Influenza

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.

Influenza is a major cause of mortality. In developed countries, mortality is at its highest during winter months, not only as a result of deaths from influenza and pneumonia but also as a result of deaths attributed to other diseases (e.g. cardiovascular disease). Understandably, much of the surveillance of influenza follows predefined geographic regions (e.g. census regions or state boundaries). However, the spread of influenza and its resulting mortality does not respect such boundaries.

Objective

To cluster cities in the United States based on their levels of mortality from influenza and pneumonia.

The threat of epidemics due to non-human strains of influenza A viruses is ever present1. Surveillance is a critical aspect of pandemic preparedness for early case detection2. Identification of the index cases of a pandemic virus can trigger public health mitigation efforts3. To develop an appropriate surveillance process, it is important to understand the two possibilities of pandemic evolution. A new pandemic may begin with mild cases, during which surveillance should be concentrated on work/school absenteeism and in physician offices. The other possibility begins with severe cases, characterized by sCAP, respiratory failure, and ICU admission. As the syndrome of pneumonia is not reportable to health agencies for public health surveillance, a year-round, hospital-based surveillance mechanism may be an important tool for early case detection in the event of an epidemic of sCAP. To fill these gaps, we developed a statewide, hospital-based surveillance network for sCAP surveillance in Kentucky.

Objective

To present the development and implementation of the SIPS project, a statewide, hospital-based surveillance system for severe community-acquired pneumonia (sCAP) in Kentucky.

Influenza-like illness (ILI) data is collected via an Influenza Sentinel Provider Surveillance Network at the state level. Because participation is voluntary, locations of the sentinel providers may not reflect optimal geographic placement. This study analyzes two different geographic placement schemes - a maximal coverage model (MCM) and a K-median model, two location-allocation models commonly used in geographic information systems. The MCM chooses sites in areas with the densest population. The K-median model chooses sites which minimize the average distance traveled by individuals to their nearest site. We have previously shown how a placement model can be used to improve population coverage for ILI surveillance in Iowa when considering the sites recruited by the Iowa Department of Public Health. We extend this work by evaluating different surveillance placement algorithms with respect to outbreak intensity and timing (i.e., being able to capture the start, peak and end of the influenza season).

Objective

To evaluate the performance of several sentinel surveillance site placement algorithms for ILI surveillance systems. We explore how these different approaches perform by capturing both the overall intensity and timing of influenza activity in the state of Iowa.

Much progress has been made on the development of novel systems for influenza surveillance, or explored the choices of algorithms for detecting the start of a peak season. The use of multiple streams of surveillance data has been shown to improve performance but few studies have explored its use in situational awareness to quantify level or trend of disease activity. In this study we propose a multivariate statistical approach which describes overall influenza activity and handles interrupted or drop-in surveillance systems.

Objective

This paper describes the use of multiple influenza surveillance data for situational awareness of influenza activity.

During the 2009 H1N1 influenza pandemic, the Washington State Department of Health (DOH) temporarily made lab-confirmed influenza hospitalizations reportable. Reporting of influenza hospitalizations is resource intensive for hospitals and local health jurisdictions. As a result, electronic sources of influenza hospitalization data are being explored. A Regional Health Information Exchange (HIE) in Washington currently sends DOH ICD9 coded discharge diagnoses and microbiology laboratory orders and results for all patients admitted to 17 hospitals throughout Washington, including four of the five hospitals in Spokane County. The HIE hospitalization and laboratory data may be a valuable replacement for mandatory notifiable condition reporting to monitor the basic epidemiology and severity of influenza in Washington.

Objective

To evaluate the sensitivity, positive predictive value (PPV), timeliness, completeness, and representativeness of lab-confirmed influenza hospitalization data from a health information exchange with respect to traditional notifiable condition reporting.

In April 2009, a novel strain of influenza A was detected in Mexico, which quickly spread to the United States and the rest of the world. In response to the pandemic, the New Hampshire Department of Health and Human Services (NH DHHS) developed a web-based school absenteeism reporting system to track and record overall absenteeism and influenza-like-illness (ILI) related absenteeism in New Hampshire schools.

Objective

To monitor community illness and detect outbreaks during the 2009 influenza A/H1N1 pandemic using a newly developed surveillance system for monitoring school absenteeism.

The electronic surveillance system for the early notification of community-based epidemics (ESSENCE) is the web-based syndromic surveillance system utilized by the Maryland Department of Health and Mental Hygiene (DHMH). ESSENCE utilizes a secure, automated process for the transfer of data to the ESSENCE system that is consistent with federal standards for electronic disease surveillance. Data sources in the Maryland ESSENCE system include ED chief complaints, poison control center calls, over-the-counter (OTC) medication sales, and pharmaceutical transaction data (specifically for anti-bacterial and anti-viral medications). All data sources have statewide coverage and are captured daily in near real-time fashion.

Objective

To examine the trends in prescription antiviral medication transactions and emergency department (ED) visits for influenza-like illness (ILI) and the relationship between these trends.

Syndromic surveillance systems were designed for early outbreak and bioterrorism event detection. As practical experience shaped development and implementation, these systems became more broadly used for general surveillance and situational awareness, notably influenza-like illness (ILI) monitoring. Beginning in 2006, ISDS engaged partners from state and local health departments to build Distribute, a distributed surveillance network for sharing de-identified aggregate emergency department syndromic surveillance data through existing state and local public health systems. To provide more meaningful cross-jurisdictional comparisons and to allow valid aggregation of syndromic data at the national level, a pilot study was conducted to assess implementation of a common ILI syndrome definition across Distribute.

Objective

Assess the feasibility and utility of adopting a common ILI syndrome across participating jurisdictions in the ISDS Distribute project.