Literature DB >> 19116082

School closure to reduce influenza transmission.

Lisa M Koonin, Martin S Cetron.   

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Year:  2009        PMID: 19116082      PMCID: PMC2660715          DOI: 10.3201/eid1501.081289

Source DB:  PubMed          Journal:  Emerg Infect Dis        ISSN: 1080-6040            Impact factor:   6.883


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To the Editor

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 (). 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 (–). 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 (). 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 (). 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 (,). 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. We agree with Koonin and Cetron () 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 (). 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 () 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 (–). 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.
  13 in total

1.  Nonpharmaceutical interventions implemented by US cities during the 1918-1919 influenza pandemic.

Authors:  Howard Markel; Harvey B Lipman; J Alexander Navarro; Alexandra Sloan; Joseph R Michalsen; Alexandra Minna Stern; Martin S Cetron
Journal:  JAMA       Date:  2007-08-08       Impact factor: 56.272

2.  Effectiveness of control measures during the SARS epidemic in Beijing: a comparison of the Rt curve and the epidemic curve.

Authors:  B J Cowling; L M Ho; G M Leung
Journal:  Epidemiol Infect       Date:  2007-06-14       Impact factor: 2.451

3.  Estimating the impact of school closure on influenza transmission from Sentinel data.

Authors:  Simon Cauchemez; Alain-Jacques Valleron; Pierre-Yves Boëlle; Antoine Flahault; Neil M Ferguson
Journal:  Nature       Date:  2008-04-10       Impact factor: 49.962

4.  Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures.

Authors:  Jacco Wallinga; Peter Teunis
Journal:  Am J Epidemiol       Date:  2004-09-15       Impact factor: 4.897

5.  Strategies for mitigating an influenza pandemic.

Authors:  Neil M Ferguson; Derek A T Cummings; Christophe Fraser; James C Cajka; Philip C Cooley; Donald S Burke
Journal:  Nature       Date:  2006-04-26       Impact factor: 49.962

6.  Public response to community mitigation measures for pandemic influenza.

Authors:  Robert J Blendon; Lisa M Koonin; John M Benson; Martin S Cetron; William E Pollard; Elizabeth W Mitchell; Kathleen J Weldon; Melissa J Herrmann
Journal:  Emerg Infect Dis       Date:  2008-05       Impact factor: 6.883

7.  Targeted social distancing design for pandemic influenza.

Authors:  Robert J Glass; Laura M Glass; Walter E Beyeler; H Jason Min
Journal:  Emerg Infect Dis       Date:  2006-11       Impact factor: 6.883

8.  Real-time estimates in early detection of SARS.

Authors:  Simon Cauchemez; Pierre-Yves Boelle; Christi A Donnelly; Neil M Ferguson; Guy Thomas; Gabriel M Leung; Anthony J Hedley; Roy M Anderson; Alain-Jacques Valleron
Journal:  Emerg Infect Dis       Date:  2006-01       Impact factor: 6.883

9.  Effects of school closures, 2008 winter influenza season, Hong Kong.

Authors:  Benjamin J Cowling; Eric H Y Lau; Conrad L H Lam; Calvin K Y Cheng; Jana Kovar; Kwok Hung Chan; J S Malik Peiris; Gabriel M Leung
Journal:  Emerg Infect Dis       Date:  2008-10       Impact factor: 6.883

10.  School closure to reduce influenza transmission.

Authors:  Lisa M Koonin; Martin S Cetron
Journal:  Emerg Infect Dis       Date:  2009-01       Impact factor: 6.883

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  14 in total

1.  Simulating school closure strategies to mitigate an influenza epidemic.

Authors:  Bruce Y Lee; Shawn T Brown; Philip Cooley; Maggie A Potter; William D Wheaton; Ronald E Voorhees; Samuel Stebbins; John J Grefenstette; Shanta M Zimmer; Richard K Zimmerman; Tina-Marie Assi; Rachel R Bailey; Diane K Wagener; Donald S Burke
Journal:  J Public Health Manag Pract       Date:  2010 May-Jun

2.  School closure as an influenza mitigation strategy: how variations in legal authority and plan criteria can alter the impact.

Authors:  Margaret A Potter; Shawn T Brown; Phillip C Cooley; Patricia M Sweeney; Tina B Hershey; Sherrianne M Gleason; Bruce Y Lee; Christopher R Keane; John Grefenstette; Donald S Burke
Journal:  BMC Public Health       Date:  2012-11-14       Impact factor: 3.295

3.  Age-specific differences in influenza A epidemic curves: do children drive the spread of influenza epidemics?

Authors:  Dena Schanzer; Julie Vachon; Louise Pelletier
Journal:  Am J Epidemiol       Date:  2011-05-20       Impact factor: 4.897

4.  Analysis of CDC social control measures using an agent-based simulation of an influenza epidemic in a city.

Authors:  Yong Yang; Peter M Atkinson; Dick Ettema
Journal:  BMC Infect Dis       Date:  2011-07-18       Impact factor: 3.090

5.  Controlling the spread of disease in schools.

Authors:  Benjamin J Ridenhour; Alexis Braun; Thomas Teyrasse; David Goldsman
Journal:  PLoS One       Date:  2011-12-29       Impact factor: 3.240

6.  Respiratory illness in households of school-dismissed students during pandemic (H1N1) 2009.

Authors:  Nicole J Cohen; David B Callahan; Vanessa Gonzalez; Victor Balaban; Rose T Wang; Paran Pordell; Ricardo Beato; Otilio Oyervides; Wan-Ting Huang; Mehran S Massoudi
Journal:  Emerg Infect Dis       Date:  2011-09       Impact factor: 6.883

7.  Measuring social networks in British primary schools through scientific engagement.

Authors:  A J K Conlan; K T D Eames; J A Gage; J C von Kirchbach; J V Ross; R A Saenz; J R Gog
Journal:  Proc Biol Sci       Date:  2010-11-03       Impact factor: 5.349

8.  Introduction of a Novel Swine-Origin Influenza A (H1N1) Virus into Milwaukee, Wisconsin in 2009.

Authors:  Swati Kumar; Michael J Chusid; Rodney E Willoughby; Peter L Havens; Sue C Kehl; Nathan A Ledeboer; Shun-Hwa Li; Kelly J Henrickson
Journal:  Viruses       Date:  2009-06-01       Impact factor: 5.048

9.  School closure to reduce influenza transmission.

Authors:  Lisa M Koonin; Martin S Cetron
Journal:  Emerg Infect Dis       Date:  2009-01       Impact factor: 6.883

10.  Effective school actions for mitigating seasonal influenza outbreaks in Niigata, Japan.

Authors:  Koshu Sugisaki; Nao Seki; Naohito Tanabe; Reiko Saito; Asami Sasaki; Satoshi Sasaki; Hiroshi Suzuki
Journal:  PLoS One       Date:  2013-09-10       Impact factor: 3.240

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