Higher risk of death from COVID-19 in low-income and non-White populations of São Paulo, Brazil

Abstract

Introduction: Little evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in São Paulo state, Brazil, and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities.

Methods: We conducted a cross-sectional study using hospitalised severe acute respiratory infections notified from March to August 2020 in the Sistema de Monitoramento Inteligente de São Paulo database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple data sets for individual-level and spatiotemporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour and comorbidities.

Results: Throughout the study period, patients living in the 40% poorest areas were more likely to die when compared with patients living in the 5% wealthiest areas (OR: 1.60, 95% CI 1.48 to 1.74) and were more likely to be hospitalised between April and July 2020 (OR: 1.08, 95% CI 1.04 to 1.12). Black and Pardo individuals were more likely to be hospitalised when compared with White individuals (OR: 1.41, 95% CI 1.37 to 1.46; OR: 1.26, 95% CI 1.23 to 1.28, respectively), and were more likely to die (OR: 1.13, 95% CI 1.07 to 1.19; 1.07, 95% CI 1.04 to 1.10, respectively) between April and July 2020. Once hospitalised, patients treated in public hospitals were more likely to die than patients in private hospitals (OR: 1.40%, 95% CI 1.34% to 1.46%). Black individuals and those with low education attainment were more likely to have one or more comorbidities, respectively (OR: 1.29, 95% CI 1.19 to 1.39; 1.36, 95% CI 1.27 to 1.45).

Conclusions: Low-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to quality healthcare, ability to self-isolate and the higher prevalence of comorbidities.

Keywords: cross-sectional survey; epidemiology; geographic information systems; mathematical modelling; public health.

Figure 1

Severe acute respiratory infection (SARI) hospitalisations in São Paulo state. (A) Number of hospitalisations per 100 000 habitants by state in Brazil between 1 March and 29 August 2020. (B) Number of SARI hospitalisations for the state of São Paulo by week of symptom onset. (C) Flow chart of Sistema de Monitoramento Inteligente de São Paulo (SIMI-SP) data processing (Source: https://covid.saude.gov.br).

Figure 2

Individual-level hospitalisation and death risk by age-standardised OR. (A) OR for severe acute respiratory infection (SARI) hospitalisation by race. (B) OR for SARI hospitalisation by income. (C) OR for death among patients with SARI by race. (D) OR for death among patients with SARI by income. (E) OR for death among patients with SARI by hospital type. PPP, purchasing power parity.

Figure 3

Hospitalisation risk by municipality in São Paulo state. (A) Human movement between municipalities based on In-Loco mobile phone data retrieved from March to August 2020. (B) Fixed effects and 95% credible intervals for socioeconomic covariates. (C) Relative risk of severe acute respiratory infection (SARI) hospitalisation at the municipality level.

Figure 4

Differential risk based on varying ability to self-isolate in the Região Metropolitana de São Paulo (RMSP). (A) Relative risk of severe acute respiratory infection (SARI) hospitalisation for the RMSP. (B) Seven-day moving average of daily isolation levels by race. (C) Seven-day moving average of daily isolation levels by income. (D) Difference in daily social isolation by race after the introduction of non-pharmaceutical intervention (NPI). (E) Difference in daily social isolation by income after the introduction of NPIs. In panels (B) and (C), solid lines show population-weighted median isolation levels and shaded areas show population-weighted IQR (25%–75%). Dashed vertical lines indicate the dates of NPIs that enabled school closure (13 March was the state NPI) and non-essential activities (18 and 22 March, municipal and state NPIs, respectively).

Figure 5

Inequalities in working conditions and comorbidities. (A) Probability of different working conditions by education attainment. (B) Probability of different working conditions by race. (C) OR (OR=1) of having one or more comorbidities by education attainment. (D) OR (OR=1) of having one or more comorbidities by race. Comorbidities considered include chronic obstructive pulmonary disease (COPD), diabetes, hypertension or cardiovascular disease such as infarction, angina and heart failure. Horizontal lines show 95% CIs (Source: Pesquisa Nacional por Amostra de Domicílios (PNAD) COVID-19/Instituto Brasileiro de Geografia e Estatística (IBGE), July to September 2020).