Evidence map›Paper›PMID 30772857›Full record

ArticleBMJ open2019

Comparison of the EQ-5D 3L and the SF-6D (SF-36) contemporaneous utility scores in patients with chronic kidney disease in Sri Lanka: a cross-sectional survey.

Sanjeewa Kularatna, Sameera Senanayake, Nalika Gunawardena, Nicholas Graves

Abstract readComparative Study
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Health-related quality of life in chronic kidney disease patients in low- and lower-middle income countries: a systematic review and meta-analysis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
    Pooled it
  2. Pooled it
  3. Psychometric properties comparison between EQ-5D-5L and EQ-5D-3L in the general Thai population.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2020
    Trial
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  7. Socio-demographic disparities in health-related quality of life in hypertensive patients in Bangladesh: a comprehensive survey analysis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
    Article
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  9. Comparative performance and mapping algorithms between EQ-5D-5L and SF-6Dv2 among the Chinese general population.The European journal of health economics : HEPAC : health economics in prevention and care · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sanjeewa KularatnaAustralian Centre for Health Services Innovation, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
Sameera SenanayakeAustralian Centre for Health Services Innovation, Queensland University of Technology, Kelvin Grove, Queensland, Australia.ORCID 0000-0002-5606-2046
Nalika GunawardenaWorld Health Organization, Colombo, Sri Lanka.
Nicholas GravesAustralian Centre for Health Services Innovation, Queensland University of Technology, Kelvin Grove, Queensland, Australia.ORCID 0000-0002-5559-3267

Funding

World Health Organization 001
6 · The paper itself

Abstract

objectivesThe aim of this study was to compare utility weights of EuroQoL-five-dimension-3 levels (EQ-5D-3L) and Short-Form six-dimension (SF-6D) in a representative cohort of patients with chronic kidney disease (CKD). A cost-utility analysis (CUA) is designed to report the change to costs required to achieve an estimated change to quality-adjusted life years (QALYs). The quality component of a QALY is measured by utility. Utility represents the preference of general population for a given health state. Classification systems of the multi-attribute utility instruments (MAUIs) are used to define these health states. Utility weights developed from different classification systems can vary and may affect the conclusions from CUAs.

designA community-based cross-sectional study.

settingAnuradhapura, a rural district in Sri Lanka.

participantsA representative sample of 1096 patients with CKD, selected using the population-based CKD register, completed the EQ-5D-3L and SF-36. SF-6D was constructed from the SF-36 according to the published algorithm. The study assessed discrimination, correlation and differences across the two instruments.

resultsStudy participants were predominantly male (62.6%). Mean EQ-5D-3L utility score was 0.540 (SD 0.35) compared with 0.534 (SD 0.09) for the SF-6D (p=0.588). The correlation (r) between the scores was 0.40 (p<0.001). Utility scores were significantly different in both males and females between the two tools, but there was no difference in age and educational categories. Both MAUI scores were significantly lower (p<0.001) among those who were in more advanced stages of the disease and the corresponding utility scores of the two instruments in different CKD stages were also significantly different (p<0.05). The largest effect size was seen among the patients on dialysis.

conclusionsThe correlation between the scores was moderate. SF-6D had the lowest floor and ceiling effect and was better at detecting different stages of the disease. Thus, based on the evidence presented in this study, SF-6D appears to be more appropriate to be used among patients with CKD.

Indexed as

Surveys and QuestionnairesAdultAgedCost-Benefit AnalysisCross-Sectional StudiesFemaleHealth StatusHumansMaleMiddle AgedPsychometricsQuality-Adjusted Life YearsQuality of LifeRenal Insufficiency, ChronicSri LankaYoung Adultchronic kidney disease (CKD)quality-adjusted life years (QALYs)utility

Identifiers

PMID30772857
PMCPMC6398797

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.