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Informing disinvestment through cost-effectiveness modelling

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  • Jonathan Karnon
  • Jill Carlton
  • Carolyn Czoski-Murray
  • Kevin Smith

Abstract

The mandatory nature of recommendations made by the National Institute for Health and Clinical Excellence (NICE) in the UK has highlighted inherent difficulties in the process of disinvestment in existing technologies to fund NICE-approved technologies. A lack of evidence on candidate technologies means that the process of disinvestment is subject to greater uncertainty than the investment process, and inefficiencies may occur as a result of the inverse evidence law. This article describes a potential disinvestment scenario and the options for the decision maker, including the conduct of value of information analyses. To illustrate the scenario, an economic evaluation of a disinvestment candidate (screening for amblyopia and strabismus) is presented. Only very limited data were available. The reference case analysis found that screening is not cost effective at currently accepted values of a QALY. However, a small utility decrement due to unilateral vision loss reduced the incremental cost per QALY gained, with screening expected to be extremely cost effective. The discussion highlights the specific options to be considered by decision makers in light of the model-based evaluation. It is shown that the evaluation provides useful information to guide the disinvestment decision, providing a range of focused options with respect to the decision and the decision-making process. A combination of explicit model-based evaluation, and pragmatic and generalizable approaches to interpreting uncertainty in the decision-making process is proposed, which should enable informed decisions around the disinvestment of technologies with weak evidence bases. Copyright Adis Data Information BV 2009

Suggested Citation

  • Jonathan Karnon & Jill Carlton & Carolyn Czoski-Murray & Kevin Smith, 2009. "Informing disinvestment through cost-effectiveness modelling," Applied Health Economics and Health Policy, Springer, vol. 7(1), pages 1-9, March.
    • Handle: RePEc:spr:aphecp:v:7:y:2009:i:1:p:1-9
    DOI: 10.1007/BF03256137
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    References listed on IDEAS

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    1. Jonathan Karnon & Carolyn Czoski-Murray & Kevin J. Smith & Christopher Brand, 2009. "A Hybrid Cohort Individual Sampling Natural History Model of Age-Related Macular Degeneration: Assessing the Cost-Effectiveness of Screening Using Probabilistic Calibration," Medical Decision Making, , vol. 29(3), pages 304-316, May.
    2. Karnon, Jonathan, 2002. "Planning the efficient allocation of research funds: an adapted application of a non-parametric Bayesian value of information analysis," Health Policy, Elsevier, vol. 61(3), pages 329-347, September.
    3. Simon Eckermann & Andrew R. Willan, 2007. "Expected value of information and decision making in HTA," Health Economics, John Wiley & Sons, Ltd., vol. 16(2), pages 195-209, February.
    4. Koopmanschap, Marc A. & Rutten, Frans F. H. & van Ineveld, B. Martin & van Roijen, Leona, 1995. "The friction cost method for measuring indirect costs of disease," Journal of Health Economics, Elsevier, vol. 14(2), pages 171-189, June.
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    Cited by:

    1. Ibargoyen-Roteta, Nora & Gutiérrez-Ibarluzea, Iñaki & Asua, José, 2010. "Guiding the process of health technology disinvestment," Health Policy, Elsevier, vol. 98(2-3), pages 218-226, December.
    2. Tania Stafinski & Devidas Menon & Deborah Marshall & Timothy Caulfield, 2011. "Societal Values in the Allocation of Healthcare Resources," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 4(4), pages 207-225, December.

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