Responding to Vaccine Safety Signals during Pandemic Influenza: A Modeling Study
Identifieur interne : 000E02 ( Main/Exploration ); précédent : 000E01; suivant : 000E03Responding to Vaccine Safety Signals during Pandemic Influenza: A Modeling Study
Auteurs : Judith C. Maro [États-Unis] ; Dennis G. Fryback [États-Unis] ; Tracy A. Lieu [États-Unis] ; Grace M. Lee [États-Unis] ; David B. Martin [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Descripteurs français
- KwdFr :
- Acceptation des soins par le patient, Crises convulsives fébriles (étiologie), Facteurs de risque, Facteurs temps, Grippe humaine (), Grippe humaine (immunologie), Grippe humaine (épidémiologie), Humains, Modèles immunologiques, Pandémies (), Prise de décision, Résultat thérapeutique, Simulation numérique, Syndrome de Guillain-Barré (étiologie), Vaccination, Vaccins (effets indésirables).
- MESH :
- effets indésirables : Vaccins.
- immunologie : Grippe humaine.
- épidémiologie : Grippe humaine.
- étiologie : Crises convulsives fébriles, Syndrome de Guillain-Barré.
- Acceptation des soins par le patient, Facteurs de risque, Facteurs temps, Grippe humaine, Humains, Modèles immunologiques, Pandémies, Prise de décision, Résultat thérapeutique, Simulation numérique, Vaccination.
English descriptors
- KwdEn :
- Computer Simulation, Decision Making, Guillain-Barre Syndrome (etiology), Humans, Influenza, Human (epidemiology), Influenza, Human (immunology), Influenza, Human (prevention & control), Models, Immunological, Pandemics (prevention & control), Patient Acceptance of Health Care, Risk Factors, Seizures, Febrile (etiology), Time Factors, Treatment Outcome, Vaccination, Vaccines (adverse effects).
- MESH :
- chemical , adverse effects : Vaccines.
- epidemiology : Influenza, Human.
- etiology : Guillain-Barre Syndrome, Seizures, Febrile.
- immunology : Influenza, Human.
- prevention & control : Influenza, Human, Pandemics.
- Computer Simulation, Decision Making, Humans, Models, Immunological, Patient Acceptance of Health Care, Risk Factors, Time Factors, Treatment Outcome, Vaccination.
Abstract
Managing emerging vaccine safety signals during an influenza pandemic is challenging. Federal regulators must balance vaccine risks against benefits while maintaining public confidence in the public health system.
We developed a multi-criteria decision analysis model to explore regulatory decision-making in the context of emerging vaccine safety signals during a pandemic. We simulated vaccine safety surveillance system capabilities and used an age-structured compartmental model to develop potential pandemic scenarios. We used an expert-derived multi-attribute utility function to evaluate potential regulatory responses by combining four outcome measures into a single measure of interest: 1) expected vaccination benefit from averted influenza; 2) expected vaccination risk from vaccine-associated febrile seizures; 3) expected vaccination risk from vaccine-associated Guillain-Barre Syndrome; and 4) expected change in vaccine-seeking behavior in future influenza seasons.
Over multiple scenarios, risk communication, with or without suspension of vaccination of high-risk persons, were the consistently preferred regulatory responses over no action or general suspension when safety signals were detected during a pandemic influenza. On average, the expert panel valued near-term vaccine-related outcomes relative to long-term projected outcomes by 3∶1. However, when decision-makers had minimal ability to influence near-term outcomes, the response was selected primarily by projected impacts on future vaccine-seeking behavior.
The selected regulatory response depends on how quickly a vaccine safety signal is identified relative to the peak of the pandemic and the initiation of vaccination. Our analysis suggested two areas for future investment: efforts to improve the size and timeliness of the surveillance system and behavioral research to understand changes in vaccine-seeking behavior.
Url:
DOI: 10.1371/journal.pone.0115553
PubMed: 25536228
PubMed Central: 4275236
Affiliations:
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Maro</name><affiliation wicri:level="2"><nlm:aff id="aff1"><addr-line>Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author><author><name sortKey="Fryback, Dennis G" sort="Fryback, Dennis G" uniqKey="Fryback D" first="Dennis G." last="Fryback">Dennis G. 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Federal regulators must balance vaccine risks against benefits while maintaining public confidence in the public health system.</p></sec><sec><title>Methods</title><p>We developed a multi-criteria decision analysis model to explore regulatory decision-making in the context of emerging vaccine safety signals during a pandemic. We simulated vaccine safety surveillance system capabilities and used an age-structured compartmental model to develop potential pandemic scenarios. We used an expert-derived multi-attribute utility function to evaluate potential regulatory responses by combining four outcome measures into a single measure of interest: 1) expected vaccination benefit from averted influenza; 2) expected vaccination risk from vaccine-associated febrile seizures; 3) expected vaccination risk from vaccine-associated Guillain-Barre Syndrome; and 4) expected change in vaccine-seeking behavior in future influenza seasons.</p></sec><sec><title>Results</title><p>Over multiple scenarios, risk communication, with or without suspension of vaccination of high-risk persons, were the consistently preferred regulatory responses over no action or general suspension when safety signals were detected during a pandemic influenza. On average, the expert panel valued near-term vaccine-related outcomes relative to long-term projected outcomes by 3∶1. However, when decision-makers had minimal ability to influence near-term outcomes, the response was selected primarily by projected impacts on future vaccine-seeking behavior.</p></sec><sec><title>Conclusions</title><p>The selected regulatory response depends on how quickly a vaccine safety signal is identified relative to the peak of the pandemic and the initiation of vaccination. Our analysis suggested two areas for future investment: efforts to improve the size and timeliness of the surveillance system and behavioral research to understand changes in vaccine-seeking behavior.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Schonberger, Lb" uniqKey="Schonberger L">LB Schonberger</name></author><author><name sortKey="Bregman, Dj" uniqKey="Bregman D">DJ Bregman</name></author><author><name sortKey="Sullivan Bolyai, Jz" uniqKey="Sullivan Bolyai J">JZ Sullivan-Bolyai</name></author><author><name sortKey="Keenlyside, Ra" uniqKey="Keenlyside R">RA Keenlyside</name></author><author><name sortKey="Ziegler, Dw" uniqKey="Ziegler D">DW Ziegler</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gaydos, Jc" uniqKey="Gaydos J">JC Gaydos</name></author><author><name sortKey="Top, Fh" uniqKey="Top F">FH Top</name></author><author><name sortKey="Hodder, Ra" uniqKey="Hodder R">RA 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uniqKey="Bults M">M Bults</name></author><author><name sortKey="Beaujean, Djma" uniqKey="Beaujean D">DJMA Beaujean</name></author><author><name sortKey="Richardus, Jh" uniqKey="Richardus J">JH Richardus</name></author><author><name sortKey="Van Steenbergen, Je" uniqKey="Van Steenbergen J">JE van Steenbergen</name></author><author><name sortKey="Voeten, Hacm" uniqKey="Voeten H">HACM Voeten</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Gold, Mr" uniqKey="Gold M">MR Gold</name></author><author><name sortKey="Stevenson, D" uniqKey="Stevenson D">D Stevenson</name></author><author><name sortKey="Fryback, Dg" uniqKey="Fryback D">DG Fryback</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Armstrong, Pk" uniqKey="Armstrong P">PK Armstrong</name></author><author><name sortKey="Dowse, Gk" uniqKey="Dowse G">GK Dowse</name></author><author><name sortKey="Effler, Pv" uniqKey="Effler P">PV Effler</name></author><author><name sortKey="Carcione, D" uniqKey="Carcione D">D Carcione</name></author><author><name sortKey="Blyth, Cc" uniqKey="Blyth C">CC Blyth</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Californie</li><li>Maryland</li><li>Massachusetts</li><li>Wisconsin</li></region></list><tree><country name="États-Unis"><region name="Massachusetts"><name sortKey="Maro, Judith C" sort="Maro, Judith C" uniqKey="Maro J" first="Judith C." last="Maro">Judith C. Maro</name></region><name sortKey="Fryback, Dennis G" sort="Fryback, Dennis G" uniqKey="Fryback D" first="Dennis G." last="Fryback">Dennis G. Fryback</name><name sortKey="Lee, Grace M" sort="Lee, Grace M" uniqKey="Lee G" first="Grace M." last="Lee">Grace M. Lee</name><name sortKey="Lieu, Tracy A" sort="Lieu, Tracy A" uniqKey="Lieu T" first="Tracy A." last="Lieu">Tracy A. Lieu</name><name sortKey="Martin, David B" sort="Martin, David B" uniqKey="Martin D" first="David B." last="Martin">David B. Martin</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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