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How Should We Measure Quality of Life Impact in Rare Disease? Recent Learnings in Spinal Muscular Atrophy

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Abstract

The measurement of quality of life in the context of spinal muscular atrophy (SMA) is challenging. This is because the disease is experienced by children and is rare, which makes data collection difficult. This Briefing reports on a symposium that outlined some lessons that can be learnt from the SMA context that might be more widely applicable. Where evidence is lacking for new treatments, because of practical or methodological difficulties, there is a risk that patients remain unable to access cost-effective care. We identify a variety of ways in which current approaches to the measurement of quality of life in SMA may be inadequate. For example, it is unlikely that existing measures of health-related quality of life capture all that is important to patients and caregivers. Based on the discussion, we highlight four possible strategies for improving the quantity and quality of data available to inform decisionmakers in the context of rare diseases - - Bespoke data collection which is relevant to HTA decisionmakers; - Simple economic modelling methods, which reflect the evidence available at thetime of the assessment; - Collaboration among the different parties involved; and - Identifying what is 'good enough' to inform decisionmaking on use at the time oflaunch or of the health technology assessment process. New approaches to research could facilitate health technology assessment processes and improve patients' access to cost-effective treatments for rare diseases.

Suggested Citation

  • Sampson, C. & Garau, M., 2019. "How Should We Measure Quality of Life Impact in Rare Disease? Recent Learnings in Spinal Muscular Atrophy," Briefings 002146, Office of Health Economics.
  • Handle: RePEc:ohe:briefg:002146
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    1. John Brazier & Yaling Yang & Aki Tsuchiya & Donna Rowen, 2010. "A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(2), pages 215-225, April.
    2. Julio López-Bastida & Juan Oliva-Moreno & Renata Linertová & Pedro Serrano-Aguilar, 2016. "Social/economic costs and health-related quality of life in patients with rare diseases in Europe," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(1), pages 1-5, April.
    3. Julie Ratcliffe & Elisabeth Huynh & Katherine Stevens & John Brazier & Michael Sawyer & Terry Flynn, 2016. "Nothing About Us Without Us? A Comparison of Adolescent and Adult Health‐State Values for the Child Health Utility‐9D Using Profile Case Best–Worst Scaling," Health Economics, John Wiley & Sons, Ltd., vol. 25(4), pages 486-496, April.
    4. Kamran Khan & Stavros Petrou & Oliver Rivero-Arias & Stephen Walters & Spencer Boyle, 2014. "Mapping EQ-5D Utility Scores from the PedsQL™ Generic Core Scales," PharmacoEconomics, Springer, vol. 32(7), pages 693-706, July.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Chris Sampson’s journal round-up for 7th September 2020
      by Chris Sampson in The Academic Health Economists' Blog on 2020-09-07 11:00:07

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    More about this item

    Keywords

    Measuring and valuing outcomes;

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health

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