Incidence of COVID-19 reinfection: an analysis of outpatient-based data in the United States of America

  1. Mahalul Azam
  2. Feddy Setio Pribadi
  3. Arief Rahadian
  4. Muhammad Zakki Saefurrohim
  5. Yudhy Dharmawan
  6. Arulita Ika Fibriana
  7. Ima Arum Lestarini
  8. Syed Mohamed Aljunid

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Abstract

Objectives

COVID-19 reinfection cases are evidence of antibody waning in recovered individuals. Previous studies had reported cases of COVID-19 reinfection both in hospital-based and community-based data. However, limited studies reported COVID-19 reinfection in large community-based data. The present study aimed to provide the incidence of COVID-19 reinfection based on secondary data in the U.S.

Study design

Cross-sectional study

Methods

A cross-sectional study was conducted using secondary data provided by COVID-19 Research Database, i.e., Healthjump. Reinfection were defined as diagnosed COVID-19 (U07.1= confirmed virus identified) twice with ≥90 days interval between diagnosis. Age, gender, and region data were also explored. A Chi-square test continued by a binary logistic regression was conducted to determine the association between parameters. Data collecting and processing were done in the Amazon workspace.

Results

The study revealed 3,778 reinfection cases of 116,932 COVID-19 infected cases (3.23%). Reinfection cases were more common in females (3.35%) than males (3.23%). Elderly subjects were the highest incidence (5.13%), followed by adult (4.14%), young adults (2.35%), and children (1.09%). Proportion in the region of living northeast was the highest (3.68%), compared to the south (3.49%), west (2.59%), and midwest (2.48%).

Conclusion

The incidence of COVID-19 reinfection was 3.23%, suggesting our concern with COVID-19 management and future research to understand COVID-19 reinfection better. The incident is more likely to occur in female and elderly patients.

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  1. ScreenIT

    SciScore for 10.1101/2021.12.07.21267206: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis as well as general data analysis was performed using STATA that was available in the workspace.
    STATA
    suggested: (Stata, RRID:SCR_012763)
    We choose Python as our tool because there are available methods that have been provided, especially in machine learning.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    The limitation of our study is that our data did not include genome sequencing data of the virus so that we could not differentiate between reinfection or reactivation of the virus. Nevertheless, the three months difference between each PCR test provided in the diagnosis data has been accepted as a period that is possible for reinfection because of the reduction of antibodies, yet the least possible for reactivation.

    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.

  2. Version published to 10.1101/2021.12.07.21267206v1 on medRxiv