1. Figure 1. Data Flow Diagram of Davis County School Absenteeism Surveillance System. Shuying Shen, MStat 1,2,3 ; Nicole Stone, MPH 4 ; Brian Hatch, MPH 4 ; Lisa Wyman, MPH 2,5 ; Robert T. Rolfs, MD, MPH 2,3,5 ; Brett R. South, MS 1,2,3 ; Adi Gundlapalli, MD, PhD, MS 1,2, ; Catherine Staes, BSN, MPH, PhD 2,3 ; Matthew Samore, MD 1,2,3 1 VA Salt Lake City Health Care System and the Department of Internal Medicine, School of Medicine University of Utah, Salt Lake City, UT, 2 Center of Excellence in Public Health Informatics, University of Utah, Salt Lake City, UT, 3 Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, 4 Davis County Health Department, Farmington, UT, 5 Utah Department of Health, Salt Lake City, UT Acknowledgements Center of Excellence in Public Health Informatics, University of Utah, Salt Lake City, UT (CDC Grant 8P01HK000030) Davis County Health Department, Farmington, UT Utah Department of Health, Salt Lake City, UT Contact Information Shuying Shen, MStat. Shuying. Shen@hsc.utah.edu Methods 10 public elementary schools in Davis County in Utah were selected as sentinel schools to collect syndrome-specific school absenteeism in addition to reason for absence. Reasons for absence include: o Sick. o Other. o Unknown. Sick absence is categorized into one of the following: o Respiratory. o Gastrointestinal (GI). o Rash. o Other. o Unknown. Clerks entered daily absences through a web-based information system and these data were available in real-time to the county surveillance coordinators. Data from September 1, 2007 to March 14, 2008 were extracted to generate descriptive statistics. Introduction School absenteeism data could be used as an early indicator for disease outbreaks. Reason for absence combined with syndrome-specific information may improve the utility of absenteeism data for early outbreak detection. A collaborative project was initiated to evaluate and improve an information system collecting syndrome-specific school absenteeism. The present study is a pilot evaluation to determine the usefulness of the system for public health surveillance. Evaluation of Syndrome-Specific School Absenteeism Data for Public Health Surveillance Results Currently, data extraction, analysis and dissemination steps are performed manually. Increase in total absences is often driven by non-sickness related absences (Figure 2). Unknown absences account for 59% of total absences. Absences due to respiratory illness increased from fall to winter, while absences due to GI illness remained relatively stable (Figure 3). Conclusion Syndrome-specific school absenteeism data provides useful information for targeted infectious disease surveillance. Recommendations An automated process could be implemented at the DCHD for data extraction, statistical analysis, and information dissemination. This would improve timeliness and reduce workload. Online reports could be generated as part of the automated process on school-level as well as county-level to increase situational awareness for school principals and nurses. Specificity of data could be improved by encouraging reports from parents. DCHDDavis County Health Department Figure 2. Absence Rate by Reason of AbsenceFigure 3. Sickness-related Absence Rate by Syndrome