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PharmacoEconomics

, Volume 30, Issue 12, pp 1157–1171 | Cite as

Cost Effectiveness of Oromucosal Cannabis-Based Medicine (Sativex®) for Spasticity in Multiple Sclerosis

  • Lanting LuEmail author
  • Hilary Pearce
  • Chris Roome
  • James Shearer
  • Iain A. Lang
  • Ken Stein
Original Research Article

Abstract

Background

Spasticity is common in patients with multiple sclerosis (MS) and is a major contributor to disability. Sativex®, an oromucosal spray containing cannabis-based medicinal products, has been found to be effective in reducing spasticity symptoms.

Objective

Our objective was to estimate the cost effectiveness of Sativex® plus oral anti-spasticity medicines compared with the current standard treatment for moderate or severe spasticity in MS in the UK.

Methods

A Markov model was used to assess the costs and benefits of Sativex® plus oral anti-spasticity medicines or current standard treatment based on their effects on the quality of life of patients. The main outcome was the incremental cost-effectiveness ratio (ICER) in terms of costs per additional QALY gained over 5 years of treatment. One-way, multi-way and probabilistic sensitivity analyses were conducted to explore the impact of uncertainties on the findings.

Results

In the base case, Sativex® plus oral anti-spasticity medicines resulted in incremental costs of d7600 and a QALY gain of 0.15 per person over 5 years (ICER = £49 300 per QALY) [year 2009 data for costs]. Findings were sensitive to the costs of Sativex® (price and dose) and differences in utilities between responders and non-responders.

Conclusions

Using a willingness-to-pay threshold of £30 000 per QALY, Sativex® appears unlikely to be considered cost effective by UK funders of healthcare for spasticity in MS. This is unfortunate, since it appears that Sativex® use is likely to benefit some patients in the management of this common consequence of MS.

Keywords

Multiple Sclerosis Botulinum Toxin Baclofen Numerical Rating Scale Botulinum Toxin Injection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was partially funded by the National Institute for Health Research (NIHR) through the Peninsula Collaboration for Leadership in Applied Health Research and Care (PenCLAHRC), which is based at the University of Exeter.

This article presents independent research and the views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

The authors declare that they have no conflicts of interest.

LL designed and implemented the model with advice from KS, HP, CR, JS and IL. LL drafted the first and final versions of the manuscript and led the submission and revision process. All authors contributed to the drafting and approval of the final manuscript. KS acts as the guarantor for the overall content.

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Copyright information

© Springer International Publishing AG 2012

Authors and Affiliations

  • Lanting Lu
    • 1
    • 2
    Email author
  • Hilary Pearce
    • 3
  • Chris Roome
    • 3
  • James Shearer
    • 4
  • Iain A. Lang
    • 2
    • 3
  • Ken Stein
    • 1
    • 2
  1. 1.Peninsula Technology Assessment Group (PenTAG), Peninsula College of Medicine and DentistryUniversity of ExeterExeterUK
  2. 2.Peninsula Collaboration for Leadership in Applied Health Research and Care (PenCLAHRC), Peninsula College of Medicine and DentistryUniversity of ExeterExeterUK
  3. 3.Public Health DirectorateNHS DevonExeterUK
  4. 4.Health Economics Group, Peninsula College of Medicine and DentistryUniversity of ExeterExeterUK

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