‘Naming and Framing’: The impact of labelling on health state values for multiple sclerosis

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’). 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.