‘Naming and Framing’: The impact of labelling on health state values for multiple sclerosis
Green, C; Goodwin, E; Hawton, A
Date: 21 May 2017
Article
Journal
Medical Decision Making
Publisher
SAGE Publications
Publisher DOI
Abstract
Introduction: Health state valuation is a key input to many economic evaluations that
inform resource allocation across competing health care interventions. Empirical evidence
has shown that respondents to preference elicitation surveys may value a health state
differently if aware of the condition causing it (‘labelling effects’). ...
Introduction: Health state valuation is a key input to many economic evaluations that
inform resource allocation across competing health care interventions. Empirical evidence
has shown that respondents to preference elicitation surveys may value a health state
differently if aware of the condition causing it (‘labelling effects’). This study investigates the
impact of including a multiple sclerosis (MS) label for valuation of MS health states.
Methods: 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 labelled and
unlabelled states are assessed using descriptive statistics and multivariate regression
methods.
Results: Adding a MS condition label had a statistically significant effect on mean health
state values, resulting in lower values for labelled MS states versus unlabelled states. Data
suggests 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 0.079-0.883 for unlabelled states, and 0.066-0.861 for labelled states.
Conclusions: Differences reported in health state values, using labelled and unlabelled
states, demonstrate that condition labels affect the results of valuation studies, and can have
important implications in decision-analytic modelling and in economic evaluations.
Institute of Health Research
Collections of Former Colleges
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