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  1. Version 2 published on medRxiv
  2. NCRC

    Our take

    This preprint, which has not yet been peer-reviewed, used a representative sample from a quarter of the Israeli population and included households with ≥1 individual who was infected with SARS-CoV-2 and ≥2 household members (n=253,564 individuals across 65,624 households). The study demonstrated that two doses of the Pfizer-BioNTech vaccine was highly effective for reducing SARS-CoV-2 transmission within Israeli households between June 2020 and March 2021. This effectiveness was the result of vaccinated people having an 81% lower risk of becoming infected and, if infected, a 41% lower infectiousness. These results clearly illustrate the importance of ongoing efforts to increase vaccine uptake to reduce the risk of transmitting SARS-CoV-2. The effectiveness of vaccines to prevent transmission should continue to be studied as new variants of concern, including Delta, continue to emerge.

    Study design

    retrospective-cohort

    Study population and setting

    established, few studies have addressed its ability to reduce transmission in a population. This study describes the impact of the Pfizer-BioNTech vaccine on household transmission in Israel between June 15, 2020, and March 24, 2021. Data were collected from the Maccabi Healthcare Services database, which covers a representative sample of about 1/4 of the Israeli population. The study population included households with at least one SARS-CoV-2 infected individual and at least two household members (253,564 individuals from 65,624 households). Researchers used two different time-to-event models to calculate estimates of vaccine effectiveness against vaccine breakthrough infection (given known exposure) and transmission.

    Summary of main findings

    Using a mechanistic model for household transmission, researchers estimated that vaccination (two doses of Pfizer-BioNTech) was 80.5% (CI: 78.9-82.1) effective against breakthrough infection (given exposure) and 41.3% (CI: 9.5-73.0) effective at reducing infectiousness in cases of vaccine breakthrough. Combining these two reductions of risk, this model estimated that vaccination led to an 88.5% (CI: 82.3-94.8) reduction in overall transmission risk for this population. An alternative model, based on an infection-hazard approach, estimated a 92.3% (CI: 90.2-94.5) reduction in hazard of infection, given exposure of a vaccinated household member to an infected, unvaccinated household member, as well as a 78.6% (CI: 74.5-82.7) reduction in hazard of infection, given exposure of an unvaccinated household member to an infected, vaccinated household member. While these models approach vaccine-associated risk reduction from different perspectives and are not directly comparable, they both demonstrate clear associations between vaccination and reductions in breakthrough infection and transmission.

    Study strengths

    Study data were collected from a well-maintained, state-sponsored database that covers a large, representative sample of the Israeli population. Models were adjusted for age, time-varied risk (based on community-level outbreak levels), and vaccination status of all household members. Two distinct models with different approaches provided similar conclusions regarding the impact of vaccination on SARS-CoV-2 breakthrough infection and transmission.

    Limitations

    Time of infection and duration of infectiousness were estimated using data augmentation. If individuals included in this dataset were infected but not tested (due to asymptomatic or mild infection), they would have been misclassified as uninfected. Analysis was restricted by household size and the requirement that one individual test positive for SARS-CoV-2. This study was completed prior to the rise of the Delta variant and does not capture its likely significant impact on vaccine effectiveness against breakthrough infection and transmission.

    Value added

    This large-scale study demonstrated that widespread vaccination with the Pfizer-BioNTech vaccine led to reduced SARS-CoV-2 transmission in households in Israel between June 2020 and March 2021.

  3. ScreenIT

    SciScore for 10.1101/2021.07.13.21260393: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data sources: We used data from Maccabi Healthcare Services (MHS) centralized computerized database, which captures all data on members’ healthcare-related interactions (including demographics, inpatient and outpatient visits, diagnoses, procedures etc).
    Maccabi Healthcare
    suggested: None

    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.
    • Thank you for including a protocol registration statement.

    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.

  4. Version 1 published on medRxiv