Federal Vaccine Policy and Interstate Variation in COVID-19 Vaccine Coverage in India

  1. Kanchan Mukherjee

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Abstract

Introduction

On August 13, 2021, India completed 30 weeks of vaccination against COVID-19 for its eligible citizens. While the vaccination has made progress, there has been no study analyzing the federal/union vaccine policy and its effect on vaccination coverage across Indian states. In this context, this study analyses the federal vaccination policy and its effect on interstate variation in vaccine coverage and the correlation of state economy with vaccination coverage.

Methods

The study analyses vaccine policy documents, secondary data on vaccination coverage and state gross domestic product (GDP) available in public domain. ANOVA test has been used to assess the effect of vaccine policy on interstate vaccine coverage and correlation-regression analysis has been conducted to assess the type and strength of association between gross state domestic product and vaccination coverage.

Results

Interstate variation in vaccination coverage in the first 15 weeks was the least (F=3.5), when vaccine procurement and supply was entirely provided by the union/federal government and vaccination was limited to priority groups. However, with the extension of vaccine policy to other groups and reduction in federal government involvement in vaccine procurement, the interstate variation in vaccination coverage increased significantly (F=10.74) by the end of 30 weeks. The highest interstate variation was observed in the period between 23-30 weeks (F=25.31). State GDP was positively and strongly correlated with state vaccination coverage with a high coefficient of correlation (R=0.94) and high coefficient of determination (R 2 = 0.88).

Conclusions

The study finds that federal procurement and supply of vaccination among prioritized groups has been the best strategy till date to address the inequity in vaccination coverage across the states of India.

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    SciScore for 10.1101/2021.08.16.21262113: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data entry and analysis was performed using Excel.
    Excel
    suggested: None

    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.

    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.

  2. Version published to 10.1101/2021.08.16.21262113v1 on medRxiv