Influenza

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.

The Centers for Disease Control and Prevention's (CDC) Emerging Infections Program (EIP) monitors and studies many infectious diseases, including influenza. In 10 states in the US, information is collected for hospitalized patients with laboratory-confirmed influenza. Data are extracted manually by EIP personnel at each site, stripped of personal identifiers and sent to the CDC. The anonymized data are received and reviewed for consistency at the CDC before they are incorporated into further analyses. This includes identifying errors, which are used for classification.

Objective

Introducing data quality checks can be used to generate feedback that remediates and/or reduces error generation at the source. In this report, we introduce a classification of errors generated as part of the data collection process for the EIP’s Influenza Hospitalization Surveillance Project at the CDC. We also describe a set of mechanisms intended to minimize and correct these errors via feedback, with the collection sites.

Predictionmarkets have been successfully used to forecast future events in other fields. We adapted this method to provide estimates of the likelihood of H5N1 influenza related events.

Objective

The purpose of this study is to compare the results of an H5N1 influenza prediction market model with a standard statistical model.

Reporting notifiable conditions to public health authorities by health-care providers and laboratories is fundamental to the prevention, control, and monitoring of population-based disease. To successfully develop community centered health, public health strives to understand and to manage the diseases in its community. Public health surveillance systems provide the mechanisms for public health professionals to ascertain the true disease burden of the population in their community. The information

necessary to determine the disease burden is primarily found in the data generated during clinical care processes.

Objective

This poster will present a predictive model to describe the actual number of confirmed cases for an outbreak (H1N1) based on the current number of confirmed cases reported to public health. The model describes the methods used to calculate the number of cases expected in a community based on the lag time in the diagnosis and reporting of these cases to public health departments.

The South Carolina Aberration Alerting Network (SCAAN) is a collaborative network of syndromic systems within South Carolina. Currently, SCAAN contains the following data sources: SC Hospital Emergency Department chief-complaint data, Poison Control Center call data, Over-the-Counter pharmaceutical sales surveillance, and CDC’s BioSense biosurveillance system. The Influenza-like Illness Network (ILINet) is a collaboration between the Centers for Disease Control, state health departments and health care providers. ILINet is one of several components of SC’s influenza surveillance.

Objective

This paper compares the SCAAN hospital-based fever–flu syndrome category with the South Carolina Outpatient ILINet provider surveillance system. This is the first comparison of South Carolina’s syndromic surveillance SCAAN data with ILINet data since SCAAN’s deployment.