Geographical disparities in access to surgical treatment and mortality rates from abdominal aortic aneurysms in Brazil: A retrospective longitudinal study.

INTRODUCTION: Abdominal aortic aneurysm (AAA) is a growing public health problem, and not all patients have access to surgery when needed. This study aimed to analyze spatiotemporal variations in AAA mortality and surgical procedures in Brazilian intermediate geographic regions and explore the impact of different surgical techniques on operative mortality. METHODS: A retrospective longitudinal study was conducted to evaluate AAA mortality from 2008 to 2020 using space-time cube (STC) analysis and the emerging hot spot analysis tool through the Getis-Ord Gi* method. RESULTS: There were 34,255 deaths due to AAA, 13,075 surgeries to repair AAA, and a surgical mortality of 14.92%. STC analysis revealed an increase in AAA mortality rates (trend statistic = +1.7693, p = 0.0769) and a significant reduction in AAA surgery rates (trend statistic = -3.8436, p = 0.0001). Analysis of emerging hotspots revealed high AAA mortality rates in the South, Southeast, and Central-West, with a reduction in procedures in São Paulo and Minas Gerais States (Southeast). In the Northeast, there were extensive areas of increasing mortality rates and decreasing procedure rates (cold spots). CONCLUSION: AAA mortality increased in several regions of the country while surgery rates decreased, demonstrating the need for implementing public health policies to increase the availability of surgical procedures, particularly in less developed regions with limited access to services.

Impact of socioeconomic factors and health determinants on preterm birth in Brazil: a register-based study.

BACKGROUND: More than 15 million children are born preterm annually. While preterm survival rates have increased in high-income countries. Low- and middle-income countries, like Brazil, continue to battle high neonatal mortality rates due to a lack of adequate postnatal care. Globally, neonatal mortality is higher for preterm infants compared to those born at term. Our study aims to map and analyze the spatial, socioeconomic, and health coverage determinants related to preterm birth in Brazil in order to understand how spatial variations in demographics and access to primary care may affect preterm birth occurrences.  METHODS: Using publicly available national-level data from the Brazilian health system for 2008-2017, we conducted an ecological study to visualize the spatial distributions of preterm birth along with socioeconomic status, the structure of health services, and primary care work process, each consisting of multiple variables reduced via principal component analysis. Regression models were created to determine predictive effects of numeric and spatial variation of these scores on preterm birth rates. RESULTS: In Brazil, preterm birth rates increased from 2008-2017, with small and rural municipalities frequently exhibiting higher rates than urban areas. Scores in socioeconomic status and work process were significant predictors of preterm birth rates, without taking into account spatial adjustment, with more positive scores in socioeconomic status predicting higher preterm birth rates (coefficient 0.001145) and higher scores in work process predicting lower preterm birth rates (coefficient -0.002416). Geographically weighted regression showed socioeconomic status to be a more significant predictor in the North, with the work process indicators being most significant in the Northeast. CONCLUSIONS: Results support that primary care work process indicators are more significant in estimating preterm birth rates than physical structures available for care. These results emphasize the importance of ensuring the presence of the minimum human resources needed, especially in the most deprived areas of Brazil. The association between social determinants of health and preterm birth rates raises questions regarding the importance of policies dedicated to foster equity in the accessibility of healthcare services, and improve income as protective proxies for preterm birth.

Oral primary care: an analysis of its impact on the incidence and mortality rates of oral cancer.

BACKGROUND: Oral cancer is a potentially fatal disease, especially when diagnosed in advanced stages. In Brazil, the primary health care (PHC) system is responsible for promoting oral health in order to prevent oral diseases. However, there is insufficient evidence to assess whether actions of the PHC system have some effect on the morbidity and mortality from oral cancer. The purpose of this study was to analyze the effect of PHC structure and work processes on the incidence and mortality rates of oral cancer after adjusting for contextual variables. METHODS: An ecological, longitudinal and analytical study was carried out. Data were obtained from different secondary data sources, including three surveys that were nationally representative of Brazilian PHC and carried out over the course of 10 years (2002-2012). Data were aggregated at the state level at different times. Oral cancer incidence and mortality rates, standardized by age and gender, served as the dependent variables. Covariables (sociodemographic, structure of basic health units, and work process in oral health) were entered in the regression models using a hierarchical approach based on a theoretical model. Analysis of mixed effects with random intercept model was also conducted (alpha = 5%). RESULTS: The oral cancer incidence rate was positively association with the proportion of of adults over 60 years (ß = 0.59; p = 0.010) and adult smokers (ß = 0.29; p = 0.010). The oral cancer related mortality rate was positively associated with the proportion of of adults over 60 years (ß = 0.24; p < 0.001) and the performance of preventative and diagnostic actions for oral cancer (ß = 0.02; p = 0.002). Mortality was inversely associated with the coverage of primary care teams (ß = -0.01; p < 0.006) and PHC financing (ß = -0.52-9; p = 0.014). CONCLUSIONS: In Brazil, the PHC structure and work processes have been shown to help reduce the mortality rate of oral cancer, but not the incidence rate of the disease. We recommend expanding investments in PHC in order to prevent oral cancer related deaths.

Bayesian Modeling and Estimation of Spatial Risk for Hospitalization and Mortality from Ischemic Heart Disease in Paraná, Brazil.

Objective: Despite significant advancements in understanding risk factors and treatment strategies, ischemic heart disease (IHD) remains the leading cause of mortality worldwide, particularly within specific regions in Brazil, where the disease is a burden. Therefore, the aim of this study was to estimate the risk of hospitalization and mortality from IHD in the state of Paraná (Brazil), using spatial analysis to identify areas with higher risk based on socioeconomic, demographic and health variables. Methods: This is an ecological study based on secondary and retrospective IHD hospitalization and mortality data obtained from the Brazilian Hospitalization and Mortality Information Systems during the 2010-2021 period. Data were analyzed for 399 municipalities and 22 health regions in the state of Paraná. To assess the spatial patterns of the disease and identify relative risk (RR) areas, we constructed a risk model by Bayesian inference using the R-INLA and SpatialEpi packages in R software. Results: A total of 333,229 hospitalizations and 73,221 deaths occurred in the analyzed period, and elevated RR of hospitalization (RR = 27.412, CI 21.801; 34.466) and mortality (RR = 15.673, CI 2.148; 114.319) from IHD occurred in small-sized municipalities. In addition, medium-sized municipalities also presented elevated RR of hospitalization (RR = 6.533, CI 1.748; 2.006) and mortality (RR = 6.092, CI 1.451; 2.163) from IHD. Hospitalization and mortality rates were higher in white men aged 40-59 years. A negative association was found between Municipal Performance Index (IPDM) and IHD hospitalization and mortality. Conclusion: Areas with increased risk of hospitalization and mortality from IHD were found in small and medium-sized municipalities in the state of Paraná, Brazil. These results suggest a deficit in health care attention for IHD cases in these areas, potentially due to a low distribution of health care resources.

Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers.

Smoking cessation is an important public health policy worldwide. However, as far as we know, there is a lack of screening of variables related to the success of therapeutic intervention (STI) in Brazilian smokers by machine learning (ML) algorithms. To address this gap in the literature, we evaluated the ability of eight ML algorithms to correctly predict the STI in Brazilian smokers who were treated at a smoking cessation program in Brazil between 2006 and 2017. The dataset was composed of 12 variables and the efficacies of the algorithms were measured by accuracy, sensitivity, specificity, positive predictive value (PPV) and area under the receiver operating characteristic curve. We plotted a decision tree flowchart and also measured the odds ratio (OR) between each independent variable and the outcome, and the importance of the variable for the best model based on PPV. The mean global values for the metrics described above were, respectively, 0.675±0.028, 0.803±0.078, 0.485±0.146, 0.705±0.035 and 0.680±0.033. Supporting vector machines performed the best algorithm with a PPV of 0.726±0.031. Smoking cessation drug use was the roof of decision tree with OR of 4.42 and importance of variable of 100.00. Increase in the number of relapses also promoted a positive outcome, while higher consumption of cigarettes resulted in the opposite. In summary, the best model predicted 72.6% of positive outcomes correctly. Smoking cessation drug use and higher number of relapses contributed to quit smoking, while higher consumption of cigarettes showed the opposite effect. There are important strategies to reduce the number of smokers and increase STI by increasing services and drug treatment for smokers.

Spatial distribution of mortality from colorectal cancer in the southern region of Brazil.

Colorectal cancer (CRC) is the leading cause of death due to cancer worldwide. In Brazil, it is the second most frequent cancer in men and women, with a mortality reaching 9.4% of those diagnosed. The aim of this study was to analyze the spatial heterogeneity of CRC deaths among municipalities in south Brazil, from 2015 to 2019, in different age groups (50-59 years, 60-69 years, 70-79 years, and 80 years old or more) and identify the associated variables. Global Spatial Autocorrelation (Moran's I) and Local Spatial Autocorrelation (LISA) analyses were used to evaluate the spatial correlation between municipalities and CRC mortality. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) were applied to evaluate global and local correlations between CRC deaths, sociodemographic, and coverage of health care services. For all age groups, our results found areas with high CRC rates surrounded by areas with similarly high rates mainly in the Rio Grande do Sul state. Even as factors associated with CRC mortality varied according to age group, our results suggested that improved access to specialized health centers, the presence of family health strategy teams, and higher rates of colonoscopies are protective factors against colorectal cancer mortality in southern Brazil.

River dataset as a potential fluvial transportation network for healthcare access in the Amazon region.

Remote areas, such as the Amazon Forest, face unique geographical challenges of transportation-based access to health services. As transportation to healthcare in most of the Amazon Forest is only possible by rivers routes, any travel time and travel distance estimation is limited by the lack of data sources containing rivers as potential transportation routes. Therefore, we developed an approach to convert the geographical representation of roads and rivers in the Amazon into a combined, interoperable, and reusable dataset. To build the dataset, we processed and combined data from three data sources: OpenStreetMap, HydroSHEDS, and GloRiC. The resulting dataset can consider distance metrics using the combination of streets and rivers as a transportation route network for the Amazon Forest. The created dataset followed the guidelines and attributes defined by OpenStreetMap to leverage its reusability and interoperability possibilities. This new data source can be used by policymakers, health authorities, and researchers to perform time-to-care analysis in the International Amazon region.

Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.

Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional University Hospital of Maringá, Paraná state, Brazil. This is an observational, descriptive, cross-sectional, and retrospective study using ML technique to develop a protocol that predicts which patients with an initial diagnosis of mild TBI should be recommended for a head CT. Among the tested models, he linear extreme gradient boosting was the best algorithm, with the highest sensitivity (0.70 ± 0.06). Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.

Correction: Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.

[This corrects the article DOI: 10.1371/journal.pone.0290721.].

Built environment influence on the incidence of elderly pedestrian collisions in a medium-large city in southern Brazil: a spatial analysis.

Trauma disproportionately affects vulnerable road users, especially the elderly. We analyzed the spatial distribution of elderly pedestrians struck by vehicles in the urban area of Maringa city, from 2014 to 2018. Hotspots were obtained by kernel density estimation and wavelet analysis. The relationship between spatial relative risks (RR) of elderly run-overs and the built environment was assessed through Qualitative Comparative Analysis (QCA). Incidents were more frequent in the central and southeast regions of the city, where the RR was up to 2.58 times higher. The QCA test found a significant association between elderly pedestrian victims and the presence of traffic lights, medical centers/hospitals, roundabouts and schools. There is an association between higher risk of elderly pedestrians collisions and specific elements of built environments in Maringa, providing fundamental data to help guide public policies to improve urban mobility aimed at protecting vulnerable road users and planning an age-friendly city.

Antivenom access impacts severity of Brazilian snakebite envenoming: A geographic information system analysis.

BACKGROUND: Snakebite envenoming (SBE) is a neglected tropical disease capable of causing both significant disability and death. The burden of SBE is especially high in low- and middle-income countries. The aim of this study was to perform a geospatial analysis evaluating the association of sociodemographics and access to care indicators on moderate and severe cases of SBE in Brazil. METHODS: We conducted an ecological, cross-sectional study of SBE in Brazil from 2014 to 2019 using the open access National System Identification of Notifiable Diseases (SINAN) database. We then collected a set of indicators from the Brazil Census of 2010 and performed a Principal Component Analysis to create variables related to health, economics, occupation, education, infrastructure, and access to care. Next, a descriptive and exploratory spatial analysis was conducted to evaluate the geospatial association of moderate and severe events. These variables related to events were evaluated using Geographically Weighted Poisson Regression. T-values were plotted in choropleth maps and considered statistically significant when values were <-1.96 or >+1.96. RESULTS: We found that the North region had the highest number of SBE cases by population (47.83/100,000), death rates (0.18/100,000), moderate and severe rates (22.96/100,000), and proportion of cases that took more than three hours to reach healthcare assistance (44.11%). The Northeast and Midwest had the next poorest indicators. Life expectancy, young population structure, inequality, electricity, occupation, and more than three hours to reach healthcare were positively associated with greater cases of moderate and severe events, while income, illiteracy, sanitation, and access to care were negatively associated. The remaining indicators showed a positive association in some areas of the country and a negative association in other areas. CONCLUSION: Regional disparities in SBE incidence and rates of poor outcomes exist in Brazil, with the North region disproportionately affected. Multiple indicators were associated with rates of moderate and severe events, such as sociodemographic and health care indicators. Any approach to improving snakebite care must work to ensure the timeliness of antivenom administration.

Exploring regional disparities in lung cancer mortality in a Brazilian state: A cross-sectional ecological study.

BACKGROUND: Lung cancer (LC) is one of the main causes of mortality in Brazil; geographic, cultural, socioeconomic and health access factors can affect the development of the disease. We explored the geospatial distribution of LC mortality, and associated factors, between 2015 and 2019, in Parana state, Brazil. METHODS AND FINDINGS: We obtained mortality (from the Brazilian Health Informatics Department) and population rates (from the Brazilian Institute of Geography and Statistics [IBGE]) in people over 40 years old, accessibility of oncology centers by municipality, disease diagnosis rate (from Brazilian Ministry of Health), the tobacco production rate (IBGE) and Parana Municipal Performance Index (IPDM) (from Parana Institute for Economic and Social Development). Global Moran's Index and Local Indicators of Spatial Association were performed to evaluate the spatial distribution of LC mortality in Parana state. Ordinary Least Squares Regression and Geographically Weighted Regression were used to verify spatial association between LC mortality and socioeconomic indicators and health service coverage. A strong spatial autocorrelation of LC mortality was observed, with the detection of a large cluster of high LC mortality in the South of Parana state. Spatial regression analysis showed that all independent variables analyzed were directly related to LC mortality by municipality in Paraná. CONCLUSIONS: There is a disparity in the LC mortality in Parana state, and inequality of socioeconomic and accessibility to health care services could be associated with it. Our findings may help health managers to intensify actions in regions with vulnerability in the detection and treatment of LC.

Emergency Care Gap in Brazil: Geographical Accessibility as a Proxy of Response Capacity to Tackle COVID-19.

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.

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

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. 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.

Mapping risk of ischemic heart disease using machine learning in a Brazilian state.

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.

Characteristics of primary care and rates of pediatric hospitalizations in Brazil.

OBJECTIVE: To evaluate the association among characteristics of primary health care center (PHCC) with hospitalizations for primary care sensitive conditions (PCSC) in Brazil. METHOD: In this study, a cross-sectional ecological study was performed. This study analyzed the 27 capitals of Brazil's federative units. Data were aggregated from the following open access databases: National Program for Access and Quality Improvement in Primary Care, the Hospital Information System of Brazilian Unified Health System and Annual Population Census conducted by the Brazilian Institute of Geography and Statistics. Associations were estimated among characteristics of primary care with the number of three PCSC as the leading causes of hospitalization in children under-5 population in Brazil: asthma, diarrhea, and pneumonia. RESULTS: In general, PHCC showed limited structural adequacy (37.3%) for pediatric care in Brazil. The capitals in South and Southeast regions had the best structure whereas the North and Northeast had the worst. Fewer PCSC hospitalizations were significantly associated with PHCC which presented appropriate equipment (RR: 0.98; 95%CI: 0.97-0.99), structural conditions (RR: 0.98; 95%CI: 0.97-0.99), and signage/identification of professionals and facilities (RR: 0.98; 95%CI: 0.97-0.99). Higher PCSC hospitalizations were significantly associated with PHCC with more physicians (RR: 1.23, 95%CI: 1.02-1.48), it forms (RR: 1.01, 95%CI: 1.01-1.02), and more medications (RR: 1.02, 95%CI: 1.01-1.03). CONCLUSION: Infrastructural adequacy of PHCC was associated with less PCSC hospitalizations, while availability medical professional and medications were associated with higher PCSC hospitalizations.

Characteristics of primary care and rates of pediatric hospitalizations in Brazil

ABSTRACT OBJECTIVE To evaluate the association among characteristics of primary health care center (PHCC) with hospitalizations for primary care sensitive conditions (PCSC) in Brazil. METHOD In this study, a cross-sectional ecological study was performed. This study analyzed the 27 capitals of Brazil's federative units. Data were aggregated from the following open access databases: National Program for Access and Quality Improvement in Primary Care, the Hospital Information System of Brazilian Unified Health System and Annual Population Census conducted by the Brazilian Institute of Geography and Statistics. Associations were estimated among characteristics of primary care with the number of three PCSC as the leading causes of hospitalization in children under-5 population in Brazil: asthma, diarrhea, and pneumonia. RESULTS In general, PHCC showed limited structural adequacy (37.3%) for pediatric care in Brazil. The capitals in South and Southeast regions had the best structure whereas the North and Northeast had the worst. Fewer PCSC hospitalizations were significantly associated with PHCC which presented appropriate equipment (RR: 0.98; 95%CI: 0.97-0.99), structural conditions (RR: 0.98; 95%CI: 0.97-0.99), and signage/identification of professionals and facilities (RR: 0.98; 95%CI: 0.97-0.99). Higher PCSC hospitalizations were significantly associated with PHCC with more physicians (RR: 1.23, 95%CI: 1.02-1.48), it forms (RR: 1.01, 95%CI: 1.01-1.02), and more medications (RR: 1.02, 95%CI: 1.01-1.03) CONCLUSION Infrastructural adequacy of PHCC was associated with less PCSC hospitalizations, while availability medical professional and medications were associated with higher PCSC hospitalizations.