Volume 15, Number 1—January 2009
School Closure to Reduce Influenza Transmission
Cowling et al. reported on the effects of school closure in Hong Kong, People’s Republic of China, during March 2008 in response to influenza-related deaths of children (1). The influenza epidemic started in January 2008 and peaked in late February, but the 2-week school closure did not begin until March 12. Consequently, the school-based epidemic was on the decline by the time officials closed schools. Other studies have suggested that early school closures can help reduce influenza illness in the community and among school children, especially during a pandemic (2–6). However, surveillance systems that rely on school absenteeism or deaths would likely provide information too late during the outbreak for school closure to effectively reduce influenza transmission.
The Centers for Disease Control and Prevention (CDC) has recommended early closure of schools as a community mitigation measure in the event of a severe pandemic (7). Specifically, CDC recommends rapidly initiating activities such as advising sick persons to stay home, dismissing children from schools, closing childcare facilities, and initiating further social distancing measures within a state or a community at the beginning of the upslope of a pandemic wave (acceleration interval), i.e., when cases are initially identified and community transmission begins to occur (8). We concur with the authors that the 2007–08 influenza season was already waning by the time the decision was made to close schools (deceleration interval).
School closure used as a single pandemic control measure is predicted to be less effective than early, concurrent use of multiple measures. Socially disruptive measures like early school closure and keeping children from congregating in the community would likely reduce community transmission of pandemic disease, but would also create secondary challenges (9,10). Therefore, to ensure maximal benefit for reducing disease transmission, interventions should be implemented early and concomitantly with other nonpharmaceutical and pharmaceutical measures, accompanied by public education, and used judiciously based on pandemic severity.
- Cowling BJ, Lau EH, Lam CL, Cheng CK, Kovar J, Chan KH, Effects of school closures, 2008 winter influenza season, Hong Kong. Emerg Infect Dis. 2008;14:1660–2. DOIPubMedGoogle Scholar
- Heymann A, Chodick G, Reichman B, Kokia E, Laufer J. Influence of school closure on the incidence of viral respiratory diseases among children and on health care utilization. Pediatr Infect Dis J. 2004;23:675–7. DOIPubMedGoogle Scholar
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- Centers for Disease Control and Prevention. Interim pre-pandemic planning guidance: community strategy for pandemic influenza mitigation in the United States—early, targeted, layered use of nonpharmaceutical interventions. Atlanta: The Centers; 2007.
- Federal guidance to assist states in improving state-level pandemic influenza operating plans. March 11, 2008 [cited 2008 Nov 26]. Available from http://www.pandemicflu.gov/news/guidance031108.pdf
- Johnson AJ, Moore ZS, Edelson PJ, Kinnane L, Davies M, Shay DK, Household responses to school closure resulting from outbreak of influenza B, North Carolina. Emerg Infect Dis. 2008;14:1024–30. DOIPubMedGoogle Scholar
- Blendon RJ, Koonin LM, Benson JM, Cetron MS, Pollard WE, Mitchell EW, Public response to community mitigation measures for pandemic influenza. Emerg Infect Dis. 2008;14:778–86. DOIPubMedGoogle Scholar
We agree with Koonin and Cetron (1) that early application of any intervention during an influenza epidemic or pandemic is critical in maximizing population health benefits. Further, the longer an intervention is sustained, the greater the likely benefit.
Whether surveillance data can inform public health interventions may depend on the timeliness of the data as well as the length of the epidemic. In tropical and subtropical settings, influenza tends to circulate longer. Although duration of the epidemic could enable delayed interventions a chance of success, social distancing interventions may need to be sustained to ensure that the epidemic does not revive when the intervention period ends.
One important study not mentioned by Koonin and Cetron is a natural experiment in France where the staggering of school holiday periods in different regions enabled Cauchemez et al. to estimate that school holidays prevent 16%–18% of seasonal influenza cases (2). In contrast to our study of a single school closure event in response to 1 seasonal outbreak, the French study considered preplanned holiday periods spanning many years.
Although pandemic plans often describe action to be taken depending on features in the epidemic curve (e.g., the acceleration interval as the upslope of the epidemic curve), we would argue that more focus should be given to underlying transmission dynamics. In our analysis of the effect of school closures in Hong Kong, we used a simple statistical technique (3) to estimate the underlying reproductive number. Changes in the epidemic curve may lag behind changes in the underlying transmission dynamics by at least 1 serial interval, as has previously been shown for severe acute respiratory syndrome (3–5). Public health practitioners must be encouraged to use these methods routinely.
Finally, we concur that a multipronged, targeted, layered approach will likely provide the best mitigation strategy in the event of a pandemic. However, we caution against conflating good public health practice of “pulling out all the stops” in the event of a pandemic with good scientific practice of evaluating the independent effect of school closures, which was the object of our article.
- Koonin LM, Cetron MS. School closure to reduce influenza transmission. Emerg Infect Dis. 2009;15:137–8. DOIPubMedGoogle Scholar
- Cauchemez S, Valleron AJ, Boelle PY, Flahault A, Ferguson NM. Estimating the impact of school closure on influenza transmission from sentinel data. Nature. 2008;452:750–4. DOIPubMedGoogle Scholar
- Cowling BJ, Ho LM, Leung GM. Effectiveness of control measures during the SARS epidemic in Beijing—a comparison of the Rt curve and the epidemic curve. Epidemiol Infect. 2008;136:562–6. DOIPubMedGoogle Scholar
- Wallinga J, Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol. 2004;160:509–16. DOIPubMedGoogle Scholar
- Cauchemez S, Boelle PY, Donnelly CA, Ferguson NM, Thomas G, Leung GM, Real-time estimates in early detection of SARS. Emerg Infect Dis. 2006;12:110–3.PubMedGoogle Scholar
Table of Contents – Volume 15, Number 1—January 2009
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Please use the form below to submit correspondence to the authors or contact them at the following address:
Lisa M. Koonin, Centers for Disease Control and Prevention, 1600 Clifton Rd NE, Mailstop A20, Atlanta, GA 30333, USA;
Benjamin J. Cowling, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 21 Sassoon Rd, Pokfulam, Hong Kong, People’s Republic of China;