Evidence map›Paper›PMID 40437395›Full record

ArticleBMC geriatrics2025

Multimorbidity patterns in older adults from Northern Netherlands: comparing factor analysis and latent class analysis solutions.

Rafael Ogaz-González, Qian Zou, Yihui Du, Luis Miguel Gutiérrez-Robledo, Ricardo Escamilla-Santiago, Malaquías López-Cervantes, Eva Corpeleijn

Abstract readComparative Study
PubMed Full text at PMC Publisher
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Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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.

Rafael Ogaz-GonzálezDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.ORCID http://orcid.org/0009-0008-9892-7678
Qian ZouDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.ORCID http://orcid.org/0009-0001-3984-3596
Yihui DuSchool of Public Health, Department of Epidemiology and Health Statistics, Hangzhou Normal University, Hangzhou, China.ORCID http://orcid.org/0000-0003-0959-8339
Luis Miguel Gutiérrez-RobledoDirección de Investigación, Instituto Nacional de Geriatría, Ciudad de México, México.ORCID http://orcid.org/0000-0002-9728-6644
Ricardo Escamilla-SantiagoFaculty of Medicine, Department of Public Health, National Autonomous University of Mexico, Mexico City, Mexico.ORCID http://orcid.org/0000-0003-3214-7536
Malaquías López-CervantesFaculty of Medicine, Department of Public Health, National Autonomous University of Mexico, Mexico City, Mexico.ORCID http://orcid.org/0000-0001-9936-8427
Eva CorpeleijnDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. e.corpeleijn@umcg.nl.ORCID http://orcid.org/0000-0002-2974-3305

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevalence of multimorbidity is increasing in aging populations globally. Multimorbidity involves various noncommunicable disease (NCD) combinations that extend beyond individual conditions. Identifying how multimorbidity patterns (MPs) configure is crucial for understanding the role of NCD patterns in health prognosis.

methodsThis study identified MPs and examined their associations with sociodemographic and economic factors in 23,452 participants aged ≥ 60 years from the Lifelines cohort in northern Netherlands (baseline: 2007-2013; follow-up: 2011-2019). Complete data on 14 NCDs at two time points were analyzed, with multimorbidity defined as ≥ 2 NCDs. Latent class and factor analyses identified clusters of NCDs, stratified into MPs based on multimorbidity presence. Multinomial logistic regression assessed the relationships between MPs and sociodemographic and economic traits.

resultsMultimorbidity prevalence was 55% at baseline. Five MPs, consistent across assessments, were identified. The 'Vascular' MP included the fewest NCDs (2-4), while the 'Complex-Treatment Spectrum' had the most (5-11). Adjusted analyses revealed that lower education, not having a partner, and lower income significantly increased the relative-risk of belonging to high-risk MPs, such as 'Metabolic Risk,' 'Major CVD-Vascular Conditions,' and 'Complex-Treatment Spectrum', compared to participants without multimorbidity. These MPs reflect profiles with distinct risk factors and prognoses.

conclusionsMultimorbidity manifests as stable patterns in this population. MPs derived from latent class analysis were more interpretable and consistent over time compared to correlation-based approaches. Income disparities influence MP profiles, highlighting the need for tailored interventions. Longitudinal studies are recommended to explore NCD contributions to MP dynamics and inform strategies addressing health and social inequities.

Indexed as

Latent Class AnalysisMultimorbidityNoncommunicable DiseasesAgedAged, 80 and overCohort StudiesFactor Analysis, StatisticalFemaleHumansMaleMiddle AgedNetherlandsPrevalenceRisk FactorsSocioeconomic FactorsChronic diseasesHealthy ageingLatent class analysisMultimorbidityNon-communicable diseases

Identifiers

PMID40437395
PMCPMC12117847

What Socratic holds

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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 Socratic graph.