Surveillance of COVID-19 vaccine effectiveness: a real-time case–control study in southern Sweden

  1. Jonas Björk
  2. Carl Bonander
  3. Mahnaz Moghaddassi
  4. Magnus Rasmussen
  5. Ulf Malmqvist
  6. Fredrik Kahn
  7. Malin Inghammar

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Abstract

The extensive register infrastructure available for coronavirus disease 2019 surveillance in Scania county, Sweden, makes it possible to classify individual cases with respect to hospitalisation and disease severity, stratify on time since last dose and demographic factors, account for prior infection and extract data for population controls automatically. In the present study, we developed a case–control sampling design to surveil vaccine effectiveness (VE) in this ethnically and socioeconomically diverse population with more than 1.3 million inhabitants. The first surveillance results show that estimated VE against hospitalisation and severe disease 0–3 months after the last dose remained stable during the study period, but waned markedly 6 months after the last dose in persons aged 65 years or over.

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  1. Version published to 10.1017/s0950268822000425
  2. ScreenIT

    SciScore for 10.1101/2021.12.09.21267515: (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

    No key resources detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2021.12.09.21267515 on medRxiv