Brazil is, at the time of writing, the global epicenter of COVID-19, but information on risk factors for hospitalization and mortality in the country is still limited. Demographic and clinical data of COVID-19 patients until June 11th, 2020 were retrieved from the State Health Secretariat of Espírito Santo, Brazil. Potential risk factors for COVID-19 hospitalization and death were analyzed by univariate and multivariable logistic regression models. A total of 10,713 COVID-19 patients were included in this study; 81.0% were younger than 60 years, 55.2% were female, 89.2% were not hospitalized, 32.9% had at least one comorbidity, and 7.7% died. The most common symptoms on admission were cough (67.7%) and fever (62.6%); 7.1% of the patients were asymptomatic. Cardiovascular diseases (23.7%) and diabetes (10.3%) were the two most common chronic diseases. Multivariate logistic regression analysis identified an association of all explanatory variables, except for cough and diarrhea, with hospitalization. Older age (odds ratio [OR] = 3.95, P < 0.001) and shortness of breath (OR = 3.55, P < 0.001) were associated with increase of odds to COVID-19 death in hospitalized patients. Our study provided evidence that older age, male gender, Asian, indigenous or unknown race, comorbidities (smoking, kidney disease, obesity, pulmonary disease, diabetes, and cardiovascular disease), as well as fever and shortness of breath increased the risk of hospitalization. For death outcome in hospitalized patients, only older age and shortness of breath increased the risk.

One of the various concerns of conservation biology is determining why certain species are more threatened than others. In this study, we aim to relate the national conservation status of Brazilian mammals with the taxonomic group to which they belong and with three of their intrinsic traits: body mass, diet, and litter size. We compiled a database containing the species, their status, and their attributes, and a multiple correspondence analysis was applied to identify relationships between traits and status. The two groups that presented the highest relative frequencies of threatened species were "ungulates" and Carnivora. Additionally, mammals with body mass of 10 kg or more and with carnivorous diet had a higher relative frequency of threatened taxa. We found not only a strong relationship between intrinsic traits and conservation status, but also among the traits themselves, which highlights the role of the "group" variable as one of the best predictors of the risk that a given species be threatened. We believe our study has a broad potential for the conservation of species at the regional level, especially regarding the species currently classified as Data Deficient, and for identifying which species are prone to becoming threatened.

Background: Studies of infective endocarditis (IE) are generally limited to institutions, underlining the need for more comprehensive epidemiological research. Objective: The aim of this study was to determine the mortality profile of IE-related deaths and related causes in Brazil at the national level and across regions. Method: We conducted a population-based study using data from the country’s mortality information system for the period 2000 to 2019. We identified IE-related deaths and related causes based on the ICD-10 codes. Cluster analysis was performed to determine the relationship between the variables. Results: There were 52,055 IE-related deaths during the study period. Deaths occurred predominantly among men and people aged between 60 and 79 years. The Southeast accounted for the largest proportion of deaths. The most frequent ICD-10 chapter mentioned in relation to IE-related deaths was diseases of the circulatory system. We identified three distinctive profiles: 1—an age of 80 years and over and women, where the most frequent chapters were endocrine, circulatory and metabolic diseases and the South and Southeast accounted for the largest proportion of deaths; 2—an age between 30 and 79 years and men, where the most frequent chapters were infectious and genitourinary diseases and the South and Southeast accounted for the largest proportion of deaths; and 3—an age between 0 and 29 years without any difference between sexes, where the most frequent chapter was diseases of the respiratory system and the North, Northeast and Midwest accounted for the largest proportion of deaths. Conclusions: The findings of the cluster analysis revealed distinctive IE-related mortality profiles, indicating regional differences.

Background: Brazil has one of the highest stroke rates in Latin America. It is important to understand the impact of other causes of death and sociodemographic factors, as this may contribute to a better comprehension of the stroke mortality process. Machine learning provides a means to explain this process. Objective: To investigate the stroke deaths profile and its subtype in Brazil using machine learning. Methods: This is a time series analysis where deaths mentioning stroke and other conditions were identified using individual death records from the country’s mortality information system (SIM) between 2000 and 2019. Strokes were grouped into the following subtypes: ischemic stroke (IS), hemorrhagic stroke (HS), and unspecified stroke (US). A decision tree model was built to identify the strongest factors distinguishing IS from HS. Results: There were 2,459,742 deaths mentioning stroke. There was a progressive increase in the number of deaths mentioning stroke over the study period. The most common type of stroke was US, accounting for more than 62% of deaths. Among HS deaths, hypertensive diseases were the most frequent group of associated causes (40.6%), while the most frequent group in subtypes IS and US was diseases of the respiratory system (48.30% and 42.30%, respectively). The decision tree analysis revealed that IS was more likely to occur in patients aged 60 years and over and in cases where respiratory diseases, endocrine diseases, arrhythmias, ischemic heart disease and heart failure were present. However, HS was more frequent in younger patients without these conditions but with nervous system diseases. Conclusions: The decision tree analysis identified the strongest factors distinguishing IS from HS, highlighting variables involved in each subtype of stroke-related death that can be recognized in clinical practice. These variables may also support the redistribution of deaths initially classified as unspecified stroke.