Hanmer Janel, Cherepanov Dasha, Palta Mari, Kaplan Robert M, Feeny David, Fryback Dennis G
Department of Internal Medicine, University of Pittsburgh, Pittsburgh, PA (JH)
Partnership for Health Analytic Research, LLC, Beverly Hills, CA (DC)
Med Decis Making. 2016 Feb;36(2):264-74. doi: 10.1177/0272989X15599546. Epub 2015 Aug 27.
Many cost-utility analyses rely on generic utility measures for estimates of disease impact. Commonly used generic preference-based indexes may generate different absolute estimates of disease burden despite sharing anchors of dead at 0 and full health at 1.0.
We compare the impact of 16 prevalent chronic health conditions using 6 utility-based indexes of health and a visual analog scale.
Data were from the National Health Measurement Study (NHMS), a cross-sectional telephone survey of 3844 adults aged 35 to 89 years in the United States.
The NHMS included the EuroQol-5D-3L, Health and Activities Limitation Index (HALex), Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3), preference-based scoring for the SF-36v2 (SF-6D), Quality of Well-Being Scale, and visual analog scale. Respondents self-reported 16 chronic conditions. Survey-weighted regression analyses for each index with all health conditions, age, and sex were used to estimate health condition impact estimates in terms of quality-adjusted life years (QALYs) lost over 10 years. All analyses were stratified by ages 35 to 69 and 70 to 89 years.
There were significant differences between the indexes for estimates of the absolute impact of most conditions. On average, condition impacts were the smallest with the SF-6D and EQ-5D-3L and the largest with the HALex and HUI3. Likewise, the estimated loss of QALYs varied across indexes. Condition impact estimates for EQ-5D-3L, HUI2, HUI3, and SF-6D generally had strong Spearman correlations across conditions (i.e., >0.69).
This analysis uses cross-sectional data and lacks health condition severity information.
Health condition impact estimates vary substantially across the indexes. These results imply that it is difficult to standardize results across cost-utility analyses that use different utility measures.
许多成本效用分析依赖通用效用指标来估计疾病影响。尽管常用的基于偏好的指标都以0代表死亡、1.0代表完全健康作为锚定标准,但它们可能会得出不同的疾病负担绝对估计值。
我们使用6种基于效用的健康指标和一个视觉模拟量表,比较16种常见慢性健康状况的影响。
数据来自美国国家健康测量研究(NHMS),这是一项对3844名年龄在35至89岁之间的成年人进行的横断面电话调查。
NHMS纳入了欧洲五维度健康量表-3水平(EuroQol-5D-3L)、健康与活动受限指数(HALex)、健康效用指数第2版(HUI2)和第3版(HUI3)、SF-36v2(SF-6D)基于偏好的评分、幸福感量表以及视觉模拟量表。受访者自行报告了16种慢性病状况。对每个指标与所有健康状况、年龄和性别的调查加权回归分析,用于估计10年内因质量调整生命年(QALY)损失而产生的健康状况影响估计值。所有分析均按35至69岁和70至89岁进行分层。
大多数状况的绝对影响估计值在各指标之间存在显著差异。平均而言,SF-6D和EQ-5D-3L得出的状况影响最小,而HALex和HUI3得出的影响最大。同样,不同指标估计的QALY损失也有所不同。EQ-5D-3L、HUI2、HUI3和SF-6D的状况影响估计值在各状况之间通常具有很强的斯皮尔曼相关性(即>0.69)。
本分析使用横断面数据,缺乏健康状况严重程度信息。
不同指标得出的健康状况影响估计值差异很大。这些结果表明,在使用不同效用指标的成本效用分析中,很难实现结果的标准化。