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To Close or Not to Close: That is the H1N1 Question


Researchers offer officials data-driven basis for deciding school closures


By Cindy Long


With H1N1 widespread in 48 states and the vaccine still in short supply, schools around the country are closing as a way to help prevent further spread of the flu. The Centers for Disease Control and Prevention and the Department of Education advise that schools close only as a last resort, but with the rapid spread of the virus – the CDC says the rates of disease are much higher than normal for this time of year – many school officials feel they’re left with no choice.

According to the Department of Education, from August 3 to November 5th there have been approximately 1,709 school dismissals affecting 565,004 students and 36,422 teachers.

But the question remains – how bad should the outbreak be for a school to close?

To help answer that question, a group of researchers from the United States and Japan studied a detailed set of Japanese data that could inform the decision making by schools and health agencies.

“Currently many U.S. schools don’t have specific or consistent algorithms for deciding when to shut down...and it may be a political or fear-based decision,” says epidemiologist John Brownstein of the Children’s Hospital Boston Informatics Program and one of the study’s authors.

Brownstein and his colleagues analyzed flu absenteeism data from a Japanese school district with 54 elementary schools. Tracking four consecutive flu seasons (2004-2008), they asked what pattern of flu absenteeism was best for detecting an actual school outbreak balanced against the practical need to keep schools open if possible to continue student learning and ease the burden of working parents.

“You’d want to get a school closed before an epidemic peaks to prevent the transmission of the virus, but you also don’t want to close a school unnecessarily,” says Brownstein.

The researchers defined a school outbreak as a daily flu absentee rate of more than 10 percent of students. After comparing more than two dozen possible scenarios for closing a school, their analysis, published by the CDC in the latest issue of Emerging Infectious Diseases, suggested three optimal scenarios for predicting a flu outbreak and therefore closing a school:

1. A single-day influenza-related absentee rate of 5 percent.
2. Absenteeism of 4 percent or more on two consecutive days.
3. Absenteeism of 3 percent or more on three consecutive days.

"Our method would give school administrators or government agencies a basis for timely closure decisions, by allowing them to predict the escalation of an outbreak using past absenteeism data," says Anne Gatewood Hoen, another researcher from Children’s Hospital in Boston. "It could be used with data from schools in other communities to provide predictions. It would leave decision-making in the hands of local officials, but provide them with a data-driven basis for making those decisions."

Last spring, during the early days of the H1N1 influenza pandemic, the CDC recommended first a seven-day school closure, then a 14-day closure after appearance of the first suspected case. Later, as more became known about the extent of community spread and disease severity, the CDC changed the recommendation to advise against school closure unless absentee rates interfered with school function. CDC's current guidelines don't provide a specific algorithm, but state that "the decision to selectively dismiss a school should be made locally," in conjunction with local and state health officials, "and should balance the risks of keeping the students in school with the social disruption that school dismissal can cause." When the decision is made to dismiss students, CDC recommends doing so for five to seven calendar days.

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