Influenza-Like-Illness (ILI)

Influenza causes significant morbidity and mortality, with attendant costs of roughly $10 billion for treatment and up to $77 billion in indirect costs annually. The Centers for Disease Control and Prevention conducts annual influenza surveillance, and includes measures of inpatient and outpatient influenza-related activity, disease severity, and geographic spread. However, inherent lags in the current methods used for data collection and transmission result in a one to two weeks delay in notification of an outbreak via the Centers for Disease Control and Prevention’s website. Early notification might facilitate clinical decision-making when patients present with acute respiratory infection during the early stages of the influenza outbreak. 

In the United States, the influenza surveillance season typically begins in October and continues through May. The Utah Health Research Network has participated in Centers for Disease Control and Prevention’s influenza surveillance since 2002, collecting data on outpatient visits for influenza-like illness (ILI, defined as fever of 100F or higher with either cough or sore throat). Over time, Utah Health Research Network has moved from data collection by hand to automated data collection that extracts information from discrete fields in patients’ electronic health records.

We used statistical process control to generate surveillance graphs of ILI and positive rapid influenza tests, using data available electronically from the electronic health records. 

Objective

The objective of this study is to describe the use of point-of-care rapid influenza testing in an outpatient, setting for the identification of the onset of influenza in the community. 

Syndromic surveillance data such as the incidence of influenza-like illness (ILI) is broadly monitored to provide awareness of respiratory disease epidemiology. Diverse algorithms have been employed to find geospatial trends in surveillance data, however, these methods often do not point to a route of transmission. We seek to use correlations between regions in time series data to identify patterns that point to transmission trends and routes. Toward this aim, we employ network analysis to summarize the correlation structure between regions, whereas also providing an interpretation based on infectious disease transmission. 

Cross-correlation has been used to quantify associations between climate variables and disease transmission. The related method of autocorrelation has been widely used to identify patterns in time series surveillance data. This research seeks to improve interpretation of time series data and shed light on the spatial–temporal transmission of respiratory infections based on cross-correlation of ILI case rates.

Objective

Time series of ILI events are often used to depict case rates in different regions. We explore the suitability of network visualization to highlight geographic patterns in this data on the basis of cross-correlation of the time series data. 

Absenteeism is regarded as an expedient and responsive marker of illness activity. It has been used as a health outcome measure for a wide spectrum of exposures and as an early indicator of influenza outbreaks.1 A handful of studies have described its validity compared with traditional ‘goldstandards’ for influenza and ILI.2,3 We sought to further quantify the relationship between ED ILI and school absenteeism because absenteeism, as it relates to illness, and subsequent loss in productivity and wages for parents, school staff and children, is an important public health outcome.

Objective

To describe the relationship between emergency department (ED) visits for influenza-like-illness (ILI) and absenteeism among school-aged children.

Biosurveillance systems commonly use emergency department (ED) patient chief complaint data (CC) for surveillance of influenza-like illness (ILI). Daily volumes are tracked using a computerized patient CC classifier for fever (CC Fever) to identify febrile patients. Limitations in this method have led to efforts to identify other sources of ED data. At many EDs the triage nurse measures the patient’s temperature on arrival and records it in the electronic medical record. This makes it possible to directly identify patients who meet the CDC temperature criteria for ILI: temperature greater than 100 degrees F (T>100F).

Objective

To evaluate whether a classifier based on temperature >100F would perform similarly to CC Fever and might identify additional patients.

Current influenza-like illness (ILI) monitoring in Idaho is based on syndromic surveillance using laboratory data, combined with periodic person-to-person reports collected by Idaho state workers. This system relies on voluntary reporting.

Electronic medical records offer a method of obtaining data in an automated fashion. The Computerized Patient Record System (CPRS) captures real-time visit information, vital signs, ICD-9, pharmacy, and lab data. The electronic medical record surveillance has been utilized for syndromic surveillance on a regional level. Funds supporting expansion of electronic medical records offer increased ability for use in biosurveillance. The addition of temporo-spatial modeling may improve identification of clusters of cases. This abstract reviews our efforts to develop a real-time system of identifying ILI in Idaho using Veterans Administration data and temporo-spatial techniques.

Objective

The objective of this study is to describe initial efforts to establish a real-time syndromic surveillance of ILI in Idaho, using data from the Veterans Administration electronic medical record (CPRS).

In June 2009, the CDC defined a confirmed case of H1N1 as a person with an ILI and laboratory confirmed novel influenza A H1N1 virus infection. ILI is defined by the CDC as fever and cough and/or sore throat, in the absence of a known cause other than influenza. ILI cases are usually reported without accounting for alternate diagnoses (that is, pneumonia). Therefore, evaluation is needed to determine the impact of alternate diagnoses on the accuracy of the ILI case definition.

Objective

This study investigates the impact of alternate diagnoses on the accuracy of the Centers for Disease Control and Prevention’s (CDC) case definition for influenza-like illness (ILI) when used as a screening tool for influenza A (H1N1) virus during the 2009 pandemic, and the implications for public health surveillance.

The United States outpatient Influenza-like Illness Surveillance Network (ILINet) is one of the five systems used for influenza surveillance in the United States. In Pennsylvania, ILINet providers are asked to report, every Monday, the total number of patients seen for any cause, and the number of patients with influenza-like illness (ILI) by age group. In order to encourage timely reporting, weekly reminders along with a data summary were sent to all sentinel providers postoutbreak recognition. Through the study period, recruitment of new sentinel sites was done through local health departments, health alerts, and training sessions. Sentinel providers were not restricted from submitting specimens to the state lab before and after the outbreak, whereas non sentinel providers had strict restrictions.

Objective

The objective of this study is to describe changes in influenza-like illness (ILI) surveillance, eight weeks before and after the 2009 A/H1N1 pandemic influenza outbreak. We examined changes in provider recruitment, composition, reporting of ILI, and we characterize ILI data in terms of timeliness, and ILI baselines by type of sentinel provider.

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.

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.