Evidence map›Paper›PMID 42120100›Full record

ArticleBMJ open diabetes research & care2026

Health literacy profiles of people living with type 2 diabetes: a cluster-based approach to inform tailored interventions-the Entred 3 study.

Roxane Agius, Laurent Rigal, Henri Panjo, Richard H Osborne, Sandrine Fosse-Edorh, Xavier Debussche

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Article in BMJ open diabetes research & care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Roxane AgiusDepartment of Non-Communicable Diseases and Trauma, F-94415, Santé publique France, Saint-Maurice, France roxane.agius.contact@gmail.com.ORCID http://orcid.org/0009-0003-1808-1355
Laurent RigalCESP, INSERM, UVSQ, Paris-Saclay University, Villejuif, France.
Henri PanjoCESP, INSERM, UVSQ, Paris-Saclay University, Villejuif, France.
Richard H OsborneGlobal Health and Equity Development Hub, Violet Vines Marshman Centre for Rural Health Research, La Trobe Rural Health School, Room W106, Level 1, David Myers Building West, Bundoora, VIC 3083, Australia.
Sandrine Fosse-EdorhDepartment of Non-Communicable Diseases and Trauma, F-94415, Santé publique France, Saint-Maurice, France.
Xavier DebusscheWound and Healing Expert Center, Max Querrien Hospital Centre of Paimpol, Paimpol, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionHealth literacy is a multidimensional determinant of health that influences diabetes outcomes and social inequities in health (SIH). Studies often measure it unidimensionally, failing to capture people's diverse health literacy skills. We identified multidimensional health literacy profiles among adults living with type 2 diabetes (T2D) in France and described their clinical, behavioral and social characteristics with a view to informing future tailored interventions. RESEARCH DESIGN AND

methodsThis study used a cross-sectional design based on a subsample of the Entred 3 national survey, combining self-reported questionnaire data with linked administrative health data covering the previous 10 years. Health literacy was assessed using the Health Literacy Questionnaire (HLQ) among 510 individuals with T2D who completed the cross-sectional survey online. Hierarchical clustering based on standardized HLQ scores identified distinct health literacy clusters.

resultsWe identified 13 health literacy clusters among the online survey participants, labelled from A to M, five requiring tailored public health and clinical responses. Substantial heterogeneity concerning health literacy skills and associations with SIH and health outcomes was observed. For example, cluster A demonstrated strong self-management skills counteracted by severe barriers to care navigation and information comprehension in a highly vulnerable setting. Furthermore, cluster D reflected globally low health literacy scores, poor health outcomes and high exposure to risk factors despite recent diabetes diagnosis. A third example is cluster K, which highlighted strong relational and cognitive skills but limited engagement in self-care and significant exposure to risk factors despite favorable socioeconomic conditions.

conclusionsHealth literacy profiles reflect complex combinations of skills, risks and social determinants, which can potentially inform how to tailor public health interventions, so that no group is left behind.

Indexed as

Diabetes Mellitus, Type 2Health Knowledge, Attitudes, PracticeHealth LiteracyAdultAgedCluster AnalysisCross-Sectional StudiesFemaleFollow-Up StudiesFranceHumansMaleMiddle AgedPrognosisSocioeconomic Disparities in HealthSurveys and QuestionnairesDiabetes Mellitus, Type 2Healthcare DisparitiesHealth Literacy

Identifiers

PMID42120100
PMCPMC13182442

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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.