Finelli Lyn

Given the periodic nature of influenza activity, it is important to develop visualization tools that enable enhanced decision-making. User-Centered Design is a set of software development methodologies that primarily employ user needs to develop applications. Similarly, Usability Heuristics provide a set of rules that increase the performance of user interfaces, and ease of use. We combined some of these techniques to develop FluView Interactive, a prototype that will enable users to better understand influenza information.

Objective

The objective of this study is to report on the use of User-Centered Design and Usability Heuristics to improve visualization of influenza-related information at the national level. The intention of the prototype is to make data more accessible to different stakeholders including the general public, public health officials at the local and state level, and other experts.

Real-time emergency department (ED) data from the BioSense surveillance program for ILI visits and ILI admissions provide valuable insight into disease severity that bridges gaps in traditional influenza surveillance systems that monitor ILI in outpatient settings and laboratory-confirmed hospitalization, but do not quantify the relationship between ILI visits and hospital admissions.

Objective

The purpose of this analysis is to gain understanding of the burden of influenza in recent years through analysis of clinically rich hospital data. Patterns of visits and severity measures such as the ratio of admissions related to influenzalike illness (ILI) by age group from 2007 to 2010 are described.

http://Google.org developed a regression model that used the volume of influenza-related search queries best correlated with the proportion of outpatient visits related to influenza-like illness (ILI) model to estimate the level of ILI activity. For calibration, the model used ILINet data from October 2003 to 2009, which report weekly ILI activity as the percentage of patient visits to health care providers for ILI from the total number patient visits for the week. Estimates of ILI in 121 cities were added in January 2010.

Objective

This paper compares estimates of ILI activity with estimates from the Centers for Disease Control’s ILINet from October 2008 through March 2010.

During the spring of 2009, a public health emergency was declared in response to the emergence of the 2009 Influenza A (H1N1) virus. Owing to the response, timely data were needed to improve situational awareness and to inform public health officials. Traditional influenza surveillance is time-consuming and resource intensive, and electronic data sources are often more timely and resource saving. Collaboration began between the Centers for Disease Control and Prevention (CDC), the International Society for Disease Surveillance, and the Public Health Informatics Institute to expand syndromic Emergency Department (ED) surveillance through the Distribute project.

Distribute collects aggregate, daily or weekly reports of influenza-like illness (ILI) and total patient visits to EDs from participating health jurisdictions, stratified by age group and other variables. Additional variables included the three digit zip code of the patient’s residence as well as the disposition and temperature, however not all jurisdictions collect these variables. Distribute data are typically extracted from ED-based electronic health data systems. The ILI definition is determined by the participating jurisdiction that can be a city, county, or state. At the time of analysis, the network consisted of 33 jurisdictions.

Because ILI data reported to Distribute had not been systematically compared with data reported through other surveillance systems, CDC planned an evaluation of the Distribute data, which included a comparison to the Influenza-like Illness Network (ILINet). 

ILINet is a collaborative effort between the CDC, local and state health departments and primary health care providers. The network currently consists of approximately 3000 healthcare providers in all 50 states, Chicago, the District of Columbia, New York City, and the US Virgin Islands. Enrolled providers send CDC weekly reports via internet or fax that consist of the total number of patients seen for any reason and the number of those patients with ILI by age group. ILI is defined as fever (temperature of X1001F (37.8 1C)) and a cough and/or sore throat in the absence of a known cause other than influenza.

Objective

To compare ILI data reported to the Distribute surveillance project to data from an existing influenza surveillance system, the US Outpatient ILINet.

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.