Prevalence Of COVID-19 In Rural Versus Urban Areas in a Low-Income Country: Findings from a State-Wide Study in Karnataka, India

  1. Manoj Mohanan
  2. Anup Malani
  3. Kaushik Krishnan
  4. Anu Acharya

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Abstract

Although the vast majority of confirmed cases of COVID-19 are in low- and middle-income countries, there are relatively few published studies on the epidemiology of SARS-CoV-2 in these countries. The few there are focus on disease prevalence in urban areas. We conducted state-wide surveillance for COVID-19, in both rural and urban areas of Karnataka between June 15-August 29, 2020. We tested for both viral RNA and antibodies targeting the receptor binding domain (RBD). Adjusted seroprevalence across Karnataka was 46.7% (95% CI: 43.3-50.0), including 44.1% (95% CI: 40.0-48.2) in rural and 53.8% (95% CI: 48.4-59.2) in urban areas. The proportion of those testing positive on RT-PCR, ranged from 1.5 to 7.7% in rural areas and 4.0 to 10.5% in urban areas, suggesting a rapidly growing epidemic. The relatively high prevalence in rural areas is consistent with the higher level of mobility measured in rural areas, perhaps because of agricultural activity. Overall seroprevalence in the state implies that by August at least 31.5 million residents had been infected by August, nearly an order of magnitude larger than confirmed cases.

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  1. Version published to 10.1101/2020.11.02.20224782v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.11.02.20224782: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Data collection and Consent: At each of the 2912 households in the KSS sample, we first sought consent from anyone at home to complete a health survey that asked about demographics, comorbidities, travel and contact history, and COVID symptoms.
    RandomizationSample selection for the Karnataka Seroprevalence Study: Our study, which we label the Karnataka Seroprevalence Study (KSS), draws a random sample from CPHS’s 9717 households in Karnataka separately for the urban and rural strata.
    Blindingnot detected.
    Power AnalysisSample size and minimum detectable effect: We did not conduct power calculations when selecting our sample.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Of these 12 were due to bad labels and the result were due to failure in the VTM. (See Table 2 for details on sample composition by consent and lab specimen availability, by location and date) Testing: At Xcyton Labs, plasma was separated and tested for IgG antibodies to the receptor binding domain (RBD) of the SARS-CoV-2 virus using an ELISA test developed by Translational Health Science and Technology Institute, India.
    IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    We employ Microsoft Excel 365 and Stata 16 to perform statistical analyses.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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.
    • 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.

  3. Version published to 10.1101/2020.11.02.20224782v1 on medRxiv