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Displaying results 1 - 8 of 9
  • Content Type: Abstract

    Research evaluating the use of spatial data for surveillance purposes is ongoing and evolving. As spatial methods evolve, it is important to characterize their effectiveness in real-world settings. Assessing the performance of… read more
    … outbreak data (signal) into actual background visit data. These studies focused on either temporal data, a … synthetic outbreak data (signal) into actual back- ground visit data.1,2,3 These studies focused on either temporal … and longitude using ESRI ArcGIS 8.3. For each week of visit data we used a cluster creation tool4 to insert one of …
  • Content Type: Abstract

    In 2004, the Indiana State Department of Health (ISDH) partnered with the Regenstrief Institute to begin collecting syndromic data from 14 ED’s to monitor bioterrorism-related events and other public health emergencies. Today, Indiana’s public… read more
    … system (PHESS) receives approximately 5,000 daily ED visits as real-time HL7 formatted surveillance data from 55 … Indiana’s PHESS receives approximately 5,000 daily ED visits as real-time HL7 formatted surveillance data from 55 … deadly tornado struck southern Indiana in 2005. As the ED visit volumes climbed, no false alerts were triggered for …
  • Content Type: Abstract

    Complex, highly parameterized data models are often used to detect syndromic outbreaks. Unfortunately, such models can pose greater maintenance challenges as parameter variations increase. As such, our work focuses on whether day… read more
    … cations of chief complaint as well as the total daily visits. To compare across hospitals, we normalized the … or insignificantly across data sources. We found that visit rates by syndrome varied significantly by DoW Further, …
  • Content Type: Abstract

    With the widespread deployment of near real time population health monitoring, there is increasing focus on spatial cluster detection for identifying disease outbreaks. These spatial epidemiologic methods rely on knowledge of patient location to… read more
    … tions, significant clusters contained fewer additional ED visit points (i.e., points that were not part of the … in small circle) and few additional points from the ED visits (red dots outside small circle). However, when the … with points at zip code centroids (right), many more ED visits were included in the detected cluster. CONCLUSIONS …
  • Content Type: Abstract

    The use of spatially-based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health datasets, by… read more
    … set- ting. METHODS Cases were emergency department (ED) visits for respiratory illness. Baseline ED visit data were in- jected with artificially-created …
  • Content Type: Abstract

    In the fall of 2006, the Ohio Department of Health (ODH) and the Indiana State Department of Health (ISDH) proactively began general discussions regarding surveillance issues of mutual interest. Both states, having operational… read more
    … gender, patient home zip code, admitted date and time, visit number, and free- text chief complaint. Approximately …
  • Content Type: Abstract

    An increase in tuberculosis (TB) among homeless men residing in Marion County, Indiana was noticed in the summer of 2008. The Marion County Public Health Department (MCPHD) hosted screening events at homeless shelters in hopes of finding… read more
    … as part of a follow-up to a TB outbreak investigation. A phone number of a MCPHD nurse on call is provided. The … that normal methods of locating these individual such as phone or address was not available. Additionally, other …
  • Content Type: Abstract

    Electronic laboratory reporting (ELR) was demonstrated just over a decade ago to be an effective method to improve the timeliness of reporting as well as the number of reports submitted to public health agencies. The quality of data (inc.… read more
    … Patient’s address 41.5% 63.3% �21.8% Patient’s home phone number 38.5% 72.8% �34.3% Ethnicity 3.5% 18.3% �14.8% …