Evidence map›Paper›PMID 26097435›Full record

ArticlePopulation health metrics2015

Level of agreement between patient-reported EQ-5D responses and EQ-5D responses mapped from the SF-12 in an injury population.

Belinda J Gabbe, Emma McDermott, Pam M Simpson, Sarah Derrett, Shanthi Ameratunga, Suzanne Polinder, Ronan A Lyons, Frederick P Rivara, James E Harrison

Abstract read
In one paragraph

Article in Population health metrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Development of algorithms to estimate EQ-5D and derive health utilities from WHOQOL-HIV Bref: a mapping study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2020
    Article
  4. Article
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  8. Validation of a new patient-reported outcome instrument of health-related quality of life specific to patients with alcohol use disorder: the Alcohol Quality of Life Scale (AQoLS).Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2016
    Article
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

9 authors.

Belinda J GabbeDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia ; Centre for Improvement of Population Health through E-records Research, Swansea University, Swansea, UK.
Emma McDermottDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.
Pam M SimpsonDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.
Sarah DerrettInjury Prevention Research Unit, Department of Preventive and Social Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand ; School of Health and Social Services, Massey University, Palmerston North, New Zealand.
Shanthi AmeratungaSection of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand.
Suzanne PolinderDepartment of Public Health, Erasmus MC, Rotterdam, The Netherlands.
Ronan A LyonsDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia ; Centre for Improvement of Population Health through E-records Research, Swansea University, Swansea, UK ; Public Health Wales NHS Trust, Swansea, Wales UK.
Frederick P RivaraDepartments of Pediatrics and Epidemiology, and the Harborview Injury Prevention and Research Center, University of Washington, Seattle, USA.
James E HarrisonResearch Centre for Injury Studies, Flinders University, Adelaide, South Australia.

Funding

Medical Research Council MR/K006525/1
6 · The paper itself

Abstract

backgroundComparing health-related quality of life (HRQL) outcomes between studies is difficult due to the wide variety of instruments used. Comparing study outcomes and facilitating pooled data analyses requires valid "crosswalks" between HRQL instruments. Algorithms exist to map 12-item Short Form Health Survey (SF-12) responses to EQ-5D item responses and preference weights, but none have been validated in populations where disability is prevalent, such as injury.

methodsData were extracted from the Validating and Improving injury Burden Estimates Study (Injury-VIBES) for 10,166 adult, hospitalized trauma patients, with both the three-level EQ-5D (EQ-5D-3L) and SF-12 data responses at six and 12-months postinjury. Agreement between actual (patient-reported) and estimated (mapped from SF-12) EQ-5D-3L item responses and preference weights was assessed using Kappa, Prevalence-Adjusted Bias-Adjusted Kappa statistics and Bland-Altman plots.

resultsModerate agreement was observed for usual activities, pain/discomfort, and anxiety/depression. Agreement was substantial for mobility and self-care items. The mean differences in preference weights were -0.024 and -0.012 at six and 12 months (p < 0.001), respectively. The Bland-Altman plot limits of agreement were large compared to the range of valid preference weight values (-0.56 to 1.00). Estimated EQ-5D-3L responses under-reported disability for all items except pain/discomfort.

conclusionsCaution should be taken when using EQ-5D-3L responses mapped from the SF-12 to describe patient outcomes or when undertaking economic evaluation, due to the underestimation of disability associated with mapped values. The findings from this study could be used to adjust expected EQ-5D-3L preference weights when estimated from SF-12 item responses when combining data from studies that use either instrument.

Indexed as

AgreementEQ-5DInjuryQuality of lifeSF-12

Identifiers

PMID26097435
PMCPMC4474565

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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.