Microdatasus: pacote para download e pré-processamento de microdados do Departamento de Informática do SUS (DATASUS)

Saldanha, Raphael de Freitas; Bastos, Ronaldo Rocha; Barcellos, Christovam

doi:10.1590/0102-311x00032419

Cited by 40 publications

(21 citation statements)

References 2 publications

(2 reference statements)

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“…The health coverage data were obtained from the Brazilian National Register of Health Facilities (CNES) [21], while the indicator variables to create the examination rates were obtained from the Brazilian Hospital Information System (SIH) by the Microdatasus package, using the R studio software [22].…”

Section: Study Variables and Sourcementioning

confidence: 99%

The Impact of Socioeconomic Factors, Coverage and Access to Health on Heart Ischemic Disease Mortality in a Brazilian Southern State: A Geospatial Analysis

et al. 2021

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Background: No other disease has killed more than ischemic heart disease (IHD) for the past few years globally. Despite the advances in cardiology, the response time for starting treatment still leads patients to death because of the lack of healthcare coverage and access to referral centers. Objectives: To analyze the spatial disparities related to IHD mortality in the Parana state, Brazil. Methods: An ecological study using secondary data from Brazilian Health Informatics Department between 2013-2017 was performed to verify the IHD mortality. An spatial analysis was performed using the Global Moran and Local Indicators of Spatial Association (LISA) to verify the spatial dependency of IHD mortality. Lastly, multivariate spatial regression models were also developed using Ordinary Least Squares and Geographically Weighted Regression (GWR) to identify socioeconomic indicators (aging, income, and illiteracy rates), exam coverage (catheterization, angioplasty, and revascularization rates), and access to health (access index to cardiologists and chemical reperfusion centers) significantly correlated with IHD mortality. The chosen model was based on p < 0.05, highest adjusted R² and lowest Akaike Information Criterion. Results: A total of 22,920 individuals died from IHD between 2013-2017. The spatial analysis confirmed a positive spatial autocorrelation global between IDH mortality rates (Moran's I: 0.633, p < 0.01). The LISA analysis identified six high-high pattern clusters composed by 66 municipalities (16.5%). GWR presented the best model (Adjusted R²: 0.72) showing that accessibility to cardiologists and chemical reperfusion centers, and revascularization and angioplasty rates differentially affect the IHD mortality rates geographically. Aging and illiteracy rate presented positive correlation with IHD mortality rate, while income ratio presented negative correlation (p < 0.05). Conclusion: Regions of vulnerability were unveiled by the spatial analysis where sociodemographic, exam coverage and accessibility to health variables impacted differently the IHD mortality rates in Paraná state, Brazil. Dutra et al: The Impact of Socioeconomic Factors, Coverage and Access to Health on Heart Ischemic Disease Mortality in a Brazilian Southern State Art. 5, page 2 of 15 Highlights • The increase in ischemic heart disease mortality rates is related to geographical disparities. • The IHD mortality is differentially associated to socioeconomic factors, exam coverage, and access to health. • Higher accessibility to chemical reperfusion centers did not necessarily improve patient outcomes in some regions of the state. • Clusters of high mortality rate are placed in regions with low amount of cardiologists, income and schooling.

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Section: Study Variables and Sourcementioning

confidence: 99%

The Impact of Socioeconomic Factors, Coverage and Access to Health on Heart Ischemic Disease Mortality in a Brazilian Southern State: A Geospatial Analysis

et al. 2021

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“…Moreover, use of R packages as tools to acquire or handle public health-related data is becoming frequent. 5,7,8 For this presentation, we considered the indicator matrix of the Health Systems Performance Evaluation Project (Projeto de Avaliação de Desempenho de Sistemas de Saúde -PROADESS), 9 given that health status indicators are fundamental for socio-spatial analyses of the health situation in Brazil. 10 The list of indicators available on TabNet on the SES/SP webpage is shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…1,2 in the form of microdata, such as microdatasus. 5 However, in the case of aggregated data, such as health status indicators, no specific tool exists other than TabNet manual retrieval or calculations using microdata.…”

Section: Introductionmentioning

confidence: 99%

Rtabnetsp: pacote R para extração de indicadores de saúde do estado de São Paulo

Morais

Konstantyner

Fazenda

et al. 2021

Epidemiol. Serv. Saúde

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Resumo Indicadores de saúde representam uma importante ferramenta de acompanhamento de desempenho de ações em Saúde Pública, permitindo a avaliação de intervenções realizadas, bem como a identificação de tendências e regiões prioritárias para alocação de recursos. Com o objetivo de aumentar a praticidade nas tarefas de análise e manipulação de dados desses indicadores, foi criado um pacote R. O pacote rtabnetsp realiza requisições aos servidores TabNet da página eletrônica da Secretaria de Estado da Saúde de São Paulo, recuperando e tratando tais dados para utilização do usuário. Este artigo apresenta o pacote rtabnetsp e suas funções, modo de instalação e uso; traz também exemplos de suas funcionalidades, que permitem a visualização, busca e seleção, entre uma lista de indicadores, do conteúdo desejado, além da obtenção dos dados agregados pelo nível de regionalização disponível na matriz de dados, conferindo maior agilidade a tarefas de gestão em saúde do estado de São Paulo.

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“…Data regarding hospitals, professionals (nurses, nursing technicians, doctors, and physiotherapists), and equipment (ventilators, ICU, and infirmary beds) were obtained from the CNES website using R through the microdatasus package (24). The population data and thematic maps were fetched from the IBGE (23).…”

Section: Methodsmentioning

confidence: 99%

Brazil Health Care System preparation against COVID-19

Silva

Dutra

Iora

et al. 2020

Preprint

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Background: The coronavirus disease outbreak from 2019 (COVID-19) is associated with a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a highly contagious virus that claimed thousands of lives around the world and disrupted the health system in many countries. The assessment of emergency capacity in every country is a necessary part of the COVID-19 response efforts. Thus, it is extremely recommended to evaluate the health care system to prepare the country to tackle COVID-19 challenges. Methods and Findings: A retrospective and ecological study was performed with data retrieved from the public national healthcare database (DATASUS). Numbers of intensive care unit and infirmary beds, general or intensivists physicians, nurses, nursing technicians, and ventilators from each Regional Health Unity were extracted, and the beds per health professionals and ventilators per population rates were assessed. The accessibility to health services was also performed using a spatial overlay approach to verify regions that lack assistance. It was found that Brazil lacks equity, integrity, and may struggle to assist with high complexity for the COVID-19 patients in many regions of the country. Conclusions: Brazilian health system is insufficient to tackle the COVID-19 in some regions of the country where the coronavirus may be responsible for high rates of morbidity and mortality.

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Microdatasus: pacote para download e pré-processamento de microdados do Departamento de Informática do SUS (DATASUS)

Cited by 40 publications

References 2 publications

The Impact of Socioeconomic Factors, Coverage and Access to Health on Heart Ischemic Disease Mortality in a Brazilian Southern State: A Geospatial Analysis

The Impact of Socioeconomic Factors, Coverage and Access to Health on Heart Ischemic Disease Mortality in a Brazilian Southern State: A Geospatial Analysis

Rtabnetsp: pacote R para extração de indicadores de saúde do estado de São Paulo

Brazil Health Care System preparation against COVID-19

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