Evidence map›Paper›PMID 34591892›Full record

ArticlePloS one2021

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

Vandana Tamrakar, Ankita Srivastava, Nandita Saikia, Mukesh C Parmar, Sudheer Kumar Shukla, Shewli Shabnam, Bandita Boro, Apala Saha, Benjamin Debbarma

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  14. A cross-sectional study of COVID-19 outbreak in Indian population.Annals of medicine and surgery (2012) · 2022
    Article
  15. District-Level Risk Factors for COVID-19 Incidence and Mortality in Nepal.International journal of environmental research and public health · 2022
    Article
  16. Article
  17. The role of demography in COVID-19 in India.The Indian journal of medical research
    Article
  18. India's Multi-Sectoral Response to Oxygen Surge Demand during COVID-19 Pandemic: A Scoping Review.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Vandana TamrakarCentre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, India.ORCID 0000-0001-6039-6164
Ankita SrivastavaCentre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, India.
Nandita SaikiaCentre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, India.ORCID 0000-0001-6735-6157
Mukesh C ParmarCentre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, India.ORCID 0000-0002-8614-1183
Sudheer Kumar ShuklaCentre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, India.
Shewli ShabnamBidhannagar College, Kolkata, India.
Bandita BoroCentre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, India.
Apala SahaBanaras Hindu University, Varanasi, India.
Benjamin DebbarmaCentre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-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.

objectiveThe 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

methodsWe 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.

resultsThe 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.

conclusionThe 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.

Indexed as

AdolescentAdultCOVID-19Family CharacteristicsHumansIndiaMiddle AgedPandemicsSARS-CoV-2Socioeconomic FactorsSpatial AnalysisUrban PopulationYoung Adult

Identifiers

PMID34591892
PMCPMC8483309

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.