1. Comparability of Electronic and Manual Influenza-like Illness (ILI) Surveillance Methods Robin M. Williams, Nebraska Department of Health & Human Services/University of Nebraska – Lincoln; Anne O’Keefe, MD, MPH Douglas County Health Department; Thomas J. Safranek, MD Nebraska Department of Health & Human Services Background Conclusions Recommendations  NDHHS will continue to participate in the ILINet program as requested by the CDC.  NDHHS will continue to recruit facilities to participate in syndromic surveillance, both from EDs and OPCs.  Excluding well visits from ILINet and OPC data may improve sensitivity and specificity of those tracking systems. Methods To complement and enhance existing influenza surveillance capacity in Nebraska, including CDC’s manual, non- automated, resource-intensive Influenza-like Illness Surveillance Network (ILINet), the Nebraska Department of Health and Human Services Division of Public Health (NDHHS) and the Douglas County Health Department (DCHD) developed two automated influenza tracking systems: 1) Emergency Department Syndromic Surveillance (ED), and 2) outpatient physician clinic (OPC) ILI surveillance program. NDHHS has a goal of establishing at least one ILINet sentinel provider and one automated OPC surveillance site per local public health department (LPHD). Currently, 16 of the 21 LHDs have at least one ILInet sentinel provider, and 3 have at least one OPC site submitting data to NDHHS.  Influenza data collected via electronic methods show a strong positive correlation with the traditional manual method used to collect ILI data.  Electronic data collection methods provide a standard surveillance approach that can be replicated more broadly across the state to provide insights into regional variation in influenza morbidity.  Automating the data collection process obviates the need for human resources to perform surveillance activity and reduces the surveillance artifact associated with variations in staffing and training at ILINet physician offices. For the 2009-2010 influenza season, the total number of patients seen (denominator) in each system and of those, the number of identified ILI cases (numerator) is shown in Table 1. The degree of correlation between the electronic and manual data systems is shown Figure 1 and Table 2.. For purposes of this study, we restricted our analysis to ILINet sentinel providers (n=2), EDs (n=1) and OPC’s (n=12) in Douglas County, the state’s most populous county. We compare the ILI surveillance data from the two electronic systems with the data from the ILInet system to establish comparability of the three systems. To measure the degree of association between the systems, the Pearson correlation coefficient statistic was utilized. Case Definition ILI was defined as the presence of fever (≥ 100 F°), plus cough or sore throat, without other known diagnosis. Data used for case classification was ascertained at the time of the clinic visit for the two traditional ILINet providers, and was collected electronically and processed via a computer algorithm for the OPC and ED systems. ILINet: The participating physicians’ offices manually tabulate the number of total visits and the number of patients presenting with ILI by age per surveillance week. This data is submitted weekly to CDC via fax or online data entry. We used the ILINet surveillance program as the benchmark for comparison of the two automated electronic systems. NDHHS and DCHD have developed two separate real-time automated processes to collect de-identified clinical data from electronic health records from which ILI visits can be tracked: OPC: Clinic providers at 12 geographically scattered OPCs all utilizing the same electronic health record agreed to assess all patients for the presence of cough, sore throat and measured temperature. Data is recorded in the electronic health record, and electronically extracted and analyzed. ED: All ED visits from one ED are processed to identify ILI using the chief complaint and ICD9 code data variables. A visit is classified as being ILI by using SAS code that analyzes natural language from the chief complaint variable and/or by the visit being coded as influenza. This same process is utilized for statewide ILI data collection and reporting to the national Distribute ILI tracking system. Results Limitations  ILINet is time consuming and data is not always collected in a standardized way.  OPC data includes all patients with fever, cough and sore throat, without excluding patients with known non-influenza diagnoses (e.g., strep throat).  ILINet and OPC data include well visits which inflates the denominator. Table 1: Numerator/Denominator Data by System, 2009-2010 Influenza Season EDOPCILINet Total # of Pt Visits 15,76790,5914,363 # ILI4,2802,534127 % ILI0.2710.0280.029 Table 2: Pearson Correlation Coefficient 2009-10 E* vx O¹0.808High Correlation O vs I°0.854High Correlation E vs I0.821High Correlation *Emergency Department ¹Automated Outpatient Physician Clinics °ILINet Contact Information Robin M. Williams 301 Centennial Mall South P.O. Box 95026 Lincoln, NE 68509-5026 Phone: 402-471-0935 Fax: 402-471-3601 Email: robin.m.williams@nebraska.gov INFLUENZA