The seroprevalence of severe acute respiratory syndrome coronavirus 2 in Delhi, India: a repeated population-based seroepidemiological study

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Abstract

Background

Three rounds of a repeated cross-sectional serosurvey to estimate the change in seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were conducted from August to October 2020 in the state of Delhi, India, in the general population ≥5 y of age.

Methods

The selection of participants was through a multistage sampling design from all 11 districts and 280 wards of the city-state, with multistage allocation proportional to population size. The blood samples were screened using immunoglobulin G (IgG) enzyme-linked immunosorbent assay kits.

Results

We observed a total of 4267 (N=150 46), 4311 (N=17 409) and 3829 (N=15 015) positive tests indicative of the presence of IgG antibody to SARS-CoV-2 during the August, September and October 2020 serosurvey rounds, respectively. The adjusted seroprevalence declined from 28.39% (95% confidence interval [CI] 27.65 to 29.14) in August to 24.08% (95% CI 23.43 to 24.74) in September and 24.71% (95% CI 24.01 to 25.42) in October. On adjusted analysis, participants with lower per capita income, living in slums or overcrowded households and those with diabetes comorbidity had significantly higher statistical odds of having antibody positivity (p<0.01).

Conclusions

Nearly one in four residents in Delhi, India ≥5 y of age had the SARS-CoV-2 infection during August–October 2020.

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  1. SciScore for 10.1101/2020.12.13.20248123: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Ethical considerations: Written and informed consent for adults and assent for minors was obtained, before recruitment.
    IACUC: The study was approved by the Institutional Ethics Committee (F.1/IEC/MAMC/(78/06/2020/No.176).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Chi-square was used to assess the significance of the association between the presence of antibodies to SARS-CoV-2 (dependent or outcome variable) and the independent variables (age, sex, overcrowding).
    SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    The data were analyzed with IBM SPSS Version 25 and Stata 14.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    Infection to case ratio (ICR) was calculated by dividing the estimated infection with SARS-CoV-2 confirmed cases by one week and two weeks before the median survey date in each round.
    SARS-CoV-2
    suggested: (Active Motif Cat# 91351, RRID:AB_2847848)

    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 overall seroprevalence in this study ranging from 24.08%-28.39% with a caveat for the likely underestimation of antibody positivity in subsequent rounds due to the waning of the antibody response in segments of the population, who may or may not have acquired immunity for protection against the infection. Consequently, future studies should estimate the incidence of COVID-19 reinfection in cases with asymptomatic or mild seroconversion which constitutes the clinical profile of most cases to validate the durability of the immune response. The temporal feasibility of such an approach is evident since ≥6 months have elapsed since the initial rounds of population serosurveys were conducted across multiple Indian cities and states. Understanding the phenomenon would help in planning early COVID-19 vaccine deployment through evidence based identification of subgroups for early vaccine coverage prioritization and achieving the minimum herd immunity threshold in the least possible time. There exist some study limitations. Sample representativeness in the August round was probably lesser compared to the September and October rounds. The sample was underpowered for detecting ward level variations, so disaggregated ward-level data were not analysed. The missing sociodemographic data were assumed to be missing at random type for estimation of the sample statistics. The testing kit did not detect IgM antibodies likely to be present in participants with acute infection but lacking dete...

    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.

    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.