Surveillance Systems

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).

State laws mandate clinicians and laboratories to report occurrences of reportable diseases to public health entities. For this purpose, a set of case-reporting specifications are published and maintained by public health departments. There are several problems with the existing case-reporting specifications: (1) they are described on individual state websites and posters and not structured or executable; (2) the specifications are often misleading representing case classification rather than case reporting criteria; (3) they vary across jurisdictions and change over time; and (4) reporting facilities are required to interpret the criteria and maintain logic in their own systems. To overcome these problems, we are designing and developing a prototype system to represent case-reporting specifications that can be authored and maintained by public health and published openly.

Objective

In this paper, we describe the content and functional requirements for a knowledge management system that can be authored by public health authorities to inform reporting facilities ‘what’s reportable where’.

As part of the United States Department of Defense strategy to counter biological threats, the Defense Threat Reduction Agency’s biological threat reduction program is enhancing the capabilities of countries in the former Soviet Union (FSU) to detect, diagnose, and report endemic and epidemic, manmade or natural cases of especially dangerous pathogens. During these engagements, it is noted that Western-trained and Soviet-trained epidemiologists have difficulty, beyond that of simple translation, in exchanging ideas. 

Before 1991, infectious disease surveillance in the FSU was centrally planned in Moscow. The methodologies of infectious disease surveillance and data analysis have remained almost unaltered since this time in most nations of the FSU. Vlassov describes that FSU physicians and other specialists are not taught epidemiology as it is understood in the West. The Soviet public health system and the scientific discipline of epidemiology developed independently of that of other nations. Consequently, many fundamental Soviet terms and concepts lack simple correlates in English and other languages outside the Soviet sphere; the same is true when attempting to translate from English to Russian and other languages of the FSU. Systematic review of the differences in FSU and Western epidemiologic concepts and terminology is therefore needed for international public health efforts, such as disease surveillance, compliance with International Health Regulations 2005, pandemic preparedness, and response to biological terrorism. A multi-language reference in the form of a dictionary would greatly improve mutual comprehension among epidemiologists in the West and the FSU.

Objective

The objective of this study is to describe the development of a multilingual lexicon of epidemiology, which is needed for improved communication in public health surveillance internationally.

Syndromic surveillance systems significantly enhance the ability of Public Health Units to identify, quantify, and respond to disease outbreaks. Existing systems provide excellent classification, identification, and alerting functions, but are limited in the range of statistical and mapping analyses that can be done. Currently available commercial off-the-shelf (COTS) statistical and GIS packages provide a much broader range of analytical and visualization tools, as well as the capacity for automation through user-friendly scripting languages. This study retrospectively evaluates the use of these packages for surveillance using syndromic data collected in Ottawa during the 2009 pH1NI outbreak.

Objective

The objective of this study was to create and evaluate a system that uses customized scripts developed for COTS statistical and GIS software to (1) analyze syndromic data and produce regular reports to public health epidemiologists, containing the information they would need to detect and manage an ILI outbreak, and (2) facilitate the generation more detailed analyses relevant to specific situations using these data.

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.

Microorganisms resistant to antibiotics (ABX) increase the mortality, morbidity and costs of infections. In the absence of a drug development pipeline that can keep pace with the emerging resistancemechanisms, these organisms are expected to threaten public health for years to come. Because exposure to ABX promotes the development of bacterial resistance, health care providers have long been urged to avoid using antibiotics to treat conditions that they are unlikely to improve, including many uncomplicated acute respiratory infections. We asked if interposing clinical decision support software at the time of electronic order entry could adjust ABX utilization toward consensus guidelines for these conditions. 

The Centers for Disease Control and Prevention's (CDC) Emerging Infections Program (EIP) monitors and studies many infectious diseases, including influenza. In 10 states in the US, information is collected for hospitalized patients with laboratory-confirmed influenza. Data are extracted manually by EIP personnel at each site, stripped of personal identifiers and sent to the CDC. The anonymized data are received and reviewed for consistency at the CDC before they are incorporated into further analyses. This includes identifying errors, which are used for classification.

Objective

Introducing data quality checks can be used to generate feedback that remediates and/or reduces error generation at the source. In this report, we introduce a classification of errors generated as part of the data collection process for the EIP’s Influenza Hospitalization Surveillance Project at the CDC. We also describe a set of mechanisms intended to minimize and correct these errors via feedback, with the collection sites.

Current methods for influenza surveillance include laboratory confirmed case reporting, sentinel physician reporting of Influenza-Like-Illness (ILI) and chief-complaint monitoring from emergency departments (EDs).

The current methods for monitoring influenza have drawbacks. Testing for the presence of the influenza virus is costly and delayed. Specific, sentinel physician reporting is subject to incomplete, delayed reporting. Chief complaint (CC) based surveillance is limited in that a patient’s chief complaint will not contain all signs and symptoms of a patient.

A possible solution to the cost, delays, incompleteness and low specificity (for CC) in current methods of influenza surveillance is automated surveillance of ILI using clinician-provided free-text ED reports.

Objective

This paper describes an automated ILI reporting system based on natural language processing of transcribed ED notes and its impact on public health practice at the Allegheny County Health Department.

On 20 April 2010, an explosion on an offshore drilling rig in the Gulf of Mexico led to a prolonged uncontrolled release of crude oil. Both clean-up workers and coastal residents were potentially at high risk for respiratory and other acute health effects from exposure to crude oil and its derivatives, yet there was no surveillance system available to monitor these health effects. The Department of Veterans Affairs (VA) conducts routine surveillance for biological threats using the Electronic Surveillance System for Early Notification of Community Based Epidemics (ESSENCE). ESSENCE captures specific patient care visit ICD-nine codes belonging to selected conditions that could represent a biological threat. VA operates 153 medical centers and over 1000 free standing patient care facilities across the United States. We describe the adaptation of ESSENCE to allow surveillance of health conditions potentially related to the oil spill.

Objective

To describe a surveillance system created to identify acute health issues potentially associated with the Deepwater Horizon oil spill among Veterans in the Gulf of Mexico coastal region.

Syndromic surveillance data have been widely shown to be useful to large health departments. Use at smaller local health departments (LHDs) has rarely been described, and the effectiveness of various methods of delivering syndromic surveillance data and information to smaller health departments is unknown. Syndromic surveillance data and information in North Carolina are available to all local public health staff by several routes. This report characterizes LHD access to syndromic surveillance data and information and their use for key public health purposes.

Objective

To characterize use of syndromic surveillance information for key public health functions at the local health department level, and to make recommendations to facilitate use of syndromic surveillance data for these functions.