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Enregistrement W4411431416 · doi:10.1016/j.ard.2025.05.584

POS0197-PARE HEALTH LITERACY AND DISEASE OUTCOMES IN INFLAMMATORY ARTHRITIS: A SYSTEMATIC LITERATURE REVIEW

2025· article· en· W4411431416 sur OpenAlexaboutno aff
M. Dey, Sanjeev Budhathoki, Helen Elwell, S. Ramiro, Kaleb Michaud, Sam Norton, Maya H Buch, Andrew P. Cope, Richard H. Osborne, J. Galloway, E. Nikiphorou

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueAutoimmune and Inflammatory Disorders Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineSystematic reviewAlternative medicineArthritisInflammatory arthritisDiseaseImmunologyFamily medicineIntensive care medicineMEDLINEDermatologyInternal medicinePathology

Résumé

récupéré en direct d'OpenAlex

Background: Health literacy (HL) is a potentially important social determinant of health in inflammatory arthritis (IA) and has been recognized by the World Health Organization (WHO) as central to the prevention and control of non-communicable diseases. Objectives: We conducted a systematic literature review (SLR) to i) summarize associations between HL and IA outcomes; ii) inform management in IA; iii) inform further research. Methods: This SLR was registered on PROSPERO (CRD42024511354). Inclusion criteria were adults (≥18 years) with a confirmed diagnosis of IA (i.e. rheumatoid arthritis, axial spondyloarthritis, psoriatic arthritis); studies assessing HL or interventions targeting HL; observational and qualitative studies, randomized controlled trials. English-language articles only were included. Searches were performed on 8th February 2024, with MeSH headings for HL and IA, within MEDLINE, Scopus, The Cochrane Library, Health Technology Assessment and PsycINFO, with no time restriction. All types of HL measures and outcomes were included, to ensure capture of all potentially relevant articles. Data were extracted on demographics, HL assessment, and relevant clinical and non-clinical outcomes. Outcomes were analyzed and grouped into themes using vote-counting. Risk of bias for each study was assessed using the Newcastle-Ottawa Scale for observational studies and Cochrane Risk-of-Bias-2 tool for randomized controlled trials, and presented as risk ratios, odds ratios, beta coefficients, or p-values (as reported). Results: Of 3087 identified articles from the search, 29 met inclusion criteria (22 cross-sectional studies; 3 cohort studies; 2 randomized controlled trials; 1 case-control study; 1 qualitative study). Four studies reported results of interventions targeting HL to improve outcomes in people with IA; the remaining 25 reported associations of HL levels with outcomes. Included articles were judged to be of uncertain risk of bias. The total number of participants across all studies was 16402, 75% female, with a mean age of 57.5 years (SD 9.6). Ethnicity was reported in 13 studies; 66% of participants were Caucasian. 89% had rheumatoid arthritis, 4% axial spondyloarthritis, 3% psoriatic arthritis and 4% unspecified IA. Studies involved cohorts in the following geographical regions: North America (n=16); Europe (n=7); Australia (n=4); South America (n=2). No studies involved participants based in Africa or Asia. Most measures of HL included early measurement tools: Test of Functional Health Literacy in Adults (short and long form; n=13); Single Item Literacy Screener (n=10); Rapid Estimate of Adult Literacy in Medicine (n=6); and 2 studies used the contemporary multidimensional Health Literacy Questionnaire (n=2). Some studies measured HL using multiple measures (Table 1). Six main associations were identified with lower HL: higher disease activity; worse disability and physical function; more mental health symptoms (including depression and anxiety); higher healthcare and medication use; lower medication adherence; and lower use of the internet, telehealth and technology (Figure 1). Meta-analysis was deemed inappropriate due to marked heterogeneity across studies. Conclusion: This is the first SLR to synthesize factors associated with HL in people with IA, including higher disease activity, worse disability, more healthcare utilization and decreased access to medication and care. Of note, our review identified no studies conducted in Africa and Asia, indicating a gap in knowledge in these regions, and a priority to support WHO's global strategy. Measurement of HL was diverse, with few studies using contemporary measures that inform prevention and quality of care. There is a need for standardized and robust methods to assess HL in the clinical setting, which can be implemented routinely to tailor prevention and management strategies according to individual needs. Our findings may be used to inform policy and resource allocation, and improve access and quality of care in IA, particularly among people with lower HL. REFERENCES: NIL . Acknowledgements: This work was generously funded by a FOREUM Early Career Grant (EN) and the King's College Hospital Charity (MD). Disclosure of Interests: None declared . © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,016
score de la tête « metaresearch » (Gemma)0,069
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,017
Score d'incertitude au seuil0,085

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0160,069
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0090,008
Bibliométrie0,0170,016
Études des sciences et des technologies0,0010,001
Communication savante0,0040,004
Science ouverte0,0030,003
Intégrité de la recherche0,0030,001
Charge utile insuffisante (le modèle a refusé de juger)0,0120,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,018
Tête enseignante GPT0,353
Écart entre enseignants0,335 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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