1	Syndromic Surveillance in North Dakota: A gastrointestinal illness case study Isaac Triebold*¹, Margaret L. Khaitsa² , Julie L. Goplin³, William Nganje⁴, Penelope Gibbs², Neil W. Dyer⁵. ¹The Great Plains Institute of Food Safety, ²Department of Veterinary and Microbiological Sciences, ⁴Department of Agribusiness and Applied Economics, ⁵Department of Veterinary Diagnostic Services, North Dakota State University, Fargo, ND. ³North Dakota Department of Health, Bismarck, ND.
2	Introduction to Syndromic Surveillance “An investigational approach where health department staff, assisted by automated data acquisition and generation of statistical alarms, monitor disease indicators in real-time or near real-time to detect outbreaks of diseases earlier than would otherwise be possible with traditional public health methods.” -CDC
3	Introduction to Syndromic Surveillance In North Dakota… RedBat is the syndromic surveillance system that takes ER complaint data and categorizes symptoms into 11 syndromes Data sent from ER to Department of Health Each syndrome is assigned score correlating with the symptoms provided High enough score is considered a case Large amounts of similar syndrome cases will trigger alarm if they exceed CUSUM baseline CUSUM (cumulative sum) is statistical method used to calculate deviations from the amount of disease expected
4	Introduction to Syndromic Surveillance Syndromes analyzed by RedBat Gastrointestinal illness Encephalitis Influenza-like illness Hepatitis Pulmonary Radiation Sickness Rash Illness SARS Sepsis Systemic Disease Neurologic Intoxication
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7	Syndromic Surveillance Sites in ND
8	Objectives Determine the effectiveness of syndromic surveillance in North Dakota using the retrospective case study 1. Set appropriate baseline for GI illness syndrome 2. Determine if syndromic surveillance would have helped in this case 3. Estimate the economic potential of the system
9	Materials and Methods Objective 1: Use RedBat outputs to determine what the appropriate sensitivity would be for GI illness Objective 2: Assuming appropriate sensitivity was set, assess the response that would have occurred during the outbreak and if it would RedBat was operational
10	Materials and Methods Objective 3: Use real options model to estimate systems value Use data from economic research service to estimate costs Cost per case of E. coli 0157:H7 Estimate benefits by time saved in detection (prevention of cases and earlier treatment)
11	Background on Case Study October 10, 2003 the NDDoH was notified of 4 cases of E. coli O157:H7 that had dined at the same restaurant Exposure date of Sept. 30 or Oct. 1 127 people interviewed with 13 cases 5 lab confirmed, 8 met case definition Case definition was anyone experiencing diarrhea 2-10 days after eating at the restaurant on exposure date
12	RedBat Output (7 Day Calculation)
13	RedBat Output (14 Day Calculation)
14	Results The 7 day CUSUM calculation sets the appropriate level of sensitivity Using the 7 day CUSUM calculation method RedBat signaled an outbreak Oct. 2 (8 day gain over the actual time NDDoH was notified) The 14 day CUSUM calculation would probably have not recognized the outbreak
15	Results Total cost of system estimated at $109,500 annually Hardware & Software – $76,890 Labor – $24,940 Other Capital – $7,670 Average benefits per case of E. coli O157:H7 in case study $2,691 Based on average treatment cost and lost productivity for cases in study
16	Conclusion Syndromic surveillance can be useful in early detection of naturally occurring outbreaks It will be economically feasible, in the case of GI illness, if the time saved by using it reduces the number of cases
17	References 1. Sosin, DM. Draft Framework for Evaluating Syndromic Surveillance Systems. Journal of Urban Health 2003; 80(2): i8-i13. 2. Foldy SL. Linking Better Surveillance to Better Outcomes. In: Syndromic Surveillance: Reports from a National Conference, 2003. MMWR 2004; 53 (Suppl): 12-17. 3. Burkom H, Elbert Y, Feldman A, Lin J. Role of Data Aggregation in Biosurveillance Detection Strategies with Applications from ESSENCE. In: Syndromic Surveillance: Reports from a National Conference, 2003. MMWR 2004; 53 (Suppl): 67-73. 4. North Dakota Department of Health. Syndromic Surveillance. Available from: http://www.health.state.nd.us/disease/Surveillance/syndromicsurveillance.htm 5. Hutwagner L, Browne T, Seeman G, Fleischauer A. Comparing Aberration Detection Methods with Simulated Data. Emerg Infect Dis 2005; 2: 314-316. 8. CDC Fact Sheet. Escherichia coli 0157:H7. December 2000. Available from http://www.cdc.gov/ncidod/dbmd/diseaseinfo/escherichiacoli_t.htm.