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Epidemiol Infect ; 148: e123, 2020 06 25.
Article in English | MEDLINE | ID: covidwho-615328

ABSTRACT

This study aims to identify the risk factors associated with mortality and survival of COVID-19 cases in a state of the Brazilian Northeast. It is a historical cohort with a secondary database of 2070 people that presented flu-like symptoms, sought health assistance in the state and tested positive to COVID-19 until 14 April 2020, only moderate and severe cases were hospitalised. The main outcome was death as a binary variable (yes/no). It also investigated the main factors related to mortality and survival of the disease. Time since the beginning of symptoms until death/end of the survey (14 April 2020) was the time variable of this study. Mortality was analysed by robust Poisson regression, and survival by Kaplan-Meier and Cox regression. From the 2070 people that tested positive to COVID-19, 131 (6.3%) died and 1939 (93.7%) survived, the overall survival probability was 87.7% from the 24th day of infection. Mortality was enhanced by the variables: elderly (HR 3.6; 95% CI 2.3-5.8; P < 0.001), neurological diseases (HR 3.9; 95% CI 1.9-7.8; P < 0.001), pneumopathies (HR 2.6; 95% CI 1.4-4.7; P < 0.001) and cardiovascular diseases (HR 8.9; 95% CI 5.4-14.5; P < 0.001). In conclusion, mortality by COVID-19 in Ceará is similar to countries with a large number of cases of the disease, although deaths occur later. Elderly people and comorbidities presented a greater risk of death.


Subject(s)
Coronavirus Infections/mortality , Pneumonia, Viral/mortality , Adult , Age Factors , Aged , Brazil/epidemiology , COVID-19 , Cardiovascular Diseases/complications , Cohort Studies , Comorbidity , Coronavirus Infections/complications , Diabetes Complications/complications , Female , Hospitalization , Humans , Intensive Care Units , Kaplan-Meier Estimate , Kidney Diseases/complications , Lung Diseases/complications , Male , Middle Aged , Nervous System Diseases/complications , Pandemics , Pneumonia, Viral/complications , Poisson Distribution , Proportional Hazards Models , Risk Factors , Sex Factors , Time Factors
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