Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system.
Identifieur interne : 000015 ( Main/Exploration ); précédent : 000014; suivant : 000016Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system.
Auteurs : Madeline A. Ward [Canada] ; Anu Stanley [Canada] ; Lorna E. Deeth [Canada] ; Rob Deardon [Canada] ; Zeny Feng [Canada] ; Lise A. Trotz-Williams [Canada]Source :
- BMC public health [ 1471-2458 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
- épidémiologie : Grippe humaine, Ontario.
- Absentéisme, Adolescent, Enfant, Humains, Saisons, Surveillance de la population, Épidémies, Établissements scolaires.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Ontario.
- epidemiology : Influenza, Human.
- methods : Population Surveillance.
- Absenteeism, Adolescent, Child, Epidemics, Humans, Schools, Seasons.
Abstract
BACKGROUND
School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics.
METHODS
Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness.
RESULTS
All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827.
CONCLUSIONS
School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.
DOI: 10.1186/s12889-019-7521-7
PubMed: 31488092
Affiliations:
Links toward previous steps (curation, corpus...)
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Ward</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada. mward06@uoguelph.ca.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1</wicri:regionArea><wicri:noRegion>N1G 2W1</wicri:noRegion></affiliation></author><author><name sortKey="Stanley, Anu" sort="Stanley, Anu" uniqKey="Stanley A" first="Anu" last="Stanley">Anu Stanley</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1</wicri:regionArea><wicri:noRegion>N1G 2W1</wicri:noRegion></affiliation></author><author><name sortKey="Deeth, Lorna E" sort="Deeth, Lorna E" uniqKey="Deeth L" first="Lorna E" last="Deeth">Lorna E. 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Ward</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada. mward06@uoguelph.ca.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1</wicri:regionArea><wicri:noRegion>N1G 2W1</wicri:noRegion></affiliation></author><author><name sortKey="Stanley, Anu" sort="Stanley, Anu" uniqKey="Stanley A" first="Anu" last="Stanley">Anu Stanley</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1</wicri:regionArea><wicri:noRegion>N1G 2W1</wicri:noRegion></affiliation></author><author><name sortKey="Deeth, Lorna E" sort="Deeth, Lorna E" uniqKey="Deeth L" first="Lorna E" last="Deeth">Lorna E. Deeth</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1</wicri:regionArea><wicri:noRegion>N1G 2W1</wicri:noRegion></affiliation></author><author><name sortKey="Deardon, Rob" sort="Deardon, Rob" uniqKey="Deardon R" first="Rob" last="Deardon">Rob Deardon</name><affiliation wicri:level="4"><nlm:affiliation>Department of Production Animal Health, University of Calgary, University Drive NW, Calgary, T2N 1N4, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Production Animal Health, University of Calgary, University Drive NW, Calgary, T2N 1N4</wicri:regionArea><orgName type="university">Université de Calgary</orgName><placeName><settlement type="city">Calgary</settlement><region type="state">Alberta</region></placeName></affiliation><affiliation wicri:level="4"><nlm:affiliation>Department of Mathematics and Statistics, University of Calgary, University Drive NW, Calgary, T2N 1N4, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Mathematics and Statistics, University of Calgary, University Drive NW, Calgary, T2N 1N4</wicri:regionArea><orgName type="university">Université de Calgary</orgName><placeName><settlement type="city">Calgary</settlement><region type="state">Alberta</region></placeName></affiliation></author><author><name sortKey="Feng, Zeny" sort="Feng, Zeny" uniqKey="Feng Z" first="Zeny" last="Feng">Zeny Feng</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1</wicri:regionArea><wicri:noRegion>N1G 2W1</wicri:noRegion></affiliation></author><author><name sortKey="Trotz Williams, Lise A" sort="Trotz Williams, Lise A" uniqKey="Trotz Williams L" first="Lise A" last="Trotz-Williams">Lise A. Trotz-Williams</name><affiliation wicri:level="1"><nlm:affiliation>Wellington-Dufferin-Guelph Public Health, Chancellors Way, Guelph, N1G 0E1, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Wellington-Dufferin-Guelph Public Health, Chancellors Way, Guelph, N1G 0E1</wicri:regionArea><wicri:noRegion>N1G 0E1</wicri:noRegion></affiliation></author></analytic><series><title level="j">BMC public health</title><idno type="eISSN">1471-2458</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Absenteeism</term><term>Adolescent</term><term>Child</term><term>Epidemics</term><term>Humans</term><term>Influenza, Human (epidemiology)</term><term>Ontario (epidemiology)</term><term>Population Surveillance (methods)</term><term>Schools</term><term>Seasons</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Absentéisme</term><term>Adolescent</term><term>Enfant</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Ontario (épidémiologie)</term><term>Saisons</term><term>Surveillance de la population ()</term><term>Épidémies</term><term>Établissements scolaires</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Ontario</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Ontario</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Absenteeism</term><term>Adolescent</term><term>Child</term><term>Epidemics</term><term>Humans</term><term>Schools</term><term>Seasons</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Absentéisme</term><term>Adolescent</term><term>Enfant</term><term>Humains</term><term>Saisons</term><term>Surveillance de la population</term><term>Épidémies</term><term>Établissements scolaires</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">31488092</PMID><DateCompleted><Year>2019</Year><Month>12</Month><Day>06</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2458</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>1</Issue><PubDate><Year>2019</Year><Month>Sep</Month><Day>05</Day></PubDate></JournalIssue><Title>BMC public health</Title><ISOAbbreviation>BMC Public Health</ISOAbbreviation></Journal><ArticleTitle>Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system.</ArticleTitle><Pagination><MedlinePgn>1232</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12889-019-7521-7</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ward</LastName><ForeName>Madeline A</ForeName><Initials>MA</Initials><Identifier Source="ORCID">http://orcid.org/0000-0003-2115-9515</Identifier><AffiliationInfo><Affiliation>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada. mward06@uoguelph.ca.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Stanley</LastName><ForeName>Anu</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Deeth</LastName><ForeName>Lorna E</ForeName><Initials>LE</Initials><AffiliationInfo><Affiliation>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Deardon</LastName><ForeName>Rob</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Department of Production Animal Health, University of Calgary, University Drive NW, Calgary, T2N 1N4, Canada.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Mathematics and Statistics, University of Calgary, University Drive NW, Calgary, T2N 1N4, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Feng</LastName><ForeName>Zeny</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Trotz-Williams</LastName><ForeName>Lise A</ForeName><Initials>LA</Initials><AffiliationInfo><Affiliation>Wellington-Dufferin-Guelph Public Health, Chancellors Way, Guelph, N1G 0E1, Canada.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>NSERC Discovery Grant</GrantID><Agency>Natural Sciences and Engineering Research Council of Canada</Agency><Country></Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>09</Month><Day>05</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Public Health</MedlineTA><NlmUniqueID>100968562</NlmUniqueID><ISSNLinking>1471-2458</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="D058872" MajorTopicYN="Y">Epidemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D009864" MajorTopicYN="N" Type="Geographic">Ontario</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011159" MajorTopicYN="N">Population Surveillance</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012574" MajorTopicYN="Y">Schools</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012621" MajorTopicYN="N">Seasons</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Absenteeism surveillance system</Keyword><Keyword MajorTopicYN="N">Disease modelling</Keyword><Keyword MajorTopicYN="N">Epidemic detection</Keyword><Keyword MajorTopicYN="N">Influenza</Keyword><Keyword MajorTopicYN="N">Seasonal logistic regression</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2019</Year><Month>05</Month><Day>22</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>08</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>9</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>9</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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Ward</name></noRegion><name sortKey="Deardon, Rob" sort="Deardon, Rob" uniqKey="Deardon R" first="Rob" last="Deardon">Rob Deardon</name><name sortKey="Deardon, Rob" sort="Deardon, Rob" uniqKey="Deardon R" first="Rob" last="Deardon">Rob Deardon</name><name sortKey="Deeth, Lorna E" sort="Deeth, Lorna E" uniqKey="Deeth L" first="Lorna E" last="Deeth">Lorna E. Deeth</name><name sortKey="Feng, Zeny" sort="Feng, Zeny" uniqKey="Feng Z" first="Zeny" last="Feng">Zeny Feng</name><name sortKey="Stanley, Anu" sort="Stanley, Anu" uniqKey="Stanley A" first="Anu" last="Stanley">Anu Stanley</name><name sortKey="Trotz Williams, Lise A" sort="Trotz Williams, Lise A" uniqKey="Trotz Williams L" first="Lise A" last="Trotz-Williams">Lise A. Trotz-Williams</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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