Influenza

Despite the number of infections, hospitalizations, and deaths from influenza each year, developing the ability to predict the timing of these outbreaks has remained elusive. Public health practitioners have lacked a reliable, easy-to-implement method for predicting the onset of a period of elevated influenza incidence in a community. We (a team of statisticians, epidemiologists, and clinicians) have developed a model to help public health practitioners develop simple, adaptable, data-driven rules to define a period of increased disease incidence in a given location. We call this method the Above Local Elevated Respiratory illness Threshold (ALERT) algorithm. The ALERT algorithm is a simple method that defines a period of elevated disease incidence in a community or hospital that systematically collects surveillance data on a particular disease.

Objective

Our objective was to develop a simple, easy-to-use algorithm to predict the onset of a period of elevated influenza incidence in a community using surveillance data.

During the past decade, public health practitioners have implemented various new syndromic and other advanced surveillance systems to supplement their existing laboratory testing and disease surveillance toolkit. While much of the development and widespread implementation of these systems had been 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 H3N2v and other novel influenza A viruses, requires agencies to carefully choose which systems will provide the most cost-effective data to support their public health practice. The global emergence of influenza A H7N9, 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 disease.

Objective

To review approaches used by public health agencies for alerting health care providers and enhancing surveillance systems to identify the presence of novel respiratory disease and to characterize their recent experience in searching for globally emerging viruses.

Influenza epidemics occur seasonally but with spatiotemporal variations in peak incidence. Many modeling studies examine transmission dynamics [1], but relatively few have examined spatiotemporal prediction of future outbreaks [2]. Bootsma et al [3] examined past influenza epidemics and found that the timing of public health interventions strongly affected the morbidity and mortality. Being able to predict when and where high influenza incidence levels will occur before they happen would provide additional lead time for public health professionals to plan mitigation strategies. These predictions are especially valuable to them when the positive predictive value is high and subsequently false positives are infrequent.

Objective

Advanced techniques in data mining and integrating evidence from multiple sources are used to predict levels of influenza incidence several weeks in advance and display results on a map in order to help public health professionals prepare mitigation measures.

For public health surveillance to achieve its desired purpose of reducing morbidity and mortality, surveillance data must be linked to public health response. While there is evidence of the growing popularity of syndromic surveillance (1,2), the impact or value added with its application to public health responses is not well described (3).

Objective

To describe if and how syndromic surveillance data influenced public health decisions made during the 2009 H1N1 pandemic within the context of other existing public health surveillance systems.

Unpublished statewide 2009 H1N1 epidemiological data suggests that rates of lab-confirmed H1N1-related hospitalization were three to four times higher in Black and Hispanic populations compared to White, non-Hispanic populations (Alfred DeMaria, MDPH, personal communication, 2010). There is an absence of socioeconomic data in most public health surveillance systems, and population-based statewide descriptions of H1N1-related hospitalizations according to race/ethnic group and SES have not been described.

Objective

1) Investigate 2009 H1N1-related ICU admissions in Massachusetts by race/ethnic group;

2) Investigate the association between ICU stay and race/ethnic group adjusted for socioeconomic status (SES).

In Reunion Island, the non-specific surveillance was developed since 2006 and was based on the activity of only one hospital emergency department and on mortality. To respond to the threat of influenza A(H1N1) pandemic emergence, this surveillance system was significantly enhanced. All hospital emergency departments of the island have been included as well as the emergency medical service regulation center. In 2010, a new surveillance was implemented from National Health Insurance data.

Objective

To demonstrate that the different surveillance systems allow to establish complementary indicators.

After the 2009 H1N1 influenza pandemic, CDC initiated community-based surveillance of self-reported influenza-like illness (ILI)[1], defined as the presence of fever with cough or sore throat. Although ILI is frequently attributed to other pathogens, including rhinovirus, routine surveillance of ILI at the population level does aid in the detection of nascent influenza outbreaks. In the United States, approximately 90% of influenza-related deaths occur among adults aged 65 years and older[2]. We explored the association of influenza vaccination with ILI, among this vulnerable age group.

Objective

To explore the association of influenza vaccination with Influenza-like illness ( ILI) among adults aged 65 years and older

The Veterans Affairs (VA) ESSENCE obtains electronic health record data from 152 medical centers plus clinics in all 50 states, U.S. territories, and the Philippines. ESSENCE analyzes ICD-9 diagnosis codes and demographic data from outpatient and emergency department visits using complex aberrancy-detection algorithms. In 2010, a new instance was stood up (VA Inpatient ESSENCE) which receives weekly feeds of inpatient data from all VA acute care hospitals starting at the beginning of the Fiscal Year (FY10, Oct. 1, 2009). Data include demographics, admission/discharge data (including ICD-9 diagnosis codes), diagnosis related group, bedsection, procedure and surgery data.

Objective

To describe the utility of inpatient data in VA ESSENCE biosurveillance system for healthcare-associated infection and public health surveillance.

Influenza is a serious disease that seasonality causes substantial but varying morbidity and mortality. In Taiwan, estimates of the influenza mortality burden were based on post-hoc analyses of national mortality statistics and not available until at least six months after the corresponding epidemic. Timely monitoring and early detection of influenza-associated excess mortality can guide antiviral or vaccine interventions and help healthcare capacity planning. Beginning April 2009, Taiwan Centers for Disease Control has been collaborating with the Department of Health Office of Statistics to develop an automated system for real-time pneumonia and influenza (P&I) mortality surveillance.

Objective 

To develop and evaluate a nationwide automated system for early detection of aberrations and real-time monitoring of P&I mortality in Taiwan.

Syndromic surveillance systems use electronic health-related data to support near-real time disease surveillance. Over the last 10 years, the use of ILI syndromes defined from emergency department (ED) data has become an increasingly accepted strategy for public health influenza surveillance at the local and national levels. However, various ILI definitions exist and few studies have used patient-level data to describe validity for influenza specifically.

Objective

Estimate and compare the accuracy of various ILI syndromes for detecting lab-confirmed influenza in children.