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Influenza epidemic surveillance and prediction based on electronic health record data from an out-of-hours general practitioner cooperative: model development and validation on 2003-2015 data.

Identifieur interne : 000017 ( Main/Exploration ); précédent : 000016; suivant : 000018

Influenza epidemic surveillance and prediction based on electronic health record data from an out-of-hours general practitioner cooperative: model development and validation on 2003-2015 data.

Auteurs : Barbara Michiels [Belgique] ; Van Kinh Nguyen [Viêt Nam, Allemagne] ; Samuel Coenen [Belgique] ; Philippe Ryckebosch [Belgique] ; Nathalie Bossuyt [Belgique] ; Niel Hens [Belgique]

Source :

RBID : pubmed:28100186

Descripteurs français

English descriptors

Abstract

BACKGROUND

Annual influenza epidemics significantly burden health care. Anticipating them allows for timely preparation. The Scientific Institute of Public Health in Belgium (WIV-ISP) monitors the incidence of influenza and influenza-like illnesses (ILIs) and reports on a weekly basis. General practitioners working in out-of-hour cooperatives (OOH GPCs) register diagnoses of ILIs in an instantly accessible electronic health record (EHR) system. This article has two objectives: to explore the possibility of modelling seasonal influenza epidemics using EHR ILI data from the OOH GPC Deurne-Borgerhout, Belgium, and to attempt to develop a model accurately predicting new epidemics to complement the national influenza surveillance by WIV-ISP.

METHOD

Validity of the OOH GPC data was assessed by comparing OOH GPC ILI data with WIV-ISP ILI data for the period 2003-2012 and using Pearson's correlation. The best fitting prediction model based on OOH GPC data was developed on 2003-2012 data and validated on 2012-2015 data. A comparison of this model with other well-established surveillance methods was performed. A 1-week and one-season ahead prediction was formulated.

RESULTS

In the OOH GPC, 72,792 contacts were recorded from 2003 to 2012 and 31,844 from 2012 to 2015. The mean ILI diagnosis/week was 4.77 (IQR 3.00) and 3.44 (IQR 3.00) for the two periods respectively. Correlation between OOHs and WIV-ISP ILI incidence is high ranging from 0.83 up to 0.97. Adding a secular trend (5 year cycle) and using a first-order autoregressive modelling for the epidemic component together with the use of Poisson likelihood produced the best prediction results. The selected model had the best 1-week ahead prediction performance compared to existing surveillance methods. The prediction of the starting week was less accurate (±3 weeks) than the predicted duration of the next season.

CONCLUSION

OOH GPC data can be used to predict influenza epidemics both accurately and fast 1-week and one-season ahead. It can also be used to complement the national influenza surveillance to anticipate optimal preparation.


DOI: 10.1186/s12879-016-2175-x
PubMed: 28100186


Affiliations:


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Le document en format XML

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<div type="abstract" xml:lang="en">
<p>
<b>BACKGROUND</b>
</p>
<p>Annual influenza epidemics significantly burden health care. Anticipating them allows for timely preparation. The Scientific Institute of Public Health in Belgium (WIV-ISP) monitors the incidence of influenza and influenza-like illnesses (ILIs) and reports on a weekly basis. General practitioners working in out-of-hour cooperatives (OOH GPCs) register diagnoses of ILIs in an instantly accessible electronic health record (EHR) system. This article has two objectives: to explore the possibility of modelling seasonal influenza epidemics using EHR ILI data from the OOH GPC Deurne-Borgerhout, Belgium, and to attempt to develop a model accurately predicting new epidemics to complement the national influenza surveillance by WIV-ISP.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>METHOD</b>
</p>
<p>Validity of the OOH GPC data was assessed by comparing OOH GPC ILI data with WIV-ISP ILI data for the period 2003-2012 and using Pearson's correlation. The best fitting prediction model based on OOH GPC data was developed on 2003-2012 data and validated on 2012-2015 data. A comparison of this model with other well-established surveillance methods was performed. A 1-week and one-season ahead prediction was formulated.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>RESULTS</b>
</p>
<p>In the OOH GPC, 72,792 contacts were recorded from 2003 to 2012 and 31,844 from 2012 to 2015. The mean ILI diagnosis/week was 4.77 (IQR 3.00) and 3.44 (IQR 3.00) for the two periods respectively. Correlation between OOHs and WIV-ISP ILI incidence is high ranging from 0.83 up to 0.97. Adding a secular trend (5 year cycle) and using a first-order autoregressive modelling for the epidemic component together with the use of Poisson likelihood produced the best prediction results. The selected model had the best 1-week ahead prediction performance compared to existing surveillance methods. The prediction of the starting week was less accurate (±3 weeks) than the predicted duration of the next season.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>CONCLUSION</b>
</p>
<p>OOH GPC data can be used to predict influenza epidemics both accurately and fast 1-week and one-season ahead. It can also be used to complement the national influenza surveillance to anticipate optimal preparation.</p>
</div>
</front>
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<Day>10</Day>
</DateRevised>
<Article PubModel="Electronic">
<Journal>
<ISSN IssnType="Electronic">1471-2334</ISSN>
<JournalIssue CitedMedium="Internet">
<Volume>17</Volume>
<Issue>1</Issue>
<PubDate>
<Year>2017</Year>
<Month>01</Month>
<Day>18</Day>
</PubDate>
</JournalIssue>
<Title>BMC infectious diseases</Title>
<ISOAbbreviation>BMC Infect. Dis.</ISOAbbreviation>
</Journal>
<ArticleTitle>Influenza epidemic surveillance and prediction based on electronic health record data from an out-of-hours general practitioner cooperative: model development and validation on 2003-2015 data.</ArticleTitle>
<Pagination>
<MedlinePgn>84</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1186/s12879-016-2175-x</ELocationID>
<Abstract>
<AbstractText Label="BACKGROUND">Annual influenza epidemics significantly burden health care. Anticipating them allows for timely preparation. The Scientific Institute of Public Health in Belgium (WIV-ISP) monitors the incidence of influenza and influenza-like illnesses (ILIs) and reports on a weekly basis. General practitioners working in out-of-hour cooperatives (OOH GPCs) register diagnoses of ILIs in an instantly accessible electronic health record (EHR) system. This article has two objectives: to explore the possibility of modelling seasonal influenza epidemics using EHR ILI data from the OOH GPC Deurne-Borgerhout, Belgium, and to attempt to develop a model accurately predicting new epidemics to complement the national influenza surveillance by WIV-ISP.</AbstractText>
<AbstractText Label="METHOD">Validity of the OOH GPC data was assessed by comparing OOH GPC ILI data with WIV-ISP ILI data for the period 2003-2012 and using Pearson's correlation. The best fitting prediction model based on OOH GPC data was developed on 2003-2012 data and validated on 2012-2015 data. A comparison of this model with other well-established surveillance methods was performed. A 1-week and one-season ahead prediction was formulated.</AbstractText>
<AbstractText Label="RESULTS">In the OOH GPC, 72,792 contacts were recorded from 2003 to 2012 and 31,844 from 2012 to 2015. The mean ILI diagnosis/week was 4.77 (IQR 3.00) and 3.44 (IQR 3.00) for the two periods respectively. Correlation between OOHs and WIV-ISP ILI incidence is high ranging from 0.83 up to 0.97. Adding a secular trend (5 year cycle) and using a first-order autoregressive modelling for the epidemic component together with the use of Poisson likelihood produced the best prediction results. The selected model had the best 1-week ahead prediction performance compared to existing surveillance methods. The prediction of the starting week was less accurate (±3 weeks) than the predicted duration of the next season.</AbstractText>
<AbstractText Label="CONCLUSION">OOH GPC data can be used to predict influenza epidemics both accurately and fast 1-week and one-season ahead. It can also be used to complement the national influenza surveillance to anticipate optimal preparation.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y">
<Author ValidYN="Y">
<LastName>Michiels</LastName>
<ForeName>Barbara</ForeName>
<Initials>B</Initials>
<AffiliationInfo>
<Affiliation>Department of Primary and Interdisciplinary Care Antwerp (ELIZA) - Centre for General Practice, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium. barbara.michiels@uantwerpen.be.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Nguyen</LastName>
<ForeName>Van Kinh</ForeName>
<Initials>VK</Initials>
<AffiliationInfo>
<Affiliation>Department of Epidemiology, Faculty of Public Health, Ho Chi Minh University of Medicine and Pharmacy, Ho Chi Minh, Vietnam.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Systems Medicine of Infectious Diseases (SMID), Department of Systems Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Coenen</LastName>
<ForeName>Samuel</ForeName>
<Initials>S</Initials>
<AffiliationInfo>
<Affiliation>Department of Primary and Interdisciplinary Care Antwerp (ELIZA) - Centre for General Practice, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Vaccine & Infectious Disease Institute (VAXINFECTIO) - Laboratory of Medical Microbiology, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Epidemiology and Social Medicine (ESOC), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Ryckebosch</LastName>
<ForeName>Philippe</ForeName>
<Initials>P</Initials>
<AffiliationInfo>
<Affiliation>Department of Primary and Interdisciplinary Care Antwerp (ELIZA) - Centre for General Practice, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Bossuyt</LastName>
<ForeName>Nathalie</ForeName>
<Initials>N</Initials>
<AffiliationInfo>
<Affiliation>Unit Epidemiology of infectious diseases - Operational Directorate Public Health and Surveillance, Belgian Scientific Institute for Public Health, Brussels, Belgium.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Hens</LastName>
<ForeName>Niel</ForeName>
<Initials>N</Initials>
<AffiliationInfo>
<Affiliation>Epidemiology and Social Medicine (ESOC), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Interuniversity Institute of Biostatistics and statistical Bioinformatics (iBIOSTAT), Hasselt University, Hasselt, Belgium.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Vaccine & Infectious Disease Institute (VAXINFECTIO) - Centre for Health Economic Research and Modelling Infectious Diseases, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList>
<PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D023361">Validation Study</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic">
<Year>2017</Year>
<Month>01</Month>
<Day>18</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo>
<Country>England</Country>
<MedlineTA>BMC Infect Dis</MedlineTA>
<NlmUniqueID>100968551</NlmUniqueID>
<ISSNLinking>1471-2334</ISSNLinking>
</MedlineJournalInfo>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList>
<MeshHeading>
<DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D039602" MajorTopicYN="Y">After-Hours Care</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D001530" MajorTopicYN="N" Type="Geographic">Belgium</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D003625" MajorTopicYN="N">Data Collection</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D057286" MajorTopicYN="Y">Electronic Health Records</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D058872" MajorTopicYN="Y">Epidemics</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D062665" MajorTopicYN="N">Epidemiological Monitoring</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D058005" MajorTopicYN="Y">General Practitioners</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D008962" MajorTopicYN="N">Models, Theoretical</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName>
</MeshHeading>
<MeshHeading>
<DescriptorName UI="D012621" MajorTopicYN="N">Seasons</DescriptorName>
</MeshHeading>
</MeshHeadingList>
<KeywordList Owner="NOTNLM">
<Keyword MajorTopicYN="Y">Epidemics</Keyword>
<Keyword MajorTopicYN="Y">Epidemiology</Keyword>
<Keyword MajorTopicYN="Y">Influenza</Keyword>
<Keyword MajorTopicYN="Y">Influenza-like illness</Keyword>
<Keyword MajorTopicYN="Y">Out-of-hours</Keyword>
<Keyword MajorTopicYN="Y">Prediction</Keyword>
<Keyword MajorTopicYN="Y">Secular</Keyword>
<Keyword MajorTopicYN="Y">Surveillance</Keyword>
</KeywordList>
</MedlineCitation>
<PubmedData>
<History>
<PubMedPubDate PubStatus="received">
<Year>2016</Year>
<Month>08</Month>
<Day>25</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted">
<Year>2016</Year>
<Month>12</Month>
<Day>27</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez">
<Year>2017</Year>
<Month>1</Month>
<Day>20</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed">
<Year>2017</Year>
<Month>1</Month>
<Day>20</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline">
<Year>2017</Year>
<Month>7</Month>
<Day>8</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>epublish</PublicationStatus>
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<ArticleId IdType="pubmed">28100186</ArticleId>
<ArticleId IdType="doi">10.1186/s12879-016-2175-x</ArticleId>
<ArticleId IdType="pii">10.1186/s12879-016-2175-x</ArticleId>
<ArticleId IdType="pmc">PMC5241973</ArticleId>
</ArticleIdList>
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</pubmed>
<affiliations>
<list>
<country>
<li>Allemagne</li>
<li>Belgique</li>
<li>Viêt Nam</li>
</country>
<region>
<li>Province d'Anvers</li>
<li>Région de Bruxelles-Capitale</li>
<li>Région flamande</li>
</region>
<settlement>
<li>Anvers</li>
<li>Bruxelles</li>
</settlement>
<orgName>
<li>Université d'Anvers</li>
</orgName>
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<name sortKey="Michiels, Barbara" sort="Michiels, Barbara" uniqKey="Michiels B" first="Barbara" last="Michiels">Barbara Michiels</name>
</region>
<name sortKey="Bossuyt, Nathalie" sort="Bossuyt, Nathalie" uniqKey="Bossuyt N" first="Nathalie" last="Bossuyt">Nathalie Bossuyt</name>
<name sortKey="Coenen, Samuel" sort="Coenen, Samuel" uniqKey="Coenen S" first="Samuel" last="Coenen">Samuel Coenen</name>
<name sortKey="Coenen, Samuel" sort="Coenen, Samuel" uniqKey="Coenen S" first="Samuel" last="Coenen">Samuel Coenen</name>
<name sortKey="Coenen, Samuel" sort="Coenen, Samuel" uniqKey="Coenen S" first="Samuel" last="Coenen">Samuel Coenen</name>
<name sortKey="Hens, Niel" sort="Hens, Niel" uniqKey="Hens N" first="Niel" last="Hens">Niel Hens</name>
<name sortKey="Hens, Niel" sort="Hens, Niel" uniqKey="Hens N" first="Niel" last="Hens">Niel Hens</name>
<name sortKey="Hens, Niel" sort="Hens, Niel" uniqKey="Hens N" first="Niel" last="Hens">Niel Hens</name>
<name sortKey="Ryckebosch, Philippe" sort="Ryckebosch, Philippe" uniqKey="Ryckebosch P" first="Philippe" last="Ryckebosch">Philippe Ryckebosch</name>
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