GIS-Enabled Surveillance System

for West Nile Virus Neurological Syndromes



Christopher Bangs, MS and Jonathan Jui, MD MPH





The specter of emerging infections and potential bioterrorist threats has lead to an increased interest in population-based syndromic surveillance systems. GIS-enabled syndromic surveillance enhances significantly the utility of such surveillance methods.

A simultaneous three-phased surveillance strategy, which includes spatio-temporal information collected through the Emergency Department Information System (EDIS) in a major metropolitan area, demonstrates no statistically significant historical spatio-temporal association between known vector spaces and the target syndrome population.


A baseline syndrome rate of .5% of 235,000 patient visits for approximately 4 years is being compared on daily, weekly, and monthly values for the year 2003 and prospectively 2004. The rapid availability of spatio-temporal mapping adds a critical informational component to both the clinician’s treatment plan and the public health decision support system.





West Nile Virus (WNV) has yet to be detected in the indigenous population of Oregon. Oregon public health authorities anticipate human cases will occur later this year. Surveillance systems for detection in birds, equines, and mosquito vectors are already operational. Public health officials are educating health workers on the signs and symptoms of the WNV disease. In spite of these efforts, the initial detection of a WNV case in our community is a major challenge.  Early detection can be linked to early intervention, which may limit the extent and impact of the disease on the population. Currently, no automated surveillance program exists for WNV infection in humans. Our investigation explores the feasibility of using an emergency department information system (EDIS) as the foundation of a surveillance system designed to detect WNV infection in our community.


An EDIS requires the collection of patient specific symptomatology and diagnoses, as well as point locational information related with each patient seen and treated. Automated query and message generation, as well as automated and semi-automated spatio-temporal analysis may provide a unique opportunity for early detection of West Nile Virus,


The Oregon Health & Science University Emergency Department (ED) is located near the downtown district of the largest metropolitan area in Oregon and Southwest Washington. This medical center is the only academic center in the state of Oregon. Each year approximately 47,000 patients are seen in the ED. As part of the registration process home addresses, as well as employer addresses, are collected in the Hospital Information System (HIS), which is connected via interface with the EDIS. This information, together with disease presentation and diagnoses, are maintained in a single enterprise relational database (Oracle). Queries are routinely run to detect diagnostic and treatment patterns, as well as many other patterns and sentinel events, enabled by the high degree of content and accessibility of the database. Due to its primary function as a departmental management application, data must be weighted for confidence, something best accomplished by investigators with a thorough understanding of ED operations practices.




This study uses five full years (1998-2002) of data from the EDIS to create baseline values for disease diagnoses and other patient characteristics, such as age, gender, complaint categories, home and work locations, laboratory orders, and laboratory results. Patient referral areas or “capture” areas were demonstrated as well through home address matching and geocoding. Both Streets, Microsoft Corporation (Bellevue, Washington), geocoding service and the Regional Land Information System (RLIS) published by Metro, a Portland Area Regional government body, were used in matching addresses.


Primary data was collected through a leading industry EDIS (EMSTAT) from A4Healthsystems, Cary, North Carolina. Oracle 7.3, Microsoft Access 2000, and SQL Server 2000 database management systems were used to store and manage the data. Patient records were analyzed using SPSS v11.0. Spatial data management and analysis were accomplished with ArcGIS v. 8.3, Esri, Redlands, CA.


Department procedures require triage and primary nurses to elicit patient information, including complaints and medical history. Registrars collect and code demographic and billing information either in the EDIS or Hospital Information System (HIS). These systems are linked through a Health Level 7 (Hl7) interface. Prior to patient discharge from the ED, OHSU emergency physicians are required to assign at least one diagnosis, but may assign two and make notes for review at discharge as well. For the period of the study the department had an average time in department of 4 hours and 40 minutes, and a standard deviation of 8 hours and 59 minutes. This is the period of time from patient presentation until the patient is admitted or discharged home. It also includes an observation unit with stays of over 24 hours being not uncommon.


Case records were analyzed with particular focus on diagnosis (ICD9 codes) and laboratory results (spinal tap), home address location, and disposition.  Chief complaint and chief complaint codes are also available, but were not found to be as consistent for the study period. One study has shown good agreement between complaint and diagnosis overall (Begier, et al, 2003). An expanded diagnosis group included both primary and secondary diagnoses deemed to be  consistent with possible WNV Infection. Two groups were formed. The expanded group included headache, altered mental status, fever of unknown origin, febrile illness, viral meningitis, and viral encephalitis. The focal group included only viral meningitis and viral encephalitis. 


There are 18 emergency departments in the Portland Metro area. While OHSU is not the only such ED in the core area, an examination of the home addresses of patients presenting to this ED over the last several years reveals a broad and highly dispersed array of points. (Figure 3) This array indicates that, over time, the address of residence of OHSU patients is representative of the entire Portland community. The potential usefulness of EDIS data in disease surveillance is supported by the continuous hours of operation, the lack of financial restrictions on access to care, and the perception of the public of this particular hospital as a place of high quality care (National Research Corporation, Lincoln, Nebraska ccahosp02.html). An increased number of indigent and uninsured seek care in this ED at a rate of 40% of admissions (P. Southard, personal communication, 2003).





Figure 1:  National Array of ED patient home addresses



Propinquity of diagnosed patients home residence to water-born disease vectors is being explored as a useful input for epidemiological studies (Kleinschmidt, I, et al). Home residences may provide a proxy for exposure period to adjacent mosquito breeding areas. While it is expected to be highly variable in the population, some groups may be identifiable as relatively homebound, thus increasing the relative risk of exposure in place. Both the old and the very young probably tend to spend more time at home (Levinson, et al, 1997). By determining home locations for patients presenting with the target syndrome, we are able to provide map visualizations demonstrating patients who reside and/or work within identified known potential mosquito vector ranges.





Figure 1:  Regional array of  patient locations





Using the target diagnoses, a broad group totaling 4,570 patient visits over more than 5 years was selected from the EDIS. These included diagnoses such as headache, febrile illness, and fever of unknown etiology as well as more specific diagnoses such as viral meningitis, and or viral encephalitis. Within this expanded group, a subgroup was identified where either the primary or secondary diagnosis was viral encephalitis or viral meningitis. Viral meningitis totaled 66%, and viral encephalitis 13% of the primary diagnoses in the 71 cases in this subgroup. Temporal or spatial clustering was not evident in direct observation of the array. No single week with more than two cases of viral meningitis was present in the data. Encephalitis diagnoses were rare. Headache diagnoses were typically primary diagnoses, with viral encephalitis as the secondary diagnosis in all but one case. Table 1 shows the diagnoses associated with these two viral diagnoses and their ICD9 codes as coded into the EDIS. In 48 of these cases, no secondary diagnosis was assigned.








Altered mental status



Encephalitis, viral



Febrile Illness






Meningitis, viral









Viral syndrome



Grand Total




Table 1: Diagnoses associated with focal group of viral encephalitis or meningitis




Outpatient (ED) diagnoses correlated very highly with inpatient diagnoses on a representative sample of admissions, with ICD9 codes matching in greater than 90% of the cases sampled, and approaching 99% for viral meningitis.


Of the 71 patients in the focused group, only 18 resided in the Metro area. Of these, 15 had valid addresses in the EDIS. Two of the others had no home address and may have been homeless. One did not match, though the address appeared valid. Patient’s home locations were observed for spatial correlations, i.e. proximity to other locations in the group or to known bodies of water. Dispersion over the Metro area was high. No consistent observable spatial association with known water features was found. The water feature dataset is limited to major and minor waterways, with wetlands.


Two one-week periods of cases with the target diagnoses were identified over the entire five years of data to have more than 1 case diagnosed. Only one week had 3 cases. No confidence interval was computable. All cases in the focused group were within a three-mile buffer of known water sources.


Figure 2: Temporal aggregation of array of patient locations



The small numbers of patients in the narrow target diagnosis group (viral meningitis and viral encephalitis), consistently low at every level of temporal aggregation over the study period, leads us to believe that any increase in the number of patients presenting with these diagnoses will be detected by the system. The diagnostic accuracy of the ED physicians with regards to the two prominent diagnoses (with more than 50 physicians practicing during the period) is encouraging.   We believe viral meningitis will be the most likely ED discharge diagnosis of patients presenting with West Nile Viral Infections. We base this conclusion on both our knowledge of the practice of emergency medicine and rationale for diagnosis coding used by clinicians. Our data suggests that when the diagnosis of “viral meningitis” is made, this diagnosis is a good predictor of the definitive diagnosis in those patients admitted to the hospital.


Because of severe time and resource limitations, ED diagnoses can frequently represent the physician’s best understanding of the patient’s disease or injury. We believe ED diagnosis correlated with spinal taps were accurate because clinicians were aware of the results of the spinal tap at the time the diagnoses were made.


An inherent limitation of the use of ED diagnosis is that in many cases, the clinician will use symptom-based diagnoses if a more specific diagnosis is not available. An example is abdominal pain. As a diagnosis this accounts for as much as 40% of patients discharged from the ED over any given period of time. However, even with the non-specific nature of this diagnosis, when combined with spatial and temporal analysis, these diagnoses may prove valuable in narrowing the focus of an investigation.


The frequently generalized nature of such diagnoses, descriptive analysis of EDIS discharge diagnoses, combined with related medical, spatial, and temporal elements in the same data set, will likely further delineate trends than can a more restricted system of inputs. Such diagnoses may represent syndromes at their early stages, until confirmed or further specified by inpatient discharge diagnoses or special testing.




The target diagnosis group will be tracked on a daily basis with map visualizations produced where cases indicate this would be useful to clinicians. Maps will be posted on an intranet web site accessible from the EDIS. Because the data set includes inter-hospital transfers from the entire state and region, the data can provide some potential as a proxy for the region, most of which is rural.


The criteria used to determine a spatial relationship between home locations and water will include the known range of the representative mosquito species of the area, the distance of each location from each other, as well as the distance of each case from represented water sources. In addition, patient spatial relationships will be compared with spatial analysis of “dead bird siting” a technique proven to be of value in previous investigations.  An expanded water feature set will be used by state and local authorities to determine if such relationships might exist, with de-identified ED records provided to them routinely through a secure data Virtual Private Network (VPN). This system of transmission is currently in place and functioning as a method of transmitting reportable disease cases every 4 hours. Locational identity will not be protected when cases are deemed reportable. As a matter of course the cases will be sent only on request of responsible Public Health authorities. As such they represent a public health concern and can be escalated to a reportable status.






National Research Corporation, Lincoln, Nebraska



1997, Levinson, David M. “Life-Cycle, Money, Space, and the Allocation of Time.”


Begier, E.M.; Sockwell, D.; Branch, L.M.; Davies-Cole, J.O.; Jones, L.H.; Edwards, L.; Casani, J.A.; and Blyth, D.: “The National Capitol Region’s Emergency Department Syndromic Surveillance System: Do Chief Complaint and Discharge Diagnosis Yield Different Results?” CDC, Vol. 9, No 3, March, 2003.


Kleinschmidt, Immo, Sharp, Brian, Mueller, Ivo, Vounatsou, Penelope: Rise in Malaria Incidence Rates in South Africa: A Small-Area Spatial Analysis of Variation in Time Trends. Am. J. Epidemiol. 2002 155:257-264.



Author Information:


Christopher Bangs, MS

Administrative Manager/Instructor

Department of Emergency Medicine

Oregon Health & Science University

3181 SW Sam Jackson Park Way

Portland, OR 97239

GH 239

Phone: 503-494-7473

Fax: 503-494-1470