Evidence synthesis and decision modelling to support complex decisions: stockpiling neuraminidase inhibitors for pandemic influenza usage

Watson, Samuel I.; Chen, Yen-Fu; Nguyen-Van-Tam, Jonathan S.; Myles, Puja R.; Venkatesan, Sudhir; Zambon, Maria; Uthman, Olalekan; Chilton, Peter J.; Lilford, Richard J.

Abstract

Objectives: The stockpiling of neuraminidase inhibitor (NAI) antivirals as a defence against pandemic influenza is a significant public health policy decision that must be made despite a lack of conclusive evidence from randomised controlled trials regarding the effectiveness of NAIs on important clinical end points such as mortality. The objective of this study was to determine whether NAIs should be stockpiled for treatment of pandemic influenza on the basis of current evidence.

Methods: A decision model for stockpiling was designed. Data on previous pandemic influenza epidemiology was combined with data on the effectiveness of NAIs in reducing mortality obtained from a recent individual participant meta-analysis using observational data. Evidence synthesis techniques and a bias modelling method for observational data were used to incorporate the evidence into the model. The stockpiling decision was modelled for adults (≥16 years old) and the United Kingdom was used as an example. The main outcome was the expected net benefits of stockpiling in monetary terms. Health benefits were estimated from deaths averted through stockpiling.

Results: After adjusting for biases in the estimated effectiveness of NAIs, the expected net benefit of stockpiling in the baseline analysis was £444 million, assuming a willingness to pay of £20,000/QALY ($31,000/QALY). The decision would therefore be to stockpile NAIs. There was a greater probability that the stockpile would not be utilised than utilised. However, the rare but catastrophic losses from a severe pandemic justified the decision to stockpile.

Conclusions: Taking into account the available epidemiological data and evidence of effectiveness of NAIs in reducing mortality, including potential biases, a decision maker should stockpile anti-influenza medication in keeping with the postulated decision rule.

Citation

Watson, S. I., Chen, Y., Nguyen-Van-Tam, J. S., Myles, P. R., Venkatesan, S., Zambon, M., …Lilford, R. J. (2017). Evidence synthesis and decision modelling to support complex decisions: stockpiling neuraminidase inhibitors for pandemic influenza usage. F1000Research, 5, Article 2293. https://doi.org/10.12688/f1000research.9414.2

• Journal Article Type: Article
• Acceptance Date: Mar 15, 2017
• Online Publication Date: Mar 16, 2017
• Publication Date: Mar 16, 2017
• Deposit Date: Aug 20, 2018
• Publicly Available Date: Aug 20, 2018
• Journal: F1000Research
• Electronic ISSN: 2046-1402
• Publisher: F1000Research
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Article Number: 2293
• DOI: https://doi.org/10.12688/f1000research.9414.2
• Keywords: Pandemic influenza; Evidence synthesis; Bias modelling; Neuraminidase inhibitors; Stockpiling
• Public URL: https://nottingham-repository.worktribe.com/output/1039434
• Publisher URL: https://f1000research.com/articles/5-2293/v2
• Additional Information: Referee status: Indexed; Referee Report: 10.5256/f1000research.10138.r17973, Pasi M. Penttinen, European Centre for Disease Prevention and Control, Stockholm, Sweden, 18 Jan 2017, version 1, 1 approved, 1 approved with reservations; Referee Comment: Sam Watson; ; Posted: 08 Mar 2017; We thank the review for their comments and detail our responses below, point by point. The referee's text is in Italics. ; This is a well-designed, carefully executed and documented study, that provides important insights into the cost-effectiveness of national stockpiles of neuraminidase inhibitors to be used during influenza pandemics. The analysis is relying on a number of key assumptions, such as the effectiveness of NAI antivirals against mortality due to influenza, the probability of a pandemic occurring during the shelf life of the stockpile and the proportion of pandemic influenza deaths occurring in hospital. Many of these assumptions are based on a limited or controversial evidence base, however the authors acknowledge and address most of these limitations. The assumption that most pandemic deaths occur in hospitals, is based on the observation during the 2009 pandemic in the UK, however in many countries, already during severe influenza A(H3N2) epidemics, and during many previous pandemics, the majority of deaths are likely to occur in the community, outside of hospitals. It is confusing that the authors compare the costs of a population wide (80%) stockpile with the estimated benefits on hospital mortality only. Although this is discussed in the second paragraph of discussion, it would be helpful to see an analysis or results taking also into account outpatient and community mortality. We did not consider non-hospital mortality as there were no data on the effectiveness of NAIs outside of the hospital setting, where there may be differences in compliance and other factors, when the study was conducted. We note that the way we have set up the analysis is to try to be as conservative as possible: the highest stated costs with a justifiable patient pool. On this basis we note that if a decision to stockpile is supported under our assumptions then it will certainly be supported if there is any benefit outside of the hospital. Recently published analyses outside of the hospital setting suggest a potential benefit (https://doi.org/10.1093/cid/cix127), however we opt to remain conservative in our analyses. ; It is likely that such an analysis would be useful for other countries than UK. Please discuss briefly the limitations of this approach and these assumptions, when replicating the study in other settings (such as differences in societal willingness to pay per QALY). We have amended the discussion to reflect this. In Box 1. the two columns are not aligned when viewing as a pop-up on MS Internet Explorer. ; This is an issue for the journal. In Figure 1. the references to UK, and the national pandemic flu service are not helpful and distract from the more general main message of this figure. ; We have removed that box from the figure.; Referee Report: 10.5256/f1000research.12012.r21030, Joel Kelso, School of Computer Science and Software Engineering, The University of Western Australia, Crawley, WA, Australia, 17 Mar 2017, version 2, indexed; Grant Information: SIW, RJL, YFC, and PJC are part-funded/supported by the National Institute for Health Research (NIHR) Collaborations for Leadership in Applied Health Research and Care West Midlands. This paper presents independent research and the views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.; Copyright: This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).