Evidence map›Paper›PMID 41933051›Full record

ArticleScientific reports2026

Identifying Long Covid phenotypes and their association with personal characteristics, healthcare use, and daily life burden: population-based study in Belgium.

Sarah Moreels, Pierre Smith, Rana Charafeddine, Diego Castanares-Zapatero, Dieter van Cauteren, Niko Speybroeck

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Article in Scientific reports, 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

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Sarah MoreelsHealth Services Research (Primary Care unit), Epidemiology and Public Health Sciensano Brussels - Belgium (Europe), Rue Juliette Wytsmanstraat 14, 1050, Brussel, Belgium. sarah.moreels@sciensano.be.
Pierre SmithInstitute of Health and Society, Faculty of Public Health, UCLouvain, Brussels, Belgium.
Rana CharafeddineHealth Information, Epidemiology and Public Health, Sciensano, Brussels, Belgium.
Diego Castanares-ZapateroBelgian Health Care Knowledge Centre, Brussels, Belgium.
Dieter van CauterenInfectious Diseases Epidemiology, Epidemiology and Public Health, Sciensano, Brussels, Belgium.
Niko SpeybroeckInstitute of Health and Society, Faculty of Public Health, UCLouvain, Brussels, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long Covid (LC), a multisystem disorder with persistent symptoms > 4 weeks post COVID-19, has impacted healthcare systems substantially. As information is scarce for Belgium, this study aimed to identify LC phenotypes and assess participant characteristics, healthcare use and daily life burden by phenotype. The last survey wave of COVimpact (a longitudinal online cohort study among Belgian adults recruited after SARS-CoV-2 infection) focused on LC with questions on healthcare utilization and perceived daily impact. A latent class analysis (LCA) identified phenotypes, based on symptom type, disease duration, and LC-related disability. Backward multivariable multinomial logistic regression explored predictors of LC class membership. Among 1,840 respondents self-reporting LC, four phenotypes emerged. Class 1 presented mild clinical outcomes, class 2 and 3 presented moderate clinical outcomes, differentiated by symptoms of memory problems and brain fog (class 2), and respiratory problems and muscle/joint pain (class 3), and class 4 included the most severe clinical outcomes. Compared to class 1, being older, female, lower educated, non-European, obese and experiencing moderate to severe acute COVID-19 symptoms predicted higher class severity. Diagnosis, healthcare use and support differed among adults with moderate and severe LC, many reported insufficient healthcare access and major disruption to daily life, including absenteeism from work/school. People with severe LC were the most financially impacted. By distinguishing LC phenotypes, this study enhances understanding of varying healthcare trajectories and daily life impacts. Healthcare planning and support should be more effectively tailored to the specific needs of those affected by LC.

Indexed as

COVID-19Patient Acceptance of Health CareAdultAgedBelgiumCost of IllnessFemaleHumansLongitudinal StudiesMaleMiddle AgedPhenotypePost-Acute COVID-19 SyndromeSARS-CoV-2Symptom BurdenBelgiumCluster analysisDisease burdenHealthcare utilizationLong Covid

Identifiers

PMID41933051
PMCPMC13194761

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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.