Green Colin, Goodwin Elizabeth, Hawton Annie
Health Economics Group, Institute of Health Research, University of Exeter, Exeter, UK (CG, EG, AH).
South West Collaboration for Leadership in Applied Health Research and Care (CLAHRC), University of Exeter Medical School, University of Exeter, Exeter, UK (CG, AH).
Med Decis Making. 2017 Aug;37(6):703-714. doi: 10.1177/0272989X17705637. Epub 2017 May 21.
Health state valuation is a key input in many economic evaluations that inform resource allocation across competing healthcare interventions. Empirical evidence has shown that, in preference elicitation surveys, respondents may value a health state differently if they are aware of the condition causing it ('labeling effects'). This study investigates the impact of including a multiple sclerosis (MS) label for valuation of MS health states.
Health state values for MS were elicited using two internet-based surveys in representative samples of the UK population ( n = 1702; n = 1788). In one survey respondents were not informed that health states were caused by MS. The second survey included a condition label for MS. Surveys were identical in all other ways. Health states were described using a MS-specific eight-dimensional classification system (MSIS-8D), and the time trade-off valuation technique was used. Differences between values for labeled and unlabeled states were assessed using descriptive statistics and multivariate regression methods.
Adding a MS condition label had a statistically significant effect on mean health state values, resulting in lower values for labeled MS states v. unlabeled states. The data suggest that the MS label had a more significant effect on values for less severe states, and no significant effect on values for the most severe states. The inclusion of the MS label had a differential impact across the dimensions of the MSIS-8D. Across the MSIS-8D, predicted values ranged from 0.079 to 0.883 for unlabeled states, and 0.066 to 0.861 for labeled states.
Differences reported in health state values, using labeled and unlabeled states, demonstrate that condition labels affect the results of valuation studies, and can have important implications in decision-analytic modelling and in economic evaluations.
健康状态评估是许多经济评估中的关键输入内容，这些评估为跨相互竞争的医疗干预措施分配资源提供依据。实证证据表明，在偏好诱导调查中，如果受访者知晓导致某种健康状态的疾病（“标签效应”），他们对该健康状态的估值可能会有所不同。本研究调查了在对多发性硬化症（MS）健康状态进行估值时加入MS标签的影响。
通过两项基于互联网的调查，在英国代表性人群样本（n = 1702；n = 1788）中获取MS的健康状态值。在一项调查中，未告知受访者健康状态是由MS引起的。第二项调查包含了MS的疾病标签。其他方面两项调查完全相同。使用特定于MS的八维分类系统（MSIS - 8D）描述健康状态，并采用时间权衡估值技术。使用描述性统计和多元回归方法评估有标签和无标签状态的估值差异。
添加MS疾病标签对平均健康状态值有统计学显著影响，导致有标签的MS状态的估值低于无标签状态。数据表明，MS标签对不太严重状态的估值影响更大，对最严重状态的估值无显著影响。MS标签的纳入在MSIS - 8D的各个维度上产生了不同的影响。在MSIS - 8D中，无标签状态的预测值范围为0.079至0.883，有标签状态的预测值范围为0.066至0.861。
使用有标签和无标签状态报告的健康状态值差异表明，疾病标签会影响估值研究结果，并且可能对决策分析建模和经济评估产生重要影响。
