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 : 002011 ( Ncbi/Merge ); précédent : 002010; suivant : 002012A 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 [Royaume-Uni] ; Jonathan S. Nguyen-Van-Tam [Royaume-Uni] ; Peer-Olaf Siebers [Royaume-Uni]Source :
- PLoS ONE [ 1932-6203 ] ; 2019.
Descripteurs français
- KwdFr :
- Algorithmes, Angleterre (épidémiologie), Antiviraux (usage thérapeutique), Grippe humaine (traitement médicamenteux), Grippe humaine (épidémiologie), Humains, Modèles théoriques, Pandémies, Prise de décision, Prise de décision clinique, Santé publique, Sous-type H1N1 du virus de la grippe A (pathogénicité).
- MESH :
- pathogénicité : Sous-type H1N1 du virus de la grippe A.
- traitement médicamenteux : Grippe humaine.
- usage thérapeutique : Antiviraux.
- épidémiologie : Angleterre, Grippe humaine.
- Algorithmes, Humains, Modèles théoriques, Pandémies, Prise de décision, Prise de décision clinique, Santé publique.
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.
Url:
DOI: 10.1371/journal.pone.0223946
PubMed: 31622404
PubMed Central: 6797120
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Links to Exploration step
PMC:6797120Le document en format XML
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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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Funk, S" uniqKey="Funk S">S Funk</name></author><author><name sortKey="Bansal, S" uniqKey="Bansal S">S Bansal</name></author><author><name sortKey="Bauch, Ct" uniqKey="Bauch C">CT Bauch</name></author><author><name sortKey="Eames, Kt" uniqKey="Eames K">KT Eames</name></author><author><name sortKey="Edmunds, Wj" uniqKey="Edmunds W">WJ Edmunds</name></author><author><name sortKey="Galvani, Ap" uniqKey="Galvani A">AP Galvani</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Funk, S" uniqKey="Funk S">S Funk</name></author><author><name sortKey="Salathe, M" uniqKey="Salathe M">M Salathé</name></author><author><name sortKey="Jansen, Vaa" uniqKey="Jansen V">VAA Jansen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Verelst, F" uniqKey="Verelst F">F Verelst</name></author><author><name sortKey="Willem, L" uniqKey="Willem L">L Willem</name></author><author><name sortKey="Beutels, P" uniqKey="Beutels P">P Beutels</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Weiss, G" uniqKey="Weiss G">G. 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Nottingham, England, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></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 wicri:level="4"><nlm:aff id="aff001"><addr-line>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></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 wicri:level="4"><nlm:aff id="aff002"><addr-line>School of Computer Science, University of Nottingham, Nottingham, England, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>School of Computer Science, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">31622404</idno><idno type="pmc">6797120</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797120</idno><idno type="RBID">PMC:6797120</idno><idno type="doi">10.1371/journal.pone.0223946</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">000C66</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000C66</idno><idno type="wicri:Area/Pmc/Curation">000C66</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000C66</idno><idno type="wicri:Area/Pmc/Checkpoint">000103</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000103</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">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 wicri:level="4"><nlm:aff id="aff001"><addr-line>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></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 wicri:level="4"><nlm:aff id="aff001"><addr-line>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></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 wicri:level="4"><nlm:aff id="aff002"><addr-line>School of Computer Science, University of Nottingham, Nottingham, England, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>School of Computer Science, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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. 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Kingdom.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></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 wicri:level="4"><nlm:affiliation>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></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 wicri:level="4"><nlm:affiliation>School of Computer Science, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>School of Computer Science, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></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><idno type="wicri:Area/PubMed/Curation">000084</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000084</idno><idno type="wicri:Area/PubMed/Checkpoint">000187</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000187</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 wicri:level="4"><nlm:affiliation>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></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 wicri:level="4"><nlm:affiliation>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></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 wicri:level="4"><nlm:affiliation>School of Computer Science, University of Nottingham, Nottingham, England, United Kingdom.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>School of Computer Science, University of Nottingham, Nottingham, England</wicri:regionArea><orgName type="university">Université de Nottingham</orgName><placeName><settlement type="city">Nottingham</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Nottinghamshire</region></placeName></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="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Angleterre (épidémiologie)</term><term>Antiviraux (usage thérapeutique)</term><term>Grippe humaine (traitement médicamenteux)</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Modèles théoriques</term><term>Pandémies</term><term>Prise de décision</term><term>Prise de décision clinique</term><term>Santé publique</term><term>Sous-type H1N1 du virus de la grippe A (pathogénicité)</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" qualifier="pathogénicité" xml:lang="fr"><term>Sous-type H1N1 du virus de la grippe A</term></keywords><keywords scheme="MESH" qualifier="traitement médicamenteux" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="usage thérapeutique" xml:lang="fr"><term>Antiviraux</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Angleterre</term><term>Grippe humaine</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><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Humains</term><term>Modèles théoriques</term><term>Pandémies</term><term>Prise de décision</term><term>Prise de décision clinique</term><term>Santé publique</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></double></record>Pour manipuler ce document sous Unix (Dilib)
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