: given a specified radius of coverage for each of the n candidate surveillance sites, we greedily choose the m sites that result in the highest population coverage. In previous work, we showed that the MCM can be used for site placement. In this paper, we introduce an alternative to the MCM - the K-median model. The K-median model, often called the P-median model in geographic literature, operates by greedily choosing the m sites which minimize the sum of the distances from each person in a population to that person’s nearest site. In other words, it minimizes the average travel distance for a population.

 

Objective

This paper describes an experiment to evaluate the performance of several alternative surveillance site placement algorithms with respect to the standard ILI surveillance system in Iowa.

Referenced File
Submitted by hparton on

The Distribute project began in 2006 as a distributed, syndromic surveillance demonstration project that networked state and local health departments to share aggregate emergency department-based influenza-like illness (ILI) syndrome data. Preliminary work found that local systems often applied syndrome definitions specific to their regions; these definitions were sometimes trusted and understood better than standardized ones because they allowed for regional variations in idiom and coding and were tailored by departments for their own surveillance needs. Originally, sites were asked to send whatever syndrome definition they had found most useful for monitoring ILI. Places using multiple definitions were asked to send their broader, higher count syndrome. In 2008, sites were asked to send both a broad syndrome, and a narrow syndrome specific to ILI.

 

Objective

To describe the initial phase of the ISDS Distribute project ILI syndrome standardization pilot.

Referenced File
Submitted by hparton on

Nationally, vaccine safety is monitored through several systems including Vaccine Adverse Event Reporting System (VAERS), a passive reporting system designed to detect potential vaccine safety concerns. Healthcare providers are encouraged to report adverse events after vaccination to VAERS, whether or not they believe that the vaccine caused the adverse event. The 2009 Pandemic H1N1 influenza vaccine became available in the United States in October 2009. By January 2010, Center for Disease Control and Prevention (Atlanta, GA, USA) estimated that 61 million persons across the United States had received the vaccine. As of January 2010, an estimated 28% of the North Carolina population greater than or equal to six months of age had been vaccinated against 2009 H1N1.

 

Objective

The objectives of this study were: (1) to compare trends in vaccine adverse events identified through emergency department (ED) diagnosis codes and reports from the VAERS, and (2) to determine whether 2009 H1N1 vaccine adverse events identified through VAERS could also be identified using ED diagnosis codes.

Referenced File
Submitted by hparton on
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