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.

OBJECTIVE To analyze the spatial distribution of risk for tuberculosis and its socioeconomic determinants in the city of Rio de Janeiro, Brazil.METHODS An ecological study on the association between the mean incidence rate of tuberculosis from 2004 to 2006 and socioeconomic indicators of the Censo Demográfico (Demographic Census) of 2000. The unit of analysis was the home district registered in the Sistema de Informação de Agravos de Notificação (Notifiable Diseases Information System) of Rio de Janeiro, Southeastern Brazil. The rates were standardized by sex and age group, and smoothed by the empirical Bayes method. Spatial autocorrelation was evaluated by Moran’s I. Multiple linear regression models were studied and the appropriateness of incorporating the spatial component in modeling was evaluated.RESULTS We observed a higher risk of the disease in some neighborhoods of the port and north regions, as well as a high incidence in the slums of Rocinha and Vidigal, in the south region, and Cidade de Deus, in the west. The final model identified a positive association for the variables: percentage of permanent private households in which the head of the house earns three to five minimum wages; percentage of individual residents in the neighborhood; and percentage of people living in homes with more than two people per bedroom.CONCLUSIONS The spatial analysis identified areas of risk of tuberculosis incidence in the neighborhoods of the city of Rio de Janeiro and also found spatial dependence for the incidence of tuberculosis and some socioeconomic variables. However, the inclusion of the space component in the final model was not required during the modeling process.

OBJECTIVE Describe the clinical and epidemiological profile of confirmed cases of yellow fever whose patients were hospitalized in a general hospital for infectious diseases in the State of Rio de Janeiro, Brazil, from March 11, 2017 to June 15, 2018, during a recent outbreak and factors associated with death.METHODS This is a retrospective observational study with analysis of secondary databases of local epidemiological surveillance system, and complementary data collection from epidemiological investigation records and clinical records. Study variables included demographic, epidemiological, clinical, and laboratory data. A descriptive statistical analysis and a bivariate and multivariate analysis by logistic regression were performed to analyze factors associated with death.RESULTS Fifty-two patients diagnosed with yellow fever were hospitalized, 86.5% male patients, median age 49.5 years, 40.4% rural workers. The most frequent signs and symptoms were fever (90.4%), jaundice (86.5%), nausea and/or vomiting (69.2%), changes in renal excretion (53.8%), bleeding (50%), and abdominal pain (48.1%), with comorbidity in 38.5% of all cases. The lethality rate was 40.4%. Factors significantly associated with a higher chance of death in the bivariate analysis were: bleeding, changes in renal excretion, and maximum values of direct bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), urea, and creatinine. In the multivariate analysis by logistic regression, only changes in renal excretion and ALT remained significant predictors of higher chance of death. A threshold effect was also observed for AST. The cutoff points identified as high risk for death were ALT > 4,000 U/L and AST > 6,000 U/L.CONCLUSIONS This study contributed to the knowledge on the profile of confirmed cases of high severity yellow fever. The main factors associated with death were changes in renal excretion and elevated serum transaminases, especially ALT. High lethality emphasizes the need for early diagnosis and treatment, and the importance of increasing vaccination coverage.

This study proposes a reflection on the uses and future prospects of hospital-based health

Resumo Introdução A tuberculose é uma doença endêmica em diversos países e sua ocorrência tem sido relacionada a situações de pior condição socioeconômica. Objetivo Analisar a distribuição espacial da tuberculose no município do Rio de Janeiro em 2006 e a associação entre ocorrência da doença e indicadores socioeconômicos. Método O setor censitário de residência do paciente foi georreferenciado a partir do endereço registrado no SINAN; intensidade de casos estimada a partir do estimador de Kernel. A associação entre variáveis socioeconômicas e existência de pelo menos um caso de tuberculose nos setores censitários foi avaliada por regressão logística. Resultados A base de dados final tinha 3.089 novos casos não institucionalizados. Identificou-se grande concentração de casos na Zona Norte e em pontos da Zona Sul. Na Zona Oeste, visualizou-se uma faixa de pontos desde Magalhães Bastos até Santa Cruz e Sepetiba. A intensidade de casos foi maior nas áreas mais carentes. No modelo final, permaneceram: percentual de domicílios particulares permanentes cuja pessoa responsável recebia 3 a 5 salários mínimos e o percentual de domicílios alugados. Conclusão A análise espacial identificou áreas de maior concentração de casos coincidente com áreas mais carentes. A má qualidade do registro dos endereços foi a principal limitação.