About: The epidemic caused by COVID-19 in Brazil is associated with an unfavorable political scenario, aggravated by intense social inequality and low number of available hospital beds. Therefore, this study aimed to analyze the survival of patients admitted to Brazilian hospitals due to the COVID-19 and estimate prognostic factors. This is a retrospective, multicenter cohort study, based on data from 46285 hospitalizations for COVID-19 in Brazil. Survival functions were calculated using the Kaplan-Meier method. The Log-rank test compared the survival functions for each variable and from that, hazard ratios were calculated and the proportional hazards model was used in Cox multiple regression. The smallest survival curves were the ones for patients at the age of 68 years or more, black / brown race, illiterate, living in the countryside, dyspnea, respiratory distress, influenza-like outbreak, O2 saturation <95%, X-ray change, length of stay in the ICU, invasive ventilatory support, previous heart disease, pneumopathy, diabetes, down's syndrome, neurological disease and kidney disease. Better survival was observed in the symptoms and in an asthmatic patient. The multiple model for increased risk of death when they were admitted to the ICU HR 1.28 (95% CI 1.21;1.35), diabetes HR 1.17 (95% CI 1.11;1.24), neurological disease HR 1.34 (95% CI 1.22;1.46), kidney disease HR 1.11 (95% CI 1.02;1.21), heart disease HR 1.14 (95% CI 1.08;1.20), black or brown race of HR 1.50 (95% CI 1.43;1.58), asthma HR 0.71 (95% CI 0.61;0.81) and pneumopathy HR 1.12 (95% CI 1.02;1.23). The overall survival time was low in hospitalizations for COVID-19 and this reinforces the importance of sociodemographic and clinical factors as a prognosis for death. The lack of a protocol for scientific clinical management puts a greater risk of death for about 80 million Brazilians, who are chronically ill or living in poverty. COVID-19 can promote selective mortality that borders the eugenics of specific social segments in Brazil.   Goto Sponge  NotDistinct  Permalink

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  • The epidemic caused by COVID-19 in Brazil is associated with an unfavorable political scenario, aggravated by intense social inequality and low number of available hospital beds. Therefore, this study aimed to analyze the survival of patients admitted to Brazilian hospitals due to the COVID-19 and estimate prognostic factors. This is a retrospective, multicenter cohort study, based on data from 46285 hospitalizations for COVID-19 in Brazil. Survival functions were calculated using the Kaplan-Meier method. The Log-rank test compared the survival functions for each variable and from that, hazard ratios were calculated and the proportional hazards model was used in Cox multiple regression. The smallest survival curves were the ones for patients at the age of 68 years or more, black / brown race, illiterate, living in the countryside, dyspnea, respiratory distress, influenza-like outbreak, O2 saturation <95%, X-ray change, length of stay in the ICU, invasive ventilatory support, previous heart disease, pneumopathy, diabetes, down's syndrome, neurological disease and kidney disease. Better survival was observed in the symptoms and in an asthmatic patient. The multiple model for increased risk of death when they were admitted to the ICU HR 1.28 (95% CI 1.21;1.35), diabetes HR 1.17 (95% CI 1.11;1.24), neurological disease HR 1.34 (95% CI 1.22;1.46), kidney disease HR 1.11 (95% CI 1.02;1.21), heart disease HR 1.14 (95% CI 1.08;1.20), black or brown race of HR 1.50 (95% CI 1.43;1.58), asthma HR 0.71 (95% CI 0.61;0.81) and pneumopathy HR 1.12 (95% CI 1.02;1.23). The overall survival time was low in hospitalizations for COVID-19 and this reinforces the importance of sociodemographic and clinical factors as a prognosis for death. The lack of a protocol for scientific clinical management puts a greater risk of death for about 80 million Brazilians, who are chronically ill or living in poverty. COVID-19 can promote selective mortality that borders the eugenics of specific social segments in Brazil.
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  • Brazil
  • Medical physics
  • Member states of Mercosur
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