Co-WIN: Really Winning? Analysing Inequity in India's Vaccination Response

TL;DR

This paper analyzes the inequities in India's COVID-19 vaccination policies, quantifies their effects, and highlights data discrepancies, providing insights into policy impacts and societal disparities during the vaccination drive.

Contribution

It offers a comprehensive analysis of policy-driven inequities in India's vaccination campaign using quantitative data, revealing unintended disparities and data inconsistencies.

Findings

Identified inequities in vaccination policy implementation

Quantified the impact of new policies on coverage

Highlighted data discrepancies across sources

Abstract

The COVID-19 pandemic has so far accounted for reported 5.5M deaths worldwide, with 8.7% of these coming from India. The pandemic exacerbated the weakness of the Indian healthcare system. As of January 20, 2022, India is the second worst affected country with 38.2M reported cases and 487K deaths. According to epidemiologists, vaccines are an essential tool to prevent the spread of the pandemic. India's vaccination drive began on January 16, 2021 with governmental policies being introduced to prioritize different populations of the society. Through the course of the vaccination drive, multiple new policies were also introduced to ensure that vaccines are readily available and vaccination coverage is increased. However, at the same time, some of the government policies introduced led to unintended inequities in the populations being targeted. In this report, we enumerate and analyze the inequities that existed in India's vaccination policy drive, and also compute the effect of the new policies that were introduced. We analyze these potential inequities not only qualitatively but also quantitatively by leveraging the data that was made available through the government portals. Specifically, (a) we discover inequities that might exist in the policies, (b) we quantify the effect of new policies introduced to increase vaccination coverage, and (c) we also point the data discrepancies that exist across different data sources.