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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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Publication details

DateAccepted/In press - 23 Feb 2020
DateE-pub ahead of print (current) - 10 Mar 2020
Number of pages18
Early online date10/03/20
Original languageEnglish


Background: In rare disease (RD) studies, generic preference-based patient-reported outcome measures (PROMs) that yield health state utility values (HSUVs) are seldom collected, as they are considered not sensitive enough for these small and heterogeneous patient populations. In such cases, a HSUV can also be obtained by ‘mapping’ a more sensitive ‘source’ (e.g., disease-specific PROM) to a ‘target' preference-based measure (e.g., EuroQol-5 Dimension (EQ-5D)) through a statistical relationship.
Objective: This study aimed to systematically review all published studies using ‘mapping’ to derive HSUVs from non-preference-based measures in RDs (i.e. affecting fewer than 1 in 2,000 people), and identify any critical issue related to the main features of RDs.
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 additional to ‘rare’ and ‘orphan’. ‘Very rare’ diseases (i.e. with less 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 thirteen different RDs. As a target measure, the majority used EQ-5D, and the others 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 existing 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, hindering the cross-validation of algorithms and application of more complex regression models, 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. Additionally, few studies explicitly referred to published recommendations for mapping. Lastly, the application of existing algorithms in non-RDs was likely 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 generalizability 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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