Reducing disease burden in an influenza pandemic by targeted delivery of neuraminidase inhibitors: mathematical models in the Australian context
Identifieur interne : 000190 ( Pmc/Curation ); précédent : 000189; suivant : 000191Reducing disease burden in an influenza pandemic by targeted delivery of neuraminidase inhibitors: mathematical models in the Australian context
Auteurs : Robert Moss [Australie] ; James M. Mccaw [Australie] ; Allen C. Cheng [Australie] ; Aeron C. Hurt [Australie] ; Jodie Mcvernon [Australie]Source :
- BMC Infectious Diseases [ 1471-2334 ] ; 2016.
Abstract
Many nations maintain stockpiles of neuraminidase inhibitor (NAI) antiviral agents for use in influenza pandemics to reduce transmission and mitigate the course of clinical infection. Pandemic preparedness plans include the use of these stockpiles to deliver proportionate responses, informed by emerging evidence of clinical impact. Recent uncertainty about the effectiveness of NAIs has prompted these nations to reconsider the role of NAIs in pandemic response, with implications for pandemic planning and for NAI stockpile size.
We combined a dynamic model of influenza epidemiology with a model of the clinical care pathways in the Australian health care system to identify effective NAI strategies for reducing morbidity and mortality in pandemic events, and the stockpile requirements for these strategies. The models were informed by a 2015 assessment of NAI effectiveness against susceptibility, pathogenicity, and transmission of influenza.
Liberal distribution of NAIs for early treatment in outpatient settings yielded the greatest benefits in all of the considered scenarios. Restriction of community-based treatment to risk groups was effective in those groups, but failed to prevent the large proportion of cases arising from lower risk individuals who comprise the majority of the population.
These targeted strategies are only effective if they can be deployed within the constraints of existing health care infrastructure. This finding highlights the critical importance of identifying optimal models of care delivery for effective emergency health care response.
The online version of this article (doi:10.1186/s12879-016-1866-7) contains supplementary material, which is available to authorized users.
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DOI: 10.1186/s12879-016-1866-7
PubMed: 27724915
PubMed Central: 5057455
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Mccaw</name><affiliation wicri:level="4"><nlm:aff id="Aff1">Modelling and Simulation Unit Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie St, Melbourne, 3010 Victoria Australia</nlm:aff><orgName type="university">Université de Melbourne</orgName><country>Australie</country><placeName><settlement type="city">Melbourne</settlement><region type="état">Victoria (État)</region></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff2">School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>School of Mathematics and Statistics, The University of Melbourne, Melbourne</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff3">Murdoch Childrens Research Institute, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Murdoch Childrens Research Institute, Melbourne</wicri:regionArea></affiliation></author><author><name sortKey="Cheng, Allen C" sort="Cheng, Allen C" uniqKey="Cheng A" first="Allen C." last="Cheng">Allen C. 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Mccaw</name><affiliation wicri:level="4"><nlm:aff id="Aff1">Modelling and Simulation Unit Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie St, Melbourne, 3010 Victoria Australia</nlm:aff><orgName type="university">Université de Melbourne</orgName><country>Australie</country><placeName><settlement type="city">Melbourne</settlement><region type="état">Victoria (État)</region></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff2">School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>School of Mathematics and Statistics, The University of Melbourne, Melbourne</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff3">Murdoch Childrens Research Institute, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Murdoch Childrens Research Institute, Melbourne</wicri:regionArea></affiliation></author><author><name sortKey="Cheng, Allen C" sort="Cheng, Allen C" uniqKey="Cheng A" first="Allen C." last="Cheng">Allen C. Cheng</name><affiliation wicri:level="1"><nlm:aff id="Aff4">Infectious Disease Epidemiology Unit, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Infectious Disease Epidemiology Unit, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff5">Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, Melbourne</wicri:regionArea></affiliation></author><author><name sortKey="Hurt, Aeron C" sort="Hurt, Aeron C" uniqKey="Hurt A" first="Aeron C." last="Hurt">Aeron C. Hurt</name><affiliation wicri:level="1"><nlm:aff id="Aff6">WHO Collaborating Centre for Reference and Research on Influenza, Peter Doherty Institute, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>WHO Collaborating Centre for Reference and Research on Influenza, Peter Doherty Institute, Melbourne</wicri:regionArea></affiliation></author><author><name sortKey="Mcvernon, Jodie" sort="Mcvernon, Jodie" uniqKey="Mcvernon J" first="Jodie" last="Mcvernon">Jodie Mcvernon</name><affiliation wicri:level="4"><nlm:aff id="Aff1">Modelling and Simulation Unit Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie St, Melbourne, 3010 Victoria Australia</nlm:aff><orgName type="university">Université de Melbourne</orgName><country>Australie</country><placeName><settlement type="city">Melbourne</settlement><region type="état">Victoria (État)</region></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff3">Murdoch Childrens Research Institute, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Murdoch Childrens Research Institute, Melbourne</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC Infectious Diseases</title><idno type="eISSN">1471-2334</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Many nations maintain stockpiles of neuraminidase inhibitor (NAI) antiviral agents for use in influenza pandemics to reduce transmission and mitigate the course of clinical infection. Pandemic preparedness plans include the use of these stockpiles to deliver proportionate responses, informed by emerging evidence of clinical impact. Recent uncertainty about the effectiveness of NAIs has prompted these nations to reconsider the role of NAIs in pandemic response, with implications for pandemic planning and for NAI stockpile size.</p></sec><sec><title>Methods</title><p>We combined a dynamic model of influenza epidemiology with a model of the clinical care pathways in the Australian health care system to identify effective NAI strategies for reducing morbidity and mortality in pandemic events, and the stockpile requirements for these strategies. The models were informed by a 2015 assessment of NAI effectiveness against susceptibility, pathogenicity, and transmission of influenza.</p></sec><sec><title>Results</title><p>Liberal distribution of NAIs for early treatment in outpatient settings yielded the greatest benefits in all of the considered scenarios. Restriction of community-based treatment to risk groups was effective in those groups, but failed to prevent the large proportion of cases arising from lower risk individuals who comprise the majority of the population.</p></sec><sec><title>Conclusions</title><p>These targeted strategies are only effective if they can be deployed within the constraints of existing health care infrastructure. This finding highlights the critical importance of identifying optimal models of care delivery for effective emergency health care response.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12879-016-1866-7) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Mytton, Ot" uniqKey="Mytton O">OT Mytton</name></author><author><name sortKey="Rutter, Pd" uniqKey="Rutter P">PD Rutter</name></author><author><name sortKey="Donaldson, Lj" uniqKey="Donaldson L">LJ Donaldson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mcvernon, J" uniqKey="Mcvernon J">J McVernon</name></author><author><name sortKey="Laurie, K" uniqKey="Laurie K">K Laurie</name></author><author><name sortKey="Nolan, T" uniqKey="Nolan T">T Nolan</name></author><author><name sortKey="Owen, R" uniqKey="Owen R">R Owen</name></author><author><name sortKey="Irving, D" uniqKey="Irving D">D Irving</name></author><author><name sortKey="Capper, H" uniqKey="Capper H">H Capper</name></author><author><name sortKey="Hyland, C" uniqKey="Hyland C">C Hyland</name></author><author><name sortKey="Faddy, H" uniqKey="Faddy H">H Faddy</name></author><author><name sortKey="Carolan, L" uniqKey="Carolan L">L Carolan</name></author><author><name sortKey="Barr, I" uniqKey="Barr I">I Barr</name></author><author><name sortKey="Kelso, A" uniqKey="Kelso A">A Kelso</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Infect Dis</journal-id><journal-id journal-id-type="iso-abbrev">BMC Infect. Dis</journal-id><journal-title-group><journal-title>BMC Infectious Diseases</journal-title></journal-title-group><issn pub-type="epub">1471-2334</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">27724915</article-id><article-id pub-id-type="pmc">5057455</article-id><article-id pub-id-type="publisher-id">1866</article-id><article-id pub-id-type="doi">10.1186/s12879-016-1866-7</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Reducing disease burden in an influenza pandemic by targeted delivery of neuraminidase inhibitors: mathematical models in the Australian context</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-4568-2012</contrib-id><name><surname>Moss</surname><given-names>Robert</given-names></name><address><email>rgmoss@unimelb.edu.au</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>McCaw</surname><given-names>James M.</given-names></name><address><email>jamesm@unimelb.edu.au</email></address><xref ref-type="aff" rid="Aff1">1</xref><xref ref-type="aff" rid="Aff2">2</xref><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Cheng</surname><given-names>Allen C.</given-names></name><address><email>allen.cheng@monash.edu</email></address><xref ref-type="aff" rid="Aff4">4</xref><xref ref-type="aff" rid="Aff5">5</xref></contrib><contrib contrib-type="author"><name><surname>Hurt</surname><given-names>Aeron C.</given-names></name><address><email>Aeron.Hurt@influenzacentre.org</email></address><xref ref-type="aff" rid="Aff6">6</xref></contrib><contrib contrib-type="author"><name><surname>McVernon</surname><given-names>Jodie</given-names></name><address><email>j.mcvernon@unimelb.edu.au</email></address><xref ref-type="aff" rid="Aff1">1</xref><xref ref-type="aff" rid="Aff3">3</xref></contrib><aff id="Aff1"><label>1</label>Modelling and Simulation Unit Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie St, Melbourne, 3010 Victoria Australia</aff><aff id="Aff2"><label>2</label>School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia</aff><aff id="Aff3"><label>3</label>Murdoch Childrens Research Institute, Melbourne, Australia</aff><aff id="Aff4"><label>4</label>Infectious Disease Epidemiology Unit, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia</aff><aff id="Aff5"><label>5</label>Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, Melbourne, Australia</aff><aff id="Aff6"><label>6</label>WHO Collaborating Centre for Reference and Research on Influenza, Peter Doherty Institute, Melbourne, Australia</aff></contrib-group><pub-date pub-type="epub"><day>10</day><month>10</month><year>2016</year></pub-date><pub-date pub-type="pmc-release"><day>10</day><month>10</month><year>2016</year></pub-date><pub-date pub-type="collection"><year>2016</year></pub-date><volume>16</volume><elocation-id>552</elocation-id><history><date date-type="received"><day>28</day><month>4</month><year>2016</year></date><date date-type="accepted"><day>23</day><month>9</month><year>2016</year></date></history><permissions><copyright-statement>© The Author(s) 2016</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold> This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p>Many nations maintain stockpiles of neuraminidase inhibitor (NAI) antiviral agents for use in influenza pandemics to reduce transmission and mitigate the course of clinical infection. Pandemic preparedness plans include the use of these stockpiles to deliver proportionate responses, informed by emerging evidence of clinical impact. Recent uncertainty about the effectiveness of NAIs has prompted these nations to reconsider the role of NAIs in pandemic response, with implications for pandemic planning and for NAI stockpile size.</p></sec><sec><title>Methods</title><p>We combined a dynamic model of influenza epidemiology with a model of the clinical care pathways in the Australian health care system to identify effective NAI strategies for reducing morbidity and mortality in pandemic events, and the stockpile requirements for these strategies. The models were informed by a 2015 assessment of NAI effectiveness against susceptibility, pathogenicity, and transmission of influenza.</p></sec><sec><title>Results</title><p>Liberal distribution of NAIs for early treatment in outpatient settings yielded the greatest benefits in all of the considered scenarios. Restriction of community-based treatment to risk groups was effective in those groups, but failed to prevent the large proportion of cases arising from lower risk individuals who comprise the majority of the population.</p></sec><sec><title>Conclusions</title><p>These targeted strategies are only effective if they can be deployed within the constraints of existing health care infrastructure. This finding highlights the critical importance of identifying optimal models of care delivery for effective emergency health care response.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12879-016-1866-7) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Influenza</kwd><kwd>Pandemic preparedness</kwd><kwd>Neuraminidase inhibitor</kwd><kwd>Emergency response</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100000925</institution-id><institution>National Health and Medical Research Council</institution></institution-wrap></funding-source><award-id>CDF1061321</award-id></award-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100000923</institution-id><institution>Australian Research Council</institution></institution-wrap></funding-source><award-id>FT11010025</award-id></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2016</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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