This study analyzed the spatial distribution of dengue in Rio de Janeiro, Brazil, in 2006, and associations between the incidence per 100,000 inhabitants and socio-environmental variables. The study analyzed reported dengue cases among the city's inhabitants, rainfall, Breteau index (for Aedes aegypti and Aedes albopictus), Gini index, and social development index. We conducted mapping and used the global Moran index to measure the indicators' spatial autocorrelation, which was positive for all variables. The generalized linear model showed a direct association between dengue incidence and rainfall, one-month rainfall time lag, Gini index, and Breteau index for A. albopictus. The conditional autoregressive model (CAR) showed a direct association with rainfall for four months of the year, rain time lag in July, and Gini index in February. The results demonstrate the importance of socio-environmental variables in the dynamics of dengue transmission and the relevance for the development of dengue control strategies.

This study analyzed the dengue fever epidemic in 2002 and the socio-demographic context of the State of Rio de Janeiro, Brazil, using spatial analysis and statistical modeling. The incidence rate was calculated for resident dengue cases in the State in 2002. The study analyzed associations between incidence and socio-demographic variables and spatial autocorrelation using the Moran Global Index, which showed spatial dependence for both the outcome and the independent variables. A multivariate linear regression model was used. The variables' proportion of urban population, percentage of the population with running water, and percentage of coverage by the Family Health Program (FHP) explained 30.2% of the total variance in the epidemic's incidence rate. The model's residuals did not show spatial autocorrelation. The associations were in the expected direction, and the findings are corroborated by other studies that showed higher dengue incidence in areas characterized by growing urbanization and deficient running water and water supply, while highlighting the FHP as an important facilitator of vector control strategies.

The trend towards decline and stabilization of the AIDS epidemic in Brazil should be analyzed carefully, since aggregate data can mask regional or local inequalities in such a large and diverse country. The current study reevaluates the epidemic’s spatial dissemination and the AIDS-related mortality pattern in Brazil. The study considered all AIDS cases diagnosed in individuals over 18 years of age and living in Brazil, as well as AIDS deaths recorded in 1998-2008. Three-year moving average rates were estimated, and a spatial analysis was conducted using a local empirical Bayesian method. The epidemic was only found to be expanding in the North and Northeast regions, while declining in the rest of the country, especially in the Southeast. According to the findings, the apparent stabilization of AIDS mortality tends to mask regional disparities. Social determinants of health and regional disparities should be taken into account in program development and policymaking.

Resumo Objetivo Descrever o perfil clínico-epidemiológico dos casos suspeitos de COVID-19 internados em hospital federal do Rio de Janeiro, RJ, Brasil, e identificar fatores associados ao óbito. Métodos Estudo seccional, utilizando dados da vigilância epidemiológica local até a Semana Epidemiológica 27 de 2020. Empregou-se regressão logística. Resultados Foram incluídos 376 casos internados suspeitos de COVID-19, dos quais 52,9% eram mulheres, 57,4% tinham 50 anos ou mais de idade e 80,1% exibiam comorbidades. Foram confirmados para COVID-19 195 casos (51,9%), cuja letalidade (37,9%) foi maior, comparada à dos descartados (24,2%). Na análise ajustada, associaram-se ao óbito, nos casos confirmados, ter 50 a 69 (OR=11,65 – IC95% 1,69;80,33) e 70 ou mais anos (OR=8,43 – IC95% 1,22;58,14), apresentar neoplasia (OR=4,34 – IC95% 1,28;14,76) e usar suporte ventilatório invasivo (OR=70,20 – IC95% 19,09;258,19). Conclusão Houve elevada prevalência de comorbidades e letalidade; os principais fatores associados ao óbito foram idade, presença de neoplasia e suporte ventilatório invasivo.

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.