Use of a prospective space-time scan statistic to prioritize shigellosis case investigations in an urban jurisdiction.
OBJECTIVE
A prospective space-time scan statistic was applied to Chicago's 2002 shigellosis surveillance data to evaluate its utility in objectively describing clusters and assisting in the prioritization of investigations.


METHODS
The prospective space-time module of SaTScan, a free software available online, was used to identify "live" clusters of disease, meaning cases that were current as of the date of the analysis and strongly associated in place and time. Fifty-two separate space-time analyses were run; one simulation for each week of 2002. Identified clusters were described in terms of space, time, risk factors reported by involved case-patients, and cases' links to venue-associated outbreaks.


RESULTS
Twelve live clusters were detected at the p < 0.05 significance level: two single-household clusters and 10 community clusters. The community clusters ranged in size from 194 to 367 census tracts (median = 294), and in disease burden from 21 to 41 cases (median = 29). Geographically, all of the community clusters were located in the west-central part of the city and had a temporal span of 28 days. Within the 10 community clusters, 15 different day care centers were identified as potential exposure settings for case-patients or their close contacts.


CONCLUSIONS
The prospective space-time scan statistic offers local health departments an objective way of describing clusters of shigellosis cases. The method used in this study could help prioritize the assignment and investigation of cases, particularly when overall shigellosis incidence exceeds expected numbers or when an agency's resources are stressed by other events, such as outbreaks.
Jones
Roderick C
RC
Communicable Disease Program, Chicago Department of Public Health, 2160 West Ogden Ave., Chicago, IL 60612, USA. Jones_Roderick@cdph.org
Liberatore
Monica
M
Fernandez
Julio R
JR
Gerber
Susan I
SI
D002641:Chicago / Q000453:epidemiology; D004405:Dysentery, Bacillary / Q000453:epidemiology*; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D013029:Space-Time Clustering; D014505:Urban Population / Q000706:statistics & numerical data*