Evidence map›Paper›PMID 38328496›Full record

ArticleOpen forum infectious diseases2024

Reduction in Long COVID Symptoms and Symptom Severity in Vaccinated Compared to Unvaccinated Adults.

Hannah E Maier, Theresa Kowalski-Dobson, Ashley Eckard, Carmen Gherasim, David Manthei, Alyssa Meyers, Dawson Davis, Kevin Bakker, Kathleen Lindsey, Zijin Chu and 6 more

Open access · goldAbstract read
In one paragraph

Article in Open forum infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
5.9field-weighted citation impact, top 3% of its field
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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Long COVID and the kidney.Nature reviews. Nephrology · 2025
    Review
  8. Article
  9. Article
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  14. Review
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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

16 authors at 2 institutions in 1 country.

Hannah E MaierDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0001-5761-0801
Theresa Kowalski-DobsonDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0003-3105-4637
Ashley EckardDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Carmen GherasimDepartment of Pathology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0009-0004-8484-6798
David MantheiDepartment of Pathology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Alyssa MeyersDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Dawson DavisDepartment of Pathology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Kevin BakkerDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-7084-9291
Kathleen LindseyDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Zijin ChuDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Lauren WarsinskeDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Matthew ArnoldDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Anna BuswinkaDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Emily StonemanDepartment of Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Riccardo ValdezDepartment of Pathology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Aubree GordonDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-9352-7877
University of Michigan · USMichigan Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The impact of vaccination prior to infection on postacute sequelae of coronavirus disease 2019 (COVID-19, PASC), also known as long COVID, remains unclear. Here we assess the protective effect of vaccination on long COVID in a community-based setting. Methods: The Immunity Associated with SARS-CoV-2 (IASO) study is an ongoing prospective cohort of working adults that began in October 2020. Participants are actively followed for severe acute respiratory syndrome coronavirus 2 infection. We compared the prevalence of symptoms and symptom severity in vaccinated compared to unvaccinated cases. Our primary definition of long COVID was the presence of symptoms at 90 days postinfection; 30 days postinfection was also examined. Results: Overall, by 90 days postinfection, 13% of cases had long COVID, with 27% of unvaccinated cases and 8% of vaccinated cases reporting long COVID (relative risk [RR], 0.31 [95% confidence interval {CI}, .22-.42]). Vaccination was also associated with significantly lower average severity scores at all timepoints (eg, relative severity at 90 days postinfection: -2.70 [95% CI, -1.68 to -3.73]). In the pre-Omicron era, 28% of unvaccinated cases and 18% of vaccinated cases reported long COVID ( Conclusions: Vaccinated cases had lower prevalence of long COVID and reduced symptom severity.

Indexed as

community-basedCOVID-19long COVIDSARS-CoV-2vaccine

Identifiers

PMID38328496
PMCPMC10847810
OpenAlexW4391463007

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.