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Modelling the impact of local reactive school closures on critical care provision during an influenza pandemic

Identifieur interne : 000F36 ( Ncbi/Checkpoint ); précédent : 000F35; suivant : 000F37

Modelling the impact of local reactive school closures on critical care provision during an influenza pandemic

Auteurs : Thomas House [Royaume-Uni] ; Marc Baguelin [Royaume-Uni] ; Albert Jan Van Hoek [Royaume-Uni] ; Peter J. White [Royaume-Uni] ; Zia Sadique [Royaume-Uni] ; Ken Eames [Royaume-Uni] ; Jonathan M. Read [Royaume-Uni] ; Niel Hens [Belgique] ; Alessia Melegaro [Italie] ; W. John Edmunds [Royaume-Uni] ; Matt J. Keeling [Royaume-Uni]

Source :

RBID : PMC:3145187

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English descriptors

Abstract

Despite the fact that the 2009 H1N1 pandemic influenza strain was less severe than had been feared, both seasonal epidemics of influenza-like-illness and future influenza pandemics have the potential to place a serious burden on health services. The closure of schools has been postulated as a means of reducing transmission between children and hence reducing the number of cases at the peak of an epidemic; this is supported by the marked reduction in cases during school holidays observed across the world during the 2009 pandemic. However, a national policy of long-duration school closures could have severe economic costs. Reactive short-duration closure of schools in regions where health services are close to capacity offers a potential compromise, but it is unclear over what spatial scale and time frame closures would need to be made to be effective. Here, using detailed geographical information for England, we assess how localized school closures could alleviate the burden on hospital intensive care units (ICUs) that are reaching capacity. We show that, for a range of epidemiologically plausible assumptions, considerable local coordination of school closures is needed to achieve a substantial reduction in the number of hospitals where capacity is exceeded at the peak of the epidemic. The heterogeneity in demand per hospital ICU bed means that even widespread school closures are unlikely to have an impact on whether demand will exceed capacity for many hospitals. These results support the UK decision not to use localized school closures as a control mechanism, but have far wider international public-health implications. The spatial heterogeneities in both population density and hospital capacity that give rise to our results exist in many developed countries, while our model assumptions are sufficiently general to cover a wide range of pathogens. This leads us to believe that when a pandemic has severe implications for ICU capacity, only widespread school closures (with their associated costs and organizational challenges) are sufficient to mitigate the burden on the worst-affected hospitals.


Url:
DOI: 10.1098/rspb.2010.2688
PubMed: 21288945
PubMed Central: 3145187


Affiliations:


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PMC:3145187

Le document en format XML

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<term>Adolescent</term>
<term>Adult</term>
<term>Aged</term>
<term>Basic Reproduction Number</term>
<term>Child</term>
<term>Child, Preschool</term>
<term>Critical Care</term>
<term>Disease Transmission, Infectious (prevention & control)</term>
<term>Health Services Needs and Demand</term>
<term>Hospitalization (statistics & numerical data)</term>
<term>Humans</term>
<term>Influenza A Virus, H1N1 Subtype</term>
<term>Influenza, Human (epidemiology)</term>
<term>Influenza, Human (prevention & control)</term>
<term>Influenza, Human (therapy)</term>
<term>Intensive Care Units (statistics & numerical data)</term>
<term>Middle Aged</term>
<term>Models, Biological</term>
<term>Pandemics</term>
<term>Schools</term>
<term>United Kingdom (epidemiology)</term>
<term>Young Adult</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr">
<term>Adolescent</term>
<term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Besoins et demandes de services de santé</term>
<term>Enfant</term>
<term>Enfant d'âge préscolaire</term>
<term>Grippe humaine ()</term>
<term>Grippe humaine (épidémiologie)</term>
<term>Hospitalisation ()</term>
<term>Humains</term>
<term>Jeune adulte</term>
<term>Modèles biologiques</term>
<term>Nombre de reproduction de base</term>
<term>Pandémies</term>
<term>Royaume-Uni (épidémiologie)</term>
<term>Soins de réanimation</term>
<term>Sous-type H1N1 du virus de la grippe A</term>
<term>Sujet âgé</term>
<term>Transmission de maladie infectieuse ()</term>
<term>Unités de soins intensifs ()</term>
<term>Établissements scolaires</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en">
<term>Influenza, Human</term>
<term>United Kingdom</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en">
<term>Disease Transmission, Infectious</term>
<term>Influenza, Human</term>
</keywords>
<keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en">
<term>Hospitalization</term>
<term>Intensive Care Units</term>
</keywords>
<keywords scheme="MESH" qualifier="therapy" xml:lang="en">
<term>Influenza, Human</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr">
<term>Grippe humaine</term>
<term>Royaume-Uni</term>
</keywords>
<keywords scheme="MESH" xml:lang="en">
<term>Adolescent</term>
<term>Adult</term>
<term>Aged</term>
<term>Basic Reproduction Number</term>
<term>Child</term>
<term>Child, Preschool</term>
<term>Critical Care</term>
<term>Health Services Needs and Demand</term>
<term>Humans</term>
<term>Influenza A Virus, H1N1 Subtype</term>
<term>Middle Aged</term>
<term>Models, Biological</term>
<term>Pandemics</term>
<term>Schools</term>
<term>Young Adult</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr">
<term>Adolescent</term>
<term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Besoins et demandes de services de santé</term>
<term>Enfant</term>
<term>Enfant d'âge préscolaire</term>
<term>Grippe humaine</term>
<term>Hospitalisation</term>
<term>Humains</term>
<term>Jeune adulte</term>
<term>Modèles biologiques</term>
<term>Nombre de reproduction de base</term>
<term>Pandémies</term>
<term>Soins de réanimation</term>
<term>Sous-type H1N1 du virus de la grippe A</term>
<term>Sujet âgé</term>
<term>Transmission de maladie infectieuse</term>
<term>Unités de soins intensifs</term>
<term>Établissements scolaires</term>
</keywords>
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</teiHeader>
<front>
<div type="abstract" xml:lang="en">
<p>Despite the fact that the 2009 H1N1 pandemic influenza strain was less severe than had been feared, both seasonal epidemics of influenza-like-illness and future influenza pandemics have the potential to place a serious burden on health services. The closure of schools has been postulated as a means of reducing transmission between children and hence reducing the number of cases at the peak of an epidemic; this is supported by the marked reduction in cases during school holidays observed across the world during the 2009 pandemic. However, a national policy of long-duration school closures could have severe economic costs. Reactive short-duration closure of schools in regions where health services are close to capacity offers a potential compromise, but it is unclear over what spatial scale and time frame closures would need to be made to be effective. Here, using detailed geographical information for England, we assess how localized school closures could alleviate the burden on hospital intensive care units (ICUs) that are reaching capacity. We show that, for a range of epidemiologically plausible assumptions, considerable local coordination of school closures is needed to achieve a substantial reduction in the number of hospitals where capacity is exceeded at the peak of the epidemic. The heterogeneity in demand per hospital ICU bed means that even widespread school closures are unlikely to have an impact on whether demand will exceed capacity for many hospitals. These results support the UK decision not to use localized school closures as a control mechanism, but have far wider international public-health implications. The spatial heterogeneities in both population density and hospital capacity that give rise to our results exist in many developed countries, while our model assumptions are sufficiently general to cover a wide range of pathogens. This leads us to believe that when a pandemic has severe implications for ICU capacity, only widespread school closures (with their associated costs and organizational challenges) are sufficient to mitigate the burden on the worst-affected hospitals.</p>
</div>
</front>
<back>
<div1 type="bibliography">
<listBibl>
<biblStruct></biblStruct>
<biblStruct></biblStruct>
<biblStruct></biblStruct>
<biblStruct>
<analytic>
<author>
<name sortKey="Cauchemez, S" uniqKey="Cauchemez S">S. Cauchemez</name>
</author>
<author>
<name sortKey="Ferguson, N M" uniqKey="Ferguson N">N. M. Ferguson</name>
</author>
<author>
<name sortKey="Wachtel, C" uniqKey="Wachtel C">C. Wachtel</name>
</author>
<author>
<name sortKey="Tegnell, A" uniqKey="Tegnell A">A. Tegnell</name>
</author>
<author>
<name sortKey="Saour, G" uniqKey="Saour G">G. Saour</name>
</author>
<author>
<name sortKey="Duncan, B" uniqKey="Duncan B">B. Duncan</name>
</author>
<author>
<name sortKey="Nicoll, A" uniqKey="Nicoll A">A. Nicoll</name>
</author>
</analytic>
</biblStruct>
<biblStruct>
<analytic>
<author>
<name sortKey="Cauchemez, S" uniqKey="Cauchemez S">S. Cauchemez</name>
</author>
<author>
<name sortKey="Valleron, A J" uniqKey="Valleron A">A.-J. Valleron</name>
</author>
<author>
<name sortKey="Boelle, P Y" uniqKey="Boelle P">P.-Y. Boëlle</name>
</author>
<author>
<name sortKey="Flahault, A" uniqKey="Flahault A">A. Flahault</name>
</author>
<author>
<name sortKey="Ferguson, N M" uniqKey="Ferguson N">N. M. Ferguson</name>
</author>
</analytic>
</biblStruct>
<biblStruct>
<analytic>
<author>
<name sortKey="Mossong, J" uniqKey="Mossong J">J. Mossong</name>
</author>
</analytic>
</biblStruct>
<biblStruct>
<analytic>
<author>
<name sortKey="Hens, N" uniqKey="Hens N">N. Hens</name>
</author>
<author>
<name sortKey="Minalu Ayele, G" uniqKey="Minalu Ayele G">G. Minalu Ayele</name>
</author>
<author>
<name sortKey="Goeyvaerts, N" uniqKey="Goeyvaerts N">N. Goeyvaerts</name>
</author>
<author>
<name sortKey="Aerts, M" uniqKey="Aerts M">M. Aerts</name>
</author>
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<name sortKey="Mossong, J" uniqKey="Mossong J">J. Mossong</name>
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</author>
<author>
<name sortKey="Beutels, P" uniqKey="Beutels P">P. Beutels</name>
</author>
</analytic>
</biblStruct>
<biblStruct>
<analytic>
<author>
<name sortKey="Baguelin, M" uniqKey="Baguelin M">M. Baguelin</name>
</author>
<author>
<name sortKey="Van Hoek, A J" uniqKey="Van Hoek A">A. J. Van Hoek</name>
</author>
<author>
<name sortKey="Jit, M" uniqKey="Jit M">M. Jit</name>
</author>
<author>
<name sortKey="Flasche, S" uniqKey="Flasche S">S. Flasche</name>
</author>
<author>
<name sortKey="White, P J" uniqKey="White P">P. J. White</name>
</author>
<author>
<name sortKey="Edmunds, W J" uniqKey="Edmunds W">W. J. Edmunds</name>
</author>
</analytic>
</biblStruct>
<biblStruct>
<analytic>
<author>
<name sortKey="Sadique, M Z" uniqKey="Sadique M">M. Z. Sadique</name>
</author>
<author>
<name sortKey="Adams, E J" uniqKey="Adams E">E. J. Adams</name>
</author>
<author>
<name sortKey="Edmunds, W J" uniqKey="Edmunds W">W. J. Edmunds</name>
</author>
</analytic>
</biblStruct>
<biblStruct>
<analytic>
<author>
<name sortKey="Smith, R D" uniqKey="Smith R">R. D. Smith</name>
</author>
<author>
<name sortKey="Keogh Brown, M R" uniqKey="Keogh Brown M">M. R. Keogh-Brown</name>
</author>
<author>
<name sortKey="Barnett, T" uniqKey="Barnett T">T. Barnett</name>
</author>
<author>
<name sortKey="Tait, J" uniqKey="Tait J">J. Tait</name>
</author>
</analytic>
</biblStruct>
</listBibl>
</div1>
</back>
</TEI>
<affiliations>
<list>
<country>
<li>Belgique</li>
<li>Italie</li>
<li>Royaume-Uni</li>
</country>
<region>
<li>Lombardie</li>
<li>Province d'Anvers</li>
<li>Région flamande</li>
</region>
<settlement>
<li>Anvers</li>
<li>Milan</li>
</settlement>
<orgName>
<li>Université d'Anvers</li>
</orgName>
</list>
<tree>
<country name="Royaume-Uni">
<noRegion>
<name sortKey="House, Thomas" sort="House, Thomas" uniqKey="House T" first="Thomas" last="House">Thomas House</name>
</noRegion>
<name sortKey="Baguelin, Marc" sort="Baguelin, Marc" uniqKey="Baguelin M" first="Marc" last="Baguelin">Marc Baguelin</name>
<name sortKey="Baguelin, Marc" sort="Baguelin, Marc" uniqKey="Baguelin M" first="Marc" last="Baguelin">Marc Baguelin</name>
<name sortKey="Eames, Ken" sort="Eames, Ken" uniqKey="Eames K" first="Ken" last="Eames">Ken Eames</name>
<name sortKey="Edmunds, W John" sort="Edmunds, W John" uniqKey="Edmunds W" first="W. John" last="Edmunds">W. John Edmunds</name>
<name sortKey="Edmunds, W John" sort="Edmunds, W John" uniqKey="Edmunds W" first="W. John" last="Edmunds">W. John Edmunds</name>
<name sortKey="Keeling, Matt J" sort="Keeling, Matt J" uniqKey="Keeling M" first="Matt J." last="Keeling">Matt J. Keeling</name>
<name sortKey="Keeling, Matt J" sort="Keeling, Matt J" uniqKey="Keeling M" first="Matt J." last="Keeling">Matt J. Keeling</name>
<name sortKey="Read, Jonathan M" sort="Read, Jonathan M" uniqKey="Read J" first="Jonathan M." last="Read">Jonathan M. Read</name>
<name sortKey="Sadique, Zia" sort="Sadique, Zia" uniqKey="Sadique Z" first="Zia" last="Sadique">Zia Sadique</name>
<name sortKey="Van Hoek, Albert Jan" sort="Van Hoek, Albert Jan" uniqKey="Van Hoek A" first="Albert Jan" last="Van Hoek">Albert Jan Van Hoek</name>
<name sortKey="White, Peter J" sort="White, Peter J" uniqKey="White P" first="Peter J." last="White">Peter J. White</name>
<name sortKey="White, Peter J" sort="White, Peter J" uniqKey="White P" first="Peter J." last="White">Peter J. White</name>
</country>
<country name="Belgique">
<noRegion>
<name sortKey="Hens, Niel" sort="Hens, Niel" uniqKey="Hens N" first="Niel" last="Hens">Niel Hens</name>
</noRegion>
<name sortKey="Hens, Niel" sort="Hens, Niel" uniqKey="Hens N" first="Niel" last="Hens">Niel Hens</name>
</country>
<country name="Italie">
<region name="Lombardie">
<name sortKey="Melegaro, Alessia" sort="Melegaro, Alessia" uniqKey="Melegaro A" first="Alessia" last="Melegaro">Alessia Melegaro</name>
</region>
</country>
</tree>
</affiliations>
</record>

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