Contribution of infection and vaccination to seroprevalence through two COVID waves in Tamil Nadu, India

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Abstract

Four rounds of serological surveys were conducted, spanning two COVID waves (October 2020 and April-May 2021), in Tamil Nadu (population 72 million) state in India. Each round included representative populations in each district of the state, totaling ≥20,000 persons per round. State-level seroprevalence was 31.5% in round 1 (October-November 2020), after India’s first COVID wave. Seroprevalence fell to 22.9% in 2 (April 2021), consistent with waning of antibodies from natural infection. Seroprevalence rose to 67.1% by round 3 (June-July 2021), reflecting infections from the Delta-variant induced second COVID wave. Seroprevalence rose to 93.1% by round 4 (December 2021-January 2022), reflecting higher vaccination rates. Antibodies also appear to wane after vaccination. Seroprevalence in urban areas was higher than in rural areas, but the gap shrunk over time (35.7 v. 25.7% in round 1, 89.8% v. 91.4% in round 4) as the epidemic spread even in low-density rural areas.

Article Summary Line

Antibodies waned after India’s first COVID wave and both vaccination and infection contributed its roughly 90% seroprevalence after its second wave.

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  1. SciScore for 10.1101/2021.11.14.21265758: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study was approved by the Directorate of Public Health and Preventive Medicine, Government of Tamil Nadu, and the Institutional Ethics Committee of Madras Medical College, Chennai, India.
    Consent: The exclusion criteria were refusal to consent and contraindication to venipuncture.
    Field Sample Permit: We focus on individuals with just 1 dose because they are more in number and have greater variation in the number of days since vaccination; this variation exists because many people who received 1 dose had not got a second dose at the time of sample collection.
    Sex as a biological variablenot detected.
    RandomizationFirst, within each HUD, the study randomly selected clusters.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Serum was analyzed for IgG antibodies to the SARS-CoV-2 spike protein using either the iFlash-SARS-CoV-2 IgG (Shenzhen YHLO Biotech; sensitivity of 95.9% and specificity of 95.7% per manufacturer)4 or the Vitros anti-SARS-CoV-2 IgG CLIA kit (Ortho-Clinical Diagnostics; sensitivity of 90% and specificity of 100% per manufacturer)5.
    IgG
    suggested: None
    iFlash-SARS-CoV-2 IgG
    suggested: None
    anti-SARS-CoV-2 IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    All statistical analyses were conducted with Microsoft Excel 365 (
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    (Microsoft, USA) and Stata 16 (StataCorp, USA).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    Our study has several limitations. First, because antibody concentrations in infected persons decline over time8, our estimate of seroprevalence in round 1 may underestimate the level of prior infection and perhaps natural immunity. Our estimate of 31.6% seroprevalence in round 2 after adjusting for infections between rounds 1 and 2 may be incorrect if our adjusted reported case rate does not accurately estimate of those infections. The fact that a 1 percentage point increase in that adjusted rate is associated with a 1 percentage point higher seropositivity rate suggests, however, that rate is a reasonable measure of infections. Second, we may not accurately untangle seropositivity in round 3 that is due to infection versus due to vaccination. Seroprevalence among the unvaccinated during round 3 may not be a reasonable measure of seropositivity if people were not to be vaccinated if age or self-selection affects vaccination status. Our estimate of seroprevalence due to vaccination may be off if our adjusted reported case rate (over 30 days prior to vaccination) does not accurately estimate infection risk.

    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.