In the COVID-19 pandemic in Brazil, do brown lives matter?

Ribeiro, Helena; Lima, Viviana Mendes; Waldman, Eliseu Alves

doi:10.1016/s2214-109x(20)30314-4

Cited by 41 publications

(28 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Poor literacy is likely to also impact negatively on healthcare access. These findings support the conclusion that socioeconomic, ethnic and geographical factors are crucial in order to correctly understand the pandemic in Brazil and plan adequate measures 17 .…”

Section: Discussionsupporting

confidence: 84%

Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors

Baqui

Marra

Alaa

et al. 2021

Sci Rep

View full text Add to dashboard Cite

The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil’s social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95% CI 0.810–0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors. In conclusion, socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.

show abstract

Section: Discussionsupporting

confidence: 84%

Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors

Baqui

Marra

Alaa

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In addition, the authors highlighted that comorbid diseases and death were more common among Brazilians from the North region compared to the Central-South (except for Rio de Janeiro). Ribeiro et al [ 66 ] highlighted that the worst public health and socioeconomic scenarios were present in the northern regions of Brazil; higher proportions of individuals living in substandard housing (slums), with reduced schooling and a lack of sanitation and piped water, may interfere with adherence to hygiene recommendations. Although both papers discussed intracountry inequalities, social and health inequalities may be even higher between different countries [ 67 ] ( Figure 1 ).…”

Section: Discussionmentioning

confidence: 99%

Depression, Anxiety, and Lifestyle Among Essential Workers: A Web Survey From Brazil and Spain During the COVID-19 Pandemic

Boni¹,

Balanzá‐Martínez²,

Mota³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

Background Essential workers have been shown to present a higher prevalence of positive screenings for anxiety and depression during the COVID-19 pandemic. Individuals from countries with socioeconomic inequalities may be at increased risk for mental health disorders. Objective We aimed to assess the prevalence and predictors of depression, anxiety, and their comorbidity among essential workers in Brazil and Spain during the COVID-19 pandemic. Methods A web survey was conducted between April and May 2020 in both countries. The main outcome was a positive screening for depression only, anxiety only, or both. Lifestyle was measured using a lifestyle multidimensional scale adapted for the COVID-19 pandemic (Short Multidimensional Inventory Lifestyle Evaluation–Confinement). A multinomial logistic regression model was performed to evaluate the factors associated with depression, anxiety, and the presence of both conditions. Results From the 22,786 individuals included in the web survey, 3745 self-reported to be essential workers. Overall, 8.3% (n=311), 11.6% (n=434), and 27.4% (n=1027) presented positive screenings for depression, anxiety, and both, respectively. After adjusting for confounding factors, the multinomial model showed that an unhealthy lifestyle increased the likelihood of depression (adjusted odds ratio [AOR] 4.00, 95% CI 2.72-5.87), anxiety (AOR 2.39, 95% CI 1.80-3.20), and both anxiety and depression (AOR 8.30, 95% CI 5.90-11.7). Living in Brazil was associated with increased odds of depression (AOR 2.89, 95% CI 2.07-4.06), anxiety (AOR 2.81, 95%CI 2.11-3.74), and both conditions (AOR 5.99, 95% CI 4.53-7.91). Conclusions Interventions addressing lifestyle may be useful in dealing with symptoms of common mental disorders during the strain imposed among essential workers by the COVID-19 pandemic. Essential workers who live in middle-income countries with higher rates of inequality may face additional challenges. Ensuring equitable treatment and support may be an important challenge ahead, considering the possible syndemic effect of the social determinants of health.

show abstract

“…Earlier studies that have examined COVID-19 in Brazil have focused on specific indicators, such as antibody prevalence, poverty, and how mortality from COVID-19 varies by ethnicity and by region. 24 , 25 , 26 , 27 In this Article, we present a comprehensive analysis of health-system preparedness and response to COVID-19 in Brazil. We examine the relationship between the availability of health-care resources in different parts of the country, the socioeconomic characteristics of the population (eg, income, housing, and employment status), risk factors for adverse COVID-19 outcomes (age and burden of chronic disease), and socioeconomic vulnerability, with the pattern of spread, response, and outcomes of the epidemic, as measured by the number of deaths by state and municipality.…”

Section: Introductionmentioning

confidence: 99%

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

Rocha¹,

Atun²,

Massuda³

et al. 2021

The Lancet Global Health

285

234

View full text Add to dashboard Cite

Background COVID-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). Methods From 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. Findings The 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. Interpretation In Brazil, existing socioeconomic inequalities, rather than age, health status, and other risk factors for COVID-19, have...

show abstract

In the COVID-19 pandemic in Brazil, do brown lives matter?

Abstract: We declare no competing interests. We thank the Brazilian National Council for Scientific and Technological Development for a productivity grant for HR and postdoc scholarship for VML.

Cited by 41 publications

References 5 publications

Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors

Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors

Depression, Anxiety, and Lifestyle Among Essential Workers: A Web Survey From Brazil and Spain During the COVID-19 Pandemic

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

Contact Info

Product

Resources

About