Evidence map›Paper›PMID 32622400›Full record

Observational studyThe Lancet. Global health2020

Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study.

Pedro Baqui, Ioana Bica, Valerio Marra, Ari Ercole, Mihaela van der Schaar

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in The Lancet. Global health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 280 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
280citing papers in PubMed, 11 pooled it
14.3field-weighted citation impact, top 1% 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

280 citing papers in PubMed, 11 syntheses or guidelines pooled it, 659 citations in OpenAlex.

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220 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 3 countries.

Pedro BaquiNúcleo de Astrofísica e Cosmologia, Universidade Federal do Espírito Santo, Vitória, ES, Brazil.
Ioana BicaDepartment of Engineering Science, University of Oxford, Oxford, UK; The Alan Turing Institute, London, UK.
Valerio MarraNúcleo de Astrofísica e Cosmologia, Universidade Federal do Espírito Santo, Vitória, ES, Brazil; Departamento de Física, Universidade Federal do Espírito Santo, Vitória, ES, Brazil. Electronic address: marra@cosmo-ufes.org.
Ari ErcoleDepartment of Medicine, University of Cambridge, Cambridge, UK; Cambridge Centre for Artificial Intelligence in Medicine, Cambridge, UK.
Mihaela van der SchaarThe Alan Turing Institute, London, UK; Cambridge Centre for Artificial Intelligence in Medicine, Cambridge, UK; Department of Applied Mathematics and Theoretical Physics and Department of Population Health, University of Cambridge, Cambridge, UK; Department of Electrical and Computer Engineering, University of California Los Angeles, Los Angeles, CA, USA.
The Alan Turing Institute · GBUniversidade Federal do Espírito Santo · BRUniversity of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBrazil ranks second worldwide in total number of COVID-19 cases and deaths. Understanding the possible socioeconomic and ethnic health inequities is particularly important given the diverse population and fragile political and economic situation. We aimed to characterise the COVID-19 pandemic in Brazil and assess variations in mortality according to region, ethnicity, comorbidities, and symptoms.

methodsWe conducted a cross-sectional observational study of COVID-19 hospital mortality using data from the SIVEP-Gripe (Sistema de Informação de Vigilância Epidemiológica da Gripe) dataset to characterise the COVID-19 pandemic in Brazil. In the study, we included hospitalised patients who had a positive RT-PCR test for severe acute respiratory syndrome coronavirus 2 and who had ethnicity information in the dataset. Ethnicity of participants was classified according to the five categories used by the Brazilian Institute of Geography and Statistics: Branco (White), Preto (Black), Amarelo (East Asian), Indígeno (Indigenous), or Pardo (mixed ethnicity). We assessed regional variations in patients with COVID-19 admitted to hospital by state and by two socioeconomically grouped regions (north and central-south). We used mixed-effects Cox regression survival analysis to estimate the effects of ethnicity and comorbidity at an individual level in the context of regional variation.

findingsOf 99 557 patients in the SIVEP-Gripe dataset, we included 11 321 patients in our study. 9278 (82·0%) of these patients were from the central-south region, and 2043 (18·0%) were from the north region. Compared with White Brazilians, Pardo and Black Brazilians with COVID-19 who were admitted to hospital had significantly higher risk of mortality (hazard ratio [HR] 1·45, 95% CI 1·33-1·58 for Pardo Brazilians; 1·32, 1·15-1·52 for Black Brazilians). Pardo ethnicity was the second most important risk factor (after age) for death. Comorbidities were more common in Brazilians admitted to hospital in the north region than in the central-south, with similar proportions between the various ethnic groups. States in the north had higher HRs compared with those of the central-south, except for Rio de Janeiro, which had a much higher HR than that of the other central-south states.

interpretationWe found evidence of two distinct but associated effects: increased mortality in the north region (regional effect) and in the Pardo and Black populations (ethnicity effect). We speculate that the regional effect is driven by increasing comorbidity burden in regions with lower levels of socioeconomic development. The ethnicity effect might be related to differences in susceptibility to COVID-19 and access to health care (including intensive care) across ethnicities. Our analysis supports an urgent effort on the part of Brazilian authorities to consider how the national response to COVID-19 can better protect Pardo and Black Brazilians, as well as the population of poorer states, from their higher risk of dying of COVID-19.

fundingNone.

Indexed as

Health Status DisparitiesAdultAgedBrazilComorbidityCoronavirus InfectionsCOVID-19Cross-Sectional StudiesEthnicityFemaleHospital MortalityHumansMaleMiddle AgedPandemicsPneumonia, Viral

Identifiers

PMID32622400
PMCPMC7332269
OpenAlexW3038328918

What OpenQuestion holds

Textmetadata
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.