Influenza

The South Carolina (SC) Department of Health and Environmental Control uses multiple surveillance systems to monitor influenza activity from October to May of each year, including participating in the U.S. Influenza Sentinel Providers Surveillance Network. A percentage of influenza-like-illness surpassing the national 2.5% baseline is considered evidence of increased influenza activity by the CDC; this baseline is historical and does not change throughout the influenza season. Though not a part of the national influenza surveillance, SC also requires health care providers in the state to report positive rapid influenza tests, by number, on a weekly basis. Currently, only a trend analysis is used on weekly reports of positive rapid influenza test data for SC. A more robust method for determining statistically significant increases in activity for these two influenza surveillance systems is needed, and would provide a more accurate assessment of the status of seasonal influenza activity in SC.

Objective

Use the Early Aberration Reporting System (EARS) to analyze influenza sentinel provider surveillance data and positive rapid influenza test reports to identify weeks where influenza activity was significantly increased in South Carolina. Demonstrate the utility of using EARS to detect increases in influenza activity using existing surveillance systems.

As public health surveillance is becoming more and more prevalent, new sources of data collection are more evident. One such data source is school absenteeism. By monitoring the symptoms of illness recorded when students are absent, health departments ideally can pinpoint potential outbreaks prior to their existence, all at little to no cost. The symptoms reported may not amount to disease, but their increase in incidence may indicate the preliminary spread of illness. This surveillance tool is also used to develop community intervention containment practices.

Objective

This paper describes the application of syndromic surveillance data from area school districts to detect influenza epidemics in a county setting.

There has been much recent interest in using disease signatures to better recognize disease outbreaks. Conversely, the metrics used to describe these signatures can also be used to better characterize the outbreaks. Recent work at the New York City Department of Health has shown the ability to identify characteristic age-specific patterns during influenza outbreaks. One issue that remains is how to implement a search for such patterns using prospective outbreak detection tools such as SatScan.

A potential approach to this problem arises from another currently active research area: the simultaneous use of multiple datastreams. One form of this is to disaggregate a data stream with respect to a third variable such as age. Two drawbacks to this approach are that the categories used to make the streams have to be defined a priori and that relationships between the streams cannot be exploited. Furthermore, the resulting description is less rich as it describes outbreaks in a few non-overlapping age-specific streams. It would be desirable to look for age specific patterns with the age groupings implicitly defined.

Objective

This paper presents an implementation of a citywide SatScan analysis that uses age as a one-dimensional spatial variable. The resulting clusters identify age-specific clusters of respiratory and fever/flu syndromes in the New York City Emergency Department Data.

Surveillance of individual data streams is a well-accepted approach to monitor community incidence of infectious diseases such as influenza, and to enable timely detection of outbreaks so that control measures can be applied. However the performance of alerts may be improved by simultaneously monitor a variety of data sources, or multiple streams (eg from different geographic locations) of the same type, rather than monitoring only aggregate data. Rates of influenza-like illness in subtropical settings typically show greater variability than in temperate regions.

Objective

This paper describes the use of time series models for simultaneous monitoring of multiple streams of influenza surveillance data.

Respiratory viruses cause substantial morbidity and costly resource utilization among young children, especially during the winter months. Accurate estimates of the impact of these viruses are important in guiding prevention efforts and measuring the impact of public health interventions. Previous studies have focused on the rate of hospitalizations resulting from viral infections, particularly those attributable to influenza virus for which a vaccine is available, but have not included healthcare use in the emergency department (ED) nor considered the impact of other viruses such as respiratory syncytial virus (RSV), for which limited preventative methods are available. We used ED surveillance data for acute respiratory infection to measure the population-based impact of specific viruses.

Objective

To use surveillance data to estimate resource utilization and parental lost productivity associated with influenza and RSV infections among young children.

In order to detect influenza outbreaks, the New York State Department of Health emergency department (ED) syndromic surveillance system uses patients’ chief complaint (CC) to assign visits to respiratory and fever syndromes. Recently, the CDC developed a more specific set of “sub-syndromes” including one that included only patients with a CC of flu or having a final ICD9 diagnosis of flu. Our own experience was that although flu may be a common presentation in the ED during the flu season, it is not commonly diagnosed as such. Emergency physicians usually use a symptomatic diagnosis in preference, probably because rapid testing is generally unavailable or may not change treatment. The flu subsyndrome is based on a specific ICD9 code for influenza. It is unknown whether patient visits that meet these restrictive criteria are sufficiently common to be of use, or whether patients who identify themselves as having the flu are correct.

Objective

Our objective was to examine the CC and ICD9 classifiers for the influenza sub-syndrome to assess the frequency of visits and the agreement between the CC, ICD9 code and chart review for these patient visits.

Influenza is an important public health problem associated with considerable morbidity and mortality. A disease traditionally monitored via legally mandated reporting, researchers have identified alternative data sources for influenza surveillance. The hospital environment presents a unique opportunity for comparative studies of biosurveillance data with high quality and various level of clinical information ranging from provisional diagnoses to laboratory confirmed cases. This study investigated the alert times achievable from hospital-based sources relative to reporting of influenza cases. The earlier detection of influenza could potentially provide more advanced warning for the medical community and the early implementation of precautionary measures in vulnerable populations.

Objective

To determine the relative alert time of influenza surveillance based on hospital data sources compared to notifiable disease reporting.

BioSense is a national system that receives, analyzes, and visualizes electronic health data and makes it available for public health use. In December 2007 CDC added the Influenza Module to the main BioSense application.

Objective

This presentation describes the new BioSense Influenza Module, its performance during the 2007-8 influenza season, and modifications for the 2008-9 influenza season.

Events of recent years, particularly concern about a possible avian (H5N1) influenza pandemic, have focused increasing attention on the need for timely surveillance, with real time surveillance as the ultimate goal. In a previous study, we reported on the utility of monitoring clinical laboratory results as a means of estimating the incidence of influenza in the U.S. within 24 hours using the Quest Diagnostics Corporate Informatics Data Warehouse. We have now begun to explore the feasibility of near real time surveillance using an internal application capable of providing alerts on unusual conditions within minutes of their occurrence. Our first application of this technology to infectious disease is monitoring activity related to the possible emergence of avian (H5N1) influenza in the United States.

Objective

To explore the utility of a system monitoring program for infectious disease surveillance with real time proactive notification.

We explored the utility of tracking emergency department (ED) visits for febrile illness as a proxy for influenza surveillance, from both a local and a regional perspective.