Higher risk of death from COVID-19 in low-income and non-White populations of São Paulo, Brazil
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Abstract
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.
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SciScore for 10.1101/2020.12.09.20246207: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has limitations that may have underestimated the level of inequality. Firstly, geocoding cases may have discarded patients from poor census tracts where accuracy is limited 24. Secondly, using data aggregated for …
SciScore for 10.1101/2020.12.09.20246207: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has limitations that may have underestimated the level of inequality. Firstly, geocoding cases may have discarded patients from poor census tracts where accuracy is limited 24. Secondly, using data aggregated for various administrative levels has inherent limitations due to ecological fallacy and the modifiable areal unit problem 25. Finally, the 2010 Brazilian population census and PNAD COVID-19 survey may have limited the capture of socioeconomic changes in the last decade and inclusion of extremely wealthy individuals 26. Additionally, disadvantaged groups can be underrepresented in health administrative records because of their lower access to healthcare. Given that São Paulo is the wealthiest state and has the most robust healthcare system in Brazil 27, it is possible that the impact of inequalities is more severe in other states. Our findings on the difference in risk of SARI death reveal stark inequalities in access to healthcare. Only 25% of Brazilians have access to private healthcare via health insurance, reflecting how inequality in access to quality healthcare is largely driven by income 28. This leaves 75% of the population solely reliant on a chronically underfunded public healthcare system, which highlights that disadvantaged populations are more likely to be infected and deprived of care. Strengthening healthcare access and its capacity will be critical for reducing health inequities during this and forthcoming public health emergencies 29. Our findi...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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