Background Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil’s municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic’s spread in the country. Methods This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). Findings The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease. Discussion Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.

Objective: To describe the clinical and epidemiological profile of suspected COVID-19 cases admitted to a federal hospital in Rio de Janeiro, RJ, Brazil, and to identify factors associated with death. Methods: This was a cross-sectional study using local epidemiological surveillance data as at epidemiological week 27 of 2020 and logistic regression. Results: 376 hospitalized suspected COVID-19 cases were included; 52.9% were female, 57.4% were 50 years old or over and 80.1% had comorbidities. 195 (51.9%) COVID-19 cases were confirmed and their lethality was higher (37.9%) than among discarded cases (24.2%). In the adjusted analysis, death among confirmed cases was associated with being in the 50-69 age group (OR=11.65-95%CI 1.69;80.33), being aged 70 or over (OR=8.43-95%CI 1.22;58.14), presence of neoplasms (OR=4.34-95%CI 1.28;14.76) and use of invasive ventilatory support (OR=70.20-95%CI 19.09;258.19). Conclusion: High prevalence of comorbidities and lethality was found; the main factors associated with death were being older, neoplasms and invasive ventilatory support.

Background COVID-19 can occur asymptomatically, as influenza-like illness, or as more severe forms, which characterize severe acute respiratory syndrome (SARS). Its mortality rate is higher in individuals over 80 years of age and in people with comorbidities, so these constitute the risk group for severe forms of the disease. We analyzed the factors associated with death in confirmed cases of COVID-19 in the state of Rio de Janeiro. This cross-sectional study evaluated the association between individual demographic, clinical, and epidemiological variables and the outcome (death) using data from the Unified Health System information systems. Methods We used the extreme boosting gradient (XGBoost) model to analyze the data, which uses decision trees weighted by the estimation difficulty. To evaluate the relevance of each independent variable, we used the SHapley Additive exPlanations (SHAP) metric. From the probabilities generated by the XGBoost model, we transformed the data to the logarithm of odds to estimate the odds ratio for each independent variable. Results This study showed that older individuals of black race/skin color with heart disease or diabetes who had dyspnea or fever were more likely to die. Conclusions The early identification of patients who may progress to a more severe form of the disease can help improve the clinical management of patients with COVID-19 and is thus essential to reduce the lethality of the disease.

Objetivo: realizar estratificação de risco para disseminação e gravidade da Covid-19 nas unidades da federação (UF) brasileiras a partir de características apontadas como situações de risco. Métodos: foram selecionados alguns indicadores sociais, demográficos e de saúde e submetidos à análise de componentes principais. Em seguida foi possível dividir as UF por análise de cluster. A partir da carga fatorial dos componentes, obtivemos um escore para as UF, que foram estratificadas quanto ao risco de disseminação e mortalidade da Covid-19. Resultados: os componentes referem-se às condições assistenciais, de saúde (incluindo fatores de risco), demográficas e sociais. Estes componentes permitiram a classificação final das 27 UF, com diferença na ordem quanto ao potencial de disseminação e a mortalidade. Conclusão: espera-se que a estratificação de risco possa ser uma medida de apoio à saúde pública, definindo áreas com maior potencial de dano, no sentido de subsidiar a criação de estratégias de intervenção prioritárias.

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Background: We analyzed the factors associated with death in confirmed cases of COVID-19 in the state of Rio de Janeiro. This cross-sectional study evaluated the association between individual demographic, clinical, and epidemiological variables and the outcome (death) using data from the Unified Health System information systems.Methods: We used the extreme boosting gradient (XGBoost) model to analyze the data, which uses decision trees weighted by the estimation difficulty. To evaluate the relevance of each independent variable, we used the SHapley Additive exPlanations (SHAP) metric. From the probabilities generated by the XGBoost model, we transformed the data to the logarithm of odds to estimate the odds ratio for each independent variable.Results: This study showed that older individuals of black race/skin color with heart disease or diabetes who had dyspnea or fever were more likely to die.Conclusions: The early identification of patients who may progress to a more severe form of the disease can help improve the clinical management of patients with COVID-19 and is thus essential to reduce the lethality of the disease.

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