A novel framework for evaluating the impact of individual decision-making on public health outcomes and its potential application to study antiviral treatment collection during an influenza pandemic.
Identifieur interne : 000084 ( PubMed/Corpus ); précédent : 000083; suivant : 000085A novel framework for evaluating the impact of individual decision-making on public health outcomes and its potential application to study antiviral treatment collection during an influenza pandemic.
Auteurs : Sudhir Venkatesan ; Jonathan S. Nguyen-Van-Tam ; Peer-Olaf SiebersSource :
- PloS one [ 1932-6203 ] ; 2019.
English descriptors
- KwdEn :
- Algorithms, Antiviral Agents (therapeutic use), Clinical Decision-Making, Decision Making, England (epidemiology), Humans, Influenza A Virus, H1N1 Subtype (pathogenicity), Influenza, Human (drug therapy), Influenza, Human (epidemiology), Models, Theoretical, Pandemics, Proof of Concept Study, Public Health.
- MESH :
- chemical , therapeutic use : Antiviral Agents.
- geographic , epidemiology : England.
- drug therapy : Influenza, Human.
- epidemiology : Influenza, Human.
- pathogenicity : Influenza A Virus, H1N1 Subtype.
- Algorithms, Clinical Decision-Making, Decision Making, Humans, Models, Theoretical, Pandemics, Proof of Concept Study, Public Health.
Abstract
The importance of accounting for social and behavioural processes when studying public health emergencies has been well-recognised. For infectious disease outbreaks in particular, several methods of incorporating individual behaviour have been put forward, but very few are based on established psychological frameworks. In this paper, we develop a decision framework based on the COM-B model of behaviour change to investigate the impact of individual decision-making on public health outcomes. We demonstrate the application of our decision framework in a proof-of-concept case study based on the 2009 A(H1N1) influenza pandemic in the UK. The National Pandemic Flu Service (NPFS) was set up in England during the pandemic as a means to provide antiviral (AV) treatment to clinically ill patients with influenza-like illness, via telephone calls or internet screening, thereby averting the need to see a doctor. The evaluated patients based on a clinical algorithm and authorised AV drugs for collection via community collection points. We applied our behavioural framework to evaluate the influence of human behaviour on AV collection rates, and subsequently to identify interventions that could help improve AV collection rates. Our model was validated against empirically collected pandemic data from 2009 in the UK. We also performed a sensitivity analysis to identify potentially effective interventions by varying model parameters. Using our behavioural framework in a proof-of-concept case study, we found that interventions geared towards increasing people's 'Capability' and 'Opportunity' are likely to result in increased AV collection, potentially resulting in fewer influenza-related hospitalisations and deaths. We note that important behavioural data from public health emergencies are largely scarce. Insights obtained from models such as ours can, not only be very useful in designing healthcare interventions, but also inform future data collection.
DOI: 10.1371/journal.pone.0223946
PubMed: 31622404
Links to Exploration step
pubmed:31622404Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A novel framework for evaluating the impact of individual decision-making on public health outcomes and its potential application to study antiviral treatment collection during an influenza pandemic.</title><author><name sortKey="Venkatesan, Sudhir" sort="Venkatesan, Sudhir" uniqKey="Venkatesan S" first="Sudhir" last="Venkatesan">Sudhir Venkatesan</name><affiliation><nlm:affiliation>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Nguyen Van Tam, Jonathan S" sort="Nguyen Van Tam, Jonathan S" uniqKey="Nguyen Van Tam J" first="Jonathan S" last="Nguyen-Van-Tam">Jonathan S. Nguyen-Van-Tam</name><affiliation><nlm:affiliation>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Siebers, Peer Olaf" sort="Siebers, Peer Olaf" uniqKey="Siebers P" first="Peer-Olaf" last="Siebers">Peer-Olaf Siebers</name><affiliation><nlm:affiliation>School of Computer Science, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno type="RBID">pubmed:31622404</idno><idno type="pmid">31622404</idno><idno type="doi">10.1371/journal.pone.0223946</idno><idno type="wicri:Area/PubMed/Corpus">000084</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000084</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A novel framework for evaluating the impact of individual decision-making on public health outcomes and its potential application to study antiviral treatment collection during an influenza pandemic.</title><author><name sortKey="Venkatesan, Sudhir" sort="Venkatesan, Sudhir" uniqKey="Venkatesan S" first="Sudhir" last="Venkatesan">Sudhir Venkatesan</name><affiliation><nlm:affiliation>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Nguyen Van Tam, Jonathan S" sort="Nguyen Van Tam, Jonathan S" uniqKey="Nguyen Van Tam J" first="Jonathan S" last="Nguyen-Van-Tam">Jonathan S. Nguyen-Van-Tam</name><affiliation><nlm:affiliation>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Siebers, Peer Olaf" sort="Siebers, Peer Olaf" uniqKey="Siebers P" first="Peer-Olaf" last="Siebers">Peer-Olaf Siebers</name><affiliation><nlm:affiliation>School of Computer Science, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Antiviral Agents (therapeutic use)</term><term>Clinical Decision-Making</term><term>Decision Making</term><term>England (epidemiology)</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype (pathogenicity)</term><term>Influenza, Human (drug therapy)</term><term>Influenza, Human (epidemiology)</term><term>Models, Theoretical</term><term>Pandemics</term><term>Proof of Concept Study</term><term>Public Health</term></keywords><keywords scheme="MESH" type="chemical" qualifier="therapeutic use" xml:lang="en"><term>Antiviral Agents</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>England</term></keywords><keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="pathogenicity" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Clinical Decision-Making</term><term>Decision Making</term><term>Humans</term><term>Models, Theoretical</term><term>Pandemics</term><term>Proof of Concept Study</term><term>Public Health</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The importance of accounting for social and behavioural processes when studying public health emergencies has been well-recognised. For infectious disease outbreaks in particular, several methods of incorporating individual behaviour have been put forward, but very few are based on established psychological frameworks. In this paper, we develop a decision framework based on the COM-B model of behaviour change to investigate the impact of individual decision-making on public health outcomes. We demonstrate the application of our decision framework in a proof-of-concept case study based on the 2009 A(H1N1) influenza pandemic in the UK. The National Pandemic Flu Service (NPFS) was set up in England during the pandemic as a means to provide antiviral (AV) treatment to clinically ill patients with influenza-like illness, via telephone calls or internet screening, thereby averting the need to see a doctor. The evaluated patients based on a clinical algorithm and authorised AV drugs for collection via community collection points. We applied our behavioural framework to evaluate the influence of human behaviour on AV collection rates, and subsequently to identify interventions that could help improve AV collection rates. Our model was validated against empirically collected pandemic data from 2009 in the UK. We also performed a sensitivity analysis to identify potentially effective interventions by varying model parameters. Using our behavioural framework in a proof-of-concept case study, we found that interventions geared towards increasing people's 'Capability' and 'Opportunity' are likely to result in increased AV collection, potentially resulting in fewer influenza-related hospitalisations and deaths. We note that important behavioural data from public health emergencies are largely scarce. Insights obtained from models such as ours can, not only be very useful in designing healthcare interventions, but also inform future data collection.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">31622404</PMID><DateCompleted><Year>2020</Year><Month>03</Month><Day>16</Day></DateCompleted><DateRevised><Year>2020</Year><Month>03</Month><Day>16</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>14</Volume><Issue>10</Issue><PubDate><Year>2019</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>A novel framework for evaluating the impact of individual decision-making on public health outcomes and its potential application to study antiviral treatment collection during an influenza pandemic.</ArticleTitle><Pagination><MedlinePgn>e0223946</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0223946</ELocationID><Abstract><AbstractText>The importance of accounting for social and behavioural processes when studying public health emergencies has been well-recognised. For infectious disease outbreaks in particular, several methods of incorporating individual behaviour have been put forward, but very few are based on established psychological frameworks. In this paper, we develop a decision framework based on the COM-B model of behaviour change to investigate the impact of individual decision-making on public health outcomes. We demonstrate the application of our decision framework in a proof-of-concept case study based on the 2009 A(H1N1) influenza pandemic in the UK. The National Pandemic Flu Service (NPFS) was set up in England during the pandemic as a means to provide antiviral (AV) treatment to clinically ill patients with influenza-like illness, via telephone calls or internet screening, thereby averting the need to see a doctor. The evaluated patients based on a clinical algorithm and authorised AV drugs for collection via community collection points. We applied our behavioural framework to evaluate the influence of human behaviour on AV collection rates, and subsequently to identify interventions that could help improve AV collection rates. Our model was validated against empirically collected pandemic data from 2009 in the UK. We also performed a sensitivity analysis to identify potentially effective interventions by varying model parameters. Using our behavioural framework in a proof-of-concept case study, we found that interventions geared towards increasing people's 'Capability' and 'Opportunity' are likely to result in increased AV collection, potentially resulting in fewer influenza-related hospitalisations and deaths. We note that important behavioural data from public health emergencies are largely scarce. Insights obtained from models such as ours can, not only be very useful in designing healthcare interventions, but also inform future data collection.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Venkatesan</LastName><ForeName>Sudhir</ForeName><Initials>S</Initials><Identifier Source="ORCID">0000-0002-2093-5781</Identifier><AffiliationInfo><Affiliation>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nguyen-Van-Tam</LastName><ForeName>Jonathan S</ForeName><Initials>JS</Initials><AffiliationInfo><Affiliation>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Siebers</LastName><ForeName>Peer-Olaf</ForeName><Initials>PO</Initials><AffiliationInfo><Affiliation>School of Computer Science, University of Nottingham, Nottingham, England, United Kingdom.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>10</Month><Day>17</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000998">Antiviral Agents</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000998" MajorTopicYN="N">Antiviral Agents</DescriptorName><QualifierName UI="Q000627" MajorTopicYN="Y">therapeutic use</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000066491" MajorTopicYN="N">Clinical Decision-Making</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003657" MajorTopicYN="Y">Decision Making</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004739" MajorTopicYN="N" Type="Geographic">England</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D053118" MajorTopicYN="N">Influenza A Virus, H1N1 Subtype</DescriptorName><QualifierName UI="Q000472" MajorTopicYN="N">pathogenicity</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000188" MajorTopicYN="Y">drug therapy</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="N">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000075082" MajorTopicYN="N">Proof of Concept Study</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011634" MajorTopicYN="N">Public Health</DescriptorName></MeshHeading></MeshHeadingList><CoiStatement>SV and JSN-V-T are currently working on outputs from the PRIDE study which is supported by an unrestricted educational grant from F. Hoffman La Roche. F. Hoffman La Roche had no role in any aspect of this study. JSN-V-T reports grants from F. Hoffmann-La Roche, personal fees from Shionogi Ltd. (in 2016) outside the submitted work; he is currently on secondment from the University of Nottingham to the Department of Health and Social Care (England). The work and conclusions presented here are those of the authors and do not necessarily represent the official position of the Department of Health and Social Care (England). P-OS and SV reports no competing interests. This does not alter our adherence to PLOS ONE policies on sharing data and materials.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>12</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>10</Month><Day>02</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>10</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>10</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">31622404</ArticleId><ArticleId IdType="doi">10.1371/journal.pone.0223946</ArticleId><ArticleId IdType="pii">PONE-D-18-36740</ArticleId><ArticleId 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