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Use for the Analytic Solutions for Real-Time Surveillance: Asyndromic Cluster Detection consultancy held June 9-10, 2015 at the University of North Carolina, Chapel Hill.

Problem Summary

A syndrome cannot be created to identify every possible cluster of potential public health... Read more

Content type: Use Case

Currently, there is an abundance of data coming from most of the surveillance environments and applications. Identification and filtering of responsive messages from this big data ocean and then processing these informative datasets to gain knowledge are the two real challenges in today’s... Read more

Content type: Abstract

We sought to compare ambulatory care (AC) and emergency department (ED) data for the detection of clusters of lower gastrointestinal illness, using AC and ED data and AC+ED data combined, from two geographically separate health plans participating in the National Bioterrorism Syndromic... Read more

Content type: Abstract

Syndromic surveillance is focused upon organizing data into categories to detect medium to large scale clusters of illness. Detection often requires that a critical threshold be surpassed. Data mining searches through data to identify records containing keywords. New Hampshire has combined data... Read more

Content type: Abstract

Los Angeles County Department of Health Services is currently testing SaTScan’s space-time permutation model to assist in identifying aberrant illness activity in the community and determine it’s ability to detect outbreaks through analyzing real-time syndromic data. SaTScan could be useful ... Read more

Content type: Abstract

The New York State (NYS) Medicaid Program provides healthcare for 34% of the population in New York City (NYC) and 4%-20% in each of the 57 county populations up-state. Prescription data are collected through the sub-mission of claims forms to the Medicaid Program and transmitted daily to the... Read more

Content type: Abstract

Prior work demonstrates the extent to which sampling strategies reduce the power to detect clusters.1 Additionally, the power to detect clusters can vary across space.2 A third, unexplored, effect is how much the sample size impacts the power of spatial cluster detection methods. This research... Read more

Content type: Abstract

The Centers for Disease Control and Prevention (CDC) uses the National Poison Data System (NPDS) to conduct surveillance of calls to United States poison centers (PCs) to identify clusters of reports of hazardous exposures and illnesses. NPDS stores basic information from PC calls including call... Read more

Content type: Abstract

Biosurveillance systems commonly depend on free-text chief complaints (CC)s for timely situational awareness. However, diagnosis codes may not be available soon enough and may have uncertain value because they are assigned for billing purposes rather than for population monitoring. Existing... Read more

Content type: Abstract

This paper describes a comparison between two statistics ñ SaTScan and FleXScan, applying to a data of absentees in primary school in Japan.

Content type: Abstract

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National Syndromic
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Email:nssp@cdc.gov

The National Syndromic Surveillance Program (NSSP) is a collaboration among states and public health jurisdictions that contribute data to the BioSense Platform, public health practitioners who use local syndromic surveillance systems, Center for Disease Control and Prevention programs, other federal agencies, partner organizations, hospitals, healthcare professionals, and academic institutions.

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