Office of Health Economics, Southside 7th Floor, 105 Victoria Street, London, SW1E 6QT, UK.
Centre for Health Economics Research and Evaluation, University of Technology Sydney, 1-59 Quay St, Haymarket, Sydney, NSW, 2000, Australia.
Eur J Health Econ. 2018 May;19(4):595-605. doi: 10.1007/s10198-017-0905-7. Epub 2017 May 31.
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' (χ = 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.
在健康状况评估研究中，健康状况通常以一系列句子呈现，每个句子描述一个健康维度和严重程度“级别”。严重程度级别的差异可能很细微，对哪个“更差”的混淆可能导致逻辑不一致的评估数据。一种解决方案是模仿患者自我报告健康状况的方式，其中级别的有序结构是明确的。我们开发并测试了在整个 EQ-5D-5L 描述系统“背景”中呈现 EQ-5D-5L 健康状况的可行性。
在英国进行了一项在线两臂离散选择实验（n=993）。受访者被随机分配到对照组（标准呈现）或“背景”组。每位受访者完成了 16 对配对比较任务和关于任务的反馈问题。使用回归分析评估臂间差异。
以“背景”呈现健康状况可以显著减少逻辑主导健康状况的选择，特别是对于“严重”和“极端”标签（χ=46.02，p<0.001）。臂间偏好差异显著（似然比统计量=42.00，p<0.05）。比较条件逻辑回归模型结果，两个臂的系数顺序都符合预期，但背景臂之间级别的降幅更大。
健康状况呈现是设计评估研究的关键考虑因素。以“背景”呈现健康状况会影响评估数据并减少逻辑不一致。我们的结果可能对其他评估任务（如时间权衡）和其他基于偏好的衡量标准的评估产生影响。
