Evidence map›Paper›PMID 32303251›Full record

ArticleArthritis research & therapy2020

Data quality predicts care quality: findings from a national clinical audit.

Mark Yates, Katie Bechman, Elaine M Dennison, Alexander J MacGregor, Jo Ledingham, Sam Norton, James B Galloway

Abstract read
In one paragraph

Article in Arthritis research & therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

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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

7 authors.

Mark YatesThe Centre for Rheumatic Diseases, School of Immunology, Infection & Inflammatory Disease, King's College London, Room 3.46 Weston Education Centre, Cutcombe Road, London, SE5 9RJ, UK. mark.yates@kcl.ac.uk.
Katie BechmanThe Centre for Rheumatic Diseases, School of Immunology, Infection & Inflammatory Disease, King's College London, Room 3.46 Weston Education Centre, Cutcombe Road, London, SE5 9RJ, UK.
Elaine M DennisonMRC Epidemiology Unit, University of Southampton, Southampton, UK.
Alexander J MacGregorNorwich Medical School, University of East Anglia, Norwich, UK.
Jo LedinghamDepartment of Rheumatology, Queen Alexandra Hospital, Portsmouth, UK.
Sam NortonInstitute of Psychiatry, Kings College London, London, UK.
James B GallowayThe Centre for Rheumatic Diseases, School of Immunology, Infection & Inflammatory Disease, King's College London, Room 3.46 Weston Education Centre, Cutcombe Road, London, SE5 9RJ, UK.

Funding

Medical Research Council MC_UP_A620_1015Medical Research Council MC_UU_12011/2Medical Research Council MR/R001332/1Versus Arthritis 19583Versus Arthritis 21824
6 · The paper itself

Abstract

backgroundMissing clinical outcome data are a common occurrence in longitudinal studies. Data quality in clinical audit is a particular cause for concern. The relationship between departmental levels of missing clinical outcome data and care quality is not known. We hypothesise that completeness of key outcome data in a national audit predicts departmental performance.

methodsThe National Clinical Audit for Rheumatoid and Early Inflammatory Arthritis (NCAREIA) collected data on care of patients with suspected rheumatoid arthritis (RA) from early 2014 to late 2015. This observational cohort study collected data on patient demographics, departmental variables, service quality measures including time to treatment, and the key RA clinical outcome measure, disease activity at baseline, and 3 months follow-up. A mixed effects model was conducted to identify departments with high/low proportions of missing baseline disease activity data with the results plotted on a caterpillar graph. A mixed effects model was conducted to assess if missing baseline disease activity predicted prompt treatment.

resultsSix thousand two hundred five patients with complete treatment time data and a diagnosis of RA were recruited from 136 departments. 34.3% had missing disease activity at baseline. Mixed effects modelling identified 13 departments with high levels of missing disease activity, with a cluster observed in the Northwest of England. Missing baseline disease activity was associated with not commencing treatment promptly in an adjusted mix effects model, odds ratio 0.50 (95% CI 0.41 to 0.61, p < 0.0001).

conclusionsWe have shown that poor engagement in a national audit program correlates with the quality of care provided. Our findings support the use of data completeness as an additional service quality indicator.

Indexed as

Data AccuracyMedical AuditQuality of Health CareAdultAgedAntirheumatic AgentsArthritis, RheumatoidCohort StudiesEnglandFemaleHumansMaleMiddle AgedObservational Studies as TopicWalesAntirheumatic AgentsCare qualityMethodologyMissing dataNational clinical auditRheumatoid arthritis

Identifiers

PMID32303251
PMCPMC7164190

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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.