Influenza

An essential theme of the US Federal Health Information Technology Strategic Plan is interoperability and the ability to effectively exchange information using specific data and technical standards.1 In 2005, in an effort to accelerate the development of a national laboratory standards-based electronic data-sharing network, APHL and CDC collaborated to launch PHLIP.2 The goals of PHLIP include, but are not limited to, improving the quality of data exchanged, piloting sustainable architecture for laboratory data exchange, sending and receiving HL7 test results from states to CDC programs (v2.3.1), increasing the use of Route-not-Read hubs for regional data exchange, and expanding these efforts beyond National Notifiable Diseases (NNDs). In an effort to achieve these goals, APHL solicited input directly from the PHL community to understand what assistance was necessary to achieve success with ELSM; in this case, Influenza as a prototype. After receiving feedback from PHLs responsible for reporting NNDs, the concept of technical assistance teams was formulated. In early 2010, APHL initiated an effort to send out the PATs to implement the ELSM message for Influenza in as many PHLs as possible by December 2010.

Objective

This paper describes the Public Health Laboratory Interoperability Project (PHLIP) assistance team (PAT) approach and the collaborative efforts between the Association of Public Health Laboratories (APHL) and the Centers for Disease Control and Prevention (CDC) to achieve electronic laboratory surveillance messaging (ELSM) for Influenza. The knowledge transfer and experience gained by state public health laboratories (PHLs) participating in PHLIP could serve as an interoperability model for other data messaging and surveillance initiatives.

We are developing a Bayesian surveillance system for realtime surveillance and characterization of outbreaks that incorporates a variety of data elements, including free-text clinical reports. An existing natural language processing (NLP) system called Topaz is being used to extract clinical data from the reports. Moving the NLP system from a research project to a real-time service has presented many challenges.

Objective

Adapt an existing NLP system to be a useful component in a system performing real-time surveillance.

School closure has long been proposed as a non-pharmaceutical intervention in reducing the transmission of pandemic influenza. Children are thought to have high transmission potential because of their low immunity to circulating influenza viruses and high contact rates. In the wake of pandemic (H1N1) 2009, simulation studies suggest that early and sustained school closure might be effective at reducing community-wide transmission of influenza. Determining when to close schools once an outbreak occurs has been difficult. Influenza-related absentee data from Japan were previously used to develop an algorithm to predict an outbreak of influenza-related absenteeism. However, the cause of absenteeism is frequently unavailable, making application of this model difficult in certain settings. For this study, we aimed to evaluate the potential for adapting the Japanese algorithm for use with all-cause absenteeism, using data on the rate of influenza-related nurse visits in

corresponding schools to validate our findings.

Objective

To determine the optimal pattern in school-specific all-cause absenteeism for use in informing school closure related to pandemic influenza.

Public health surveillance using death data is critical for tracking the impact of diseases such as influenza. However, utility of such systems is compromised by delayed reporting, particularly when it is paper based. In Nebraska, funeral directors are encouraged to initiate death certificates electronically by an electronic death registration system (EDRS). Although paper-based or mixed (electronic followed by paper) registration is still accepted statewide, EDRS usage has gradually increased over time. Fact of death (FOD) data that includes time and place of death, and a deceased person’s identity are usually recorded by a funeral director. Cause of death data in the medical portion are provided by physicians or medical examiners at a later date. FOD data entered into EDRS are immediately available, whereas paper-based data must first be mailed to vital records whereupon staff enter it into EDRS. Although implemented in 2006, epidemiology surveillance staff did not have realtime access to EDRS data until early 2009, when a collaboration was formed between the Office of vital records and the Office of epidemiology within the Nebraska Department of Health and Human Services. Daily electronic access by surveillance staff to death certificate data was established enabling the conductance of public health death surveillance.

Objective

This report describes use and evaluation of a near real time, novel electronic influenza mortality surveillance system developed in Nebraska.

It has been postulated that school absenteeism, a non-traditional surveillance data source, may allow for early detection of disease outbreaks, particularly among school-aged children who may not seek emergency medical attention. Although a New York City-based study showed moderate utility of school absenteeism in biosurveillance, no study to date has been reported on school absenteeism in Los Angeles County, which contains the second largest school district in the US.

Objective

To evaluate the utility of school absenteeism surveillance data in Los Angeles County during the 2009–2010 influenza season.

Accurately assigning causes or contributing causes to deaths remains a universal challenge, especially in the elderly with underlying disease. Cause of death statistics commonly record the underlying cause of death, and influenza deaths in winter are often attributed to underlying circulatory disorders. Estimating the number of deaths attributable to influenza is, therefore, usually performed using statistical models. These regression models (usually linear or poisson regression are applied) are flexible and can be built to incorporate trends in addition to influenza virus activity such as surveillance data on other viruses, bacteria, pure seasonal trends and temperature trends.

Objective

Mortality exhibits clear seasonality mainly caused by an increase in deaths in the elderly in winter. As there may be substantial hidden mortality for a number of common pathogens, we estimated the number of elderly deaths attributable to common seasonal viruses and bacteria for which robust weekly laboratory surveillance data were available.

Our laboratory previously established the value of over-the-counter (OTC) sales data for the early detection of disease outbreaks. We found that thermometer sales (TS) increased significantly and early during influenza (flu) season. Recently, the 2009 H1N1 outbreak has highlighted the need for developing methods that not only detect an outbreak but also estimate incidence so that public-health decision makers can allocate appropriate resources in response to an outbreak. Although a few studies have tried to estimate the H1N1 incidence in the 2009 outbreak, these were done months afterward and were based on data that are either not easy to collect or not available in a timely fashion (for example, surveys or confirmed laboratory cases).

Here, we explore the hypothesis that OTC sales data can also be used for predicting a disease activity. Towards that end, we developed a model to predict the number of Emergency Departments (ED) flu cases in a region based on TS. We obtain sales information from the National Retail Data Monitor (NRDM) project. NRDM collects daily sales data of 18 OTC categories across the US.

Objective

We developed a model that predicts the incidence of flu cases that present to ED in a given region based on TS.

Nationally, vaccine safety is monitored through several systems including Vaccine Adverse Event Reporting System (VAERS), a passive reporting system designed to detect potential vaccine safety concerns. Healthcare providers are encouraged to report adverse events after vaccination to VAERS, whether or not they believe that the vaccine caused the adverse event. The 2009 Pandemic H1N1 influenza vaccine became available in the United States in October 2009. By January 2010, Center for Disease Control and Prevention (Atlanta, GA, USA) estimated that 61 million persons across the United States had received the vaccine. As of January 2010, an estimated 28% of the North Carolina population greater than or equal to six months of age had been vaccinated against 2009 H1N1.

Objective

The objectives of this study were: (1) to compare trends in vaccine adverse events identified through emergency department (ED) diagnosis codes and reports from the VAERS, and (2) to determine whether 2009 H1N1 vaccine adverse events identified through VAERS could also be identified using ED diagnosis codes.

The global H1N1 influenza A pandemic in 2009 heightened the need for automated disease surveillance capabilities. After an initial surge in confirmatory testing, clinicians

moved to diagnosis based on patient assessment for fever combined with cough or sore throat, the influenza-like indicators (ILI). Although some organizations used automated data capture or national systems with manual data entry (www.cdc.gov/flu/weekly/fluactivity.htm), there was not a turnkey national automated system in place to support syndromic surveillance for ILI among non-affiliated organizations. Semantic interoperability through standards utilization is widely expected to simplify large-scale data initiatives but is challenging with widely disparate uses of terminology.

Objective

This paper describes a national initiative connecting 850 non-affiliated healthcare provider organizations throughout the United States in order to provide situational awareness during the 2009–2010 H1N1 influenza A pandemic. We addressed the challenge of semantic variability between organizations through a centralized data-mapping approach.

Seasonal influenza epidemics are responsible for over 200,000 hospitalizations in the United States per year, and 39,000 of them are in children. In the United States, the Advisory Committee on Immunization Practices guides immunization practices, including influenza vaccination, with recommendations revised on an annual basis. For the 2006–2007 flu season, the Advisory Committee on Immunization Practices recommendations for influenza vaccination began including healthy children aged 24–59 months (two to four years), a shift that added 10.6 million children to the target group.

Canada has a parallel federal organization, the National Advisory Committee on Immunization, which is responsible for guiding the use of vaccines. Recommendations made by the National Advisory Committee on Immunization and the Advisory Committee on Immunization Practices around seasonal influenza vaccination was concordant until the 2006–2007 season. Starting in the 2010–2011 season, the National Advisory Committee on Immunization has further expanded its recommendations to additional pediatric age groups by including two- to four-year-olds for targeted seasonal influenza vaccination.

We took advantage of this divergence in policy between two neighboring countries with similar annual seasonal influenza epidemics to try to understand the effects of the

policy change in the United States to expand influenza vaccination coverage to other pediatric populations.

Objective

The objective of this study is to estimate the effect of expanding recommendations for routine seasonal influenza vaccination to include 24–59-month-old children.