Background: The new coronavirus disease (COVID-19) has claimed thousands of lives worldwide and disrupted the health system in many countries. As the national emergency care capacity is a crucial part of the COVID-19 response, we evaluated the Brazilian Health Care System response preparedness against the COVID-19 pandemic.Methods: A retrospective and ecological study was performed with data retrieved from the Brazilian Information Technology Department of the Public Health Care System. The numbers of intensive care (ICU) and hospital beds, general or intensivist physicians, nurses, nursing technicians, physiotherapists, and ventilators from each health region were extracted. Beds per health professionals and ventilators per population rates were assessed. A health service accessibility index was created using a two-step floating catchment area (2SFCA). A spatial analysis using Getis-Ord Gi* was performed to identify areas lacking access to high-complexity centers (HCC).Results: As of February 2020, Brazil had 35,682 ICU beds, 426,388 hospital beds, and 65,411 ventilators. In addition, 17,240 new ICU beds were created in June 2020. The South and Southeast regions have the highest rates of professionals and infrastructure to attend patients with COVID-19 compared with the northern region. The north region has the lowest accessibility to ICUs.Conclusions: The Brazilian Health Care System is unevenly distributed across the country. The inequitable distribution of health facilities, equipment, and human resources led to inadequate preparedness to manage the COVID-19 pandemic. In addition, the ineffectiveness of public measures of the municipal and federal administrations aggravated the pandemic in Brazil.

Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD based on location and risk factors. Secondary data, geographical information system (GIS) and machine learning were used to validate the HHCI and stratify the IHD municipality risk in the state of Paraná. A positive spatial autocorrelation was found (Moran’s I = 0.6472, p-value = 0.001), showing clusters of high IHD mortality. The Support Vector Machine, which had an RMSE of 0.789 and error proportion close to one (0.867), was the best for prediction among eight machine learning algorithms after validation. In the north and northwest regions of the state, HHCI was low and mortality clusters patterns were high. By creating an HHCI through ML, we can predict IHD mortality rate at municipal level, identifying predictive characteristics that impact health conditions of these localities’ guided health management decisions for improvements for IHD within the emergency care network in the state of Paraná.

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.

Background In 2017, the World Health Organization declared the snakebite envenomation as a neglected tropical disease. Annually, snakebite envenomation causes approximately 400,000 permanent disabilities and 95,000 deaths worldwide. People with the greatest risk of envenomation lack access to adequate health care, including treatment with antivenom. We developed na analysis of accessibility to antivenom in Brazil in order to verify the impacts on mortality. Methods and Findings Information about number of accidents, deaths, antivenom, medical assistance, and species, were retrieved from the Brazilian Health Informatics Department (DATASUS) from 2010 to 2015 and analyzed using geostatistics to evaluate the association between snakebite acidentes and mortality. An Spatial analysis using Global Morans I was performed in order to verify the presence of spatiality as an independent variable to the distribution of the accidents. In addition, we also tested three different analysis of regression using Ordinary Least Square (OLS), Spatial Error, and Geographically Weighed Regression (GWR), together with the information obtained from the DATASUS and sociodemographic indicators, to verify the spatial-temporal distribution of envenomation cases and time to reach the healthcare centers. The regression presenting the lowest Akaike Criterion Information (AIC), highest adjusted R2, and variables with p < 0.05 was selected to represent our model. Lastly, the accessibility index was performed using 2-step floating catchment area based on the amount of hospital beds and inhabitants. This study revealed 141,039 cases of snakebites, 598 deaths, and mortality rate of 3.13 per 1,000,000 inhabitants. Moreover, GWR presented the best fit (AIC = 55477.56; adjusted R2 = 0.55) and showed that illiteracy, income, percentage of urban population, percentage of antivenom, accessibility index for hospital beds with antivenom, proportion of cases with more than 3 hours to reach healthcare are correlated with the mortality rate by snakebite (p < 0.05). Conclusion This study identified regions affected by snakebite and how the accessibility to antivenom treatment plays an important role in the mortality in Brazil. Public interventions can located to those most vulnerable regions in order to improve the accident outcome.

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