“Naming and Framing”: The Impact of Labeling on Health State Values for Multiple Sclerosis

Green, Colin; Goodwin, Elizabeth; Hawton, Annie

doi:10.1177/0272989x17705637

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…Each participant was randomly assigned a set of health states. Previous MSIS-8D surveys had demonstrated that this represented an acceptable workload for participants [11,24]; this was confirmed during pilot testing with people with MS.…”

Section: Valuation Surveysupporting

confidence: 56%

“…Independent t tests were used to determine the ability of the MSIS-8D-P index to distinguish between subgroups of respondents to the valuation survey, based on type of MS and duration since diagnosis. Additional analysis was undertaken using MSIS-8D responses from previous surveys of the general population [11,24] to assess the ability of the MSIS-8D-P index to distinguish between people with and without MS.…”

Section: Discriminative Validitymentioning

confidence: 99%

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Health State Values Derived from People with Multiple Sclerosis for a Condition-Specific Preference-Based Measure: Multiple Sclerosis Impact Scale–Eight Dimensions–Patient Version (MSIS-8D-P)

2018

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In economic evaluation, health outcomes are commonly quantified using quality-adjusted life-years (QALYs) derived from the preferences of a sample of the general population. It can be argued that this approach ignores the preferences of people with experience of the condition, and that patient preferences have a place in the valuation of health outcomes. Here we report the estimation of a preference-based index for an existing condition-specific preferencebased measure for multiple sclerosis (MS), the MSIS-8D, based on the preferences of people with MS. Study design: Internet time trade-off (TTO) survey, eliciting preferences from people with MS. Methods: We elicited preferences from a sample of people with MS (N ¼ 1635) across 169 MSIS-8D health states, using the TTO technique. We fitted ordinary least squares and random effects models to the survey data to estimate values for all health states described by the MSIS-8D. Results: The new patient-derived index (the MSIS-8D-P) provides values ranging from 0.893 for the best possible health state to 0.138 for the worst state. The MSIS-8D-P exhibits good discriminative validity, identifying expected significant differences between groups based on presence/absence of MS, type of MS, and duration since diagnosis. Conclusions: The MSIS-8D-P index of values for MSspecific health states provides an opportunity to estimate QALYs based on patient preferences, for use in economic evaluations of treatments for MS. More broadly, it adds to the methods and data available to consider the health-related quality of life of people with MS to inform resource allocation and individual-level decisions regarding treatments for MS.

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Section: Valuation Surveysupporting

confidence: 56%

Section: Discriminative Validitymentioning

confidence: 99%

Health State Values Derived from People with Multiple Sclerosis for a Condition-Specific Preference-Based Measure: Multiple Sclerosis Impact Scale–Eight Dimensions–Patient Version (MSIS-8D-P)

2018

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“…The sociodemographic questions were worded to allow comparison with normative U.K. data (see Table for more details). Respondents were not informed that the health states were cancer‐related, as it has been found previously that the mention of disease labels impacts health state values, probably because prior knowledge or preconceptions of health conditions distort survey participants' values and preferences (Rowen, Brazier, Tsuchiya, Young, & Ibbotson, ; Green, Goodwin, & Hawton, ). Respondents completed the QLU‐C10D prior to the DCE tasks that familiarised respondents with the classification system.…”

Section: Methodsmentioning

confidence: 99%

U.K. utility weights for the EORTC QLU‐C10D

et al. 2019

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The EORTC QLU‐C10D is a new multi‐attribute utility instrument derived from the widely used cancer‐specific quality of life questionnaire, EORTC QLQ‐C30. It contains 10 dimensions (physical functioning, role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems), each with four levels. The aim of this study was to provide U.K. general population utility weights for the QLU‐C10D. A U.K. online panel was quota‐sampled to align the sample to the general population proportions of sex and age (≥18 years). The online valuation survey included a discrete choice experiment (DCE). Each participant was asked to complete 16 choice‐pairs, each comprising two QLU‐C10D health states plus duration. DCE data were analysed using conditional logistic regression to generate utility weights. Data from 2,187 respondents who completed at least one choice set were included in the DCE analysis. The final U.K. QLU‐C10D utility weights comprised decrements for each level of each health dimension. For nine of the 10 dimensions (all except appetite), the expected monotonic pattern was observed across levels: Utility decreased as severity increased. For the final model, consistent monotonicity was achieved by merging inconsistent adjacent levels for appetite. The largest utility decrements were associated with physical functioning and pain. The worst possible health state (the worst level of each dimension) is −0.083, which is considered slightly worse than being dead. The U.K.‐specific utility weights will enable cost–utility analysis (CUA) for the economic evaluation of new oncology therapies and technologies in the United Kingdom, where CUA is commonly used to inform resource allocation.

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“…The naming of health conditions or types of disability (for example, using the term 'dementia') may have encouraged the introduction by participants of external knowledge and points of reference, but it is reasonable to expect that when providing proxy responses for an actual close person, the proxy rater would be aware of their health constraints and diagnoses. Previous quantitative research has found that labelling health states influences health state valuation [19,20]; clearly the context and methodologies differ, but the qualitative evidence from this study of proxy raters introducing external considerations relating to a particular illness/diagnosis may be relevant to those undertaking future research involving health state valuation and choosing whether to label health states.…”

Section: Discussionmentioning

confidence: 84%

Proxy responses to ICECAP-A: Exploring variation across multiple proxy assessments of capability well-being for the same individuals

Kinghorn

Afentou

2020

PLoS ONE

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Background The ICECAP capability measures are increasingly being used to capture the impact of health and social care interventions on well-being. In cases where the recipient of an intervention is highly vulnerable, proxy completion may be necessary. This study adds to the limited existing evidence on proxy completion of ICECAP-A specifically and adopts the novel approach of investigating multiple proxy responses for the same four (hypothetical) individuals. Methods 62 members of the public who were participating in a series of one day deliberative workshops on public health and social care completed ICECAP-A on behalf of four hypothetical individuals, described in vignettes. Quantitative analysis explored the range of proxy responses for each of the four hypothetical individuals, and any possible correlation between participants' own characteristics and their proxy responses. Participants discussed their proxy responses after completing the task; this discussion was audio recorded and analysed using Framework Analysis. Results Wide variation in ICECAP-A scores was observed across proxy respondents for each hypothetical individual. Participants' demographic characteristics and own well-being do not appear to have systematically influenced proxy responses. Qualitative analysis suggests two principal approaches (or perspectives) were adopted by participants: Empathetic (adopting the perspective of the 'subject') and factual (a factual assessment of the subject's well-being). Participants also drew on their own experiences to varying degrees. There were differing interpretations of the Independence attribute on ICECAP-A and some evidence that participants' ideas of what constituted achievement were context (including life-stage and condition/health) specific.

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“Naming and Framing”: The Impact of Labeling on Health State Values for Multiple Sclerosis

Abstract: Introduction: Health state valuation is a key input to many economic evaluations that

Cited by 6 publications

References 26 publications

Health State Values Derived from People with Multiple Sclerosis for a Condition-Specific Preference-Based Measure: Multiple Sclerosis Impact Scale–Eight Dimensions–Patient Version (MSIS-8D-P)

Health State Values Derived from People with Multiple Sclerosis for a Condition-Specific Preference-Based Measure: Multiple Sclerosis Impact Scale–Eight Dimensions–Patient Version (MSIS-8D-P)

U.K. utility weights for the EORTC QLU‐C10D

Proxy responses to ICECAP-A: Exploring variation across multiple proxy assessments of capability well-being for the same individuals

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