title: Responding to vaccine safety signals during pandemic influenza: a modeling study.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America.
authors.forename: Judith C
authors.initials: JC
authors.lastname: Maro
authors.affiliation: Department of Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, United States of America.
authors.forename: Dennis G
authors.initials: DG
authors.lastname: Fryback
authors.affiliation: Division of Research, Kaiser Permanente Northern California, Oakland, California, United States of America.
authors.forename: Tracy A
authors.initials: TA
authors.lastname: Lieu
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America.
authors.forename: Grace M
authors.initials: GM
authors.lastname: Lee
authors.affiliation: Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America.
authors.forename: David B
authors.initials: DB
authors.lastname: Martin
mesh_terms: D003198:Computer Simulation; D003657:Decision Making; D020275:Guillain-Barre Syndrome / Q000209:etiology; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology* / Q000276:immunology* / Q000517:prevention & control; D018448:Models, Immunological; D058873:Pandemics / Q000517:prevention & control*; D010342:Patient Acceptance of Health Care; D012307:Risk Factors; D003294:Seizures, Febrile / Q000209:etiology; D013997:Time Factors; D016896:Treatment Outcome; D014611:Vaccination; D014612:Vaccines / Q000009:adverse effects*
chemical_list: D014612:Vaccines