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

Abstract

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 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.

Funding: None.

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

Figure 1

Spatial distribution of socioeconomic vulnerabilities, COVID-19 health risks, hospital capacity, and COVID-19 death rates Maps show state-level indicators. A socioeconomic vulnerability index of 0 indicates the least vulnerable and 1 the most vulnerable. ICU=intensive care unit.

Figure 2

Correlation matrix of indicators of socioeconomic vulnerability, health risk factors, pre-existing health-system resources, and responses to COVID-19 Correlations are expressed as Pearson coefficients for bilateral associations across all pairs of indicators, in the range of −1 to +1. adj=age-adjusted. ICU=intensive care unit. SUS=Sistema Único de Saúde. *Significant at the 5% level.

Figure 3

Correlations between pre-existing hospital capacity, socioeconomic vulnerability, age and chronic disease burden, response, and COVID-19 death rates (A) Pearson coefficients for state-level bilateral correlations between selected indicators and pre-existing SUS ICU beds per 100 000 people. (B) State-level bilateral correlations between selected indicators and the socioeconomic vulnerability index. Scatterplots show linear associations at the state level. States are represented by their two-letter ISO 3166-2 codes. adj=age-adjusted. ICU=intensive care unit. SUS=Sistema Único de Saúde.

Figure 4

Differentials on outcome variables by socioeconomic vulnerability and month in 2020 The plots show coefficients and 95% CIs (error bars) of linear regressions that measure, for each month, the difference in average outcomes between municipalities with HDI below the median and those with HDI above the median (February is the omitted category). (A) Deaths per 100 000 people. (B) Physical distancing adherence and policy stringency indicators. Positive estimates indicate that the respective outcome increased relatively more in municipalities with HDI below the median, and negative estimates indicate that the respective outcome decreased relatively more in municipalities with HDI below the median. HDI=Human Development Index.