SARS-CoV-2 Seroprevalence in 12 Cities of India from July-December 2020

  1. Arokiaswamy Velumani
  2. Chaitali Nikam
  3. Wilson Suraweera
  4. Sze Hang Fu
  5. Hellen Gelband
  6. Patrick Brown
  7. Isaac Bogoch
  8. Nico Nagelkerke
  9. Prabhat Jha

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Abstract

Objectives

We sought to understand the spread of SARS-CoV-2 infection in urban India, which has surprisingly low COVID-19 deaths.

Design

Cross-sectional and trend analyses of seroprevalence in self-referred test populations, and of reported cases and COVID mortality data.

Participants

448,518 self-referred individuals using a nationwide chain of private laboratories with central testing of SARS-CoV-2 antibodies and publicly available case and mortality data.

Setting

12 populous cities with nearly 92 million total population.

Main outcome measures

Seropositivity trends and predictors (using a Bayesian geospatial model) and prevalence derived from mortality data and infection fatality rates (IFR).

Results

For the whole of India, 31% of the self-referred individuals undergoing antibody testing were seropositive for SARS-CoV-2 antibodies. Seropositivity was higher in females (35%) than in males (30%) overall and in nearly every age group. In these 12 cities, seroprevalence rose from about 18% in July to 41% by December, with steeper increases at ages <20 and 20-44 years than at older ages. The “M-shaped” age pattern is consistent with intergenerational transmission. Areas of higher childhood measles vaccination in earlier years had lower seropositivity. The patterns of increase in seropositivity and in peak cases and deaths varied substantially across cities. In Delhi, death rates and cases first peaked in June and again in November; Chennai had a single peak in July. Based local IFRs and COVID deaths (adjusted for undercounts), we estimate that 43%-65% of adults above age 20 had been infected (range of mid-estimates of 12%-77%) corresponding 26 to 36 million infected adults in these cities, or an average of 9-12 infected adults per confirmed case.

Conclusion

Even with relatively low death rates, the large cities of India had remarkably high levels of SARS-CoV-2 infection. Vaccination strategies need to consider widespread intergenerational transmission.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.19.21253429: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableThe model also adjusted for sex, non-linear effect of age, percentage of urban settlement, population size, population density in the pincode, subdistrict-level solid-fuel use (as a measure of rural residence and lower socioeconomic status), female illiteracy, and language (as a proxy for geographic regions).

    Table 2: Resources

    Antibodies
    SentencesResources
    Population tested: We included 448,518 people of all ages who underwent testing for SARS-CoV-2 antibodies between June 12, 2020 and December 31, 2020 by Thyrocare Laboratories.12 Thyrocare conducts central laboratory testing in Navi Mumbai from over 2200 franchised collection centers in all major cities in India.
    SARS-CoV-2
    suggested: None
    SARS-CoV-2 IgG serological assays: The SARS-CoV-2 antibody assays involved collection of 3 ml of serum, shipment overnight to Navi Mumbai, and central high-throughput analyses using one or both of two widely-used assays that detect antibodies to the SARS-Cov-2 nucleocapsid protein: the Abbott SARS-CoV-2 chemiluminescent microparticle immunoassay for IgG (positive cutoff: >=1.40),14 and the Cobas, Roche Elecsys Anti-SARS-CoV-2 electrochemiluminescence immunoassay for total immunoglobulin (most of which is IgG; positive cutoff: >=1.0).15 For both assays, the laboratory followed the manufacturers’ quality control procedures.
    total immunoglobulin
    suggested: None
    Software and Algorithms
    SentencesResources
    SARS-CoV-2 IgG serological assays: The SARS-CoV-2 antibody assays involved collection of 3 ml of serum, shipment overnight to Navi Mumbai, and central high-throughput analyses using one or both of two widely-used assays that detect antibodies to the SARS-Cov-2 nucleocapsid protein: the Abbott SARS-CoV-2 chemiluminescent microparticle immunoassay for IgG (positive cutoff: >=1.40),14 and the Cobas, Roche Elecsys Anti-SARS-CoV-2 electrochemiluminescence immunoassay for total immunoglobulin (most of which is IgG; positive cutoff: >=1.0).15 For both assays, the laboratory followed the manufacturers’ quality control procedures.
    Abbott
    suggested: (Abbott, RRID:SCR_010477)

    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:
    Nonetheless, the study has some limitations. First, we could not provide reliable estimates of COVID infection or mortality in rural areas, where most of India’s 10 million annual deaths occur.4 In rural areas, SARS-CoV-2 infection and deaths might already be substantial. The Registrar General of India needs to re-start its Million Death Study,4,37 a mortality surveillance that provides cause of death information in over 8000 randomly-selected villages and urban blocks, as only maternal death data have been published after 2013. Repeat properly-representative large serosurveys that avoid some of the sampling limitations of current surveys29 are needed to monitor changes in natural and vaccine-induced immunity. Second, we had to rely on published estimates of IFR from three cities in Western India. IFRs may well vary by city and on variables such as background rates of obesity and other risk factors which are not well understood.5 The mortality-derived seroprevalence, while crude, is transparent and can be updated with improved IFR estimates.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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

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  2. Version published to 10.1101/2021.03.19.21253429 on medRxiv