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 distri- butions have been described previously. Although the number of cases varies substantially over time, a stable geo- graphic 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.
A population-adjusted stable geospatial baseline for outbreak detection in syndromic surveillance (poster with abstract published in proceedings)
BONETTI, MARCO;
2004
Abstract
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 distri- butions have been described previously. Although the number of cases varies substantially over time, a stable geo- graphic 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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