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 : 000018Influenza 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 :
- BMC infectious diseases [ 1471-2334 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
- Wicri :
- geographic : Belgique.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Belgium.
- epidemiology : Influenza, Human.
- Adult, After-Hours Care, Data Collection, Electronic Health Records, Epidemics, Epidemiological Monitoring, Female, General Practitioners, Humans, Incidence, Male, Models, Theoretical, Retrospective Studies, Seasons.
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:
- Allemagne, Belgique, Viêt Nam
- Province d'Anvers, Région de Bruxelles-Capitale, Région flamande
- Anvers, Bruxelles
- Université d'Anvers
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Le document en format XML
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<term>Médecins généralistes</term>
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<term>Épidémies</term>
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<term>After-Hours Care</term>
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<front><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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<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>
<ArticleIdList><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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<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>
</list>
<tree><country name="Belgique"><region name="Région flamande"><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>
</country>
<country name="Viêt Nam"><noRegion><name sortKey="Nguyen, Van Kinh" sort="Nguyen, Van Kinh" uniqKey="Nguyen V" first="Van Kinh" last="Nguyen">Van Kinh Nguyen</name>
</noRegion>
</country>
<country name="Allemagne"><noRegion><name sortKey="Nguyen, Van Kinh" sort="Nguyen, Van Kinh" uniqKey="Nguyen V" first="Van Kinh" last="Nguyen">Van Kinh Nguyen</name>
</noRegion>
</country>
</tree>
</affiliations>
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