Evidence map›Paper›PMID 41115964›Full record

ArticleCommunications medicine2025

Dynamics of multimorbidity patterns in older population across socioeconomic areas.

Jesus Carretero-Bravo, Esther Ortega-Martin, Javier Alvarez-Galvez

Abstract read
In one paragraph

Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Jesus Carretero-BravoDepartment of Quantitative Methods, Universidad Loyola Andalucía, Seville, Spain.ORCID http://orcid.org/0000-0002-3524-9565
Esther Ortega-MartinComputational Social Science DataLab (CS2 DataLab), University Research Institute for Sustainable Social Development (INDESS)), University of Cádiz, Jerez de la Frontera, Spain.ORCID http://orcid.org/0000-0002-5182-1824
Javier Alvarez-GalvezComputational Social Science DataLab (CS2 DataLab), University Research Institute for Sustainable Social Development (INDESS)), University of Cádiz, Jerez de la Frontera, Spain. javier.alvarezgalvez@uca.es.ORCID http://orcid.org/0000-0001-9512-7853

Funding

Fundación Pública Andaluza para la Gestión de la Investigación en Salud de Sevilla (Andalusian Public Foundation for the Management of Health Research in Seville) ITI-0028-2019
6 · The paper itself

Abstract

backgroundRecent studies have addressed the problem of the complex classification and discovery of multimorbidity patterns; however, to date there are still many doubts about the conceptualization of multimorbidity and its adequate measurement due to the need to work with very large samples and longitudinal designs that allow us to study the dynamics of multimorbidity patterns and the social contexts from which they emerge.

methodsThe present study makes use of a sample of 341,343 patients over 50 years of age residing in southern Spain with the aim of analysing the trajectories of multimorbidity patterns that are linked to different socioeconomic areas.

resultsEmploying Hidden Markov Models for the study of changes in the latent states of multimorbidity profiles, our analysis demonstrates significant associations between multimorbidity patterns and the socioeconomic gradient of residential areas. Moreover, we provide additional evidence that enables a characterisation of transitions from initial disease patterns to multisystemic profiles of chronicity (or complex multimorbidity) that offer a worse prognosis of health and quality of life for the patient.

conclusionsOur study adds relevant and contextualized information on the evolution of multimorbidity patterns that can serve as a basis for further research and policies aimed at the prevention and control of multimorbidity from an early age.

Identifiers

PMID41115964
PMCPMC12537859

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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.