Evidence map›Paper›PMID 36914858›Full record

ArticleScientific reports2023

Spatial-temporal distribution of incidence, mortality, and case-fatality ratios of coronavirus disease 2019 and its social determinants in Brazilian municipalities.

Carlos Eduardo Raymundo, Marcella Cini Oliveira, Tatiana de Araujo Eleuterio, Édnei César de Arruda Santos Junior, Marcele Gonçalves da Silva, Suzana Rosa André, Ana Inês Sousa, Roberto de Andrade Medronho

Full text read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

  1. Pooled it
  2. 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

8 authors.

Carlos Eduardo RaymundoInstituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, 100 - Cidade Universitária, Rio de Janeiro, RJ, CEP 21941-598, Brazil. caducer@gmail.com.ORCID 0000-0002-4150-4403
Marcella Cini OliveiraFaculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.ORCID 0000-0001-5305-6823
Tatiana de Araujo EleuterioInstituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, 100 - Cidade Universitária, Rio de Janeiro, RJ, CEP 21941-598, Brazil.ORCID 0000-0002-8043-2350
Édnei César de Arruda Santos JuniorInstituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, 100 - Cidade Universitária, Rio de Janeiro, RJ, CEP 21941-598, Brazil.ORCID 0000-0002-6956-6139
Marcele Gonçalves da SilvaFaculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.ORCID 0000-0002-6554-0900
Suzana Rosa AndréEscola de Enfermagem Anna Nery, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.ORCID 0000-0002-4219-9279
Ana Inês SousaEscola de Enfermagem Anna Nery, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.ORCID 0000-0002-0214-0723
Roberto de Andrade MedronhoInstituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, 100 - Cidade Universitária, Rio de Janeiro, RJ, CEP 21941-598, Brazil.ORCID 0000-0003-4073-3930

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic caused impact on public health worldwide. Brazil gained prominence during the pandemic due to the magnitude of disease. This study aimed to evaluate the spatial-temporal dynamics of incidence, mortality, and case fatality of COVID-19 and its associations with social determinants in Brazilian municipalities and epidemiological week. We modeled incidence, mortality, and case fatality rates using spatial-temporal Bayesian model. "Bolsa Família Programme" (BOLSAFAM) and "proportional mortality ratio" (PMR) were inversely associated with the standardized incidence ratio (SIR), while "health insurance coverage" (HEALTHINSUR) and "Gini index" were directly associated with the SIR. BOLSAFAM and PMR were inversely associated with the standardized mortality ratio (SMR) and standardized case fatality ratio (SCFR). The highest proportion of excess risk for SIR and the SMR started in the North, expanding to the Midwest, Southeast, and South regions. The highest proportion of excess risk for the SCFR outcome was observed in some municipalities in the North region and in the other Brazilian regions. The COVID-19 incidence and mortality in municipalities that most benefited from the cash transfer programme and with better social development decreased. The municipalities with a higher proportion of non-whites had a higher risk of becoming ill and dying from the disease.

Indexed as

COVID-19Bayes TheoremBrazilCitiesHumansIncidencePandemicsSocial Determinants of Health

Identifiers

PMID36914858
PMCPMC10009864

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read13
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.