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Population-Adjusted Stable Geospatial Baseline for Outbreak Detection in Syndromic Surveillance
Karen L. Olson,1,2 M. Bonetti,3 M. Pagano,3 K. Mandl1,2
1Children's Hospital Boston, Boston, Massachusetts; 2Harvard Medical School, Boston, Massachusetts; 3Harvard School of Public Health, Boston, Massachusetts
Introduction: Surveillance systems should detect outbreaks that become evident when cases cluster
geographically. Identifying an illness with an abnormal spatial pattern of disease requires a stable model of what is normal, adjusting for underlying population density.
Objectives: Observations indicate that the distribution of all pairwise interpoint distances among patients in
the catchment area of a hospital is stable over time. This study sought to demonstrate that baseline spatial distributions can be established.
Methods: Emergency department visits made during 2 years at two urban academic medical centers (one a
pediatric hospital) were classified into syndromes according to chief complaints and
International Classification of Diseases,
Ninth Revision codes. Distances between all pairs of patient addresses were calculated. The number of visits and the distance distributions for respiratory and gastrointestinal syndrome at each hospital, by season, were determined.
Results: For respiratory syndrome at one hospital, the number of visits ranged from a summer low of 1,932 to
a winter high of 4,457 (mean: 3,203; standard deviation: 795). Variability and seasonal effects were present.
By contrast, the interpoint-distance distributions were characterized by remarkable similarity over time without seasonal effects. When individual distance distributions for each season for 3 years are plotted, they overlap to
substantially, demonstrating their stability. This same pattern of results was identified for respiratory visits at one hospital and gastrointestinal visits at both hospitals.
Conclusions: Empirical and parametric methods that rely on detecting differences between interpoint-distance distributions have been described previously. Although the number of cases varies substantially over time, a stable geographic baseline can be established against which clusters can be detected. Therefore, syndromic surveillance
is enhanced when location is incorporated into a system that can detect outbreaks in space, even when the number of cases is too small to generate alerts on the basis of frequency.
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