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District level correlates of COVID-19 pandemic in India during March-October 2020

BACKGROUND: COVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19’s infection ratio (IR) is essential for formulating policies and interventions. OBJECTIVE: The present study aims to investigate the district level variation in COVID-19 during M...

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Autores principales: Tamrakar, Vandana, Srivastava, Ankita, Saikia, Nandita, Parmar, Mukesh C., Shukla, Sudheer Kumar, Shabnam, Shewli, Boro, Bandita, Saha, Apala, Debbarma, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8483309/
https://www.ncbi.nlm.nih.gov/pubmed/34591892
http://dx.doi.org/10.1371/journal.pone.0257533
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author Tamrakar, Vandana
Srivastava, Ankita
Saikia, Nandita
Parmar, Mukesh C.
Shukla, Sudheer Kumar
Shabnam, Shewli
Boro, Bandita
Saha, Apala
Debbarma, Benjamin
author_facet Tamrakar, Vandana
Srivastava, Ankita
Saikia, Nandita
Parmar, Mukesh C.
Shukla, Sudheer Kumar
Shabnam, Shewli
Boro, Bandita
Saha, Apala
Debbarma, Benjamin
author_sort Tamrakar, Vandana
collection PubMed
description BACKGROUND: COVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19’s infection ratio (IR) is essential for formulating policies and interventions. OBJECTIVE: The present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India’s socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level. DATA AND METHODS: We used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio. RESULTS: The results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran’s- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15–59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts. CONCLUSION: The COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas.
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spelling pubmed-84833092021-10-01 District level correlates of COVID-19 pandemic in India during March-October 2020 Tamrakar, Vandana Srivastava, Ankita Saikia, Nandita Parmar, Mukesh C. Shukla, Sudheer Kumar Shabnam, Shewli Boro, Bandita Saha, Apala Debbarma, Benjamin PLoS One Research Article BACKGROUND: COVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19’s infection ratio (IR) is essential for formulating policies and interventions. OBJECTIVE: The present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India’s socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level. DATA AND METHODS: We used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio. RESULTS: The results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran’s- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15–59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts. CONCLUSION: The COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas. Public Library of Science 2021-09-30 /pmc/articles/PMC8483309/ /pubmed/34591892 http://dx.doi.org/10.1371/journal.pone.0257533 Text en © 2021 Tamrakar et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tamrakar, Vandana
Srivastava, Ankita
Saikia, Nandita
Parmar, Mukesh C.
Shukla, Sudheer Kumar
Shabnam, Shewli
Boro, Bandita
Saha, Apala
Debbarma, Benjamin
District level correlates of COVID-19 pandemic in India during March-October 2020
title District level correlates of COVID-19 pandemic in India during March-October 2020
title_full District level correlates of COVID-19 pandemic in India during March-October 2020
title_fullStr District level correlates of COVID-19 pandemic in India during March-October 2020
title_full_unstemmed District level correlates of COVID-19 pandemic in India during March-October 2020
title_short District level correlates of COVID-19 pandemic in India during March-October 2020
title_sort district level correlates of covid-19 pandemic in india during march-october 2020
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8483309/
https://www.ncbi.nlm.nih.gov/pubmed/34591892
http://dx.doi.org/10.1371/journal.pone.0257533
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