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Emergency Department (ED)

Description

Real-world public health data often provide numerous challenges. There may be a limited amount of background data, data dropouts, noise, and human error. The data from an emergency department (ED) in Urbana, IL includes a diagnosis field with multiple terms and notes separated by semicolons. There are over 7000 distinct terms, excluding the notes. Because it begins in April 2009, there is not yet adequate background data to use some of the regressionbased alerting algorithms. Values for some days are missing, so we also needed an algorithm that would tolerate data dropouts. 

INDICATOR is a workflow-based biosurveillance system developed at the National Center for Supercomputing Applications. One of the fundamental concepts of INDICATOR is that the burden of cleaning and processing incoming data should be on the software, rather than on the health care providers.

 

Objective

This paper compares different approaches with classification and anomaly detection of data from an ED.

Submitted by hparton on
Description

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.

Submitted by hparton on
Description

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.

Submitted by hparton on
Description

Our laboratory previously established the value of over-the-counter (OTC) sales data for the early detection of disease outbreaks. We found that thermometer sales (TS) increased significantly and early during influenza (flu) season. Recently, the 2009 H1N1 outbreak has highlighted the need for developing methods that not only detect an outbreak but also estimate incidence so that public-health decision makers can allocate appropriate resources in response to an outbreak. Although a few studies have tried to estimate the H1N1 incidence in the 2009 outbreak, these were done months afterward and were based on data that are either not easy to collect or not available in a timely fashion (for example, surveys or confirmed laboratory cases).

Here, we explore the hypothesis that OTC sales data can also be used for predicting a disease activity. Towards that end, we developed a model to predict the number of Emergency Departments (ED) flu cases in a region based on TS. We obtain sales information from the National Retail Data Monitor (NRDM) project. NRDM collects daily sales data of 18 OTC categories across the US.

 

Objective

We developed a model that predicts the incidence of flu cases that present to ED in a given region based on TS.

Submitted by hparton on
Description

Florida Department of Health has developed a statewide syndromic surveillance system based on the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). Authorized users can currently access data from the Florida Poison Information Center Network (FPICN), Emergency Room chief complaints, Florida reportable disease system (Merlin) and the Florida death records through ESSENCE under one portal. The purpose of this paper is to summarize efforts to enhance statewide real-time chemical surveillance by incorporating FPICN data into ESSENCE.

Submitted by hparton on
Description

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.

Submitted by hparton on
Description

Seasonal influenza epidemics are responsible for over 200,000 hospitalizations in the United States per year, and 39,000 of them are in children. In the United States, the Advisory Committee on Immunization Practices guides immunization practices, including influenza vaccination, with recommendations revised on an annual basis. For the 2006–2007 flu season, the Advisory Committee on Immunization Practices recommendations for influenza vaccination began including healthy children aged 24–59 months (two to four years), a shift that added 10.6 million children to the target group.

Canada has a parallel federal organization, the National Advisory Committee on Immunization, which is responsible for guiding the use of vaccines. Recommendations made by the National Advisory Committee on Immunization and the Advisory Committee on Immunization Practices around seasonal influenza vaccination was concordant until the 2006–2007 season. Starting in the 2010–2011 season, the National Advisory Committee on Immunization has further expanded its recommendations to additional pediatric age groups by including two- to four-year-olds for targeted seasonal influenza vaccination.

We took advantage of this divergence in policy between two neighboring countries with similar annual seasonal influenza epidemics to try to understand the effects of the

policy change in the United States to expand influenza vaccination coverage to other pediatric populations.

 

Objective

The objective of this study is to estimate the effect of expanding recommendations for routine seasonal influenza vaccination to include 24–59-month-old children.

Submitted by hparton on
Description

Ontologies representing knowledge from the public health and surveillance domains currently exist. However, they focus on infectious diseases (infectious disease ontology), reportable diseases (PHSkbFretired) and internet surveillance from news text (BioCaster ontology), or are commercial products (OntoReason public health ontology). From the perspective of biosurveillance text mining, these ontologies do not adequately represent the kind of knowledge found in clinical reports. Our project aims to fill this gap by developing a stand-alone ontology for the public health/biosurveillance domain, which (1) provides a starting point for standard development, (2) is straightforward for public health professionals to use for text analysis, and (3) can be easily plugged into existing syndromic surveillance systems.

 

Objective

To develop an application ontology - the extended syndromic surveillance ontology - to support text mining of ER and radiology reports for public health surveillance. The ontology encodes syndromes, diagnoses, symptoms, signs and radiology results relevant to syndromic surveillance (with a special focus on bioterrorism).

Submitted by hparton on
Description

The New York City (NYC) Department of Health and Mental Hygiene monitors visits daily from 49 of 54 NYC emergency departments (EDs), capturing 95% of all ED visits. ED visits for influenza-like illness (ILI) have reflected influenza activity in NYC, better than the more broadly defined fever/flu and respiratory syndromes, but the correlation with H1N1 is unknown. 

Laboratory-confirmed influenza and respiratory syncytial virus (RSV) were made reportable in NYC in February 2008. DOHMH receives electronic reports of positive tests. 

As part of 2009–10 influenza surveillance, five hospitals were selected for ‘sentinel’ surveillance of hospitalized influenza cases, to test all patients with a respiratory condition for influenza. Sentinel hospitals ensured that patient medical record numbers were in the daily ED syndromic file and in the electronic laboratory reports.

 

Objective

To determine the correlation of the ILI syndrome with laboratory-confirmed H1N1 and RSV during the October 2009 to March 2010 H1N1 season in NYC.

Submitted by hparton on
Description

During the spring of 2009, a public health emergency was declared in response to the emergence of the 2009 Influenza A (H1N1) virus. Owing to the response, timely data were needed to improve situational awareness and to inform public health officials. Traditional influenza surveillance is time-consuming and resource intensive, and electronic data sources are often more timely and resource saving. Collaboration began between the Centers for Disease Control and Prevention (CDC), the International Society for Disease Surveillance, and the Public Health Informatics Institute to expand syndromic Emergency Department (ED) surveillance through the Distribute project.

Distribute collects aggregate, daily or weekly reports of influenza-like illness (ILI) and total patient visits to EDs from participating health jurisdictions, stratified by age group and other variables. Additional variables included the three digit zip code of the patient’s residence as well as the disposition and temperature, however not all jurisdictions collect these variables. Distribute data are typically extracted from ED-based electronic health data systems. The ILI definition is determined by the participating jurisdiction that can be a city, county, or state. At the time of analysis, the network consisted of 33 jurisdictions.

Because ILI data reported to Distribute had not been systematically compared with data reported through other surveillance systems, CDC planned an evaluation of the Distribute data, which included a comparison to the Influenza-like Illness Network (ILINet). 

ILINet is a collaborative effort between the CDC, local and state health departments and primary health care providers. The network currently consists of approximately 3000 healthcare providers in all 50 states, Chicago, the District of Columbia, New York City, and the US Virgin Islands. Enrolled providers send CDC weekly reports via internet or fax that consist of the total number of patients seen for any reason and the number of those patients with ILI by age group. ILI is defined as fever (temperature of X1001F (37.8 1C)) and a cough and/or sore throat in the absence of a known cause other than influenza.

 

Objective

To compare ILI data reported to the Distribute surveillance project to data from an existing influenza surveillance system, the US Outpatient ILINet.

Submitted by hparton on