ILINet

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 influenza-like illness (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.

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.

http://Google.org developed a regression model that used the volume of influenza-related search queries best correlated with the proportion of outpatient visits related to influenza-like illness (ILI) model to estimate the level of ILI activity. For calibration, the model used ILINet data from October 2003 to 2009, which report weekly ILI activity as the percentage of patient visits to health care providers for ILI from the total number patient visits for the week. Estimates of ILI in 121 cities were added in January 2010.

Objective

This paper compares estimates of ILI activity with estimates from the Centers for Disease Control’s ILINet from October 2008 through March 2010.

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.

The South Carolina Aberration Alerting Network (SCAAN) is a collaborative network of syndromic systems within South Carolina. Currently, SCAAN contains the following data sources: SC Hospital Emergency Department chief-complaint data, Poison Control Center call data, Over-the-Counter pharmaceutical sales surveillance, and CDC’s BioSense biosurveillance system. The Influenza-like Illness Network (ILINet) is a collaboration between the Centers for Disease Control, state health departments and health care providers. ILINet is one of several components of SC’s influenza surveillance.

Objective

This paper compares the SCAAN hospital-based fever–flu syndrome category with the South Carolina Outpatient ILINet provider surveillance system. This is the first comparison of South Carolina’s syndromic surveillance SCAAN data with ILINet data since SCAAN’s deployment.

The Syndromic Surveillance Program (SSP) of the Acute Disease Epidemiology Section of the Georgia Division of Public Health, provides electronic influenza- like- illness (ILI) data to the Center for Disease Control and Prevention’s Influenza-like Illness Surveillance Network Program that characterizes the burden of influenza in states on a weekly basis.

ILI is defined as a fever of 1001, plus a cough or sore throat. This definition is used to classify ILI by the SSP, as well as in diagnosis at the pediatric hospital system. During the 2009 H1N1 pandemic, the SSP was provided a daily data transfer to the Center for Disease Control and Prevention to heighten situational awareness of the burden of ILI in Georgia. Throughout the peak of the pandemic, data from the pediatric hospital system identified when the percentage of daily visits for ILI had substantively increased. The data includes patient chief complaint (CC) data from emergency department visits for two facilities at Facilities A and B. The data received by SSP does not include diagnosis data.

Patient emergency department discharge data (DD) for ‘FLU’ was provided to SSP retrospectively to compare with the CC data routinely collected and analyzed. The data was derived from the pediatric health system’s month end, internal, syndromic surveillance report based upon emergency department visits, and including physician’s diagnosis at the time of patient’s discharge. The case definition of ‘FLU’ from the pediatric health system facilities is acute onset of fever, with cough and/or sore throat in the absence of a known cause other than influenza.

Objective

The objective of this study is to describe the difference between patient CC, ILI data provided daily to the Georgia SSP during the 2009 H1N1 pandemic, and patient DD subsequently provided for comparison with the SSP from its participating pediatric hospital system, and its two affiliated emergency rooms.

During summer 2012, Washington State Department of Health (WA DOH) surveyed ILINet providers and found that more than half either utilize their electronic medical record system (EMRS) to gather and report weekly ILINet data, or intend to implement queries to do so in the future. There are a variety of EMRS being used state-wide, and providers that currently utilize these systems to report ILINet data apply a wide range of methods to query their data. There exists great interest in the evaluation of ambulatory care data within the context of Meaningful Use and little research is published in this area. WA DOH sought to evaluate electronic data from WA outpatient clinic networks in order to determine if a syndromic ILI definition previously validated for emergency department (ED) data accurately identified ILI visits in electronic ambulatory care data.

Objective:

To determine if a syndromic influenza-like illness (ILI) definition previously validated for emergency department (ED) data accurately identified ILI visits in electronic ambulatory care data.

A Neolithic transformation is underway in public health, where the ubiquity of digital healthcare (HC) data is changing public health’s traditional role as data hunter-gatherers to one of data farmers harvesting huge reserves of electronic data. ILINet 1.0 is the current U.S. outpatient ILI surveillance network dependent on ~2000 volunteer sentinel providers recruited by States to report syndromic ILI. ILINet 1.0 began in the 1980s and represents a largely unchanged, ongoing hunter-gatherer approach to ILI outpatient surveillance involving the independent efforts of all state health departments. Many significant changes have occurred in the U.S. healthcare system since ILINet 1.0 was initiated. For example, eCommerce standards emerged in the 1990s creating ubiquitous amounts of easily accessible electronic healthcare administrative data. Since 2001 new public health surveillance approaches and investments have emerged including methods for syndromic surveillance (e.g. BioSense). Most recently healthcare reform efforts hold great promise (as yet largely unrealized) for public health to access electronic information derived from EHRs/HIEs (e.g., Meaningful Use). Could and should the current U.S. gold standard for ILI outpatient surveillance benefit from these new opportunities, and if so, what approach should be used and who should be responsible?

Objective

This paper outlines the current state of ILINet (ILINet 1.0), the accepted national gold standard for outpatient influenza-like illness (ILI) surveillance, and demonstrates how ILINet 2.0 could be more automated, timely, and locally representative if it were to utilize increasingly available electronic healthcare data rather than a specific group of recruited sentinel providers.