The European Journal of Public Health
Volume 23 Issue 4 August 2013
Modelling the risk–benefit impact of H1N1 influenza vaccines
Lawrence D. Phillips1,2, Barbara Fasolo1,2, Nikolaos Zafiropoulous1, Hans-Georg Eichler1, Falk Ehmann1, Veronika Jekerle1, Piotr Kramarz3, Angus Nicoll3 and Thomas Lönngren4
Background: Shortly after the H1N1 influenza virus reached pandemic status in June 2009, the benefit–risk project team at the European Medicines Agency recognized this presented a research opportunity for testing the usefulness of a decision analysis model in deliberations about approving vaccines soon based on limited data or waiting for more data. Undertaken purely as a research exercise, the model was not connected to the ongoing assessment by the European Medicines Agency, which approved the H1N1 vaccines on 25 September 2009. Methods: A decision tree model constructed initially on 1 September 2009, and slightly revised subsequently as new data were obtained, represented an end-of-September or end-of-October approval of vaccines. The model showed combinations of uncertain events, the severity of the disease and the vaccines’ efficacy and safety, leading to estimates of numbers of deaths and serious disabilities. The group based their probability assessments on available information and background knowledge about vaccines and similar pandemics in the past. Results: Weighting the numbers by their joint probabilities for all paths through the decision tree gave a weighted average for a September decision of 216 500 deaths and serious disabilities, and for a decision delayed to October of 291 547, showing that an early decision was preferable. Conclusions: The process of constructing the model facilitated communications among the group’s members and led to new insights for several participants, while its robustness built confidence in the decision. These findings suggest that models might be helpful to regulators, as they form their preferences during the process of deliberation and debate, and more generally, for public health issues when decision makers face considerable uncertainty.