A space-time permutation scan statistic for disease outbreak detection.

Martin Kulldorff,Richard Heffernan,Farzad Mostashari,Renato Assunção,Jessica Hartman,Sally M Blower

doi:10.1371/journal.pmed.0020059

Abstract

BackgroundThe ability to detect disease outbreaks early is important in order to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Many national, state, and local health departments are launching disease surveillance systems with daily analyses of hospital emergency department visits, ambulance dispatch calls, or pharmacy sales for which population-at-risk information is unavailable or irrelevant.Methods and FindingsWe propose a prospective space–time permutation scan statistic for the early detection of disease outbreaks that uses only case numbers, with no need for population-at-risk data. It makes minimal assumptions about the time, geographical location, or size of the outbreak, and it adjusts for natural purely spatial and purely temporal variation. The new method was evaluated using daily analyses of hospital emergency department visits in New York City. Four of the five strongest signals were likely local precursors to citywide outbreaks due to rotavirus, norovirus, and influenza. The number of false signals was at most modest.ConclusionIf such results hold up over longer study times and in other locations, the space–time permutation scan statistic will be an important tool for local and national health departments that are setting up early disease detection surveillance systems.

Highlights

The number of false signals was at most modest. If such results hold up over longer study times and in other locations, the space–time permutation scan statistic will be an important tool for local and national health departments that are setting up early disease detection surveillance systems
The World Trade Center and anthrax terrorist attacks in 2001, as well as the recent West Nile virus and SARS outbreaks, have motivated many public health departments to develop early disease outbreak detection systems using non-diagnostic information, often derived from electronic data collected for other purposes (‘‘syndromic surveillance’’) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]
These include systems that monitor the number of emergency department visits, primary care visits, ambulance dispatches, nurse hot line calls, pharmaceutical sales, and West Nile–related dead bird reports

Summary

Introduction

The World Trade Center and anthrax terrorist attacks in 2001, as well as the recent West Nile virus and SARS outbreaks, have motivated many public health departments to develop early disease outbreak detection systems using non-diagnostic information, often derived from electronic data collected for other purposes (‘‘syndromic surveillance’’) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] These include systems that monitor the number of emergency department visits, primary care visits, ambulance dispatches, nurse hot line calls, pharmaceutical sales, and West Nile–related dead bird reports. They found a way to deal with incomplete data, when, for example, one hospital did not report any data for a particular day

Methods

Results

Conclusion