Collaboration

NPDS is the only source for national surveillance data regarding human exposures to hazardous substances and their health effects. It is a near real-time surveillance system operated by the American Association of Poison Control Centers (AAPCC) in cooperation with CDC’s National Center for Environmental Health. The system receives, analyzes, and displays data from 60 regional Poison Centers (PCs). On 20 April 2010, an explosion occurred on the Deepwater Horizon oil rig, causing oil to be continuously spilled into the Gulf of Mexico. In response, AAPCC created a code that was sent to all 60 PCs, allowing the centers to identify and properly code all calls associated with the oil spill at the local level. This enabled CDC to track all spill-related exposure and information calls.

Objective

The objective of this study was to describe how National Poison Data System (NPDS) was used for surveillance of human health effects associated with crude oil and dispersant exposures during the Deepwater Horizon Oil Spill.

Prediction markets are a type of futures market in which users trade shares that pay off if the event to which they are connected occurs. They are used to aggregate knowledge on a large scale, as the prices of the various contracts can be interpreted as probabilities of their events. Since 2006, our group has been using prediction markets and testing their utility in predicting the spread and impact of diseases, including seasonal influenza, syphilis, and others on a market called the Iowa Electronic Health Markets (IEhM), found at http://iehm.uiowa.edu. For example, in 2009, a series of markets were run on novel influenza A (H1N1), which showed success in predicting the extent and duration of the outbreak.1 We currently plan to move into a new phase of development that will allow the community of users to submit proposals for new prediction markets, which will then be approved by site editors and referees. We call the new system Samos.

Objective

This poster presents a software system to provide a community-driven, user-generated, low-overhead, web-based prediction market system called Samos.

Emerging and re-emerging infectious diseases are a serious threat to global public health. The World Health Organization (WHO) has identified more than 1100 epidemic events worldwide in the last 5 years alone. Recently, the emergence of the novel 2009 influenza A (H1N1) virus and the SARS coronavirus has demonstrated how rapidly pathogens can spread worldwide. This infectious disease threat, combined with a concern over man-made biological or chemical events, spurred WHO to update their International Health Regulations (IHR) in 2005. The new 2005 IHR, a legally binding instrument for all 194 WHO member countries, significantly expanded the scope of reportable conditions, and are intended to help prevent and respond to global public health threats. SAGES aims to improve local public health surveillance and IHR compliance, with particular emphasis on resource-limited settings.

Objective

This paper describes the development of the Suite for Automated Global bioSurveillance (SAGES), a collection of freely available software tools intended to enhance electronic disease surveillance in resource-limited settings around the world.

Funded by the Army’s Telemedicine and Advanced Technology Research Center, we developed the BioSINE toolset to provide visualization and collaboration capabilities to improve the accessibility and utility of health surveillance data. Investigation of public health (PH) practitioners’ needs with cognitive engineering methods revealed two key objectives: 

1. To provide analysts and decision makers with an intuitive, visually driven workspace. 

2. To support a web presence to provide rapid updating and facilitate greater interaction with data analysis in the PH community.

To better serve under-resourced PH organizations, both domestic and abroad, it is necessary to minimize information technology requirements and expertise in complex analytic tools.

BioSINE provides decision makers with the ability to create customized visualizations, focus on specific aspects of the data, or conduct hypothesis testing. Users can also view or hide variables, specify data ranges, and filter data relevant to their interests. Figure 1 shows a display in which a user investigated seasonal effects by narrowing the analysis to the summer months. Intuitive filtering is a key characteristic of the application to quickly produce snapshots of local interests.

Objective

BioSINE strives to improve situational awareness by making data visualization and collaboration capabilities intuitive and readily available for a wide range of PH stakeholders.

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.

Given the clear relationship between spatial contexts and health, the Indiana Center of Excellence in Public Health Informatics (ICEPHI) aims to serve both the needs of public health researchers and practitioners by contextualizing the health information of large populations. Specifically, ICEPHI will integrate one of the nation’s largest health information exchanges, the Indiana Network for Patient Care, with well-established community information systems that collect, geocode, organize, and present integrated data on communities in Indiana and surrounding states, including data on public safety, welfare, education, economics, and demographics.

Objective

This presentation describes a collaborative approach for realizing the public health potential of a geospatially enabled statewide health information exchange.