Epidemiology and Infection

A model-adjusted space–time scan statistic with an application to syndromic surveillance

K. P. KLEINMAN a1c1, A. M. ABRAMS a1a2, M. KULLDORFF a1a3 and R. PLATT a1a4
a1 Department of Ambulatory Care and Prevention, Harvard Medical School, Harvard Pilgrim Health Care, and CDC Eastern Massachusetts Prevention Epicenter and HMO Research Network Center for Education and Research in Therapeutics, Boston, MA, USA
a2 University of Minnesota School of Public Health, Minneapolis, MN, USA
a3 University of Connecticut Health Center, Farmington, CT, USA
a4 Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

Article author query
kleinman kp   [PubMed][Google Scholar] 
abrams am   [PubMed][Google Scholar] 
kulldorff m   [PubMed][Google Scholar] 
platt r   [PubMed][Google Scholar] 


The space–time scan statistic is often used to identify incident disease clusters. We introduce a method to adjust for naturally occurring temporal trends or geographical patterns in illness. The space–time scan statistic was applied to reports of lower respiratory complaints in a large group practice. We compared its performance with unadjusted populations from: (1) the census, (2) group-practice membership counts, and on adjustments incorporating (3) day of week, month, and holidays; and (4) additionally, local history of illness. Using a nominal false detection rate of 5%, incident clusters during 1 year were identified on 26, 22, 4 and 2% of days for the four populations respectively. We show that it is important to account for naturally occurring temporal and geographic trends when using the space–time scan statistic for surveillance. The large number of days with clusters renders the census and membership approaches impractical for public health surveillance. The proposed adjustment allows practical surveillance.

(Accepted November 19 2004)

c1 Department of Ambulatory Care and Prevention, 133 Brookine Ave, 6th Floor, Boston, MA 02215, USA. (Email: ken_kleinman@harvardpilgrim.org)