Background Brazil became the epicenter of the COVID-19 epidemic in a brief period of a few months after the first officially registered case. The knowledge of the epidemiological/clinical profile and the risk factors of Brazilian COVID-19 patients can assist in the decision making of physicians in the implementation of early and most appropriate measures for poor prognosis patients. However, these reports are missing. Here we present a comprehensive study that addresses this demand. Methods This data-driven study was based on the Brazilian Ministry of Health Database (SIVEP-Gripe) regarding notified cases of hospitalized COVID-19 patients during the period from February 26th to August 10th, 2020. Demographic data, clinical symptoms, comorbidities and other additional information of patients were analyzed. Results The hospitalization rate was higher for male gender (56.56%) and for older age patients of both sexes. Overall, the lethality rate was quite high (41.28%) among hospitalized patients, especially those over 60 years of age. Most prevalent symptoms were cough, dyspnoea, fever, low oxygen saturation and respiratory distress. Cardiac disease, diabetes, obesity, kidney disease, neurological disease, and pneumopathy were the most prevalent comorbidities. A high prevalence of hospitalized COVID-19 patients with cardiac disease (65.7%) and diabetes (53.55%) and with a high lethality rate of around 50% was observed. The intensive care unit (ICU) admission rate was 39.37% and of these 62.4% died. 24.4% of patients required invasive mechanical ventilation (IMV), with high mortality among them (82.98%). The main mortality risk predictors were older age and IMV requirement. In addition, socioeconomic conditions have been shown to significantly influence the disease outcome, regardless of age and comorbidities. Conclusion Our study provides a comprehensive overview of the hospitalized Brazilian COVID-19 patients profile and the mortality risk factors. The analysis also evidenced that the disease outcome is influenced by multiple factors, as unequally affects different segments of population.

Background: Brazil became the epicenter of the COVID-19 epidemic in a brief period of a few months after the first officially registered case. The knowledge of the epidemiological/clinical profile and the risk factors of Brazilian COVID-19 patients can assist in the decision making of physicians in the implementation of early and most appropriate measures for poor prognosis patients. However, these reports are missing. Here we present a comprehensive study that addresses this demand. Methods: This data-driven study was based on the Brazilian Ministry of Health Database (SIVEP-Gripe, 2020) regarding notified cases of hospitalized COVID-19 patients during the period from February 26 to August 10, 2020. Demographic data, clinical symptoms, comorbidities and other additional information of patients were analyzed. Results: The hospitalization rate was higher for male gender (56.56%) and for older age patients of both sexes. Overall, the mortality rate was quite high (41.28%) among hospitalized patients, especially those over 60 years of age. Most prevalent symptoms were cough, dyspnoea, fever, low oxygen saturation and respiratory distress. Heart disease, diabetes, obesity, kidney disease, neurological disease, and pneumopathy were the most prevalent comorbidities. A high prevalence of hospitalized COVID-19 patients with heart disease (65.7%) and diabetes (53.55%) and with a high mortality rate of around 50% was observed. The ICU admission rate was 39.37% and of these 62.4% died. 24.4% of patients required invasive mechanical ventilation (IMV), with high mortality among them (82.98%). The main mortality risk predictors were older age and IMV requirement. In addition, socioeconomic conditions have been shown to significantly influence the disease outcome, regardless of age and comorbidities. Conclusion: Our study provides a comprehensive overview of the hospitalized Brazilian COVID-19 patients profile and the mortality risk factors. The analysis also evidenced that the disease outcome is influenced by multiple factors, as unequally affects different segments of population.

A COVID-19 é uma infecção causada pelo coronavírus SARS-CoV-2, sendo que seus primeiros registros foram na cidade chinesa de Wuhan em dezembro de 2019, e foi considerada pela Organização Mundial da Saúde (OMS) uma pandemia mundial em março de 2020.  No Brasil, a COVID-19 se espalhou atingindo as 27 unidades federativas (UFs). Com isso, as tomadas de decisões para diminuir a velocidade de transmissão foram baseadas nas recomendações da OMS, onde a principal é isolamento social. Entretanto, devido a heterogeneidade da população em cada uma das UFs, a pandemia se difundiu de forma distinta.  Deste modo, é interessante fazer o agrupamento das UFs por similaridade devido algumas características, e assim, observar as medidas de combate a COVID-19 realizadas em cada um desse grupos. O objetivo deste estudo foi agrupar as UFs usando análise de cluster pelo método não-hierárquico k-means considerando os coeficientes epidemiológicos como incidência, prevalência e letalidade. Os dados foram obtidos do site do Ministério da Saúde do Brasil e foi constituído pelas variáveis número de casos e óbitos novos e acumulados nas UFs, além da população em risco.  Para análise de cluster a base de dados foi dividida em três períodos cronológicos para os três coeficientes em estudo. Com a análise de cluster foi possível verificar a estratificação da UFs conforme suas similaridades em relação a COVID-19. Assim, a estratificação da incidência, prevalência e letalidade por UFs pode se apresentar como um recurso adicional para sinalizar quais locais e quais medidas deverão ser adotadas e onde essas medidas foram eficazes.

RESUMOPara o dimensionamento de obras hidráulicas, tanto urbanas, como rurais, é necessário o conhecimento da precipitação esperada, de modo que a estrutura planejada possa resistir adequadamente. Foram analisadas as séries históricas dos valores máximos diários de precipitação de uma série histórica de 48 anos (1964 a 2011) dos registros da estação meteorológica Jerônimo Rosado da UFERSA em Mossoró/RN com o objetivo de analisar o ajuste dos dados de precipitação máxima a distribuição de Gumbel por meio do teste de aderência de Kolmogorov-Smirnov aos níveis de 1 e 5% de probabilidade e estimar a precipitação máxima diária anual provável para diferentes períodos de retorno (2, 5, 10, 20, 50, 100 e 500 anos) com o intuito de auxiliar no planejamento de obras hidráulicas. As análises estatísticas foram feitas em planilhas do Excel 2010 e no software R versão 2.12.1. A precipitação máxima diária anual ajustou-se ao modelo probabilístico de Gumbel e em função do tempo de retorno apresentaram um ajuste logarítmico satisfatório.Palavras-chave: distribuição de probabilidade, teste estatístico, parâmetros hidrológicos

In order to search for an ideal test for multiple comparison procedures, this study aimed to develop two tests, similar to the Tukey and SNK tests, based on the distribution of the externally studentized amplitude. The test names are Tukey Midrange (TM) and SNK Midrange (SNKM). The tests were evaluated based on the experimentwise error rate and power, using Monte Carlo simulation. The results showed that the TM test could be an alternative to the Tukey test, since it presented superior performances in some simulated scenarios. On the other hand, the SNKM test performed less than the SNK test.