Valuing EQ-5D-5L health states 'in context' using a discrete choice experiment

In health state valuation studies, health states are typically presented as a series of sentences, each describing a health dimension and severity 'level'. Differences in the severity levels can be subtle, and confusion about which is 'worse' can lead to logically inconsistent valuation data. A solution could be to mimic the way patients self-report health, where the ordinal structure of levels is clear. We develop and test the feasibility of presenting EQ-5D-5L health states in the 'context' of the entire EQ-5D-5L descriptive system.

An online two-arm discrete choice experiment was conducted in the UK (n = 993). Respondents were randomly allocated to a control (standard presentation) or 'context' arm. Each respondent completed 16 paired comparison tasks and feedback questions about the tasks. Differences across arms were assessed using regression analyses.

Presenting health states 'in context' can significantly reduce the selection of logically dominated health states, particularly for labels 'severe' and 'extreme' (χ² = 46.02, p < 0.001). Preferences differ significantly between arms (likelihood ratio statistic = 42.00, p < 0.05). Comparing conditional logit modeling results, coefficients are ordered as expected for both arms, but the magnitude of decrements between levels is larger for the context arm.

Health state presentation is a key consideration in the design of valuation studies. Presenting health states 'in context' affects valuation data and reduces logical inconsistencies. Our results could have implications for other valuation tasks such as time trade-off, and for the valuation of other preference-based measures.