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.

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.

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.

Development and validation of a minimum requirements checklist for snakebite envenoming treatment in the Brazilian Amazonia.

BACKGROUND: Currently, antivenoms are the only specific treatment available for snakebite envenoming. In Brazil, over 30% of patients cannot access antivenom within its critical care window. Researchers have therefore proposed decentralizing to community health centers to decrease time-to-care and improve morbidity and mortality. Currently, there is no evidence-based method to evaluate the capacity of health units for antivenom treatment, nor what the absolute minimum supplies and staff are necessary for safe and effective antivenom administration and clinical management. METHODS: This study utilized a modified-Delphi approach to develop and validate a checklist to evaluate the minimum requirements for health units to adequately treat snakebite envenoming in the Amazon region of Brazil. The modified-Delphi approach consisted of four rounds: 1) iterative development of preliminary checklist by expert steering committee; 2) controlled feedback on preliminary checklist via expert judge survey; 3) two-phase nominal group technique with new expert judges to resolve pending items; and 4) checklist finalization and closing criteria by expert steering committee. The measure of agreement selected for this study was percent agreement defined a priori as ≥75%. RESULTS: A valid, reliable, and feasible checklist was developed. The development process highlighted three key findings: (1) the definition of community health centers and its list of essential items by expert judges is consistent with the Brazilian Ministry of Health, WHO snakebite strategic plan, and a general snakebite capacity guideline in India (internal validity), (2) the list of essential items for antivenom administration and clinical management is feasible and aligns with the literature regarding clinical care (reliability), and (3) engagement of local experts is critical to developing and implementing an antivenom decentralization strategy (feasibility). CONCLUSION: This study joins an international set of evidence advocating for decentralization, adding value in its definition of essential care items; identification of training needs across the care continuum; and demonstration of the validity, reliability, and feasibility provided by engaging local experts. Specific to Brazil, further added value comes in the potential use of the checklist for health unit accreditation as well as its applications to logistics and resource distribution. Future research priorities should apply this checklist to health units in the Amazon region of Brazil to determine which community health centers are or could be capable of receiving antivenom and translate this expert-driven checklist and approach to snakebite care in other settings or other diseases in low-resource settings.

Scaling up antivenom for snakebite envenoming in the Brazilian Amazon: a cost-effectiveness analysis.

Background: Snakebite envenoming (SBE) affects nearly three million people yearly, causing up to 180,000 deaths and 400,000 cases of permanent disability. Brazil's state of Amazonas is a global hotspot for SBE, with one of the highest annual incidence rates per 100,000 people, worldwide. Despite this burden, snake antivenom remains inaccessible to a large proportion of SBE victims in Amazonas. This study estimates the costs, and health and economic benefits of scaling up antivenom to community health centers (CHCs) and hospitals in the state. Methods: We built a decision tree model to simulate three different antivenom scale-up scenarios: (1) scale up to 95% of hospitals, (2) scale up to 95% of CHCs, and (3) scale up to 95% of hospitals and 95% of CHCs. We consider each scenario with and without a 10% increase in demand for antivenom among SBE victims. For each scenario, we model the treatment costs averted, deaths averted, and disability-adjusted life years (DALYs) averted from a societal, health system, and patient perspective relative to the status quo and over a time horizon of one year. For each scenario and perspective, we also calculate the incremental cost per DALY averted and per death averted. We use a willingness to pay threshold equal to the 2022 gross domestic product (GDP) per capita of Brazil. Findings: Scaling up antivenom to 95% of hospitals averts up to 2022 DALYs, costs up to USD $460 per DALY averted from a health system perspective, but results in net economic benefits up to USD $4.42 million from a societal perspective. Scaling up antivenom to 95% of CHCs averts up to 3179 DALYs, costs up to USD $308 per DALY averted from a health system perspective, but results in net economic benefits up to USD $7.35 million from a societal perspective. Scaling up antivenom to 95% of hospitals and CHCs averts up to 3922 DALYs, costs up to USD $328 per DALY averted from a health system perspective, but results in net economic benefits up to USD $8.98 million from a societal perspective. Interpretation: All three antivenom scale up scenarios - scale up to 95% of hospitals, scale up to 95% of CHCs, and scale up to 95% of hospitals and 95% of CHCs - avert a substantial proportion of the SBE burden in Amazonas and are cost-saving from a societal perspective and cost-effective from a health system perspective. Funding: W.M. and J.S. were funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq productivity scholarships). W.M. was funded by Fundação de Amparo à Pesquisa do Estado do Amazonas (PRÓ-ESTADO, call n. 011/2021-PCGP/FAPEAM, call n. 010/2021-CT&I ÁREAS PRIORITÁRIAS, call n. 003/2022-PRODOC/FAPEAM, POSGRAD/FAPEAM) and by the Ministry of Health, Brazil (Proposal No. 733781/19-035). Research reported in this publication was supported by the Fogarty International Center of the National Institutes of Health under Award Number R21TW011944. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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.

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.

The Impact of Time, Region, and Income Level on Stillbirth and Neonatal Mortality in Brazil, 2000-2019.

OBJECTIVE: To describe trends in perinatal loss across Brazil, a country that transitioned in 2006 from a lower-middle income to an upper-middle income country, from 2000 to 2019 and analyze the effect of municipal wealth status on perinatal outcomes. STUDY DESIGN: We conducted an ecological cohort study, based on publicly available data from the Brazilian Ministry of Health's data repository on live births and deaths. The Atlas of Human Development in Brazil was used to associate each region with a World Bank income classification. RESULTS: The national neonatal mortality rate (NMR) for infants born at ≥22 weeks of gestation decreased from 21.2 in 2000 to 12.4 in 2019. The stillbirth rate (SBR) decreased from 12.0 to 10.2 during this period. For infants born between 22 and 27 weeks of gestation, worsening perinatal outcomes were seen after 2012. In 2019, the median rates of neonatal mortality and stillbirth were both 4 points higher in lower- to middle-income municipalities compared with high-income municipalities (P < .01). CONCLUSION: Brazil has made significant progress in neonatal mortality and stillbirth from 2000 to 2019, yet inequity in perinatal outcomes remains and is correlated with municipal economic status. Nationally, ongoing improvement is needed for infants <28 weeks of gestation, and closer exploration is needed into why there are increasing rates of negative perinatal outcomes among infants born at 22-27 weeks of gestation after 2012.

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.

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.

Perspectives on snakebite envenoming care needs across different sociocultural contexts and health systems: A comparative qualitative analysis among US and Brazilian health providers.

With the advancements in therapeutics and available treatment options, almost all deaths and permanent disabilities from snakebite envenoming (SBE) are preventable. The challenge lies in implementing these evidence-based treatments and practices across different settings and populations. This study aims to compare data on provider perceptions of SBE care across health systems and cultural contexts to inform potential implementation science approaches. We hypothesize different health systems and cultural contexts will influence specific perceived needs to provide adequate snakebite care within central tenets of care delivery (e.g., cost, access, human resources). We previously conducted exploratory descriptive studies in the US and Brazil in order to understand the experience, knowledge, and perceptions of health professionals treating SBE. In the US, in-depth interviews were performed with emergency physicians from January 2020 to March 2020. In BR, focus group discussions were conducted with health professionals from community health centers at the end of June 2021. The focus group discussions (BR) were originally analyzed through an inductive thematic analysis approach. We conducted a secondary qualitative analysis in which this codebook was then applied to the interviews (US) in a deductive content analysis. The analysis concluded in August 2022. Brazil participants were physicians (n=5) or nurses (n=20) from three municipalities in the State of Amazonas with an average of three years of professional experience. US participants were emergency physicians (n=16) with an average of 15 years of professional experience. Four main themes emerged: 1) barriers to adequate care on the patient and/or community side and 2) on the health system side, 3) perceived considerations for how to address SBE, and 4) identified needs for improving care. There were 25 subthemes within the four themes. These subthemes were largely the same across the Brazil and US data, but the rationale and content within each shared subtheme varied significantly. For example, the subtheme "role of health professionals in improving care" extended across Brazil and the US. Brazil emphasized the need for task-shifting and -sharing amongst health care disciplines, whereas the US suggested specialized approaches geared toward increasing access to toxicologists and other referral resources. Despite similar core barriers to adequate snakebite envenoming care and factors to consider when trying to improve care delivery, health professionals in different health systems and sociocultural contexts identified different needs. Accounting for, and understanding, these differences is crucial to the success of initiatives intended to strengthen snakebite envenoming care. Implementation science efforts, with explicit health professional input, should be applied to develop new and/or adapt existing evidence-based treatments and practices for SBE.

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.

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.

Mapping of clinical management resources for snakebites and other animal envenomings in the Brazilian Amazon.

Snakebite envenomings (SBEs) and other envenomings triggered by venomous animals (VAEs) represent a significant disease burden in Brazil, with 29,152 SBEs reported in 2021 alone with nearly half of those occurring in the remote Brazilian Amazon. In 2021, Brazil recorded 240,294 envenomings from snakes, scorpions, spiders, and caterpillars. Therefore, there is an unequal distribution of SBEs with high morbidity and mortality in the Brazilian Amazon. The severity of SBEs increases when patients require more than 6 h to access antivenom treatment, a common issue for the rural and indigenous populations. Understanding currently available resources and practices in Amazon remote areas of Brazil can serve to inform future interventions and guide health care policies. This study aims to develop a resource map of existing healthcare resources for the Brazilian Amazon's clinical management of VAEs with emphasis in SBEs, which will aid future strategic interventions. Data collection included a literature review, secondary data collected by government departments and organizational records, GIS mapping activities, and expert input. Our framework was guided by the three levels of healthcare service ecosystem analysis (macro, meso, and micro). Our resource map lays out a comprehensive overview of antivenom access, the distribution landscape, differences in patient transportation, and barriers to access healthcare that face populations in the Brazilian Amazon.

Validation of a Culturally Relevant Snakebite Envenomation Clinical Practice Guideline in Brazil.

Snakebite envenoming (SBE) is a neglected tropical disease with significant global morbidity and mortality. Even when antivenom is available in low-resource areas, health workers do not receive adequate training to manage SBEs. This study aims to develop and validate a clinical practice guideline (CPG) for SBE management across Brazil. A panel of expert judges with academic and/or technical expertise in SBE management performed content validation. The content validity index (CVI) score was 90% for CPG objectives, 89% for structure and presentation and 92% for relevance and classified the CPG as valid. A semantic validation was performed by analyzing focus group discussions with doctors and nurses from three municipalities of the Brazilian Amazon, after a 5-day meeting during which the CPG was presented. Two central themes emerged: knowledge acquired during the meeting and recommendations for improving the CPG. Based on these results, the CPG was revised into a final version. This study presents the successful development and validation process of a CPG for SBE management, which is targeted to a specific low-resource, high-burden setting. This development and validation process can be adapted to other settings and/or other neglected tropical diseases.

[Spatial analysis of the quality of Primary Health Care services in reducing child mortality]. / Análise espacial da qualidade dos serviços de Atenção Primária à Saúde na redução da mortalidade infantil.

This study sought to analyze the correlation of the quality of Primary Health Care services in reducing child mortality, via geoprocessing. It involved an ecological study, with a cross-sectional approach, in which secondary data from all 5,565 Brazilian municipalities were used to analyze the infant mortality rate (IMR) and cause of infant death. The data related to IMR was obtained from the Mortality Information System. For the spatial analysis, 5,011 municipalities were included. The clustering analyses were performed using GEODA software and the spatial regression analyses were performed using ARCGIS 10.5 software. In Brazil, there was a 45.07% reduction in IMR between the years 2000 and 2015. The greatest reduction occurred in the northeastern region of the country, although it is still the region with the highest IMR. Of the 749 municipalities analyzed in the differential cluster for infant death, 153 had high IMR. The areas with the greatest increase in IMR were found in the North and Northeast regions. In Brazil, IMR proved to be inversely associated with the accessibility to high complexity services, health management strata and population size, reference for childbirth, live birth rate, per capita income and unemployment rate. A progressive reduction in IMR was recorded between 2000 and 2015.

Objetivou-se analisar a correlação da qualidade dos serviços da Atenção Primária na redução da mortalidade infantil, através do geoprocessamento. Um estudo ecológico, de abordagem transversal, em que foram utilizados dados secundários de todos os 5.565 municípios brasileiros para análise da taxa de mortalidade infantil (TMI) e causa de óbito infantil. Os dados da TMI foram obtidos no Sistema de Informação de Mortalidade. Para a análise espacial, incluímos 5.011 municípios. As análises de clusterização ocorreram no software GEODA e as análises de regressão espacial no ARCGIS 10.5. No Brasil houve uma redução de 45,07% da TMI entre os anos 2000 e 2015. A maior redução ocorreu na região nordeste do país, apesar de ainda ser a região com maior número na TMI. Dos 749 municípios analisados no cluster diferencial para óbito infantil, 153 apresentaram alta TMI. As áreas com maior expansão de alta TMI foram encontradas nas regiões Norte e Nordeste. No Brasil, a TMI mostrou-se inversamente associada à acessibilidade aos serviços de alta complexidade, ao estrato da gestão em saúde e porte populacional, à referência para o parto, à taxa de nascidos vivos, à renda per capita e à taxa de desemprego. Verificou-se uma crescente redução da TMI entre o período de 2000 a 2015.

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.

Análise espacial da qualidade dos serviços de Atenção Primária à Saúde na redução da mortalidade infantil / Spatial analysis of the quality of Primary Health Care services in reducing child mortality

Resumo Objetivou-se analisar a correlação da qualidade dos serviços da Atenção Primária na redução da mortalidade infantil, através do geoprocessamento. Um estudo ecológico, de abordagem transversal, em que foram utilizados dados secundários de todos os 5.565 municípios brasileiros para análise da taxa de mortalidade infantil (TMI) e causa de óbito infantil. Os dados da TMI foram obtidos no Sistema de Informação de Mortalidade. Para a análise espacial, incluímos 5.011 municípios. As análises de clusterização ocorreram no software GEODA e as análises de regressão espacial no ARCGIS 10.5. No Brasil houve uma redução de 45,07% da TMI entre os anos 2000 e 2015. A maior redução ocorreu na região nordeste do país, apesar de ainda ser a região com maior número na TMI. Dos 749 municípios analisados no cluster diferencial para óbito infantil, 153 apresentaram alta TMI. As áreas com maior expansão de alta TMI foram encontradas nas regiões Norte e Nordeste. No Brasil, a TMI mostrou-se inversamente associada à acessibilidade aos serviços de alta complexidade, ao estrato da gestão em saúde e porte populacional, à referência para o parto, à taxa de nascidos vivos, à renda per capita e à taxa de desemprego. Verificou-se uma crescente redução da TMI entre o período de 2000 a 2015.

Abstract This study sought to analyze the correlation of the quality of Primary Health Care services in reducing child mortality, via geoprocessing. It involved an ecological study, with a cross-sectional approach, in which secondary data from all 5,565 Brazilian municipalities were used to analyze the infant mortality rate (IMR) and cause of infant death. The data related to IMR was obtained from the Mortality Information System. For the spatial analysis, 5,011 municipalities were included. The clustering analyses were performed using GEODA software and the spatial regression analyses were performed using ARCGIS 10.5 software. In Brazil, there was a 45.07% reduction in IMR between the years 2000 and 2015. The greatest reduction occurred in the northeastern region of the country, although it is still the region with the highest IMR. Of the 749 municipalities analyzed in the differential cluster for infant death, 153 had high IMR. The areas with the greatest increase in IMR were found in the North and Northeast regions. In Brazil, IMR proved to be inversely associated with the accessibility to high complexity services, health management strata and population size, reference for childbirth, live birth rate, per capita income and unemployment rate. A progressive reduction in IMR was recorded between 2000 and 2015.

Microplanning for designing vaccination campaigns in low-resource settings: A geospatial artificial intelligence-based framework.

Existing campaign-based healthcare delivery programs used for immunization often fall short of established health coverage targets due to a lack of accurate estimates for population size and location. A microplan, an integrated set of detailed planning components, can be used to identify this information to support programs such as equitable vaccination efforts. Here, we presents a series of steps necessary to create an artificial intelligence-based framework for automated microplanning, and our pilot implementation of this analysis tool across 29 countries of the Americas. Further, we describe our processes for generating a conceptual framework, creating customized catchment areas, and estimating up-to-date populations to support microplanning for health campaigns. Through our application of the present framework, we found that 68 million individuals across the 29 countries are within 5 km of a health facility. The number of health facilities analyzed ranged from 2 in Peru to 789 in Argentina, while the total population within 5 km ranged from 1,233 in Peru to 15,304,439 in Mexico. Our results demonstrate the feasibility of using this methodological framework to support the development of customized microplans for health campaigns using open-source data in multiple countries. The pandemic is demanding an improved capacity to generate successful, efficient immunization campaigns; we believe that the steps described here can increase the automation of microplans in low resource settings.