Evidence map›Paper›PMID 33571268›Full record

ArticlePloS one2021

Spatial pattern of COVID-19 deaths and infections in small areas of Brazil.

Everton Emanuel Campos de Lima, Ezra Gayawan, Emerson Augusto Baptista, Bernardo Lanza Queiroz

Open access · goldAbstract 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 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
5.3field-weighted citation impact, top 4% of its field
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

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.

  1. Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022
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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

4 authors at 4 institutions in 3 countries.

Everton Emanuel Campos de LimaCollege of Philosophy and Human Sciences (IFCH), University of Campinas (UNICAMP), Campinas, Brazil.ORCID 0000-0001-6275-9854
Ezra GayawanDepartment of Statistics, Federal University of Technology, Akure, Nigeria.
Emerson Augusto BaptistaAsian Demographic Research Institute (ADRI), Shanghai University, Shanghai, China.
Bernardo Lanza QueirozDepartment of Demography, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil.
Federal University of Technology · NGShanghai University · CNUniversidade Estadual de Campinas (UNICAMP) · BRUniversidade Federal de Minas Gerais · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As of mid-August 2020, Brazil was the country with the second-highest number of cases and deaths by the COVID-19 pandemic, but with large regional and social differences. In this study, using data from the Brazilian Ministry of Health, we analyze the spatial patterns of infection and mortality from Covid-19 across small areas of Brazil. We apply spatial autoregressive Bayesian models and estimate the risks of infection and mortality, taking into account age, sex composition of the population and other variables that describe the health situation of the spatial units. We also perform a decomposition analysis to study how age composition impacts the differences in mortality and infection rates across regions. Our results indicate that death and infections are spatially distributed, forming clusters and hotspots, especially in the Northern Amazon, Northeast coast and Southeast of the country. The high mortality risk in the Southeast part of the country, where the major cities are located, can be explained by the high proportion of the elderly in the population. In the less developed areas of the North and Northeast, there are high rates of infection among young adults, people of lower socioeconomic status, and people without access to health care, resulting in more deaths.

Indexed as

AdolescentAdultAgedAged, 80 and overAge FactorsBayes TheoremBrazilChildChild, PreschoolCOVID-19FemaleHumansInfantMaleMiddle AgedRisk Factors

Identifiers

PMID33571268
PMCPMC7877657
OpenAlexW3128035221

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.