Vaccination against pandemic influenza A/H1N1v in England: A real-time economic evaluation

Decisions on how to mitigate an evolving pandemic are technically challenging. We present a real-time assessment of the effectiveness and cost-effectiveness of alternative influenza A/H1N1v vaccination strategies. A transmission dynamic model was fitted to the estimated number of cases in real-time, and used to generate plausible autumn scenarios under different vaccination options. The proportion of these cases by age and risk group leading to primary care consultations, National Pandemic Flu Service consultations, emergency attendances, hospitalisations, intensive care and death was then estimated using existing data from the pandemic. The real-time model suggests that the epidemic will peak in early November, with the peak height being similar in magnitude to the summer wave. Vaccination of the high-risk groups is estimated to prevent about 45 deaths (80% credibility interval 26-67), and save around 2900 QALYs (80% credibility interval 1600-4500). Such a programme is very likely to be cost-effective if the cost of vaccine purchase itself is treated as a sunk cost. Extending vaccination to low-risk individuals is expected to result in more modest gains in deaths and QALYs averted. Extending vaccination to school-age children would be the most cost-effective extension. The early availability of vaccines is crucial in determining the impact of such extensions. There have been a considerable number of cases of H1N1v in England, and so the benefits of vaccination to mitigate the ongoing autumn wave are limited. However, certain groups appear to be at significantly higher risk of complications and deaths, and so it appears both effective and cost-effective to vaccinate them. The United Kingdom was the first country to have a major epidemic in Europe. In countries where the epidemic is not so far advanced vaccination of children may be cost-effective. Similar, detailed, real-time modelling and economic studies could help to clarify the situation. (C) 2010 Elsevier Ltd. All rights reserved.