Evidence map›Paper›PMID 39830235›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Global Prevalence of Long COVID, its Subtypes and Risk factors: An Updated Systematic Review and Meta-Analysis.

Yiren Hou, Tian Gu, Zhouchi Ni, Xu Shi, Megan L Ranney, Bhramar Mukherjee

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yiren HouDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0009-0005-0422-4268
Tian GuDepartment of Biostatistics, Columbia Mailman School of Public Health, New York, NY 10032, USA.ORCID 0000-0002-0856-8843
Zhouchi NiDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Xu ShiDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0001-8566-9552
Megan L RanneyDepartment of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut.ORCID 0000-0002-8450-9642
Bhramar MukherjeeDepartment of Biostatistics, Yale School of Public Health, New Haven, CT 06511, USA.ORCID 0000-0003-0118-4561

Funding

MI-CARES: The Michigan Cancer and Research on the Environment StudyUG3CA267907 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DOLINOY, DANA, MUKHERJEE, BHRAMAR · 2021 to 2022
$2.2M
NCI NIH HHS UG3 CA267907
6 · The paper itself

Abstract

Importance: Updated knowledge regarding the global prevalence of long COVID (or post-COVID-19 condition), its subtypes, risk factors, and variations across different follow-up durations and geographical regions is necessary for informed public health recommendations and healthcare delivery. Objective: The primary objective of this systematic review is to evaluate the global prevalence of long COVID and its subtypes and symptoms in individuals with confirmed COVID-19 diagnosis, while the secondary objective is to assess risk factors for long COVID in the same population. Data Sources: Studies on long COVID published from July 5, 2021, to May 29, 2024, searched from PubMed, Embase, and Web of Science were used for this systematic review. Supplemental updates to the original search period were made. Study Selection: There were four inclusion criteria: (1) human study population with confirmed COVID-19 diagnosis; (2) appropriate index diagnosis date; (3) outcome must include either prevalence, risk factors, duration, or symptoms of long COVID; and (4) follow-up time of at least two months after the index date. The exclusion criteria were: (1) non-human study population; (1) case studies or reviews; (2) studies with imaging, molecular, and/or cellular testing as primary results; (3) studies with specific populations such as healthcare workers, residents of nursing homes, and/or those living in long-term care facilities; and (4) studies that did not meet the sample size threshold needed to estimate overall prevalence with margin of error of 0.05. Data Extraction and Synthesis: Two screeners independently performed screenings and data extraction, and decision conflicts were collectively resolved. The data were pooled using a random-effects meta-analysis framework with a DerSimonian-Laird inverse variance weighted estimator. Main Outcomes and Measures: The primary estimand (target population parameter of interest) was the prevalence of long COVID and its subtypes among individuals with confirmed COVID-19 diagnoses, and the secondary estimand was effect sizes corresponding to ten common risk factors of long COVID in the same population. Results: A total of 442 studies were included in this mega-systematic review, and 429 were meta-analyzed for various endpoints, avoiding duplicate estimates from the same study. Of the 442 studies, 17.9% of the studies have a high risk of bias. Heterogeneity is evident among meta-analyzed studies, where the Conclusions and Relevance: This study shows long COVID is globally prevalent in the COVID-19 positive population with highly varying estimates. The prevalence of long COVID persists over extended follow-up, with a high burden of symptoms 1 to 2 years post-infection. Our findings highlight long COVID and its subtypes as a continuing health challenge worldwide. The heterogeneity of the estimates across populations and geographical regions argues for the need for carefully designed follow-up with representative studies across the world.

Identifiers

PMID39830235
PMCPMC11741453

What OpenQuestion holds

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LicenceCC BY-ND
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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 OpenQuestion graph.