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Should additional domains be added to the EQ-5D health-related quality of life instrument for community-based studies? An analytical descriptive study

Overview of attention for article published in Population Health Metrics, June 2015
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Title
Should additional domains be added to the EQ-5D health-related quality of life instrument for community-based studies? An analytical descriptive study
Published in
Population Health Metrics, June 2015
DOI 10.1186/s12963-015-0046-0
Pubmed ID
Authors

Jennifer Jelsma, Soraya Maart

Abstract

There is increasing interest in monitoring the health-related quality of life (HRQoL) of populations as opposed to clinical populations. The EQ-5D identifies five domains as being most able to capture the HRQoL construct. The question arises as to whether these domains are adequate within a community-based population or whether additional domains would add to the explanatory power of the instrument. As part of a community-based survey, the responses of 310 informants who reported at least one problem in one domain filled in the EQ-5D three-level version and the WHOQOL-BREF (World Health Organization Quality of Life Scale - Abbreviated version). Using the EQ-5D visual analogue scale (VAS) of rating of health as a dependent variable, the five EQ-5D and four selected WHOQOL-BREF items were entered as dummy variables in multiple regression analysis. The additional domains increased the explanatory power of the model from 52 % (EQ-5D only) to 57 % (all domains). The coefficients of Self-Care and Usual Activities were not significant in any model. The most parsimonious model included the EQ-5D domains of Mobility, Pain/Discomfort, Anxiety/Depression, Concentration, and Sleep (adjusted r(2) = .57). The EQ-5D-3L performed well, but the addition of domains such as Concentration and Sleep increased the explanatory power. The user needs to weigh the advantage of using the EQ-5D, which allows for the calculation of a single summary index, against the use of a set of domains that are likely to be more responsive to differences in HRQoL within community living respondents. The poor predictive power of the Self-Care and Usual Activities domains within this context needs to be further examined.

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Mendeley readers

Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 28%
Student > Bachelor 6 15%
Researcher 5 13%
Student > Ph. D. Student 5 13%
Student > Doctoral Student 2 5%
Other 3 8%
Unknown 8 20%
Readers by discipline Count As %
Medicine and Dentistry 12 30%
Nursing and Health Professions 7 18%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Psychology 2 5%
Social Sciences 2 5%
Other 4 10%
Unknown 11 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 June 2015.
All research outputs
#18,810,584
of 23,312,088 outputs
Outputs from Population Health Metrics
#342
of 392 outputs
Outputs of similar age
#194,655
of 268,905 outputs
Outputs of similar age from Population Health Metrics
#4
of 5 outputs
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