Evidence map›Paper›PMID 33857500›Full record

ArticleThe Lancet. Global health2021

Effect of socioeconomic inequalities and vulnerabilities on health-system preparedness and response to COVID-19 in Brazil: a comprehensive analysis.

Rudi Rocha, Rifat Atun, Adriano Massuda, Beatriz Rache, Paula Spinola, Letícia Nunes, Miguel Lago, Marcia C Castro

Abstract read
In one paragraph

Article in The Lancet. Global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 171 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
171citing 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

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Contributing Factors to Safety: What Hospitalized Patients Can Tell Us? A Cross-Sectional Study.The International journal of health planning and management · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Rudi RochaSão Paulo School of Business Administration, Fundação Getulio Vargas, São Paulo, Brazil; Instituto de Estudos para Políticas de Saúde, São Paulo, Brazil.
Rifat AtunDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA. Electronic address: ratun@hsph.harvard.edu.
Adriano MassudaSão Paulo School of Business Administration, Fundação Getulio Vargas, São Paulo, Brazil.
Beatriz RacheInstituto de Estudos para Políticas de Saúde, São Paulo, Brazil.
Paula SpinolaCentre for Global Health Economics, University College London, London, UK.
Letícia NunesInstituto de Estudos para Políticas de Saúde, São Paulo, Brazil.
Miguel LagoInstituto de Estudos para Políticas de Saúde, Rio de Janeiro, Brazil.
Marcia C CastroDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 spread rapidly in Brazil despite the country's well established health and social protection systems. Understanding the relationships between health-system preparedness, responses to COVID-19, and the pattern of spread of the epidemic is particularly important in a country marked by wide inequalities in socioeconomic characteristics (eg, housing and employment status) and other health risks (age structure and burden of chronic disease).

methodsFrom several publicly available sources in Brazil, we obtained data on health risk factors for severe COVID-19 (proportion of the population with chronic disease and proportion aged ≥60 years), socioeconomic vulnerability (proportions of the population with housing vulnerability or without formal work), health-system capacity (numbers of intensive care unit beds and physicians), coverage of health and social assistance, deaths from COVID-19, and state-level responses of government in terms of physical distancing policies. We also obtained data on the proportion of the population staying at home, based on locational data, as a measure of physical distancing adherence. We developed a socioeconomic vulnerability index (SVI) based on household characteristics and the Human Development Index. Data were analysed at the state and municipal levels. Descriptive statistics and correlations between state-level indicators were used to characterise the relationship between the availability of health-care resources and socioeconomic characteristics and the spread of the epidemic and the response of governments and populations in terms of new investments, legislation, and physical distancing. We used linear regressions on a municipality-by-month dataset from February to October, 2020, to characterise the dynamics of COVID-19 deaths and response to the epidemic across municipalities.

findingsThe initial spread of COVID-19 was mostly affected by patterns of socioeconomic vulnerability as measured by the SVI rather than population age structure and prevalence of health risk factors. The states with a high (greater than median) SVI were able to expand hospital capacity, to enact stringent COVID-19-related legislation, and to increase physical distancing adherence in the population, although not sufficiently to prevent higher COVID-19 mortality during the initial phase of the epidemic compared with states with a low SVI. Death rates accelerated until June, 2020, particularly in municipalities with the highest socioeconomic vulnerability. Throughout the following months, however, differences in policy response converged in municipalities with lower and higher SVIs, while physical distancing remained relatively higher and death rates became relatively lower in the municipalities with the highest SVIs compared with those with lower SVIs.

interpretationIn Brazil, existing socioeconomic inequalities, rather than age, health status, and other risk factors for COVID-19, have affected the course of the epidemic, with a disproportionate adverse burden on states and municipalities with high socioeconomic vulnerability. Local government responses and population behaviour in the states and municipalities with higher socioeconomic vulnerability have helped to contain the effects of the epidemic. Targeted policies and actions are needed to protect those with the greatest socioeconomic vulnerability. This experience could be relevant in other low-income and middle-income countries where socioeconomic vulnerability varies greatly.

fundingNone. TRANSLATION: For the Portuguese translation of the abstract see Supplementary Materials section.

Indexed as

BrazilCOVID-19Delivery of Health CareHumansSocioeconomic FactorsVulnerable Populations

Identifiers

PMID33857500
PMCPMC8041360

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.