School absenteeism as an adjunct surveillance indicator: experience during the second wave of the 2009 H1N1 pandemic in Quebec, Canada.
Identifieur interne : 000432 ( Main/Exploration ); précédent : 000431; suivant : 000433School absenteeism as an adjunct surveillance indicator: experience during the second wave of the 2009 H1N1 pandemic in Quebec, Canada.
Auteurs : Christelle Aïcha Kom Mogto [Canada] ; Gaston De Serres ; Monique Douville Fradet ; Germain Lebel ; Steve Toutant ; Rodica Gilca ; Manale Ouakki ; Naveed Zafar Janjua ; Danuta M. SkowronskiSource :
- PloS one [ 1932-6203 ] ; 2012.
Descripteurs français
- KwdFr :
- Absentéisme (MeSH), Adolescent (MeSH), Enfant (MeSH), Grippe humaine (épidémiologie), Humains (MeSH), Incidence (MeSH), Jeune adulte (MeSH), Modèles statistiques (MeSH), Pandémies (MeSH), Québec (épidémiologie), Sous-type H1N1 du virus de la grippe A (génétique), Surveillance de la population (méthodes), Épidémies de maladies (MeSH), Établissements scolaires (MeSH), Étudiants (MeSH).
- MESH :
- génétique : Sous-type H1N1 du virus de la grippe A.
- méthodes : Surveillance de la population.
- épidémiologie : Grippe humaine, Québec.
- Absentéisme, Adolescent, Enfant, Humains, Incidence, Jeune adulte, Modèles statistiques, Pandémies, Épidémies de maladies, Établissements scolaires, Étudiants.
English descriptors
- KwdEn :
- Absenteeism (MeSH), Adolescent (MeSH), Child (MeSH), Disease Outbreaks (MeSH), Humans (MeSH), Incidence (MeSH), Influenza A Virus, H1N1 Subtype (genetics), Influenza, Human (epidemiology), Models, Statistical (MeSH), Pandemics (MeSH), Population Surveillance (methods), Quebec (epidemiology), Schools (MeSH), Students (MeSH), Young Adult (MeSH).
- MESH :
- geographic , epidemiology : Quebec.
- epidemiology : Influenza, Human.
- genetics : Influenza A Virus, H1N1 Subtype.
- methods : Population Surveillance.
- Absenteeism, Adolescent, Child, Disease Outbreaks, Humans, Incidence, Models, Statistical, Pandemics, Schools, Students, Young Adult.
Abstract
BACKGROUND
A school absenteeism surveillance system was implemented in the province of Quebec, Canada during the second wave of the 2009 H1N1 pandemic. This paper compares this surveillance approach with other available indicators.
METHOD
All (3432) elementary and high schools from Quebec were included. Each school was required to report through a web-based system any day where the proportion of students absent for influenza-like illness (ILI) exceeded 10% of current school enrolment.
RESULTS
Between October 18 and December 12 2009, 35.6% of all schools met the 10% absenteeism threshold. This proportion was greater in elementary compared to high schools (40% vs 19%) and in smaller compared to larger schools (44% vs 22%). The maximum absenteeism rate was reached the first day of reporting or within the next two days in 55% and 31% of schools respectively. The first reports and subsequent peak in school absenteeism provincially preceded the peak in paediatric hospitalization by two and one weeks, respectively. Trends in school surveillance otherwise mirrored other indicators.
CONCLUSION
During a pandemic, school outbreak surveillance based on a 10% threshold appears insufficient to trigger timely intervention within a given affected school. However, school surveillance appears well-correlated and slightly anticipatory compared to other population indicators. As such, school absenteeism warrants further evaluation as an adjunct surveillance indicator whose overall utility will depend upon specified objectives, and other existing capacity for monitoring and response.
DOI: 10.1371/journal.pone.0034084
PubMed: 22479531
PubMed Central: PMC3316605
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Skowronski</name></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Absenteeism (MeSH)</term><term>Adolescent (MeSH)</term><term>Child (MeSH)</term><term>Disease Outbreaks (MeSH)</term><term>Humans (MeSH)</term><term>Incidence (MeSH)</term><term>Influenza A Virus, H1N1 Subtype (genetics)</term><term>Influenza, Human (epidemiology)</term><term>Models, Statistical (MeSH)</term><term>Pandemics (MeSH)</term><term>Population Surveillance (methods)</term><term>Quebec (epidemiology)</term><term>Schools (MeSH)</term><term>Students (MeSH)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Absentéisme (MeSH)</term><term>Adolescent (MeSH)</term><term>Enfant (MeSH)</term><term>Grippe humaine (épidémiologie)</term><term>Humains (MeSH)</term><term>Incidence (MeSH)</term><term>Jeune adulte (MeSH)</term><term>Modèles statistiques (MeSH)</term><term>Pandémies (MeSH)</term><term>Québec (épidémiologie)</term><term>Sous-type H1N1 du virus de la grippe A (génétique)</term><term>Surveillance de la population (méthodes)</term><term>Épidémies de maladies (MeSH)</term><term>Établissements scolaires (MeSH)</term><term>Étudiants (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Quebec</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Sous-type H1N1 du virus de la grippe A</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Surveillance de la population</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Québec</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Absenteeism</term><term>Adolescent</term><term>Child</term><term>Disease Outbreaks</term><term>Humans</term><term>Incidence</term><term>Models, Statistical</term><term>Pandemics</term><term>Schools</term><term>Students</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Absentéisme</term><term>Adolescent</term><term>Enfant</term><term>Humains</term><term>Incidence</term><term>Jeune adulte</term><term>Modèles statistiques</term><term>Pandémies</term><term>Épidémies de maladies</term><term>Établissements scolaires</term><term>Étudiants</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>A school absenteeism surveillance system was implemented in the province of Quebec, Canada during the second wave of the 2009 H1N1 pandemic. This paper compares this surveillance approach with other available indicators.</p></div><div type="abstract" xml:lang="en"><p><b>METHOD</b></p><p>All (3432) elementary and high schools from Quebec were included. Each school was required to report through a web-based system any day where the proportion of students absent for influenza-like illness (ILI) exceeded 10% of current school enrolment.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Between October 18 and December 12 2009, 35.6% of all schools met the 10% absenteeism threshold. This proportion was greater in elementary compared to high schools (40% vs 19%) and in smaller compared to larger schools (44% vs 22%). The maximum absenteeism rate was reached the first day of reporting or within the next two days in 55% and 31% of schools respectively. The first reports and subsequent peak in school absenteeism provincially preceded the peak in paediatric hospitalization by two and one weeks, respectively. Trends in school surveillance otherwise mirrored other indicators.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>During a pandemic, school outbreak surveillance based on a 10% threshold appears insufficient to trigger timely intervention within a given affected school. However, school surveillance appears well-correlated and slightly anticipatory compared to other population indicators. As such, school absenteeism warrants further evaluation as an adjunct surveillance indicator whose overall utility will depend upon specified objectives, and other existing capacity for monitoring and response.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22479531</PMID><DateCompleted><Year>2012</Year><Month>11</Month><Day>05</Day></DateCompleted><DateRevised><Year>2020</Year><Month>03</Month><Day>05</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>7</Volume><Issue>3</Issue><PubDate><Year>2012</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>School absenteeism as an adjunct surveillance indicator: experience during the second wave of the 2009 H1N1 pandemic in Quebec, Canada.</ArticleTitle><Pagination><MedlinePgn>e34084</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0034084</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">A school absenteeism surveillance system was implemented in the province of Quebec, Canada during the second wave of the 2009 H1N1 pandemic. This paper compares this surveillance approach with other available indicators.</AbstractText><AbstractText Label="METHOD" NlmCategory="METHODS">All (3432) elementary and high schools from Quebec were included. Each school was required to report through a web-based system any day where the proportion of students absent for influenza-like illness (ILI) exceeded 10% of current school enrolment.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Between October 18 and December 12 2009, 35.6% of all schools met the 10% absenteeism threshold. This proportion was greater in elementary compared to high schools (40% vs 19%) and in smaller compared to larger schools (44% vs 22%). The maximum absenteeism rate was reached the first day of reporting or within the next two days in 55% and 31% of schools respectively. The first reports and subsequent peak in school absenteeism provincially preceded the peak in paediatric hospitalization by two and one weeks, respectively. Trends in school surveillance otherwise mirrored other indicators.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">During a pandemic, school outbreak surveillance based on a 10% threshold appears insufficient to trigger timely intervention within a given affected school. However, school surveillance appears well-correlated and slightly anticipatory compared to other population indicators. As such, school absenteeism warrants further evaluation as an adjunct surveillance indicator whose overall utility will depend upon specified objectives, and other existing capacity for monitoring and response.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kom Mogto</LastName><ForeName>Christelle Aïcha</ForeName><Initials>CA</Initials><AffiliationInfo><Affiliation>Department of Social and Preventive Medicine, Université Laval, Quebec City, Quebec, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>De Serres</LastName><ForeName>Gaston</ForeName><Initials>G</Initials></Author><Author ValidYN="Y"><LastName>Douville Fradet</LastName><ForeName>Monique</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Lebel</LastName><ForeName>Germain</ForeName><Initials>G</Initials></Author><Author ValidYN="Y"><LastName>Toutant</LastName><ForeName>Steve</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Gilca</LastName><ForeName>Rodica</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Ouakki</LastName><ForeName>Manale</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Janjua</LastName><ForeName>Naveed Zafar</ForeName><Initials>NZ</Initials></Author><Author ValidYN="Y"><LastName>Skowronski</LastName><ForeName>Danuta M</ForeName><Initials>DM</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>03</Month><Day>30</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000041" MajorTopicYN="Y">Absenteeism</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="N">Disease Outbreaks</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D053118" MajorTopicYN="N">Influenza A Virus, H1N1 Subtype</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="N">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011159" MajorTopicYN="N">Population Surveillance</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011792" MajorTopicYN="N" Type="Geographic">Quebec</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012574" MajorTopicYN="N">Schools</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013334" MajorTopicYN="N">Students</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2011</Year><Month>11</Month><Day>14</Day></PubMedPubDate><PubMedPubDate 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Naveed Zafar" uniqKey="Janjua N" first="Naveed Zafar" last="Janjua">Naveed Zafar Janjua</name><name sortKey="Lebel, Germain" sort="Lebel, Germain" uniqKey="Lebel G" first="Germain" last="Lebel">Germain Lebel</name><name sortKey="Ouakki, Manale" sort="Ouakki, Manale" uniqKey="Ouakki M" first="Manale" last="Ouakki">Manale Ouakki</name><name sortKey="Skowronski, Danuta M" sort="Skowronski, Danuta M" uniqKey="Skowronski D" first="Danuta M" last="Skowronski">Danuta M. Skowronski</name><name sortKey="Toutant, Steve" sort="Toutant, Steve" uniqKey="Toutant S" first="Steve" last="Toutant">Steve Toutant</name></noCountry><country name="Canada"><region name="Québec"><name sortKey="Kom Mogto, Christelle Aicha" sort="Kom Mogto, Christelle Aicha" uniqKey="Kom Mogto C" first="Christelle Aïcha" last="Kom Mogto">Christelle Aïcha Kom Mogto</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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