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Application of an automated surveillancedata–analysis system in a laboratory-based early-warning system for detection of an abortion outbreak in mares

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  • 1 Department of Comparative Medicine, College of Veterinary Medicine, University of Tennessee, Knoxville, TN 37996.
  • | 2 Livestock Disease Diagnostic Center, Department of Veterinary Science, College of Agriculture, University of Kentucky, Lexington, KY 40546.
  • | 3 Hensley Elam and Associates, 167 W Main St, Ste 1400, Lexington, KY 40507.
  • | 4 Livestock Disease Diagnostic Center, Department of Veterinary Science, College of Agriculture, University of Kentucky, Lexington, KY 40546.
  • | 5 Maxwell H. Gluck Equine Research Center, Department of Veterinary Science, College of Agriculture, University of Kentucky, Lexington, KY 40546.

Abstract

Objective—To develop an early-warning automated surveillance-data–analysis system for early outbreak detection and reporting and to assess its performance on an abortion outbreak in mares in Kentucky.

Sample Population—426 data sets of abortions in mares in Kentucky during December 2000 to July 2001.

Procedures—A custom software system was developed to automatically extract and analyze data from a Laboratory Information Management System database. The software system was tested on data on abortions in mares in Kentucky reported between December 1, 2000, and July 31, 2001. The prospective space-time permutations scan statistic, proposed by Kulldorff, was used to detect and identify abortion outbreak signals.

Results—Results indicated that use of the system would have detected the abortion outbreak approximately 1 week earlier than traditional surveillance systems. However, the geographic scale of analysis was critical for highest sensitivity in outbreak detection. Use of the lower geographic scale of analysis (ie, postal [zip code]) enhanced earlier detection of significant clusters, compared with use of the higher geographic scale (ie, county).

Conclusions and Clinical Relevance—The automated surveillance-data–analysis system would be useful in early detection of endemic, emerging, and foreign animal disease outbreaks and might help in detection of a bioterrorist attack. Manual analyses of such a large number of data sets (ie, 426) with a computationally intensive algorithm would be impractical toward the goal of achieving near real-time surveillance. Use of this early-warning system would facilitate early interventions that should result in more positive health outcomes.

Contributor Notes

Mr. Riley's present address is Keane Inc, 9 Fountain Pl, Frankfort, KY 40601.

Supported by the US Department of Homeland Security.

Presented in part at the Conference of Research Workers in Animal Diseases, Chicago, December 2007, and the Canadian Association of Veterinary Epidemiology and Preventive Medicine Meeting, Charlottetown, PE, Canada, May 2008.

Address correspondence to Dr. Odoi.