Institute for Choice, University of South Australia, Australia.
Population Health Sciences, Bristol Medical School, University of Bristol, UK.
Soc Sci Med. 2017 Sep;189:114-128. doi: 10.1016/j.socscimed.2017.07.012. Epub 2017 Jul 21.
End of life care may have elements of value that go beyond health. A generic measure of the benefits of end of life care could be helpful to decision makers. Such a measure, based on the capability approach, has recently been developed: the ICECAP Supportive Care Measure. This paper reports the first valuation exercise for that measure, with data from 6020 individuals collected from an on-line general population panel during June 2013. Individuals were asked to complete a stated choice experiment that combined best-worst scaling and a standard discrete choice experiment. Analysis of the best-worst data used limited dependent variable models within the random utility framework including the multinomial logit models and latent class choice model analysis. Exploratory steps were taken to determine the similarity of the best-worst and DCE data before formal testing and pooling of the two data sources. Combined data were analysed in a heteroscedastic conditional logit model adjusting for continuous scale. Two sets of tariffs were generated, one from the best-worst data capturing only main effects, and a second from the pooled data allowing for two-way interactions. Either tariff could be used in economic evaluation of interventions at the end of life, although there are advantages and disadvantages with each. This extensive valuation exercise for the ICECAP Supportive Care Measure, with a large number of members of the general public, could be complemented in the future with best-worst scaling studies amongst those experiencing the end of life.
生命终末期关怀可能具有超越健康的价值元素。一种通用的生命终末期关怀效益衡量标准可能对决策者有所帮助。最近，基于能力方法开发了这样一种衡量标准：ICECAP 支持性关怀衡量标准。本文报告了该衡量标准的首次估值研究，数据来自于 2013 年 6 月从在线一般人群小组中收集的 6020 名个体。要求个体完成一项表述选择实验，该实验将最佳最差分级法和标准离散选择实验相结合。对最佳最差数据的分析使用了随机效用框架内的受限因变量模型，包括多项逻辑模型和潜在类别选择模型分析。在正式测试和合并两个数据源之前，采取了探索性步骤来确定最佳最差和 DCE 数据的相似性。结合数据在异方差条件逻辑模型中进行分析，对连续量表进行调整。生成了两套费率，一套来自最佳最差数据，仅捕获主要效应，另一套来自允许双向交互的合并数据。可以使用这两种费率中的任何一种来评估生命终末期的干预措施的经济效果，尽管每种费率都有其优点和缺点。对于 ICECAP 支持性关怀衡量标准的这项广泛估值研究，涉及到大量普通公众，未来可以在经历生命终末期的人群中进行最佳最差分级研究来加以补充。
