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Open Access Highly Accessed Research article

Evaluation of school absenteeism data for early outbreak detection, New York City

Melanie Besculides12*, Richard Heffernan1, Farzad Mostashari3 and Don Weiss1

Author Affiliations

1 Communicable Disease, New York City Department of Health and Mental Hygiene, New York, NY, USA

2 Mathematica Policy Research, Inc, Cambridge, MA, USA

3 Epidemiology and Surveillance, New York City Department of Health and Mental Hygiene, New York, NY, USA

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BMC Public Health 2005, 5:105  doi:10.1186/1471-2458-5-105

Published: 7 October 2005

Abstract

Background

School absenteeism data may have utility as an early indicator of disease outbreaks, however their value should be critically examined. This paper describes an evaluation of the utility of school absenteeism data for early outbreak detection in New York City (NYC).

Methods

To assess citywide temporal trends in absenteeism, we downloaded three years (2001–02, 2002–03, 2003–04) of daily school attendance data from the NYC Department of Education (DOE) website. We applied the CuSum method to identify aberrations in the adjusted daily percent absent. A spatial scan statistic was used to assess geographic clustering in absenteeism for the 2001–02 academic year.

Results

Moderate increases in absenteeism were observed among children during peak influenza season. Spatial analysis detected 790 significant clusters of absenteeism among elementary school children (p < 0.01), two of which occurred during a previously reported outbreak.

Conclusion

Monitoring school absenteeism may be moderately useful for detecting large citywide epidemics, however, school-level data were noisy and we were unable to demonstrate any practical value in using cluster analysis to detect localized outbreaks. Based on these results, we will not implement prospective monitoring of school absenteeism data, but are evaluating the utility of more specific school-based data for outbreak detection.