Post-exposure prophylaxis during pandemic outbreaks
Identifieur interne : 000B61 ( Pmc/Checkpoint ); précédent : 000B60; suivant : 000B62Post-exposure prophylaxis during pandemic outbreaks
Auteurs : Seyed M. Moghadas [Canada] ; Christopher S. Bowman [Canada] ; Gergely Röst [Hongrie] ; David N. Fisman [Canada] ; Jianhong Wu [Canada]Source :
- BMC Medicine [ 1741-7015 ] ; 2009.
Abstract
With the rise of the second pandemic wave of the novel influenza A (H1N1) virus in the current season in the Northern Hemisphere, pandemic plans are being carefully re-evaluated, particularly for the strategic use of antiviral drugs. The recent emergence of oseltamivir-resistant in treated H1N1 patients has raised concerns about the prudent use of neuraminidase inhibitors for both treatment of ill individuals and post-exposure prophylaxis of close contacts.
We extended an established population dynamical model of pandemic influenza with treatment to include post-exposure prophylaxis of close contacts. Using parameter estimates published in the literature, we simulated the model to evaluate the combined effect of treatment and prophylaxis in minimizing morbidity and mortality of pandemic infections in the context of transmissible drug resistance.
We demonstrated that, when transmissible resistant strains are present, post-exposure prophylaxis can promote the spread of resistance, especially when combined with aggressive treatment. For a given treatment level, there is an optimal coverage of prophylaxis that minimizes the total number of infections (final size) and this coverage decreases as a higher proportion of infected individuals are treated. We found that, when treatment is maintained at intermediate levels, limited post-exposure prophylaxis provides an optimal strategy for reducing the final size of the pandemic while minimizing the total number of deaths. We tested our results by performing a sensitivity analysis over a range of key model parameters and observed that the incidence of infection depends strongly on the transmission fitness of resistant strains.
Our findings suggest that, in the presence of transmissible drug resistance, strategies that prioritize the treatment of only ill individuals, rather than the prophylaxis of those suspected of being exposed, are most effective in reducing the morbidity and mortality of the pandemic. The impact of post-exposure prophylaxis depends critically on the treatment level and the transmissibility of resistant strains and, therefore, enhanced surveillance and clinical monitoring for resistant mutants constitutes a key component of any comprehensive plan for antiviral drug use during an influenza pandemic.
Url:
DOI: 10.1186/1741-7015-7-73
PubMed: 19954514
PubMed Central: 2794871
Affiliations:
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Moghadas</name><affiliation wicri:level="1"><nlm:aff id="I1">Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba</wicri:regionArea><wicri:noRegion>Manitoba</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="I2">Department of Mathematics and Statistics, The University of Winnipeg, Winnipeg, Manitoba, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Mathematics and Statistics, The University of Winnipeg, Winnipeg, Manitoba</wicri:regionArea><wicri:noRegion>Manitoba</wicri:noRegion></affiliation></author><author><name sortKey="Bowman, Christopher S" sort="Bowman, Christopher S" uniqKey="Bowman C" first="Christopher S" last="Bowman">Christopher S. Bowman</name><affiliation wicri:level="1"><nlm:aff id="I1">Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba</wicri:regionArea><wicri:noRegion>Manitoba</wicri:noRegion></affiliation></author><author><name sortKey="Rost, Gergely" sort="Rost, Gergely" uniqKey="Rost G" first="Gergely" last="Röst">Gergely Röst</name><affiliation wicri:level="1"><nlm:aff id="I3">Analysis and Stochastics Research Group, Hungarian Academy of Sciences, Bolyai Institute, University of Szeged, Szeged, Hungary</nlm:aff><country xml:lang="fr">Hongrie</country><wicri:regionArea>Analysis and Stochastics Research Group, Hungarian Academy of Sciences, Bolyai Institute, University of Szeged, Szeged</wicri:regionArea><wicri:noRegion>Szeged</wicri:noRegion></affiliation></author><author><name sortKey="Fisman, David N" sort="Fisman, David N" uniqKey="Fisman D" first="David N" last="Fisman">David N. Fisman</name><affiliation wicri:level="1"><nlm:aff id="I4">Dalla Lana School of Public Health, University of Toronto and Ontario Agency for Health Protection and Promotion, Toronto, Ontario, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Dalla Lana School of Public Health, University of Toronto and Ontario Agency for Health Protection and Promotion, Toronto, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author><author><name sortKey="Wu, Jianhong" sort="Wu, Jianhong" uniqKey="Wu J" first="Jianhong" last="Wu">Jianhong Wu</name><affiliation wicri:level="1"><nlm:aff id="I5">Centre for Disease Modelling, York Institute of Health Research, York University, Toronto, Ontario, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Centre for Disease Modelling, York Institute of Health Research, York University, Toronto, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">19954514</idno><idno type="pmc">2794871</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2794871</idno><idno type="RBID">PMC:2794871</idno><idno type="doi">10.1186/1741-7015-7-73</idno><date when="2009">2009</date><idno type="wicri:Area/Pmc/Corpus">000934</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000934</idno><idno type="wicri:Area/Pmc/Curation">000934</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000934</idno><idno type="wicri:Area/Pmc/Checkpoint">000B61</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000B61</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Post-exposure prophylaxis during pandemic outbreaks</title><author><name sortKey="Moghadas, Seyed M" sort="Moghadas, Seyed M" uniqKey="Moghadas S" first="Seyed M" last="Moghadas">Seyed M. Moghadas</name><affiliation wicri:level="1"><nlm:aff id="I1">Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba</wicri:regionArea><wicri:noRegion>Manitoba</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="I2">Department of Mathematics and Statistics, The University of Winnipeg, Winnipeg, Manitoba, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Mathematics and Statistics, The University of Winnipeg, Winnipeg, Manitoba</wicri:regionArea><wicri:noRegion>Manitoba</wicri:noRegion></affiliation></author><author><name sortKey="Bowman, Christopher S" sort="Bowman, Christopher S" uniqKey="Bowman C" first="Christopher S" last="Bowman">Christopher S. Bowman</name><affiliation wicri:level="1"><nlm:aff id="I1">Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba</wicri:regionArea><wicri:noRegion>Manitoba</wicri:noRegion></affiliation></author><author><name sortKey="Rost, Gergely" sort="Rost, Gergely" uniqKey="Rost G" first="Gergely" last="Röst">Gergely Röst</name><affiliation wicri:level="1"><nlm:aff id="I3">Analysis and Stochastics Research Group, Hungarian Academy of Sciences, Bolyai Institute, University of Szeged, Szeged, Hungary</nlm:aff><country xml:lang="fr">Hongrie</country><wicri:regionArea>Analysis and Stochastics Research Group, Hungarian Academy of Sciences, Bolyai Institute, University of Szeged, Szeged</wicri:regionArea><wicri:noRegion>Szeged</wicri:noRegion></affiliation></author><author><name sortKey="Fisman, David N" sort="Fisman, David N" uniqKey="Fisman D" first="David N" last="Fisman">David N. Fisman</name><affiliation wicri:level="1"><nlm:aff id="I4">Dalla Lana School of Public Health, University of Toronto and Ontario Agency for Health Protection and Promotion, Toronto, Ontario, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Dalla Lana School of Public Health, University of Toronto and Ontario Agency for Health Protection and Promotion, Toronto, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author><author><name sortKey="Wu, Jianhong" sort="Wu, Jianhong" uniqKey="Wu J" first="Jianhong" last="Wu">Jianhong Wu</name><affiliation wicri:level="1"><nlm:aff id="I5">Centre for Disease Modelling, York Institute of Health Research, York University, Toronto, Ontario, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Centre for Disease Modelling, York Institute of Health Research, York University, Toronto, Ontario</wicri:regionArea><wicri:noRegion>Ontario</wicri:noRegion></affiliation></author></analytic><series><title level="j">BMC Medicine</title><idno type="eISSN">1741-7015</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>With the rise of the second pandemic wave of the novel influenza A (H1N1) virus in the current season in the Northern Hemisphere, pandemic plans are being carefully re-evaluated, particularly for the strategic use of antiviral drugs. The recent emergence of oseltamivir-resistant in treated H1N1 patients has raised concerns about the prudent use of neuraminidase inhibitors for both treatment of ill individuals and post-exposure prophylaxis of close contacts.</p></sec><sec><title>Methods</title><p>We extended an established population dynamical model of pandemic influenza with treatment to include post-exposure prophylaxis of close contacts. Using parameter estimates published in the literature, we simulated the model to evaluate the combined effect of treatment and prophylaxis in minimizing morbidity and mortality of pandemic infections in the context of transmissible drug resistance.</p></sec><sec><title>Results</title><p>We demonstrated that, when transmissible resistant strains are present, post-exposure prophylaxis can promote the spread of resistance, especially when combined with aggressive treatment. For a given treatment level, there is an optimal coverage of prophylaxis that minimizes the total number of infections (final size) and this coverage decreases as a higher proportion of infected individuals are treated. We found that, when treatment is maintained at intermediate levels, limited post-exposure prophylaxis provides an optimal strategy for reducing the final size of the pandemic while minimizing the total number of deaths. We tested our results by performing a sensitivity analysis over a range of key model parameters and observed that the incidence of infection depends strongly on the transmission fitness of resistant strains.</p></sec><sec><title>Conclusion</title><p>Our findings suggest that, in the presence of transmissible drug resistance, strategies that prioritize the treatment of only ill individuals, rather than the prophylaxis of those suspected of being exposed, are most effective in reducing the morbidity and mortality of the pandemic. The impact of post-exposure prophylaxis depends critically on the treatment level and the transmissibility of resistant strains and, therefore, enhanced surveillance and clinical monitoring for resistant mutants constitutes a key component of any comprehensive plan for antiviral drug use during an influenza pandemic.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Democratis, J" uniqKey="Democratis J">J Democratis</name></author><author><name sortKey="Pareek, M" uniqKey="Pareek M">M Pareek</name></author><author><name sortKey="Stephenson, I" uniqKey="Stephenson I">I Stephenson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lipsitch, M" uniqKey="Lipsitch M">M Lipsitch</name></author><author><name sortKey="Cohen, T" uniqKey="Cohen T">T Cohen</name></author><author><name sortKey="Murray, M" uniqKey="Murray M">M Murray</name></author><author><name sortKey="Levin, Br" 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<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Med</journal-id><journal-title-group><journal-title>BMC Medicine</journal-title></journal-title-group><issn pub-type="epub">1741-7015</issn><publisher><publisher-name>BioMed Central</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">19954514</article-id><article-id pub-id-type="pmc">2794871</article-id><article-id pub-id-type="publisher-id">1741-7015-7-73</article-id><article-id pub-id-type="doi">10.1186/1741-7015-7-73</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research article</subject></subj-group></article-categories><title-group><article-title>Post-exposure prophylaxis during pandemic outbreaks</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes" id="A1"><name><surname>Moghadas</surname><given-names>Seyed M</given-names></name><xref ref-type="aff" rid="I1">1</xref><xref ref-type="aff" rid="I2">2</xref><email>seyed.moghadas@nrc-cnrc.gc.ca</email></contrib><contrib contrib-type="author" id="A2"><name><surname>Bowman</surname><given-names>Christopher S</given-names></name><xref ref-type="aff" rid="I1">1</xref><email>chris.bowman@nrc-cnrc.gc.ca</email></contrib><contrib contrib-type="author" id="A3"><name><surname>Röst</surname><given-names>Gergely</given-names></name><xref ref-type="aff" rid="I3">3</xref><email>rost@math.u-szeged.hu</email></contrib><contrib contrib-type="author" id="A4"><name><surname>Fisman</surname><given-names>David N</given-names></name><xref ref-type="aff" rid="I4">4</xref><email>david.fisman@gmail.com</email></contrib><contrib contrib-type="author" id="A5"><name><surname>Wu</surname><given-names>Jianhong</given-names></name><xref ref-type="aff" rid="I5">5</xref><email>wujh@mathstat.yorku.ca</email></contrib></contrib-group><aff id="I1"><label>1</label>Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba, Canada</aff><aff id="I2"><label>2</label>Department of Mathematics and Statistics, The University of Winnipeg, Winnipeg, Manitoba, Canada</aff><aff id="I3"><label>3</label>Analysis and Stochastics Research Group, Hungarian Academy of Sciences, Bolyai Institute, University of Szeged, Szeged, Hungary</aff><aff id="I4"><label>4</label>Dalla Lana School of Public Health, University of Toronto and Ontario Agency for Health Protection and Promotion, Toronto, Ontario, Canada</aff><aff id="I5"><label>5</label>Centre for Disease Modelling, York Institute of Health Research, York University, Toronto, Ontario, Canada</aff><pub-date pub-type="collection"><year>2009</year></pub-date><pub-date pub-type="epub"><day>2</day><month>12</month><year>2009</year></pub-date><volume>7</volume><fpage>73</fpage><lpage>73</lpage><history><date date-type="received"><day>17</day><month>10</month><year>2009</year></date><date date-type="accepted"><day>2</day><month>12</month><year>2009</year></date></history><permissions><copyright-statement>Copyright ©2009 Moghadas et al; licensee BioMed Central Ltd.</copyright-statement><copyright-year>2009</copyright-year><copyright-holder>Moghadas et al; licensee BioMed Central Ltd.</copyright-holder><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:href="http://www.biomedcentral.com/1741-7015/7/73"></self-uri><abstract><sec><title>Background</title><p>With the rise of the second pandemic wave of the novel influenza A (H1N1) virus in the current season in the Northern Hemisphere, pandemic plans are being carefully re-evaluated, particularly for the strategic use of antiviral drugs. The recent emergence of oseltamivir-resistant in treated H1N1 patients has raised concerns about the prudent use of neuraminidase inhibitors for both treatment of ill individuals and post-exposure prophylaxis of close contacts.</p></sec><sec><title>Methods</title><p>We extended an established population dynamical model of pandemic influenza with treatment to include post-exposure prophylaxis of close contacts. Using parameter estimates published in the literature, we simulated the model to evaluate the combined effect of treatment and prophylaxis in minimizing morbidity and mortality of pandemic infections in the context of transmissible drug resistance.</p></sec><sec><title>Results</title><p>We demonstrated that, when transmissible resistant strains are present, post-exposure prophylaxis can promote the spread of resistance, especially when combined with aggressive treatment. For a given treatment level, there is an optimal coverage of prophylaxis that minimizes the total number of infections (final size) and this coverage decreases as a higher proportion of infected individuals are treated. We found that, when treatment is maintained at intermediate levels, limited post-exposure prophylaxis provides an optimal strategy for reducing the final size of the pandemic while minimizing the total number of deaths. We tested our results by performing a sensitivity analysis over a range of key model parameters and observed that the incidence of infection depends strongly on the transmission fitness of resistant strains.</p></sec><sec><title>Conclusion</title><p>Our findings suggest that, in the presence of transmissible drug resistance, strategies that prioritize the treatment of only ill individuals, rather than the prophylaxis of those suspected of being exposed, are most effective in reducing the morbidity and mortality of the pandemic. The impact of post-exposure prophylaxis depends critically on the treatment level and the transmissibility of resistant strains and, therefore, enhanced surveillance and clinical monitoring for resistant mutants constitutes a key component of any comprehensive plan for antiviral drug use during an influenza pandemic.</p></sec></abstract></article-meta></front></pmc><affiliations><list><country><li>Canada</li><li>Hongrie</li></country></list><tree><country name="Canada"><noRegion><name sortKey="Moghadas, Seyed M" sort="Moghadas, Seyed M" uniqKey="Moghadas S" first="Seyed M" last="Moghadas">Seyed M. Moghadas</name></noRegion><name sortKey="Bowman, Christopher S" sort="Bowman, Christopher S" uniqKey="Bowman C" first="Christopher S" last="Bowman">Christopher S. Bowman</name><name sortKey="Fisman, David N" sort="Fisman, David N" uniqKey="Fisman D" first="David N" last="Fisman">David N. Fisman</name><name sortKey="Moghadas, Seyed M" sort="Moghadas, Seyed M" uniqKey="Moghadas S" first="Seyed M" last="Moghadas">Seyed M. Moghadas</name><name sortKey="Wu, Jianhong" sort="Wu, Jianhong" uniqKey="Wu J" first="Jianhong" last="Wu">Jianhong Wu</name></country><country name="Hongrie"><noRegion><name sortKey="Rost, Gergely" sort="Rost, Gergely" uniqKey="Rost G" first="Gergely" last="Röst">Gergely Röst</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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