The critical impact of time-to-pandemic uncertainty on pandemic cost-effectiveness analyses

Health Policy and Planning
Volume 30 Issue 1 February 2015

Buy now, saved later? The critical impact of time-to-pandemic uncertainty on pandemic cost-effectiveness analyses
Tom Drake1,2,3,*, Zaid Chalabi1 and Richard Coker1,4
Author Affiliations
1London School of Hygiene and Tropical Medicine, Kepple Street, London, WC1E 7HT, UK, 2Nuffield Department of Clinical Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7BN, UK, 3Mahidol University Rajvithi Road, Bangkok 10400, Thailand and 4National University of Singapore, Lower Kent Ridge Road, Singapore 119077
Accepted November 21, 2013.
Background Investment in pandemic preparedness is a long-term gamble, with the return on investment coming at an unknown point in the future. Many countries have chosen to stockpile key resources, and the number of pandemic economic evaluations has risen sharply since 2009. We assess the importance of uncertainty in time-to-pandemic (and associated discounting) in pandemic economic evaluation, a factor frequently neglected in the literature to-date.
Methods We use a probability tree model and Monte Carlo parameter sampling to consider the cost effectiveness of antiviral stockpiling in Cambodia under parameter uncertainty. Mean elasticity and mutual information (MI) are used to assess the importance of time-to-pandemic compared with other parameters. We also consider the sensitivity to choice of sampling distribution used to model time-to-pandemic uncertainty.
Results Time-to-pandemic and discount rate are the primary drivers of sensitivity and uncertainty in pandemic cost effectiveness models. Base case cost effectiveness of antiviral stockpiling ranged between is US$112 and US$3599 per DALY averted using historical pandemic intervals for time-to-pandemic. The mean elasticities for time-to-pandemic and discount rate were greater than all other parameters. Similarly, the MI scores for time to pandemic and discount rate were greater than other parameters. Time-to-pandemic and discount rate were key drivers of uncertainty in cost-effectiveness results regardless of time-to-pandemic sampling distribution choice.
Conclusions Time-to-pandemic assumptions can “substantially” affect cost-effectiveness results and, in our model, is a greater contributor to uncertainty in cost-effectiveness results than any other parameter. We strongly recommend that cost-effectiveness models include probabilistic analysis of time-to-pandemic uncertainty.