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‘Mapping’ Health State Utility Values from Non-preference-Based Measures: A Systematic Literature Review in Rare Diseases

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Abstract

Background

The use of patient-reported outcome measures (PROMs) to monitor the effects of disease and treatment on patient symptomatology and daily life is increasing in rare diseases (RDs) (i.e. those affecting less than one in 2000 people); however, these instruments seldom yield health state utility values (HSUVs) for cost-utility analyses. In such a context, ‘mapping’ allows HSUVs to be obtained by establishing a statistical relationship between a ‘source’ (e.g. a disease-specific PROM) and a ‘target’ preference-based measure [e.g. the EuroQol-5 Dimension (EQ-5D) tool].

Objective

This study aimed to systematically review all published studies using ‘mapping’ to derive HSUVs from non–preference-based measures in RDs, and identify any critical issues related to the main features of RDs, which are characterised by small, heterogeneous, and geographically dispersed patient populations.

Methods

The following databases were searched during the first half of 2019 without time, study design, or language restrictions: MEDLINE (via PubMed), the School of Health and Related Research Health Utility Database (ScHARRHUD), and the Health Economics Research Centre (HERC) database of mapping studies (version 7.0). The keywords combined terms related to ‘mapping’ with Orphanet’s list of RD indications (e.g. ‘acromegaly’) in addition to ‘rare’ and ‘orphan’. ‘Very rare’ diseases (i.e. those with fewer than 1000 cases or families documented in the medical literature) were excluded from the searches. A predefined, pilot-tested extraction template (in Excel®) was used to collect structured information from the studies.

Results

Two groups of studies were identified in the review. The first group (n = 19) developed novel mapping algorithms in 13 different RDs. As a target measure, the majority used EQ-5D, and the others used the Short-Form Six-Dimension (SF-6D) and 15D; most studies adopted ordinary least squares (OLS) regression. The second group of studies (n = 9) applied previously published algorithms in non-RDs to comparable RDs, mainly in the field of cancer. The critical issues relating to ‘mapping’ in RDs included the availability of very few studies, the relatively high number of cancer studies, and the absence of research in paediatric RDs. Moreover, the reviewed studies recruited small samples, showed a limited overlap between RD-specific and generic PROMs, and highlighted the presence of cultural and linguistic factors influencing results in multi-country studies. Lastly, the application of existing algorithms developed in non-RDs tended to produce inaccuracies at the bottom of the EQ-5D scale, due to the greater severity of RDs.

Conclusions

More research is encouraged to develop algorithms for a broader spectrum of RDs (including those affecting young children), improve mapping study quality, test the generalisability of algorithms developed in non-RDs (e.g. HIV) to rare variants or evolutions of the same condition (e.g. AIDS wasting syndrome), and verify the robustness of results when mapped HSUVs are used in cost-utility models.

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Data Availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgments

The authors are thankful to Dr. Karen Facey (University of Edinburgh) for her constructive suggestions and comments.

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Authors and Affiliations

  1. Research Centre on Health and Social Care Management (CERGAS), Bocconi University, Milan, Italy

    Michela Meregaglia, Amanda Whittal & Elena Nicod

  2. Centre for Health Economics, University of York, York, UK

    Michael Drummond

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Contributions

All authors contributed to the study conception and design. MM and AW performed the literature searches. MM analysed the data and wrote the first draft of the manuscript. All authors commented on previous versions of the manuscript and approved the final manuscript.

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Correspondence to Michela Meregaglia.

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Funding

This research was funded by the European Commission’s Horizon 2020 research and innovation programme and was undertaken under the auspices of IMPACT-HTA (Grant number 779312). The results presented here reflect the authors’ views and not the views of the European Commission. The European Commission is not liable for any use of the information communicated.

Conflict of interest

MM has no conflict of interest. AW has no conflict of interest. EN reports personal fees from Dolon Ltd outside the submitted work and has no conflict of interest. MD has no conflict of interest.

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Meregaglia, M., Whittal, A., Nicod, E. et al. ‘Mapping’ Health State Utility Values from Non-preference-Based Measures: A Systematic Literature Review in Rare Diseases. PharmacoEconomics 38, 557–574 (2020). https://doi.org/10.1007/s40273-020-00897-4

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