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

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.