Evidence map›Paper›PMID 40234823›Full record

ArticleBMC public health2025

Follow-up of long COVID based on the definition of WHO: a multi-centre cross-sectional questionnaire-based study.

Danying Yan, Ying Liu, Ran Chen, Lei Zhou, Chuwen Wang, Ada Hoi Yan Ma, Xueqin Chen, Qifa Song, Guoqing Qian

Abstract readMulticenter Study
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Barriers to Long COVID Care in the U.S.: An Application of Levesque et al.'s Access Framework.Health care analysis : HCA : journal of health philosophy and policy · 2026
    Article
  2. Article
  3. Long COVID-19: A Concept Analysis.Infectious disease reports · 2025
    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

9 authors.

Danying Yan *Department of Infectious Diseases, The First Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China.
Ying Liu *School of Medicine, Ningbo University, Ningbo, Zhejiang Province, China.
Ran ChenGraduate School of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Lei ZhouXimen and Wangchun Community Hospital, Ningbo, Zhejiang Province, China.
Chuwen WangDepartment of Infectious Diseases, The First Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China.
Ada Hoi Yan MaNottingham University Business School, University of Nottingham Ningbo China, Ningbo, Zhejiang Province, China.
Xueqin ChenDepartment of Traditional Chinese Medicine, The First Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China.
Qifa SongMedical Data Center, The First Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China. qifasong@126.com.
Guoqing QianDepartment of Infectious Diseases, The First Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China. bill.qian@outlook.com.

Funding

Academy Level Project of the First Affiliated Hospital of Ningbo University XGZ2301Youth Science and Technology Innovation Leading Talent project of Ningbo 2023QL055Zhejiang Province Traditional Chinese Medicine Science and Technology Project Evaluation of the effect of traditional Chinese medicine on the elderly patients with severe coronavirus disease 2019Zhejiang Provincial Natural Science Foundation of China LTGG23H260002Zhejiang Provincial Natural Science Foundation of China Y23H190011
6 · The paper itself

Abstract

backgroundSince long COVID has hindered people from normal life, it is essential to understand its full spectrum of manifestation. However, it was heterogeneous in the existing studies and few large-scale studies have been conducted on Asian populations. Here, we conducted a multi-centre questionnaire-based study among Chinese people to explore the long COVID based on the definition of WHO.

methodsDuring March 20, 2023 and June 18, 2023, individuals with a history of confirmed or self-reported SARS-CoV-2 infection were recruited from three hospitals to fill out the questionnaire for long COVID. Each symptom was assigned with 0 to 3 points based on their severity. And the long COVID score was a sum of these points of each symptom. The reporting rate, time trend and risk factors of long COVID stratified by different systems were explored.

results3,693 individuals were recruited for the study. The reporting rate of at least one persistent long COVID symptoms and symptoms impacting daily life was 30.2% (1,117) and 10.7% (394). Systemic symptoms (20.7%, 765) were most easily reported. The most common symptoms were fatigue (16.3%, 602), cough (6.3%, 234) and expectoration (5.5%, 203). The reporting rate of long COVID and long COVID score decreased over time during a 180-day follow-up period (P < 0.05). For long COVID, older age (OR: 1.63, 1.38-1.93), female (OR: 1.19, 1.03-1.38) and SARS-CoV-2 reinfection (OR: 3.56, 2.63-4.80) were risk factors; while number of COVID-19 vaccine doses (OR: 0.87, 0.81-0.94) was a protective factor. The use of traditional Chinese medicine (OR: 0.51, 0.37-0.71) was a protective factor for symptoms impacting daily life.

conclusionsEarly interventions should be taken to minimize the impact of long COVID, especially for the elderly, females and those with SARS-CoV-2 reinfection. COVID-19 booster vaccination might play a potential role in minimizing the impact of long COVID.

Indexed as

COVID-19AdultAgedChinaCross-Sectional StudiesFemaleFollow-Up StudiesHumansMaleMiddle AgedPost-Acute COVID-19 SyndromeRisk FactorsSARS-CoV-2Severity of Illness IndexSurveys and QuestionnairesWorld Health OrganizationCOVID-19Long COVIDPost COVID-19 syndromeSurvey

Identifiers

PMID40234823
PMCPMC11998132

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.