Influenza-Like-Illness (ILI)

In 2004, the BioDefend (BD) syndromic surveillance (SS) system was implemented in Duval County hospitals (Jacksonville, FL). Daily emergency department chief complaints are manually classified and entered into the BD system by triage personnel. As part of a statewide implementation, the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) began collecting data in the Jacksonville area during the winter of 2007-08. ESSENCE uses an automated data collection, chief complaint parsing and analysis process for data management and analysis. The use of two systems during the same period of time in one area provided a unique opportunity to retrospectively analyze characteristics of the BD and ESSENCE systems.

Objective

To compare detection of a community outbreak of influenza-like illness using two SS systems, one using a clinician’s classification of reason for visit and the other using an automated chief complaint parsing algorithm.

Emerging infections, both natural and intentional, have provided an impetus for improved disease surveillance and response. The recognition of the interdependence of health care systems and public health infrastructure provides an opportunity to expand beyond traditional disease-based surveillance to a more comprehensive, integrated approach that leverages existing electronic information. The Veterans Affairs (VA) hospital system is uniquely positioned to perform multi-institutional enhanced electronic surveillance. A wealth of electronic information and technology resources are available in all VA hospitals and their associated clinics, as each facility uses the same standardized Computer Patient Record System. Influenza-like illness (ILI) is a common clinical syndrome of diverse etiology that presents with respiratory and systemic symptoms. The NC health department mandates the reporting of ILI from emergency departments to facilitate monitoring of seasonal ILI and serve as an important component of pandemic preparedness. Existing surveillance systems utilize an ICD-9 respiratory code screen and subsequent manual chart review which is timeconsuming and insensitive. Automated medical record review using more comprehensive electronic data may improve the system’s timeliness and efficiency.

Objective

To use data collected by NC-VET to create an automated ILI surveillance program and compare its accuracy and efficiency to the existing program.

Surveillance systems utilizing early indicator of disease activity would be useful for monitoring community disease pattern and facilitating timely decision making on public health interventions in an evidence-based manner. School absenteeism has been previously considered as a possible syndromic approach for monitoring influenza activity. We explored the feasibility and practicability of establishing an electronic school absenteeism surveillance system in Hong Kong for monitoring influenza-like illness (ILI) and other diseases using automatically captured data employing smart card technology.

Objective

We examined the utility of an electronic school absenteeism system for monitoring multiple types of diseases.

Coordinated proactive school closures can help to reduce disease transmission in communities during an influenza pandemic; however, limited information is available about effects of school closures during influenza-like illness (ILI) outbreaks. A rural school district (District A) in Kentucky was closed during January 29-February 1, 2013, in response to an increase in ILI-related student absenteeism.

Objective

We investigated effects of this closure by comparing self-reported illness among household members of students enrolled in District A with 2 adjacent districts (Districts B and C) that remained open during that period.

National telephone health advice service data have been investigated as a source for syndromic surveillance of influenza-like illness and gastroenteritis . Providing a high level of coverage, the system might serve as an early outbreak detection tool. We have previously found that telephone triage service data of acute gastroenteritis was superior to web queries as well as over-the-counter pharmacy sales of anti-diarrhea medication to detect large water- and foodborne outbreaks of gastrointestinal illness in Sweden during the years 2007–2011 (4). However, information is limited regarding the usefulness, characteristics, and signal properties of local telephone triage data for monitoring and identifying outbreaks at the community level.

Objective

Our aim was to use telephone triage data to develop a model for community-level syndromic surveillance that can detect local outbreaks of acute gastroenteritis (AGE) and influenza-like illness (ILI) and allow targeted local disease control information.

The Houston Department of Health Department of Health and Human Services (HDHHS) monitors emergency departments (ED) chief complaints across the Houston metropolitan area, Harris County, and the surrounding jurisdictions by Real-time Outbreak Disease Surveillance (RODS). The influenza-like illnesses (ILI) data is collected by sentinel surveillance provider network of 12 physicians and RODS, an electronic syndromic surveillance database consisting of about 30 EDs in metropolitan Houston. Previous research indicates that there is a relationship between new HIV diagnoses and neighborhood poverty. However, there is limited research on health disparity to investigate the association between influenza-like illnesses (ILI) and social determinants of health (SDH), such as poverty.

Objective

To investigate the association between social determinants of health and influenza-like illnesses in Houston/Harris County and to identify neighborhoods for targeted surveillance or interventions.

The threat of pandemic and seasonal influenza has drawn attention to syndromic surveillance systems for early detection of influenza-like illness. Since 2005, the Miami-Dade County Health Department has implemented ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) to monitor emergency department data for influenza-like Illness (ILI) using chief complaint information. This study evaluates the ability of the ESSENCE ILI chief complaint grouping for identifying true ICD-9 diagnosed influenza.

Objective

Previous studies have examined the utility of different methods of syndromic grouping. This study evaluates the utility of ESSENCE for ILI surveillance.

Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition. Through a manual electronic medical record (EMR) review of 5,127 outpatient encounters at the Veterans Administration health system (VA), we previously developed single-case detection algorithms (CDAs) aimed at uncovering individuals with influenza-like illness (ILI). In this work, we evaluate the impact of using CDAs of varying statistical performance on the time and workload required to find a community-wide influenza outbreak through a VA-based syndromic surveillance system (SSS). The CDAs utilize various logical arrangements of EMR data, including ICD-9 codes, structured clinical parameters, and/or an automated analysis of the free-text of the full clinical note. The 18 ILI CDAs used here are limited to the most successful representatives of ICD-9-only and EMR-based case detectors.

Objective

This work uses a mathematical model of a plausible influenza epidemic to begin to test the influence of CDAs on the performance of a SSS.

A major goal of biosurveillance is the timely detection of an infectious disease outbreak. Once a disease has been identified, another very important goal is to find all known cases of the disease to assist public health investigators. Natural language processing (NLP) systems may be able to assist in identifying epidemiological variables and decrease time-consuming manual review of records.

Objective

To identify epidemiologically important factors such as infectious disease exposure history, travel or specific variables from unstructured data using NLP methods.

Emergency Department (ED) syndromic surveillance data for influenza-like illness (ILI) have been found to provide timely and representative information about current influenza activity in NYC. DOHMH monitors visits daily from 50 of 61 EDs, capturing about 94% of all ED visits in NYC. Since January 1, 2007, DOHMH has been receiving disposition data (e.g., hospitalized, discharged) from a subset of EDs. Currently, disposition data is received from 37 EDs (approximately 1/3 of all visits by the next day and >60% of all visits within 1 week).

More detailed hospitalization data, including date, demographics, and diagnosis on all NYC hospitalizations are routinely collected by the New York State Department of Health Statewide Planning and Research Cooperative System (SPARCS). SPARCS is subject to a 2-3 year reporting lag, thus limiting its timeliness and prospective use. However, SPARCS data from prior to January 1, 2007 can supplement the ED syndromic data to develop a model for ILI hospitalizations and calculate excess hospitalizations attributable to influenza that can be used in near realtime, particularly in the event of a pandemic.

Objective

To use ED syndromic surveillance data to monitor hospitalizations for ILI and calculate excess hospitalizations attributable to influenza.