Brazil is in a critical situation due to the COVID-19 pandemic. Healthcare workers that are in the front line face challenges with a shortage of personal protective equipment, high risk of contamination, low adherence to the social distancing measures by the population, low coronavirus testing with underestimation of cases, and also financial concerns due to the economic crisis in a developing country. This study compared the impact of COVID-19 pandemic among three categories of healthcare workers in Brazil: physicians, nurses, and dentists, about workload, income, protection, training, feelings, behavior, and level of concern and anxiety. The sample was randomly selected and a Google Forms questionnaire was sent by WhatsApp messenger. The survey comprised questions about jobs, income, workload, PPE, training for COVID-19 patient care, behavior and feelings during the pandemic. The number of jobs reduced for all healthcare workers in Brazil during the pandemic, but significantly more for dentists. The workload and income reduced to all healthcare workers. Most healthcare workers did not receive proper training for treating COVID-19 infected patients. Physicians and nurses were feeling more tired than usual. Most of the healthcare workers in all groups reported difficulties in sleeping during the pandemic. The healthcare workers reported a significant impact of COVID-19 pandemic in their income, workload and anxiety, with differences among physicians, nurses and dentists.

Pacientes oncológicos possuem riscos mais elevados para o desenvolvimento da doença por COVID-19 em sua forma mais severa. Objetivo: analisar a mortalidade por câncer de mama associada a COVID-19 em mulheres brasileiras. Metodologia: Trata-se de um estudo quantitativo. Os dados foram coletados no site do Open Data SUS, no período de janeiro a agosto de 2020. Utilizou-se estatística descritiva para análise dos dados. Resultados: o câncer de mama associado ao COVID-19 causou 69 óbitos nesse período, as idades que tiveram maior número de óbitos foram nos intervalos de idade 45-49 (14.5%), 60-64 (14.5%) e 65-69 anos (14.5%). Sobre a raça, (56,52%) eram brancos, seguida da cor parda (31,88%). Apenas (31,88%) apresentaram 8 anos ou mais de estudos e (49.27%) eram casadas. Em relação a causa básica da morte, o CID B34.2 (Infecção pelo Coronavírus de localização não especificada) se apresentou em maior número, com uma frequência de 52 (75.36%) e o CID C50.9 (Neoplasia maligna de mama, não especificada) teve uma frequência de 17 (24.64%). A cidade com maior número de óbitos por câncer de mama associada a COVID-19 no qual atingiu (20,29%) dos casos foi o Rio de Janeiro (RJ), seguida pela cidade de São Bernardo do Campo (SP) com (7.25%). Conclusão: O aumento da mortalidade por câncer de mama no período da pandemia do COVID-19 no Brasil pode estar atribuído à imunossupressão dessas mulheres e as medidas de enfrentamento ao COVID-19, no qual reduziu a procura por cuidados de saúde, acesso e disponibilidade de serviços de diagnóstico.

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Objective: To analyze the diagnostic accuracy of predictive models of breast cancer risk for the Brazilian population. Method: A cross-sectional, study was conducted in a sample of 382 women aged 35-69 years who were users of the Unified Health System (SUS) residing in a municipality in southern Brazil. Results: The results showed that the Tyrer-Cuzick model had the highest mean risk values and estimates (proportion) for predicting the 5-year risk of breast cancer, reaching a maximum risk of ±1.63% in the 60-64 year age group. For the 90-year risk, a maximum risk of ±12.8% was predicted for the 50-54 year age group using this model. The 5-year risk calculated by the three tools increased progressively with increasing age, where the mean risk was ±0.8% in women aged 35-39 and reached ±1.50% in women aged 65-69. The 90-year risk declined with increasing age only in the Tyrer-Cuzick model, from ±10.8% to ±9%. The BRCAPRO model presented a greater sensitivity compared to the Gail and Tyrer-Cuzick models. And, the model that presented greater specificity was Gail. Conclusion: The Tyrer-Cuzick model presented the highest risk estimates for 5 years and 90 years in the studied population, however, this data is not enough to validate this tool, since when analyzing the sensitivity and specificity the BRCAPRO and Gail have the highest values respectively.