Influenza

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Unpublished statewide 2009 H1N1 epidemiological data suggests that rates of lab-confirmed H1N1-related hospitalization were three to four times higher in Black and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.

Historically, it has been the role of local health departments to administer, monitor, and report flu vaccinations of its residents to the state health department. In 2009, the looming threat of an influenza outbreak (H1N1) led to the extension of the Public Readiness and Emergency Preparedness Act (PREP) (1). On June 15, 2009, Kathleen Sebelius, Secretary of Health and Human Services, assigned all entities, including organizational and individual, tort liability immunity in the distribution and administration of H1N1 vaccines (1). This extension subsequently impaired local health departments ability to capture accurate estimates of flu immunizations being administered to their respective populations. Stark County Health Department, located in Ohio, in collaboration with Kent State University's College of Public Health, designed, developed, and deployed FITS based on the urgent need of accurate population data regarding influenza immunization at the county level.

Objective

To develop and implement a web-based, county-level flu immunization record keeping system that accurately tracks non-identifiable vaccine recipients and seamlessly uploads to the state record keeping system.

Work on vaccination timing and promotion largely precedes the 2009 pandemic. Post-pandemic studies examining the wide range of local vaccination efforts mostly have been limited to surveys assessing the role of administrative strategies, logistical challenges, and perceived deterrents of vaccination [1].

Objective

To assess the effectiveness of a Public Health automated phone campaign to increase vaccination uptake in targeted neighborhoods. To identify alternative predictors of variation in vaccination uptake, specifically to assess the association between vaccination uptake, and weather conditions and day-of-week.