Influenza

NBIC integrates, analyzes, and shares national biosurveillance information provided from capabilities distributed across public and private sectors. The integration of information enables early warning and shared situational awareness of nationally significant biological events to inform critical decisions directing response and recovery efforts.

The 2014-2015 HPAI H5 outbreak in the U.S. was the largest HPAI outbreak in the country’s history and resulted in the culling of millions of domestic birds and significant economic losses through loss wages, direct production losses, cost of recovery, consumer price increases, and trade restrictions.

NBIC worked closely with liaisons from USDA/APHIS and DOI/ NWHC over the course of the outbreak to integrate information from both agencies and open source reporting into reports and data sets providing early and sustained shared situational awareness to over 1400 federal, state, and local authorities.

Objective

The National Biosurveillance Integration Center (NBIC) coordinated information sharing with the U.S. Department of Agriculture (USDA/APHIS) and the Department of Interior (DOI/ NWHC) to integrate information and provide shared situational awareness of the 2014-2015 Highly Pathogenic Avian Influenza (HPAI) outbreak in the U.S. across all levels of government.

While the link between excess winter mortality and winter respiratory diseases in the elderly is well described, the impact of the epidemic of influenza in the elderly is mainly assessed in France through specific surveillance in the general population. Syndromic surveillance data enables to monitor ED attendances and hospitalizations for various diagnostic codes groupings throughout the influenza epidemic, some of which often cited as influenza proxies, such as cardiorespiratory diagnostic groups.

In mainland France, the 2014-15 season was characterized by an intense influenza epidemic in the community (sub-type A(H3N2) dominant virus). Hospital overcrowding was early reported, partly linked to serious clinical presentations among the elderly, and leading to the triggering of a national emergency plan.

We hypothesized that ED numbers of clinical influenza cases underestimate the influenza burden among patients aged 65 years and over, especially when a A(H3N2) influenza subtype circulates.

Objective

To estimate the real burden of influenza epidemic on emergency departments (ED) attendances and hospitalizations among patients over 65 years in order to better understand determinants of overcrowding and mortality excess.

Each year several thousands contract the seasonal flu, and it is estimated that these viruses are responsible for the deaths of over six thousand individuals [1]. Further, when a new strain is detected (e.g. 2009), the result can be substantially more dramatic [2]. Because of the potential threats flu viruses pose, the United States, like many developed countries, has a very well established flu surveillance system consisting of 10 components collecting laboratory data, mortality data, hospitalization data and sentinel outpatient care data [3]. Currently, this surveillance system is estimated to lag behind the actual seasonal outbreak by one to two weeks. As new data streams come online, it is important to understand what added benefit they bring to the flu surveillance system complex. For data streams to be effective, they should provide data in a more timely fashion or provide additional data that current surveillance systems cannot provide. Two types of multiplexed diagnostic tools designed to test syndromically relevant pathogens and wirelessly upload data for rapid integration and interpretation were evaluated to see how they fit into the influenza surveillance scheme in California.

Objective

Evaluate utility of point of need diagnostic tests in relationship to current standard influenza detection methods.

National Influenza Sentinel Surveillance (NISS) was established in Nigeria in 2006 to monitor influenza occurrence in humans in Nigeria and provide a foundation for detecting outbreaks of novel strains of influenza. Surveillance for influenza-like illness (ILI) and severe acute respiratory infection (SARI) is carried out in 4 sentinel sites. Specimens and epidemiological data are collected and transported 4 days a week from the sentinel sites to the National Influenza Reference Laboratory. At the laboratory, they are tested for influenza A and B viruses and further subtyped if positive for influenza A virus.

Objective

To assess the performance of the surveillance system and identify factors affecting the performance.

Flu Near You allows individuals to volunteer to be a sentinel node of the syndromic surveillance (SyS) network. The platform has the potential to provide insight into the spread of influenza-like illness (ILI). CDC’s ILINet is the gold standard for tracking ILI at the national level, but does not track into the local level. Local health departments (LHD) frequently express a need for granular data specific to their jurisdictions. FNY attempts to meet this need by collecting and sharing data at the zip code level. Knowing how well FNY data correlates to ILINet data will give local health departments an important tool to communicate the arrival of influenza to their jurisdiction. However, there is significant skepticism at the quality of FNY data as compared to validated datasets.

Objective

Our objective is to provide evidence for the data quality of Flu Near You (FNY) by evaluating the national and Houston datasets against CDC ILI data.

Evidence from over 100 years of epidemiological study demonstrates a consistent, negative association between health and economic prosperity. In many settings, it is clear that causal links exist between lower socioeconomic status and both reduced access to healthcare and increased disease burden. However, our study is the first to demonstrate that the increased disease burden in at-risk populations interacts with their reduced access to healthcare to hinder surveillance.

Objective

Improve situational awareness for influenza by combining multiple data sources to predict influenza outbreaks in at-risk populations.

 Influenza surveillance is conducted through a complex network of laboratory and epidemiologic systems essential for estimating population burden of disease, selecting influenza vaccine viruses, and detecting novel influenza viruses with pandemic potential (1). Influenza surveillance faces numerous challenges, such as constantly changing influenza viruses, substantial variability in the number of affected people and the severity of disease, nonspecific symptoms, and need for laboratory testing to confirm diagnosis. Exploring additional components that provide morbidity information may enhance current influenza surveillance. School-aged children have the highest influenza incidence rates among all age groups. Due to the close interaction of children in schools and subsequent introduction of influenza into households, it is recognized that schools can serve as amplification points of influenza transmission in communities. For this reason, pandemic preparedness recommendations include possible pre-emptive school closures, before transmission is widespread within a school system or broader community, to slow influenza transmission until appropriate vaccines become available. During seasonal influenza epidemics, school closures are usually reactive, implemented in response to high absenteeism of students and staff after the disease is already widespread in the community. Reactive closures are often too late to reduce influenza transmission and are ineffective. To enhance timely influenza detection, a variety of nontraditional data sources have been explored. School absenteeism was suggested by several research groups to improve school-based influenza surveillance. A study conducted in Japan demonstrated that influenzaassociated absenteeism can predict influenza outbreaks with high sensitivity and specificity (2). Another study found the use of allcauses absenteeism to be too nonspecific for utility in influenza surveillance (3). Creation of school-based early warning systems for pandemic influenza remains an interest, and further studies are needed. The panel will discuss how school-based surveillance can complement existing influenza surveillance systems.

Objective

This session will provide an overview of the current systems for influenza surveillance; review the role of schools in influenza transmission; discuss relationships between school closures, school absenteeism, and influenza transmission; and explore the usefulness of school absenteeism and unplanned school closure monitoring for early detection of influenza in schools and broader communities.

Clinical quality measures (CQMs) are tools that help measure and track the quality of health care services. Measuring and reporting CQMs helps to ensure that our health care system is delivering effective, safe, efficient, patient-centered, equitable, and timely care. The CQM for influenza immunization measures the percentage of patients aged 6 months and older seen for a visit between October 1 and March 31 who received (or reports previous receipt of) an influenza immunization. Centers for Disease Control and Prevention recommends that everyone 6 months of age and older receive an influenza immunization every season, which can reduce influenzarelated morbidity and mortality and hospitalizations.

Objective

To explain the utility of using an automated syndromic surveillance program with advanced natural language processing (NLP) to improve clinical quality measures reporting for influenza immunization.

Public health practitioners endeavor to expand and refine their syndromic and other advanced surveillance systems which are designed to supplement their existing laboratory testing and disease surveillance toolkit. While much of the development and widespread implementation of these systems was previously supported by public health preparedness funding, the reduction of these monies has greatly constrained the ability of public health agencies to staff and maintain these systems. The appearance of highly-pathogenic avian influenza (HPAI) H3N2v, and other novel influenza A viruses required agencies to carefully identify systems which provide the most cost-effective data to support their public health practice. The global emergence of influenza A (H7N9), Ebola virus strains, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and other viruses associated with high mortality, emphasize the importance of maintaining vigilance for the presence of emerging diseases.

Objective

To continue efforts in characterizing the challenges experienced by influenza surveillance coordinators and other practitioners conducting surveillance for the presence of avian influenza, novel respiratory diseases, and other globally emerging viruses in an era of limited resources among public health agencies.

Rates of student absenteeism in schools have been mainly used to detect outbreaks in schools and prompt public health action to stop local transmission. A report by Kim Mogto et al.  stated that aggregated counts of school absenteeism (SAi) were correlated with PPFluA, but the sample may have been biased. The purpose of this study was to assess the correlation between aggregated rates of SAi and PPFluA for two cities, Calgary and Edmonton, in Alberta. In such situations, SAi could potentially be used as a proxy for PPFluA when there are not enough samples for stable laboratory estimates.

Objective

To assess the correlations between weekly rates of elementary school absenteeism due to illness (SAi) and percent positivity for influenza A from laboratory testing (PPFluA) when conducted at a city level from September to December over multiple years.