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

Published online by Cambridge University Press:  14 January 2005

K. P. KLEINMAN ,
A. M. ABRAMS ,
M. KULLDORFF  and
R. PLATT
K. P. KLEINMAN
Affiliation:
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
A. M. ABRAMS
Affiliation:
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 University of Minnesota School of Public Health, Minneapolis, MN, USA
M. KULLDORFF
Affiliation:
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 University of Connecticut Health Center, Farmington, CT, USA
R. PLATT
Affiliation:
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 Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Abstract

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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.

Type
Research Article
Information
Epidemiology & Infection , Volume 133 , Issue 3 , June 2005 , pp. 409 - 419
Copyright
© 2005 Cambridge University Press