OBJETIVO: Analisar a pertinência do uso do Sistema de Informações Hospitalares do Sistema Único de Saúde na avaliação da qualidade da assistência ao infarto agudo do miocárdio. MÉTODOS: Foram analisados 1.936 formulários de Autorização de Internação Hospitalar (AIH) do Sistema de Informações Hospitalares registrados com o diagnóstico principal de infarto agudo do miocárdio no Município do Rio de Janeiro em 1997. Também foi analisada uma amostra aleatória de 391 prontuários médicos estratificada por hospitais. Avaliou-se o grau de confirmação do diagnóstico dessa doença segundo critérios estabelecidos pela literatura. A análise da precisão de variáveis foi realizada pelo Kappa. RESULTADOS: A qualidade do diagnóstico de infarto agudo do miocárdio da AIH foi satisfatória, com percentual de confirmação elevado, segundo critérios estabelecidos pela literatura (91,7%; IC95%=88,3-94,2). Em geral, a precisão das variáveis demográficas (sexo, faixa etária), de processo (uso de procedimentos e intervenções) e de resultado (óbito, motivo da saída) foi satisfatória. A precisão das variáveis demográficas e de resultado foi superior a das variáveis de processo. O elevado sub-registro do diagnóstico secundário na AIH foi a maior limitação observada. CONCLUSÕES: Considerando-se a ampla disponibilidade e os resultados descritos, avalia-se como pertinente o uso do Sistema de Informações Hospitalares na avaliação da qualidade da assistência ao infarto agudo do miocárdio.

Background Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil’s municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic’s spread in the country. Methods This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). Findings The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease. Discussion Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.

This study used spatial analysis to identify areas at greatest risk of visceral leishmaniasis (VL) in the urban area of Teresina, Brazil during 2001–2006. The results from kernel ratios showed that peripheral census tracts were the most heavily affected. Local spatial analysis showed that in the beginning of the study period local clusters of high incidence of VL were mostly located in the southern and northeastern parts of the city, but in subsequent years those clusters also appeared in the northern region of the city, suggesting that the pattern of VL is not static, and the disease may occasionally spread to other areas of the municipality. We also observed a spatial correlation between VL rates and all socioeconomic and demographic indicators evaluated (P < 0.01). The concentration of interventions in high-risk areas could be an effective strategy to control the disease in the urban setting.

OBJECTIVE:To analyze the dengue epidemic in relation to the socioeconomic context according to geographical areas. METHODS:An ecological study was conducted in the municipality of Rio de Janeiro (Southeastern Brazil), in areas delimited as neighborhoods, based on information about notifi ed dengue cases concerning residents in the municipality. The average incidence rate of dengue was calculated between the epidemiological weeks: 48 th of 2001 and 20 th of 2002. The occurrence of dengue was correlated with socioeconomic variables through Pearsons' correlation coeffi cient. Moran's global and local indexes were used to assess the spatial auto-correlation between dengue and the variables that signifi cantly correlated with the disease. The multiple linear regression model and the conditional auto-regression spatial model were used to analyze the relationship between dengue and socioeconomic context. RESULTS:The neighborhoods located in the west zone of the municipality presented high rates of average dengue incidence. The variables presenting signifi cant correlation were: percentage of households connected with the general sanitary network, households with washing machines, and population density per urban area. Moran's spatial auto-correlation index revealed spatial dependence between dengue and the selected variables. The utilized models indicated percentage of households connected with the general sanitary network as the sole variable signifi cantly associated with the disease. The residual fi gures in both models revealed signifi cant spatial auto-correlation, with a positive Moran Index (p<0.001) for linear regression model, and a negative one (p=0.005) for the conditional auto-regression one. CONCLUSIONS:Problems related to basic sanitation contribute decisively to increase the risk of the disease.

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