Influenza

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.

Emergency Departments (ED) supply critical infrastructure to provide medical care in the event of a disaster or disease outbreak, including seasonal and pandemic influenza [1]. Already over-crowded and stretched to near-capacity, influenza activity augments patient volumes and increases ED crowding [2,3]; high ED patient volumes expected during a true influenza pandemic represents a significant threat to the nation's healthcare infrastructure [4]. EDs ability to manage both seasonal and pandemic influenza surges is dependent on coupling early detection with graded rapid response. Although many EDs have devised influenza response measures, the potential utility of coupling early warning systems with various response strategies for managing influenza outbreaks in the ED setting has not been rigorously studied. While practical use of traditional surveillance systems has been limited due to the several week lag associated with reporting, new internet-based surveillance tools, such as GFT, report surveillance data in near-real time, thus allowing rapid integration into healthcare response planning [5].

Objective

Google Flu Trends (GFT) is a novel internet-based influenza surveillance system that uses search engine query data to estimate influenza activity. This study assesses the temporal correlation of city GFT data to both confirmed cases of influenza, as well as standard crowding indices from one inner-city emergency department (ED).

Syndromic surveillance of health care data such as the International Classification of Diseases, Ninth Revision (ICD-9), codes related to Influenza-Like-Illness (ILI), was used to track the progression of the 2009 Fall Novel H1N1 Outbreak in the Madison area. Early studies focused on prediction of an outbreak, however further investigation of patient resource utilization would be helpful in developing an action plan for addressing community and patient needs during future outbreaks. There is a paucity of research comparing emergency department (ED) and urgent care utilization rates during the 2009 Novel H1N1 Pandemic, though there is regional data suggesting that urgent care centers bore a larger portion of the burden of H1N1 influenza than emergency departments. Furthermore, one group found that ILI related phone calls to urgent care centers predicted influenza outbreak at least one week ahead of peaks in the ILI hospital care consultation rates. ED data on its own has proven useful for public health disease surveillance and many studies group urgent care and ED care together. The literature is lacking subgroup analysis of these two very different care environments. Understanding the correlation between urgent care and ED utilization rates will provide a more in depth understanding of the stress that the 2009 Fall Novel H1N1 placed on community resources in our geographic region.

Objective

To compare the proportion of patients presenting with ILI to urgent care centers versus the ED during the 2009 Fall Novel H1N1 Outbreak.