Literature DB >> 15586842

Self-valuation and societal valuations of health state differ with disease severity in chronic and disabling conditions.

Kathryn McPherson1, Julie Myers, William J Taylor, Harry K McNaughton, Mark Weatherall.   

Abstract

OBJECTIVES: We sought to determine the relationship between self-reported ratings of health-related quality of life (HRQoL) by people with 3 chronic and disabling conditions and population estimates of those health states and to model factors that might explain the disagreement between these 2 ratings. RESEARCH
DESIGN: A cross-sectional postal survey was sent in which each participant completed a set of questionnaires addressing HRQoL. Data from self-valuation on a visual analog scale (VAS) was compared with a population-based VAS-equivalent valuation, using the EQ-5D instrument. Different ways of scaling the 2 VAS scores were also explored. Data were analyzed using descriptive statistics and analysis of covariance.
SUBJECTS: A community sample of 1036 people took part in the study (rheumatoid arthritis n = 142, stroke n = 585, multiple sclerosis n = 309). MEASUREMENT INSTRUMENT: The EQ-5D health state profile and accompanying visual analog scale were used.
RESULTS: Self-ratings were significantly different than the population-based ratings, and agreement was poor, both on the original scale of the data and by analyses of rescaled data. On the original scales the mean difference was 0.13 (95% confidence interval 0.117-0.143). Diagnosis, health state severity, and its square explained 35.3% of the variation in the differences between self and population ratings with a curvilinear relationship suggesting that the differences increased as the health state worsened, but at a decreasing rate as health state severity increased.
CONCLUSIONS: This study provides evidence that EQ-5D population valuation estimates of treatment benefit for people with disabling and chronic conditions may well be inaccurate representations of the degree of change actually experienced by the individual with the condition. The varying magnitude of difference between the 2 forms of valuation has important implications for interpreting shifts in health status valuation following interventions for these populations.

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Year:  2004        PMID: 15586842     DOI: 10.1097/00005650-200411000-00014

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  9 in total

1.  Measuring preferences for cost-utility analysis: how choice of method may influence decision-making.

Authors:  Christine M McDonough; Anna N A Tosteson
Journal:  Pharmacoeconomics       Date:  2007       Impact factor: 4.981

2.  Are patients' judgments of health status really different from the general population?

Authors:  Paul Fm Krabbe; Noor Tromp; Theo Jm Ruers; Piet Lcm van Riel
Journal:  Health Qual Life Outcomes       Date:  2011-05-11       Impact factor: 3.186

3.  Health-related quality of life variations by sociodemographic factors and chronic conditions in three metropolitan cities of South Asia: the CARRS study.

Authors:  Kavita Singh; Dimple Kondal; Roopa Shivashankar; Mohammed K Ali; Rajendra Pradeepa; Vamadevan S Ajay; Viswanathan Mohan; Muhammad M Kadir; Mark Daniel Sullivan; Nikhil Tandon; K M Venkat Narayan; Dorairaj Prabhakaran
Journal:  BMJ Open       Date:  2017-10-15       Impact factor: 2.692

4.  Whom should we ask? A systematic literature review of the arguments regarding the most accurate source of information for valuation of health states.

Authors:  Olivia Ernstsson; Mimmi Åström; Gert Helgesson; Kristina Burström
Journal:  Qual Life Res       Date:  2020-02-03       Impact factor: 4.147

5.  Comparison of three generic quality-of-life metrics in peripheral arterial disease patients undergoing conservative and invasive treatments.

Authors:  Svenja Petersohn; Bram L T Ramaekers; Renske H Olie; Arina J Ten Cate-Hoek; Jan-Willem H C Daemen; Hugo Ten Cate; Manuela A Joore
Journal:  Qual Life Res       Date:  2019-03-30       Impact factor: 4.147

6.  Correspondence between EQ-5D health state classifications and EQ VAS scores.

Authors:  David K Whynes
Journal:  Health Qual Life Outcomes       Date:  2008-11-07       Impact factor: 3.186

7.  Swedish experience-based value sets for EQ-5D health states.

Authors:  Kristina Burström; Sun Sun; Ulf-G Gerdtham; Martin Henriksson; Magnus Johannesson; Lars-Åke Levin; Niklas Zethraeus
Journal:  Qual Life Res       Date:  2013-08-22       Impact factor: 4.147

8.  Does the correspondence between EQ-5D health state description and VAS score vary by medical condition?

Authors:  David K Whynes
Journal:  Health Qual Life Outcomes       Date:  2013-09-13       Impact factor: 3.186

9.  Relative importance of the EQ-5D five dimensions among patients with chronic diseases in South Korea - a comparison with the general population preference weights.

Authors:  Jihyung Hong
Journal:  Health Qual Life Outcomes       Date:  2018-08-03       Impact factor: 3.186

  9 in total

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