Evidence map›Paper›PMID 33629938›Full record

ArticleEpidemiology and infection2021

Spatial dynamics of the COVID-19 pandemic in Brazil.

R R Castro, R S C Santos, G J B Sousa, Y T Pinheiro, R R I M Martins, M L D Pereira, R A R Silva

Abstract read
In one paragraph

Article in Epidemiology and infection, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. What Makes Urban Communities More Resilient to COVID-19? A Systematic Review of Current Evidence.International journal of environmental research and public health · 2022
    Pooled it
  2. Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022
    Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Can the Content of Social Networks Explain Epidemic Outbreaks?Population research and policy review · 2023
    Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

7 authors.

R R CastroPostgraduate program in Clinical Nursing Care and Health, Universidade Estadual do Ceará, Fortaleza, Ceará, Brasil.ORCID 0000-0002-9260-4148
R S C SantosPostgraduate program in Nursing, Faculdade Metropolitana de Ciências e Tecnologia, Parnamirim, Rio Grande do Norte, Brasil.
G J B SousaPostgraduate program in Clinical Nursing Care and Health, Universidade Estadual do Ceará, Fortaleza, Ceará, Brasil.ORCID 0000-0003-0291-6613
Y T PinheiroFaculdade Maurício de Nassau, João Pessoa, Paraíba, Brasil.
R R I M MartinsFaculdade Santo Antônio de Caçapava, São Paulo, Brasil.
M L D PereiraPostgraduate program in Clinical Care in Nursing and Health, Universidade Estadual do Ceará, Fortaleza, Ceará, Brasil.
R A R SilvaPostgraduate program in Nursing, Universidade Federal do Rio Grande do Norte, Natal, Rio Grande do Norte, Brasil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The objective of this study was to analyse the dynamics of spatial dispersion of the coronavirus disease 2019 (COVID-19) in Brazil by correlating them to socioeconomic indicators. This is an ecological study of COVID-19 cases and deaths between 26 February and 31 July 2020. All Brazilian counties were used as units of analysis. The incidence, mortality, Bayesian incidence and mortality rates, global and local Moran indices were calculated. A geographic weighted regression analysis was conducted to assess the relationship between incidence and mortality due to COVID-19 and socioeconomic indicators (independent variables). There were confirmed 2 662 485 cases of COVID-19 reported in Brazil from February to July 2020 with higher rates of incidence in the north and northeast. The Moran global index of incidence rate (0.50, P = 0.01) and mortality (0.45 with P = 0.01) indicate a positive spatial autocorrelation with high standards in the north, northeast and in the largest urban centres between cities in the southeast region. In the same period, there were 92 475 deaths from COVID-19, with higher mortality rates in the northern states of Brazil, mainly Amazonas, Pará and Amapá. The results show that there is a geospatial correlation of COVID-19 in large urban centres and regions with the lowest human development index in the country. In the geographic weighted regression, it was possible to identify that the percentage of people living in residences with density higher than 2 per dormitory, the municipality human development index (MHDI) and the social vulnerability index were the indicators that most contributed to explaining incidence, social development index and the municipality human development index contributed the most to the mortality model. We hope that the findings will contribute to reorienting public health responses to combat COVID-19 in Brazil, the new epicentre of the disease in South America, as well as in other countries that have similar epidemiological and health characteristics to those in Brazil.

Indexed as

Bayes TheoremBrazilCitiesCOVID-19HumansIncidenceLinear ModelsPandemicsRisk FactorsSocioeconomic FactorsSpatial AnalysisBrazilCOVID-19pandemicspatial analysis

Identifiers

PMID33629938
PMCPMC7985898

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.