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Influenza-Like-Illness (ILI)

Bio-surveillance is an area providing real time or near real time data sets with a rich structure. In this area, the new wave of interest lies in incorporating medical-based data such as percentage of Influenza-Like-Illnesses (ILI) or count of ILI observed during visits to Emergency Room as intelligence function; since many different bioterrorist agents present with flu-like symptoms. Developing a control technique for ILI however is a complex process which involves the unpredictability of the time of emergence of influenza, the severity of the outbreak and the effectiveness of influenza epidemic interventions. Furthermore, the need to detect the beginning of epidemic in an on-line fashion as data are received one at the time and sequentially make the problems surrounding ILI's even more challenging. Statistical tools for analyzing these data are currently well short of being able to capture all their important structural details. Tools from statistical process control are on the face of it ideally suited for the task, since they address the exact problem of detecting a sudden shift against a background of random variability. Bayesian statistical methods are ideally suited to the setting of partial but imperfect information on the statistical parameters describing time series data such as are gathered in BioSense and Sentinel settings.

 

Objective

This paper presents a Bayesian approach to quality control through the use of sequential update technique in order built a fast detection method for influenza outbreak and potential intentional release of biological agents. The objective is to find evidence of outbreaks against a background in which markers of possible intentional release are non-stationary and serially dependent. This work takes on the US Sentinel ILI data to find this evidence and to address some issues related to the control of infectious diseases. A sensitivity analysis is conducted through simulation to assess timeliness, correct alarm and missed alarm rates of our technique.

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Submitted by elamb on

OBJECTIVE

Syndromic surveillance systems (SSS) seek early detection of infectious diseases outbreaks by focusing on pre-diagnostic symptoms. We do not yet know which respiratory syndrome should be monitored for a SSS to discover an influenza epidemic as soon as possible. This works compares the delay and workload required to detect an influenza epidemic using a SSS that targets either (1) all cases of acute respiratory infections (ARI) or (2) only those ARI cases that are febrile and satisfy CDC's definition for an influenza-like illness.

Referenced File
Submitted by elamb on

1) Describe a near real-time school-based syndromic surveillance program that integrates electronic data records and a two-way health alert system for early outbreak detection, notification, and possible intervention for Arizona schools. 2) Demonstrate the public health utility of this system for early detection of influenza among school children.

Referenced File
Submitted by elamb on

To compare age-group-specific correlation of influenza-like syndrome (ILS) emergency department (ED) visits with influenza laboratory data in Boston and NYC using locally defined ILS definitions.

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Submitted by elamb on

To compare locally-developed influenza-like syndrome definitions (derived from emergency department (ED) chief complaints) when applied to data from two ISDS DiSTRIBuTE Project participants: Boston and New York City (NYC) [1].

Referenced File
Submitted by elamb on

Research has shown that Canadian First Nation (FN) populations were disproportionately affected by the 2009 H1N1 influenza pan- demic. However, the mechanisms for the disproportionate outcomes are not well understood. Possibilities such as healthcare access, in- frastructure and housing issues, and pre-existing comorbidities have been suggested. We estimated the odds of hospitalization and inten- sive care unit admission for cases of H1N1 influenza among FN liv- ing in Manitoba, Canada, to determine the effect of location of residency and other factors on disease outcomes during the 2009 H1N1 pandemic.

Objective

We sought to measure from surveillance data the effect of prox- imity to an urban centre (rurality) and other risk factors, (e.g., age, residency on a FN reservation, and pandemic wave) on hospitaliza- tion and intensive care unit admission for severe influenza.

Submitted by dbedford on

Screening for Influenza Like Illness (ILI) is an important infection control activity within emergency departments (ED). When ILI screening is routinely completed in the ED it becomes clinically useful in isolating potentially infectious persons and protecting others from exposure to disease. When routinely collected, ILI screening in an electronic clinical application, with real time reporting, can be useful in Public Health surveillance activities and can support resource allocation decisions e.g. increasing decontamination cleaning. However, the reliability of documentation is unproven. Efforts to support the adoption of ILI screening documentation in a computer application, without mandatory field support, can lead to long term success and increased adherence.

 

Submitted by uysz on

In a 2007 survey of public health officials in the United States, International Society for Disease Surveillance found that only 7% used pharmacy prescription sales data for surveillance (1). There have been many reports suggesting effective use of prescription sales data in syndromic surveillance (2, 3, 4, 5). Community pharmacies can provide a valuable supplementary tool for syndromic surveillance of infectious diseases.

Objective

To examine if the prescription sales data from a large retail pharmacy chain in the US were comparable to Google Flu trends and CDC’s US ILI Network data as flu activity indicator.

 

 

Submitted by uysz on

Surveillance of influenza in the US, UK and other countries is based primarily on measures of influenza-like illness (ILI), through a combination of syndromic surveillance systems, however, this method may not capture the full spectrum of illness or the total burden of disease. Care seeking behaviour may change due to public beliefs, for example more people in the UK sought care for pH1N1 in the summer of 2009 than the winters of 2009/2010 and 2010/2011, resulting in potential inaccurate estimates from ILI. There may also be underreporting of or delays in reporting ILI in the community, for example in the UK those with mild illness are less likely to see a GP, and visits generally occur two or more days after onset of symptoms. Work absences, if the reason is known, could fill these gaps in detection.

Objective

To address the feasibility and efficiency of a novel syndromic surveillance method, monitoring influenza-like absence (ILA) among hospital staff, to improve national ILI surveillance and inform local hospital preparedness.

Submitted by teresa.hamby@d… on
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