Evidence map›Paper›PMID 39881668›Full record

ArticleThe Lancet regional health. Western Pacific2024

Long COVID facts and findings: a large-scale online survey in 74,075 Chinese participants.

Shijie Qin, Yanan Zhang, Yanhua Li, Ling Huang, Ting Yang, Jiahui Si, Likui Wang, Xin Zhao, Xiaopeng Ma, George F Gao

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 2 pooled it
–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

32 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

10 authors.

Shijie QinDepartment of Infectious Diseases, Shenzhen Children's Hospital, Shenzhen, 518038, China.
Yanan ZhangDepartment of Infectious Diseases, Shenzhen Children's Hospital, Shenzhen, 518038, China.
Yanhua LiCAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.
Ling HuangDepartment of Critical Medicine, Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China.
Ting YangCAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.
Jiahui SiCAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.
Likui WangCAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences (CAS), Beijing, 100101, China.
Xin ZhaoCAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.
Xiaopeng MaDepartment of Infectious Diseases, Shenzhen Children's Hospital, Shenzhen, 518038, China.
George F GaoDepartment of Infectious Diseases, Shenzhen Children's Hospital, Shenzhen, 518038, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Research on long COVID in China is limited, particularly in terms of large-sample epidemiological data and the effects of recent SARS-CoV-2 sub-variants. China provides an ideal study environment owing to its large infection base, high vaccine coverage, and stringent pre-pandemic measures. Methods: This retrospective study used an online questionnaire to investigate SARS-CoV-2 infection status and long COVID symptoms among 74,075 Chinese residents over one year. The relationships between baseline characteristics, vaccination status, pathogenic infection, and long COVID were analyzed using multinomial logistic regression, and propensity matching. Findings: Analysis of 68,200 valid responses revealed that the most frequent long COVID symptoms include fatigue (30.53%), memory decline (27.93%), decreased exercise ability (18.29%), and brain fog (16.87%). These symptoms were less prevalent among those infected only once: fatigue (24.85%), memory decline (18.11%), and decreased exercise ability (12.52%), etc. Women were more likely to experience long COVID, with symptoms varying by age group, except for sleep disorders and muscle/joint pain, which were more common in older individuals. Northern China exhibits a higher prevalence of long COVID, potentially linked to temperature gradients. Risk factors included underlying diseases, alcohol consumption, smoking, and the severity of acute infection (OR > 1, FDR < 0.05). Reinfection was associated with milder symptoms but led to a higher incidence and severity of long COVID (OR > 1, FDR < 0.05). Vaccination, particularly multiple boosters, significantly reduced long-term symptoms by 30%-70% (OR < 1, FDR < 0.05). COVID-19 participants also self-reported more bacterial, influenza and mycoplasma infections, and 8%-10% of patients felt SARS-CoV-2-induced chronic diseases. Interpretation: This survey provides valuable insights into long COVID situation among Chinese residents, with 10%-30% (including repeated infection) reporting symptoms. Monitoring at-risk individuals based on identified risk factors is essential for public health efforts. Funding: This study was funded by the China Postdoctoral Science Foundation (2022M723344, 2023M743729), Guangdong Basic and Applied Basic Research Foundation (2023A1515110489), and the Bill & Melinda Gates Foundation (INV-027420).

Indexed as

COVID-19EpidemiologyLong COVIDQuestionnaire surveySARS-CoV-2Vaccine background

Identifiers

PMID39881668
PMCPMC11776084

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.