Evidence map›Paper›PMID 37666546›Full record

ArticleBMJ open2023

Understanding Heterogeneity in Patients' Conceptualisation of Treatment for Rheumatoid Arthritis: A Cluster Analysis.

Betty Hsiao, Julie S Downs, Mandy Lanyon, Susan J Blalock, Jeffrey R Curtis, Leslie R Harrold, William Benjamin Nowell, Carole Wiedmeyer, Shilpa Venkatachalam, Mark T Patterson and 4 more

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 74% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

14 authors at 7 institutions in 1 country.

Betty HsiaoYale University, New Haven, Connecticut, USA betty.hsiao@yale.edu.ORCID 0000-0003-4515-4591
Julie S DownsCarnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Mandy LanyonCarnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Susan J BlalockUniversity of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Jeffrey R CurtisThe University of Alabama at Birmingham, Birmingham, Alabama, USA.
Leslie R HarroldUniversity of Massachusetts Medical School, Worcester, Massachusetts, USA.
William Benjamin NowellGlobal Healthy Living Foundation, Upper Nyack, New York, USA.
Carole WiedmeyerGlobal Healthy Living Foundation, Upper Nyack, New York, USA.
Shilpa VenkatachalamGlobal Healthy Living Foundation, Upper Nyack, New York, USA.
Mark T PattersonCarnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Kelly GaviganGlobal Healthy Living Foundation, Upper Nyack, New York, USA.ORCID 0000-0003-2615-4940
Laura StradfordGlobal Healthy Living Foundation, Upper Nyack, New York, USA.
Danielle AliGlobal Healthy Living Foundation, Upper Nyack, New York, USA.
Liana FraenkelYale University, New Haven, Connecticut, USA.
Global Healthy Living Foundation · USCarnegie Mellon University · USBerkshire Medical Center · USUniversity of Alabama at Birmingham · USUniversity of Massachusetts Chan Medical School · USUniversity of North Carolina at Chapel Hill · USYale University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

objectiveUptake of treat-to-target (TTT) strategies for rheumatoid arthritis (RA) management is low. Our objective was to understand the heterogeneity in patients' conceptualisation of RA treatment to inform interventions improving TTT uptake.

designEligible participants recruited from an online research registry rated 56 items (on 5-point scales) reflecting concepts raised from patient interviews. Using items describing adhering to physician recommendations to create a binary criterion variable for medication adherence, we conducted a principal components analysis on the remaining items using Varimax rotation, describing how these factors predict adherence over and above demographic characteristics. We further use optimal sets in regression to identify the individual concepts that are most predictive of medication adherence.

resultsWe found significant heterogeneity in patients' conceptualisation of RA treatment among 621 persons with RA. A scree plot revealed a four-factor solution explained 38.4% of the variance. The four factors expected to facilitate TTT uptake were (% variance explained): (1) Access to high quality care and support (11.3%); (2) low decisional conflict related to changing disease-modifying antirheumatic drugs (DMARDs) (10.1%); (3) endorsement of a favourable DMARD risk/benefit ratio (9.9%); and (4) confidence that testing reflects disease activity (7.2%). These factors account for 13.8% of the variance in full medication adherence, fully explaining the only significant demographic predictor, age of the patient. The individual items most predictive of poor adherence centre on the lack of effective patient-physician communication, specifically insufficient access to information from rheumatologists, along with the need to seek information elsewhere.

conclusionPatients' conceptualisation of RA treatment varies; however, almost all patients have difficulty escalating DMARDs, even with access to quality information and an understanding of the benefits of TTT. Tailored interventions are needed to address patient hesitancy to escalate DMARDs.

Indexed as

Antirheumatic AgentsArthritis, RheumatoidCluster AnalysisConcept FormationHumansMental ProcessesAntirheumatic AgentsQUALITATIVE RESEARCHRheumatologyRHEUMATOLOGY

Identifiers

PMID37666546
PMCPMC10481841
OpenAlexW4386415838

What OpenQuestion holds

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LicenceCC BY-NC
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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.