Evidence map›Paper›PMID 39352725›Full record

ArticleJMIR public health and surveillance2024

Associations Between Acute COVID-19 Symptom Profiles and Long COVID Prevalence: Population-Based Cross-Sectional Study.

Jana L Hirschtick, Elizabeth Slocum, Yanmei Xie, Laura E Power, Michael R Elliott, Robert C Orellana, Nancy L Fleischer

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2024. 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. Article
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.

Jana L HirschtickDepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States.ORCID 0000-0001-6532-1372
Elizabeth SlocumDepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States.ORCID 0000-0003-4373-4744
Yanmei XieDepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States.ORCID 0000-0002-6313-1660
Laura E PowerDepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States.ORCID 0000-0002-9069-512X
Michael R ElliottDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States.ORCID 0000-0003-4128-5527
Robert C OrellanaCenters for Disease Control and Prevention Foundation, Atlanta, GA, United States.ORCID 0000-0001-9655-9033
Nancy L FleischerDepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States.ORCID 0000-0002-4371-9133

Funding

NCEZID CDC HHS U01 CK000510
6 · The paper itself

Abstract

Background: Growing evidence suggests that severe acute COVID-19 illness increases the risk of long COVID (also known as post-COVID-19 condition). However, few studies have examined associations between acute symptoms and long COVID onset. Objective: This study aimed to examine associations between acute COVID-19 symptom profiles and long COVID prevalence using a population-based sample. Methods: We used a dual mode (phone and web-based) population-based probability survey of adults with polymerase chain reaction-confirmed SARS-CoV-2 between June 2020 and May 2022 in the Michigan Disease Surveillance System to examine (1) how acute COVID-19 symptoms cluster together using latent class analysis, (2) sociodemographic and clinical predictors of symptom clusters using multinomial logistic regression accounting for classification uncertainties, and (3) associations between symptom clusters and long COVID prevalence using modified Poisson regression. Results: In our sample (n=4169), 15.9% (n=693) had long COVID, defined as new or worsening symptoms at least 90 days post SARS-CoV-2 infection. We identified 6 acute COVID-19 symptom clusters resulting from the latent class analysis, with flu-like symptoms (24.7%) and fever (23.6%) being the most prevalent in our sample, followed by nasal congestion (16.4%), multi-symptomatic (14.5%), predominance of fatigue (10.8%), and predominance of shortness of breath (10%) clusters. Long COVID prevalence was highest in the multi-symptomatic (39.7%) and predominance of shortness of breath (22.4%) clusters, followed by the flu-like symptom (15.8%), predominance of fatigue (14.5%), fever (6.4%), and nasal congestion (5.6%) clusters. After adjustment, females (vs males) had greater odds of membership in the multi-symptomatic, flu-like symptom, and predominance of fatigue clusters, while adults who were Hispanic or another race or ethnicity (vs non-Hispanic White) had greater odds of membership in the multi-symptomatic cluster. Compared with the nasal congestion cluster, the multi-symptomatic cluster had the highest prevalence of long COVID (adjusted prevalence ratio [aPR] 6.1, 95% CI 4.3-8.7), followed by the predominance of shortness of breath (aPR 3.7, 95% CI 2.5-5.5), flu-like symptom (aPR 2.8, 95% CI 1.9-4.0), and predominance of fatigue (aPR 2.2, 95% CI 1.5-3.3) clusters. Conclusions: Researchers and clinicians should consider acute COVID-19 symptom profiles when evaluating subsequent risk of long COVID, including potential mechanistic pathways in a research context, and proactively screen high-risk patients during the provision of clinical care.

Indexed as

COVID-19Post-Acute COVID-19 SyndromeAdolescentAdultAgedCross-Sectional StudiesFemaleHumansMaleMichiganMiddle AgedPrevalenceSymptom AssessmentYoung AdultCOVID-19epidemiologypost-acute COVID-19 syndromeSARS-CoV-2surveillance

Identifiers

PMID39352725
PMCPMC11460306

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.