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Rolka Henry

West Nile Virus (WNV) is a mosquito-borne virus that can cause meningitis and encephalitis. Since its discovery in New York City during an encephalitis outbreak in 1999, WNV has become endemic in North America. In the United States, 16,000 human WNV disease cases (including West Nile fever, meningitis, encephalitis, and unspecified clinical illness) and over 600 WNV-related deaths have been reported to the Centers for Disease Control from 46 states. Perennial WNV epidemics occur during summer months, peaking during late August. BioSense Early Event Detection and Situation Awareness System receives daily laboratory test order data feed in HL7 from Laboratory Corporation of America. In this study, test orders were studied for their correlation with WNV activity.

 

Objective

To determine the feasibility of using BioSense laboratory test order data for West Nile disease surveillance in the United States. 

Referenced File
Submitted by elamb on

The Early Aberration Reporting System was developed at the Centers for Disease Control and Prevention to help assist local and state health officials to focus limited resources on appropriate activities of public health surveillance. Outbreaks of

infectious diseases are indicated in multiple spatial and temporal data sources, such as emergency department visits, drug store sales, and ambulatory clinic visits. Based on this premise, we provided correlated data sets and investigated disease clusters.

 

Objective

We present a pilot study of simulation of correlated outbreak signals for early aberration reporting and evaluating detection methods.

Referenced File
Submitted by elamb on

BioSense is a CDC initiative to promote situational awareness through summarizing, analyzing, and presenting health related event information. Among the data sources collected and analyzed through the BioSense application are the Department of Defense and Department of Veterans Affairs ambulatory clinic care data. Clinical diagnoses and procedures are quantified, and analytic results are presented and categorized into 94 state and metropolitan areas.

 

Objective

Precise geographic location of health events is a challenging but critical component to determine the likely site of exposure for disease surveillance. This paper describes a method used by BioSense to develop and implement a reasonable set of rules in defining geographic locations of health events.

Referenced File
Submitted by elamb on

Accurately gauging the health status of a population during an event of public health significance (e.g. hurricanes, H1N1 2009 pandemic) in support of emergency response and situation awareness efforts can be a challenge for established public health surveillance systems in terms of geographic and population coverage as well as the appropriateness of health indicators. The demand for timely, accurate, and event-specific data can require the rapid development of new data assets to “fill-in” existing information gaps to better characterize the scope, scale, magnitude, and population health impact of a given event within a very narrow time-window. Such new data assets may be concurrently under development and evaluation while being used to support response efforts. Recent examples include the “drop-in” surveillance processes deployed at evacuation centers following Hurricane Katrina1 and the illness and injury surveillance systems established for response workers during the Deepwater Horizon Oil spill response. During the 2009 H1N1 pandemic response, CDC acquired access to data from several national-level health information systems that previously had been un-vetted as public health information sources. These sources provided data extracts from massive administrative or electronic medical records (EMR) based in hospital and primary care settings. It was hoped that such data could supplement existing influenza surveillance systems and aid in the characterization of the pandemic. Few of these new data sources had formal documentation or concise information on the underlying populations and geographies represented.

 

Objective

To describe data management and analytic processes undertaken to rapidly acquire and use previously unavailable data during a public health emergency response.

Submitted by hparton on
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