AJVR, Vol 70, No. 2, February 2009 247 S urveillance systems can provide information on geographic and temporal patterns of disease distribution. Health events in these systems are typically captured by surveillance databases (such as LIMS) daily, weekly, or monthly depending on the disease of interest or the capacity of the system. Timely acquisition and analysis of these data is a prerequisite for early detection of new or reemerging threats. Moreover, implementing an efficient surveillance system for early Application of an automated surveillance- data–analysis system in a laboratory-based early-warning system for detection of an abortion outbreak in mares Agricola Odoi, BVM, PhD; Craig N. Carter, DVM, PhD; Jeremy W. Riley, BS; Jackie L. Smith, MS; Roberta M. Dwyer, DVM, MS 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 out- break 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 ana- lyze data from a Laboratory Information Management System database. The software sys- tem 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 out- break approximately 1 week earlier than traditional surveillance systems. However, the geo- graphic 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 out- breaks 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 impracti- cal toward the goal of achieving near real-time surveillance. Use of this early-warning sys- tem would facilitate early interventions that should result in more positive health outcomes. (Am J Vet Res 2009;70:247–256) disease detection of outbreaks, such as in bioterrorist attacks, is becoming an expected function of veterinary and health professionals. In outbreak situations, such as bioterrorism attacks, the window of opportunity to initiate effective response is quite short. Thus, it is becoming increasingly important that veterinary and medical professionals have in place systems that can detect outbreaks reliably. This information can be used to make informed decisions on priorities for conducting outbreak investigations or intervention strategies. A number of sources are available to help furnish the necessary data required for such systems. These include laboratories, 1 syndromic systems, 2–4 over- the-counter–drug sales in pharmacies, 5,6 and notifiable disease databases. 1 Syndromic and pharmacy data are useful in identi- fying clusters of syndromes before the identification of Received April 4, 2008. Accepted May 8, 2008. From the Department of Comparative Medicine, College of Vet- erinary Medicine, University of Tennessee, Knoxville, TN 37996 (Odoi); Livestock Disease Diagnostic Center (Carter, Smith) and Maxwell H. Gluck Equine Research Center (Dwyer), Department of Veterinary Science, College of Agriculture, University of Kentucky, Lexington, KY 40546; and Hensley Elam and Associates, 167 W Main St, Ste 1400, Lexington, KY 40507 (Riley). 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. ABBREVIATIONS ASDAS Automated surveillance-data–analysis system LIMS Laboratory Information Management Systems Unauthenticated | Downloaded 10/29/23 09:33 AM UTC