Real-Time Syndromic Surveillance
Spatial Distribution Mapping
Disease mapping is a major focus of spatial epidemiology. It provides insight into possible causes of a disease, clusters of disease outbreaks across a geographic area, and the evolution of disease outbreaks. In this study, one of the goals of spatial mapping was identifying regions with unusually high numbers of cases during disease outbreaks or a bioterrorism attack (also referred to as "cluster detection"). Another goal was determining high-risk areas for a disease of interest. This information could be related to other factors such as environmental pollution, weather conditions, or demographic factors. Visits for each syndrome are used to map syndrome distribution at the ZIP Code level. To evaluate dynamic spatial patterns over time, the system supports disease mapping over any given time frame.
Syndromic surveillance is a rapidly evolving discipline that is driven by timely concerns about emerging disease outbreaks or bioterrorism attacks. Most published literature emphasizes early detection of outbreaks, and this is certainly a worthwhile goal. Possibly even more important is the use of associated monitoring tools to analyze an outbreak that has already been detected. This would be an invaluable epidemiological tool to aid in understanding and controlling such an outbreak.
Health officials and epidemiologists must consider such problems as the dynamics of spread, associated ecological or climatic factors, possible quarantine decisions, and resource allocation. For this reason, a real-time geographic picture of the situation is essential. A more prosaic, but probably ultimately more useful, task for such a real-time GIS tool would be for routine epidemiological studies. If such a tool were Web-based and widely available to epidemiologists with differing interests, it would certainly augment public health analysis immeasurably and point to unsuspected problems much more rapidly than currently possible.
The development of such a tool is beset with several difficult but not insurmountable challenges. The first is data acquisition. In the past, epidemiological studies have relied on classical methods of physician and hospital reporting, chart reviews, and patient interviews. This is an expensive and time-consuming method that limits data collection in both time and scope. The final release of the data is often delayed by months or years. This is unacceptable when dealing with rapidly evolving situations, for example, emerging infectious disease epidemics such as severe acute respiratory syndrome (SARS) or a bioterrorism attack with smallpox.
Electronic hospital administrative records have allowed the rapid retrieval of simple information, such as presenting chief complaint and demographic information, that can be used for analysis of real-time disease trends. Because this data is usually collected by nonmedical personnel, algorithms are applied that attempt to determine the related diseases. The gradual deployment of complete electronic medical records has improved the information available with timely and specific diagnosis information.
The authors have the advantage of working in a hospital where a completely electronic medical record has been in place in the emergency department database for the past four years. This data is available, can be imported into GeoMedStat on a real-time basis, and includes the relevant clinical and demographic information enabling mapping to appropriate syndromes.
Using GeoMedStat, real-time syndrome data can be mapped at the ZIP Code level within the state over a Web-based interface. Current research is focused on the best methods for automating the presentation and interpretation of this data. A major problem with the interpretation of spatial mapping is data normalization. There is a large amount of both temporal and spatial variability that must be taken into account. For example, a known temporal variability is the seasonal variation in respiratory diseases with increases during the winter months.
Spatial variability is even more problematic. UMMC is centrally located and draws patients from the entire state. However, the number of patients seen and the severity of their illnesses are associated with the distance the patient must travel to reach the hospital. Rural areas also have large variations in population density that must be considered.
These normalization issues are a complex topic. Implementation of algorithms to study the advantages and efficiency of different techniques is currently under active research using tools such as time series analysis, cluster analysis (including spatial scan statistics), neural networks, and simulation modeling.
GIS can play an important role in disease outbreak surveillance systems and help decision makers interpret and analyze both routine and outbreak-related health data.
Bravata, D. M., et al. 2004. "Systematic Review: Surveillance Systems for Early Detection of Bioterrorism-Related Diseases." Annals of Internal Medicine, 140 (11):910-922.
Mandl, K. D., et al. 2004 (Mar.-Apr.). "Implementing Syndromic Surveillance: A Practical Guide Formed by the Early Experience." Journal of the American Medical Informatics Association, 11(2): 141-150.
"Syndrome Definitions for Diseases Associated with Critical Bioterrorism-Associated Agents." Available at http://www.bt.cdc.gov/surveillance/syndromedef/index.asp.
Ivanov, M., et al. 2002. "Accuracy of Three Classifiers of Acute Gastrointestinal Syndrome for Syndromic Surveillance." American Medical Informatics Association Fall Symposium, Journal of the American Medical Informatics Association (Supplement), 345-349.
Beitel, A. J., K. L. Olson, B. Y. Reis, K. D. Mandl. 2004. "Use of Emergency Department Chief Complaint and Diagnostic Codes for Identifying Respiratory Illness in a Pediatric Population." Pediatric Emergency Care, 20(6):355-360.
Espino, J. U., et al. 2004. "The RODS Open Source Project: Removing a Barrier to Syndromic Surveillance." CDC's Morbidity and Mortality Weekly Report, 53 (Supplement 1):32-39.
About the Authors
Hui Li is a GIS analyst for the Office for Strategic Research Alliance at the University of Mississippi Medical Center. He holds a doctorate in GIS/remote sensing from Indiana State University. He is interested in applications for public health analysis and disease surveillance using GIS, remote sensing, and spatial statistics.
Fazlay S. Faruque is a professor of health systems and founder and director of the GIS program at the University of Mississippi Medical Center. He teaches graduate-level GIS, environmental health, and medical geology courses and is also an Esri Authorized Instructor for ArcView and ArcGIS courses. He is a principal investigator of many health-related geospatial research projects including one recently funded by NASA titled Integration of NASA Research Results to Enhance a Decision Support Tool for Asthma Surveillance, Prediction, and Intervention. He holds a doctorate in geological engineering and is a registered professional geologist in Mississippi. He may be reached at email@example.com.
Worth Williams is a senior analyst/programmer in the Office for Strategic Research Alliance in the Department of GIS at the University of Mississippi Medical Center. He received a bachelor's degree in electronics engineering technology with a minor in computer engineering technology from the University of Southern Mississippi. He is interested in building evaluating, automated, object-oriented GIS predictor and surveillance models. He is also interested in spectral analysis of multi- and hyper spectral data and classification methodologies.
Richard Finley is a professor of medicine and emergency medicine at the University of Mississippi Medical Center. He has a master's degree in theoretical physics from the University of Colorado, Denver, and a medical degree from the Tulane School of Medicine in New Orleans, Louisiana. His areas of interest include bioinformatics, epidemiology, and the dynamics of infectious disease utilizing real-time monitoring, spatial statistics, and GIS.