Evidence map›Paper›PMID 35416871›Full record

ArticleRevista da Sociedade Brasileira de Medicina Tropical2022

COVID-19's intra-urban inequalities and social vulnerability in a medium-sized city.

Mário Círio Nogueira, Isabel Cristina Gonçalves Leite, Maria Teresa Bustamante Teixeira, Marcel de Toledo Vieira, Fernando Antonio Basile Colugnati

Abstract read
In one paragraph

Article in Revista da Sociedade Brasileira de Medicina Tropical, 2022. 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. 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

5 authors.

Mário Círio NogueiraUniversidade Federal de Juiz de Fora, Faculdade de Medicina, Departamento de Saúde Coletiva, Juiz de Fora, MG, Brasil.ORCID http://orcid.org/0000-0001-9688-4557
Isabel Cristina Gonçalves LeiteUniversidade Federal de Juiz de Fora, Faculdade de Medicina, Departamento de Saúde Coletiva, Juiz de Fora, MG, Brasil.ORCID http://orcid.org/0000-0003-1258-7331
Maria Teresa Bustamante TeixeiraUniversidade Federal de Juiz de Fora, Faculdade de Medicina, Departamento de Saúde Coletiva, Juiz de Fora, MG, Brasil.ORCID http://orcid.org/0000-0003-0727-4170
Marcel de Toledo VieiraUniversidade Federal de Juiz de Fora, Instituto de Ciências Exatas, Departamento de Estatística, Juiz de Fora, MG, Brasil.ORCID http://orcid.org/0000-0002-0456-380X
Fernando Antonio Basile ColugnatiUniversidade Federal de Juiz de Fora, Faculdade de Medicina, Departamento de Internato, Juiz de Fora, MG, Brasil.ORCID http://orcid.org/0000-0002-8288-203X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSocial conditions are related to the impact of epidemics on human populations. This study aimed to investigate the spatial distribution of cases, hospitalizations, and deaths from COVID-19 and its association with social vulnerability.

methodsAn ecological study was conducted in 81 urban regions (UR) of Juiz de Fora from March to November 2020. Exposure was measured using the Health Vulnerability Index (HVI), a synthetic indicator that combines socioeconomic and environmental variables from the Demographic Census 2010. Regression models were estimated for counting data with overdispersion (negative binomial generalized linear model) using Bayesian methods, with observed frequencies as the outcome, expected frequencies as the offset variable, and HVI as the explanatory variable. Unstructured random-effects (to capture the effect of unmeasured factors) and spatially structured effects (to capture the spatial correlation between observations) were included in the models. The models were estimated for the entire period and quarter.

resultsThere were 30,071 suspected cases, 8,063 confirmed cases, 1,186 hospitalizations, and 376 COVID-19 deaths. In the second quarter of the epidemic, compared to the low vulnerability URs, the high vulnerability URs had a lower risk of confirmed cases (RR=0.61; CI95% 0.49-0.76) and a higher risk of hospitalizations (RR=1.65; CI95% 1.23-2.22) and deaths (RR=1.73; CI95% 1.08-2.75).

conclusionsThe lower risk of confirmed cases in the most vulnerable UR probably reflected lower access to confirmatory tests, while the higher risk of hospitalizations and deaths must have been related to the greater severity of the epidemic in the city's poorest regions.

Indexed as

COVID-19Bayes TheoremCitiesHumansSocial VulnerabilitySocioeconomic Factors

Identifiers

PMID35416871
PMCPMC9009887

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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.