Long COVID and its associated factors among COVID survivors in the community from a middle-income country—An online cross-sectional study

  1. Foong Ming Moy
  2. Noran Naqiah Hairi
  3. Eugene Ri Jian Lim
  4. Awang Bulgiba

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Abstract

Patients with COVID-19 usually recover and return to normal health, however some patients may have symptoms that last for weeks or even months after recovery. This persistent state of ill health is known as Long COVID if it continues for more than three months and are not explained by an alternative diagnosis. Long Covid has been overlooked, especially in the low- and middle-income countries. Therefore, we conducted an online survey among the COVID-19 survivors in the community to explore their Long COVID symptoms, factors associated with Long COVID and how Long COVID affected their work. A total of 732 COVID-19 survivors responded, with 56% were without or with mild symptoms during their acute COVID-19 conditions. One in five COVID-19 survivors reported of experiencing Long COVID. The most commonly reported symptoms were fatigue, brain fog, depression, anxiety and insomnia. Females had 58% higher odds (95% CI: 1.02, 2.45) of experiencing Long COVID. Patients with moderate and severe levels of acute COVID-19 symptoms had OR of 3.01 (95% CI: 1.21, 7.47) and 3.62 (95% CI: 1.31, 10.03) respectively for Long COVID. Recognition of Long COVID and its associated factors is important in planning prevention, rehabilitation, clinical management to improve recovery from COVID-19.

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  1. Version published to 10.1371/journal.pone.0273364
  2. ScreenIT

    SciScore for 10.1101/2022.03.09.22272168: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: Ethics clearance was obtained from the University Malaya Research Ethics Committee (Reference number: UM.TNC2/UMREC_1439).
    Consent: Informed consent was obtained before data collection.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data from RedCap were exported into the SPSS version 23 software for data analysis.
    RedCap
    suggested: (REDCap, RRID:SCR_003445)
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    While interpreting the results, there are some limitations that need to be addressed. First, this is a cross sectional study where respondents were asked to recall their symptoms and duration of experience. This may subject to recall bias and it is impossible to retrospectively confirm the length of COVID-19 related symptoms. In order to overcome these issues, a prospective cohort study may be a better study design. We also did not assess whether the symptoms reported were intermittent or continuous for the entire three or six months. Next, selection bias cannot be avoided in an online study as younger, more highly educated and tech-savvy individuals were more likely to respond. Therefore, our respondents may not be fully representative of the general population. We could not ascertain the effect of vaccination on Long COVID. Some studies suggested that vaccination may lead to potentially lower prevalence of Long Covid (36). This should be explored in future studies. The definition of Long COVID used in the current study may be different from the latest definition proposed by WHO on the 6 October 2021 (37). The latest definition defines Long COVID or Post COVID-19 condition as condition that occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. The difference between the two definitions is that the symptom...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2022.03.09.22272168 on medRxiv