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

  • K. P. KLEINMAN (a1), A. M. ABRAMS (a1) (a2), M. KULLDORFF (a1) (a3) and R. PLATT (a1) (a4)

Abstract

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.

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Copyright

Corresponding author

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

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

  • K. P. KLEINMAN (a1), A. M. ABRAMS (a1) (a2), M. KULLDORFF (a1) (a3) and R. PLATT (a1) (a4)

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