Social inequalities and COVID-19 mortality in the city of São Paulo, Brazil

Ribeiro, Karina Braga; Ribeiro, Ana Freitas; Veras, Maria Amélia de Sousa Mascena; Castro, Márcia C.

doi:10.1093/ije/dyab022

Cited by 97 publications

(101 citation statements)

References 32 publications

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“…There was a relationship in mortality between the economic States conditions and the health services distribution; for example, difference in the fatality rate was twice higher in the Edo Mex than in the Cd Mx despite that Edo Mex population is twice as high (16,992,418 vs. 9,209,944 people) ( 23), but the gross domestic product is 45% lower in Edo Mex, a condition reflected in the provision of health services; in Cd Mx the number of third level hospitals is 3.5 higher (56 vs. 16 hospital centers) (24). The impact of sociodemographic conditions on mortality in Brazil was evaluated by Braga-Ribeiro et al, who found an association between less education, more household crowding, lower income, and a higher population concentration in subnormal areas; mortality was found to be four times higher in a population with a lower degree of education compared with that having a higher degree of education, showing that socio-economic inequity impacts fatality during this pandemic (25). Nation-wide, the proportion of confirmed cases that required hospitalization was 23.8%, 17.5% were intubated and 8.6% were inpatient in an ICU and 43.8% of hospitalized patients died with a CFR of 10.4%.…”

Section: Discussionmentioning

confidence: 99%

Coronavirus Disease-2019 Survival in Mexico: A Cohort Study on the Interaction of the Associated Factors

Márquez-González

Méndez-Gálvan

Reyes‐López

et al. 2021

Front. Public Health

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The pandemic caused by the new coronavirus Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is currently affecting more than 200 countries. The most lethal clinical presentation is respiratory insufficiency, requiring attention in intensive care units (ICU). The most susceptible people are over 60 years old with comorbidities. The health systems organization may represent a transcendental role in survival.Objective: To analyze the correlation of sociodemographic factors, comorbidities and health system organization variables with survival in cases infected by SARS-CoV-2 during the first 7 months of the pandemic in Mexico.Methods: The cohort study was performed in a health system public basis from March 1st to September 30th, 2020. The included subjects were positive for the SARS-CoV-2 test, and the target variable was mortality in 60 days. The risk variables studied were: age, sex, geographic distribution, comorbidities, health system, hospitalization, and access to ICU. Bivariate statistics (X2-test), calculation of fatality rates, survival analyses and adjustment of confusing variables with Cox proportional-hazards were performed.Results: A total of 753,090 subjects were analyzed, of which the 52% were men. There were 78,492 deaths (10.3% of general fatality and 43% inpatient). The variables associated with a higher risk of hospital mortality were age (from 60 years onwards), care in public sectors, geographic areas with higher numbers of infection and endotracheal intubation without management in the ICU.Conclusions: The variables associated with a lower survival in cases affected by SARS-CoV-2 were age, comorbidities, and respiratory insufficiency (with endotracheal intubation without care in the ICU). Additionally, an interaction was observed between the geographic location and health sector where they were treated.

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Section: Discussionmentioning

confidence: 99%

Coronavirus Disease-2019 Survival in Mexico: A Cohort Study on the Interaction of the Associated Factors

Márquez-González

Méndez-Gálvan

Reyes‐López

et al. 2021

Front. Public Health

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“…Another study in the United States showed that education level with a bachelor's degree was associated with a lower rate of mortality due to COVID-19 (estimate, −0.246; 95% CI, −0.388 to −0.103; P = 0.0008) across various ethnicities in the seven most affected states (11). In São Paulo, Brazil, among patients under 60 years of age and living in areas with the lowest percentage (below 8.61%) of the population with a university degree, COVID-19 mortality was four times higher than that among those living in areas with the highest percentage (over 34.80%) of population with a university degree (rate ratio, 4.02; 95% CI, 3.42-4.72) (12). However, our MR analysis was based on populations of European ancestry, and the findings are unlikely to be generalized to other populations and ethnicities.…”

Section: Discussionmentioning

confidence: 97%

“…Observational studies report that the severity of COVID-19 depends not only on physical conditions such as age, cardiovascular disease, and obesity (3)(4)(5)(6)(7)(8) but also on socioeconomic status (SES) indicators such as lower incomes and lower educational level among various populations (9)(10)(11)(12). In the European population, lower education level was associated with a higher risk of severe COVID-19 cases that were confirmed either at emergency departments or as inpatients and, therefore, likely reflect severe illness as well as a higher risk of asymptomatic COVID-19 cases in a prospective cohort study using UK Biobank data (9).…”

Section: Introductionmentioning

confidence: 99%

Educational Attainment Decreases the Risk of COVID-19 Severity in the European Population: A Two-Sample Mendelian Randomization Study

Yoshikawa

Asaba

2021

Front. Public Health

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Observational studies have reported that the severity of COVID-19 depends not only on physical conditions but also on socioeconomic status, including educational level. Because educational attainment (EA), which measures the number of years of schooling, is moderately heritable, we investigated the causal association of EA on the risk of COVID-19 severity using the Mendelian randomization (MR) approach. A two-sample MR analysis was performed using publicly available summary-level data sets of genome-wide association studies (GWASs). A total of 235 single-nucleotide polymorphisms (SNPs) were extracted as instrumental variables for the exposure of EA from the Social Science Genetic Association Consortium GWAS summary data of 766,345 participants of European ancestry. The effect of each SNP on the outcome of COVID-19 severity risk was obtained from the GWAS summary data of 1,059,456 participants of European ancestry gathered from the COVID-19 Host Genetics Initiative. Using inverse variance weighted method, our MR study shows that EA was significantly associated with a lower risk of COVID-19 severity (odds ratio per one standard deviation increase in years of schooling, 0.540; 95% confidence interval, 0.376–0.777, P = 0.0009). A series of sensitivity analyses showed little evidence of bias. In conclusion, we show for the first time using a two-sample MR approach the associations between higher EA and the lower risk of COVID-19 severity in the European population. However, the genetic or epidemiological mechanisms underlying the association between EA and the risk of COVID-19 severity remain unknown, and further studies are warranted to validate the MR findings and investigate underlying mechanisms.

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“…And in Brazil, the governmental response to COVID-19 has been marked by the lack of leadership at the federal level, distrust of science, denial of the importance of the virus and progressive cuts to health and research funding. There are racial and gender differences in the fight against novel corona-virus 44 . Brazil, of course, has by far the worst outbreak, and the number of confirmed cases is still rising dramatically, at the same time our experimental results also indicate this point.…”

Section: Discussionmentioning

confidence: 99%

The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method

Zheng

Wang

et al. 2021

Sci Rep

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Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many countries. Since there is still no effective treatment, it is essential to making effective predictions for relevant departments to make responses and arrangements in advance. Under the limited data, the prediction error of LSTM model will increase over time, and its prone to big bias for medium- and long-term prediction. To overcome this problem, our study proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTM model. Based on confirmed case data in the US, Britain, Brazil and Russia, we calculated the training errors of LSTM and constructed the probability transfer matrix of the Markov model by the errors. And finally, the prediction results were obtained by combining the output data of LSTM model with the prediction errors of Markov Model. The results show that: compared with the prediction results of the classical LSTM model, the average prediction error of LSTM-Markov is reduced by more than 75%, and the RMSE is reduced by more than 60%, the mean $${R}^{2}$$ R 2 of LSTM-Markov is over 0.96. All those indicators demonstrate that the prediction accuracy of proposed LSTM-Markov model is higher than that of the LSTM model to reach more accurate prediction of COVID-19.

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Social inequalities and COVID-19 mortality in the city of São Paulo, Brazil

Cited by 97 publications

References 32 publications

Coronavirus Disease-2019 Survival in Mexico: A Cohort Study on the Interaction of the Associated Factors

Coronavirus Disease-2019 Survival in Mexico: A Cohort Study on the Interaction of the Associated Factors

Educational Attainment Decreases the Risk of COVID-19 Severity in the European Population: A Two-Sample Mendelian Randomization Study

The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method

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