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A Systematic Review of the Literature on the Development of Condition-Specific Preference-Based Measures of Health

  • Systematic Review
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Abstract

Background

Health state utility values (HSUVs) are required to calculate quality-adjusted life-years (QALYs). They are frequently derived from generic preference-based measures of health. However, such generic measures may not capture health attributes of relevance to specific conditions. In such cases, a condition-specific preference-based measure (CSPBM) may be more appropriate.

Objective

This systematic review aimed to identify all published accounts of developing CSPBMs to describe and appraise the methods used.

Method

We undertook a systematic search (of Embase, MEDLINE, PsycINFO, Web of Science, the Cochrane Library, CINAHL, EconLit, ASSIA and the Health Management Information Consortium database) to identify published accounts of CSPBM development up to July 2015. Studies were reviewed to investigate the methods used to design classification systems, estimate HSUVs, and validate the measures.

Results

A total of 86 publications were identified, describing 51 CSPBMs. Around two-thirds of these were QALY measures; the remainder were designed for clinical decision making only. Classification systems for 33 CSPBMs were derived from existing instruments; 18 were developed de novo. HSUVs for 34 instruments were estimated using a ‘composite’ approach, involving statistical modelling; the remainder used a ‘decomposed’ approach based on multi-attribute utility theory. Half of the papers that described the estimation of HSUVs did not report validating their measures.

Conclusion

Various methods have been used at all stages of CSPBM development. The choice between developing a classification system de novo or from an existing instrument may depend on the availability of a suitable existing measure, while the choice between a decomposed or composite approach appears to be determined primarily by the purpose for which the instrument is designed. The validation of CSPBMs remains an area for further development.

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Notes

  1. Unlike traditional psychometric approaches, Rasch models produce interval scales that are based on an estimate of the true score rather than relying on observed scores. Item parameters are estimated independently of the sample used for scale construction; similarly, person values are estimated independently of the items used. This avoids two limitations of traditional psychometric methods, in which results for scales are sample dependent and vice versa. Rasch models are more tolerant of missing data and do not require imputation of missing values [94].

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Authors’ contributions

Elizabeth Goodwin drafted and revised the manuscript for content, was involved in conceptualisation and design of the work, analysed and interpreted the data, and is guarantor for overall content. Colin Green drafted and revised the manuscript for content, was involved in conceptualisation and design of the work and analysed and interpreted the data.

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  1. Health Economics Group, University of Exeter Medical School, University of Exeter, Room 1.06, South Cloisters, St Luke’s Campus, Exeter, EX1 2LU, UK

    Elizabeth Goodwin & Colin Green

  2. Collaboration for Leadership in Applied Health Research and Care South West Peninsula, University of Exeter Medical School, University of Exeter, Exeter, UK

    Colin Green

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Correspondence to Elizabeth Goodwin.

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Funding

This study formed part of a PhD studentship funded by The Multiple Sclerosis Society of Great Britain and Northern Ireland (Grant Reference Number 928/10). This research was supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula at the Royal Devon and Exeter NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

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Elizabeth Goodwin and Colin Green have no conflicts of interest.

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Goodwin, E., Green, C. A Systematic Review of the Literature on the Development of Condition-Specific Preference-Based Measures of Health. Appl Health Econ Health Policy 14, 161–183 (2016). https://doi.org/10.1007/s40258-015-0219-9

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