Evidence map›Paper›PMID 39469495›Full record

ArticleF1000Research2024

Understanding the habitat vulnerability of Slums to COVID 19: Case of two megacities of India.

Sudha Panda, Soumyendu Shankar Ray

Abstract read
In one paragraph

Article in F1000Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Healthcare Utilization Patterns for Acute Undifferentiated Febrile Illness in an Urban Slum of Delhi: A Prospective Cohort Study.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2026
    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

2 authors.

Sudha PandaSchool of Architecture and Planning, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India.ORCID https://orcid.org/0000-0001-9707-1961
Soumyendu Shankar RaySchool of Architecture and Planning, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. Methodology: This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. Results: The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. Conclusion: This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

Indexed as

COVID-19EcosystemPoverty AreasCitiesHumansIndiaPandemicsSARS-CoV-2Habitat vulnerability; COVID-19; urban slums; congestion; access to basic services; risk exposure;

Identifiers

PMID39469495
PMCPMC11514380

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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.