Evidence map›Paper›PMID 42168930›Full record

ArticleBMC infectious diseases2026

Long COVID longitudinal symptoms burden clusters within a national community-based cohort.

Yanhan Shen, Zach Shahn, McKaylee M Robertson, Kelly Gebo, Denis Nash

Abstract read
In one paragraph

Article in BMC infectious diseases, 2026. 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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Yanhan ShenInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA. jenny.shen@sph.cuny.edu.
Zach ShahnInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA.
McKaylee M RobertsonInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA.
Kelly GeboDepartment of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Denis NashInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA.

Funding

Following a U.S. National Cohort of Vulnerable Men to Improve HIV Prevention and CareUH3AI133675 · NIAID · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI GROV, CHRISTIAN · 2019 to 2021
$6.9M
NIAID NIH HHS UH3 AI133675
6 · The paper itself

Abstract

backgroundLong COVID is clinically heterogeneous, with evolving symptom trajectories that complicate classification. Prior clustering studies often rely on Electronic Health Records data, risking underreporting. We examined longitudinal, community-based symptom clusters and associated factors.

methodsWe analyzed CHASING COVID Cohort participants with confirmed SARS-CoV-2 infection between December 2020 and December 2022, ≥ 12 months of follow-up, and long COVID (≥ 1 new symptom and concurrent activity limitation 3-12 months post-infection, both absent pre-infection). The infection in this window was the index infection; those with pre-index long COVID were excluded. Symptoms were self-reported pre-infection and at ~ 3, 6, 9, and 12 months. Missing data were handled via multiple imputation by chained equations (30 datasets). Longitudinal K-means clustering was performed within each imputed dataset, with hierarchical aggregation to derive final assignments. Logistic regression (age- and sex-adjusted) assessed associations of demographic, clinical, and social factors with cluster membership. Within the highest-burden cluster, hierarchical clustering identified symptom phenotypes.

resultsOf 1,787 infected participants, 511 met criteria (22% ≥50 years; 55% female; 60% White non-Hispanic; 54% mental health disorder; 7.2% immunodeficiency; 21% ≥2 comorbidities). Three longitudinal clusters emerged: highest, moderate, and lowest burden. The highest-burden cluster showed persistent multisystem symptoms (median 6 symptoms at 6-9 months) with frequent fatigue, concentration difficulty, post-exertional malaise, myalgia, sleep disturbance, gastrointestinal symptoms, headache, irritability, and mobility limitations. The moderate cluster peaked at 3 symptoms, while the lowest remained at 1 symptom. Older age (aOR 2.68, 95% CI 1.59-4.53), female sex (2.36, 1.48-3.76), mental health disorder (2.65, 1.65-4.25), and immunodeficiency (4.23, 1.71-10.51) were associated with highest vs. lowest burden. Within the highest-burden cluster, three phenotypes emerged: multisystemic dysfunction, psychiatric/neurological dysfunction, and physical/respiratory dysfunction.

conclusionsDistinct long COVID clusters and phenotypes underscore heterogeneity and support tailored management and risk stratification.

Indexed as

COVID-19AdultAgedCluster AnalysisClustering AlgorithmsCohort StudiesFemaleHumansLongitudinal StudiesMaleMiddle AgedPost-Acute COVID-19 SyndromeSARS-CoV-2Symptom BurdenClustersLong COVIDLongitudinalSARS-CoV-2Symptoms

Identifiers

PMID42168930
PMCPMC13483701

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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.