SARS-CoV-2 Seroprevalence in Tamil Nadu in October-November 2020

  1. Anup Malani
  2. Sabareesh Ramachandran
  3. Vaidehi Tandel
  4. Rajeswari Parasa
  5. Sofia Imad
  6. S. Sudharshini
  7. V. Prakash
  8. Y. Yogananth
  9. S. Raju
  10. T.S. Selvavinayagam

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Abstract

A population-representative serological study was conducted in all districts of the state of Tamil Nadu (population 72 million), India, in October-November 2020. State-level seroprevalence was 31.6%. However, this masks substantial variation across the state. Seroprevalence ranged from just 11.1% in The Nilgris to 51.0% in Perambalur district. Seroprevalence in urban areas (36.9%) was higher than in rural areas (26.9%). Females (30.8%) had similar seroprevalence to males (30.3%). However, working age populations (age 40-49: 31.6%) have significantly higher seroprevalence than the youth (age 18-29: 30.7%) or elderly (age 70+: 25.8%). Estimated seroprevalence implies that at least 22.6 million persons were infected by the end of November, roughly 36 times the number of confirmed cases. Estimated seroprevalence implies an infection fatality rate of 0.052%.

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  1. Version published to 10.1101/2021.02.03.21250949v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.02.03.21250949: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIACUC: This study was approved by the Directorate of Public Health & Preventative Medicine, Government of Tamil Nadu, and the Institutional Ethics Committee of Madras Medical College, Chennai, India. Outcomes: The first primary endpoint of the study is the rate of positive results on CLIA antibody tests at the district-level.
    Consent: The exclusion criteria were refusal to consent and contraindication to venipuncture.
    RandomizationFirst, within each HUD, the study randomly selected clusters.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot 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. One is that, because antibody concentrations in infected persons decline over time11, our estimate of seroprevalence may underestimate the level of prior infection and perhaps natural immunity. Second, we may underestimate IFR. The number of deaths per million is positively correlated (ρ=0.96; p<0.001) with testing rate per million at the district level (Table 3). Perhaps increasing the testing rate would show greater deaths from SARS-CoV-2.

    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 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 10.1101/2021.02.03.21250949v1 on medRxiv