Influenza-Like-Illness (ILI)

The Louisiana Office of Public Health conducts ED syndromic surveillance using the Louisiana Early Event Detection System (LEEDS). Using outpatient data for syndromic surveillance is a relatively new concept, brought about due to the increasing use of EHRs and HIEs making such data readily available. Previously, there has been limited means of syndrome classification validation for the LEEDS data and the GNOHIE data has not been studied widely as a population sample, so this analysis and comparison is valuable on both fronts. Due to differences in the types of data (ADT messages from EDs and CCD from outpatient clinics), as well as different patient populations and site visit capability, the percentages of patients classified as ILI between data sets are unequal. The main focus of this analysis is determining whether the ILI classifiers applied to both data sets detect similar syndrome trends.

Each indicator used in the study represents the percentage of total patients seen that week who are classified as ILI cases. The study period covered the 13-14 influenza season, CDC week 1340 through 1420 (9/29/2013-5/17/2014). Two ILI classifiers were applied to both the GNOHIE and LEEDS data:the first classifier consisted of ICD-9 influenza codes and the second classifier consisted of keywords applied to encounter notes(GNOHIE data) and chief complaint, admit reason and diagnoses (LEEDS data). A graph of the data, below, shows the four data sets.

Objective

The goal of this analysis is to compare the results of influenza-like illness (ILI) text and International Classification of Diseases (ICD) code classifiers applied to the Louisiana Office of Public Health’s (OPH) syndromic surveillance data reported by New Orleans area emergency departments and the Greater New Orleans Health Information Exchange’s (GNOHIE) data reported by New Orleans area outpatient clinics.

Weather events such as a heat wave or a cold snap can cause a change to the number of patients and types of symptoms seen at a healthcare facility. Understanding the impact of weather patterns on ILI surveillance may be useful for early detection and trend analysis. In addition, weather patterns limit our ability to extrapolate data collected in one region to a different region, which may not share the same weather or periodic trends. By modeling these sources of variation, we can factor out their effects and increase the sensitivity of our overall surveillance system.

Objective

To develop a statistical model to account for weather variation in influenza-like illness (ILI) surveillance.

Decreasing contact between infectious and susceptible people in community settings may reduce influenza transmission. Examining the temporal relationship between the winter holiday break and seasonal influenza activity can provide insight of alternative contact patterns on influenza spread.

Objective

To explore the relationship between influenza-like illness observed by influenza out-patient network and winter holiday breaks in US.

Influenza is a highly contagious, acute respiratory disease that causes periodic seasonal epidemics and global pandemics[1]. Yunnan Province is characterized by poverty, multi-ethnic, and cross-border movement, which maybe be susceptible of influenza (Fig-1). Finding from spatial patter of ILI will promote to control and prevent the respiratory diseases epidemic

Objective

The purpose of the study was to determine spatial clustering of the spread of influenza like illness (ILI) epidemic in Yunnan province, China with the aim of producing useful information for prevention and control measures.

 

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.

Active surveillance for influenza is a useful but costly endeavor. In recent years infoveillance tools have been developed to track and analyze data available on the Internet and social media (Eysenbach 2011). While infoveillance tools have been developed, few tools focus on geo-targeted data collection at a local level combined with Geographic Information Systems (GIS) capability.

Objective

We developed geo-targeted social media application program interfaces (APIs) for Twitter and a web-based social media analytics and research testbed (SMART) dashboard to analyze “flu” related tweets. During the 2013-14 flu season, for 10 cities with active surveillance for influenza (ILI), we correlated weekly tweeting rates and visual patterns of flu tweeting rates. To facilitate widespread use and testing of this system, we developed an interactive webbased dashboard “SMART” that allows practitioners to monitor and visualize daily changes of flu trends and related flu news.