Evidence map›Paper›PMID 24369125›Full record

ArticleHealth policy and planning2015

Buy now, saved later? The critical impact of time-to-pandemic uncertainty on pandemic cost-effectiveness analyses.

Tom Drake, Zaid Chalabi, Richard Coker

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Health policy and planning, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Tom DrakeLondon School of Hygiene and Tropical Medicine, Kepple Street, London, WC1E 7HT, UK, Nuffield Department of Clinical Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7BN, UK, Mahidol University Rajvithi Road, Bangkok 10400, Thailand and National University of Singapore, Lower Kent Ridge Road, Singapore 119077 London School of Hygiene and Tropical Medicine, Kepple Street, London, WC1E 7HT, UK, Nuffield Department of Clinical Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7BN, UK, Mahidol University Rajvithi Road, Bangkok 10400, Thailand and National University of Singapore, Lower Kent Ridge Road, Singapore 119077 London School of Hygiene and Tropical Medicine, Kepple Street, London, WC1E 7HT, UK, Nuffield Department of Clinical Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7BN, UK, Mahidol University Rajvithi Road, Bangkok 10400, Thailand and National University of Singapore, Lower Kent Ridge Road, Singapore 119077 tom.d@tropmedres.ac.
Zaid ChalabiLondon School of Hygiene and Tropical Medicine, Kepple Street, London, WC1E 7HT, UK, Nuffield Department of Clinical Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7BN, UK, Mahidol University Rajvithi Road, Bangkok 10400, Thailand and National University of Singapore, Lower Kent Ridge Road, Singapore 119077.
Richard CokerLondon School of Hygiene and Tropical Medicine, Kepple Street, London, WC1E 7HT, UK, Nuffield Department of Clinical Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7BN, UK, Mahidol University Rajvithi Road, Bangkok 10400, Thailand and National University of Singapore, Lower Kent Ridge Road, Singapore 119077 London School of Hygiene and Tropical Medicine, Kepple Street, London, WC1E 7HT, UK, Nuffield Department of Clinical Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7BN, UK, Mahidol University Rajvithi Road, Bangkok 10400, Thailand and National University of Singapore, Lower Kent Ridge Road, Singapore 119077.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInvestment 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.

methodsWe 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.

resultsTime-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.

conclusionsTime-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.

Indexed as

Antiviral AgentsCambodiaCost-Benefit AnalysisDecision MakingHealth Care CostsHumansModels, StatisticalMonte Carlo MethodPandemicsTime FactorsUncertaintyAntiviral Agentscost effectivenessdiscountingeconomic evaluationmodellingPandemicuncertainty

Identifiers

PMID24369125
PMCPMC7574603

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.