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Brazil counts among the countries the hardest hit by the Covid-19 pandemic. A great deal has been said about the negative role played by President Bolsonaro's denialism, but relatively few studies have attempted to measure precisely what impact it actually had on the pandemic. Our paper conducts econometric estimates based on observational data at municipal level to quantitatively assess the 'Bolsonaro effect' over time from March 2020 to December 2022. To our knowledge, this paper presents the most comprehensive investigation of Bolsonaro's influence in the spread of the pandemic from two angles: considering Covid-19 mortality and two key transmission mitigation channels (social distancing and vaccination); and exploring the full pandemic cycle (2020-2022) and its dynamics over time. Controlling for a rich set of relevant variables, our results find a strong and persistent 'Bolsonaro effect' on the death rate: municipalities that were more pro-Bolsonaro recorded significantly more fatalities. Furthermore, evidence suggests that the president's attitude and decisions negatively influenced the population's behaviour. Firstly, pro-Bolsonaro municipalities presented a lower level of compliance with social distancing measures. Secondly, vaccination was relatively less widespread in places more in favour of the former president. Finally, our analysis points to longer-lasting and damaging repercussions. Regression results are consistent with the hypothesis that the 'Bolsonaro effect' impacted not only on Covid-19 vaccination, but has affected vaccination campaigns in general thereby jeopardizing the historical success of the National Immunization Program in Brazil., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2024 Razafindrakoto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The COVID-19 pandemic in Brazil has brought many challenges, particularly regarding the management of hospital capacity, and a new demand for healthcare that added to the preexisting demands, such as neoplasms, cardiovascular diseases and births. In this paper, we estimate the impact of the pandemic on the number of deaths and hospitalizations for other diseases. We construct a monthly panel data of deaths and hospitalizations for various causes by the municipality of residence and relate them to COVID-19 hospitalizations using regression models that control for municipalities fixed-effects and interactions between State and month fixed-effects. The standard errors are clustered at the municipality level. Our estimates imply that 100 more hospitalizations by COVID-19 is associated with a drop of 49 non-COVID-19 hospitalizations and an additional four deaths for other reasons (all measured per 100,000 pop.). The impact of intensive care units COVID-19 hospitalizations on mortality is larger. The groups most affected are the African Brazilians, less-educated and the elderly. Additional deaths occurred both at households and at hospitals. The main causes of additional deaths were diseases related to the circulatory and endocrine system. The decline in hospitalizations for other causes seems to be related to the overcrowding of hospitals in periods of surge in the COVID-19, alongside with the fall in the demand for care by the citizens who were afraid of COVID-19 infection. These mechanisms affected more strongly the vulnerable groups of the population. Our results highlight the importance of promoting the awareness of heightened risk of non-communicable chronic diseases during a health emergency context. This should be done preferably through already established channels with community outreach, such as the Family Health Program in Brazil., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Menezes-Filho et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The COVID-19 pandemic has highlighted a need for better understanding of countries' vulnerability and resilience to not only pandemics but also disasters, climate change, and other systemic shocks. A comprehensive characterization of vulnerability can inform efforts to improve infrastructure and guide disaster response in the future. In this paper, we propose a data-driven framework for studying countries' vulnerability and resilience to incident disasters across multiple dimensions of society. To illustrate this methodology, we leverage the rich data landscape surrounding the COVID-19 pandemic to characterize observed resilience for several countries (USA, Brazil, India, Sweden, New Zealand, and Israel) as measured by pandemic impacts across a variety of social, economic, and political domains. We also assess how observed responses and outcomes (i.e., resilience) of the COVID-19 pandemic are associated with pre-pandemic characteristics or vulnerabilities, including (1) prior risk for adverse pandemic outcomes due to population density and age and (2) the systems in place prior to the pandemic that may impact the ability to respond to the crisis, including health infrastructure and economic capacity. Our work demonstrates the importance of viewing vulnerability and resilience in a multi-dimensional way, where a country's resources and outcomes related to vulnerability and resilience can differ dramatically across economic, political, and social domains. This work also highlights key gaps in our current understanding about vulnerability and resilience and a need for data-driven, context-specific assessments of disaster vulnerability in the future., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Beesley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Socioeconomic factors have exacerbated the impact of COVID-19 worldwide. Brazil, already marked by significant economic inequalities, is one of the most affected countries, with one of the highest mortality rates. Understanding how inequality and income segregation contribute to excess mortality by COVID-19 in Brazilian cities is essential for designing public health policies to mitigate the impact of the disease. This paper aims to fill in this gap by analyzing the effect of income inequality and income segregation on COVID-19 mortality in large urban centers in Brazil. We compiled weekly COVID-19 mortality rates from March 2020 to February 2021 in a longitudinal ecological design, aggregating data at the city level for 152 Brazilian cities. Mortality rates from COVID-19 were compared across weeks, cities and states using mixed linear models. We estimated the associations between COVID-19 mortality rates with income inequality and income segregation using mixed negative binomial models including city and week-level random intercepts. We measured income inequality using the Gini index and income segregation using the dissimilarity index using data from the 2010 Brazilian demographic census. We found that 88.2% of COVID-19 mortality rates variability was between weeks, 8.5% between cities, and 3.3% between states. Higher-income inequality and higher-income segregation values were associated with higher COVID-19 mortality rates before and after accounting for all adjustment factors. In our main adjusted model, rate ratios (RR) per 1 SD increases in income inequality and income segregation were associated with 17% (95% CI 9% to 26%) and 11% (95% CI 4% to 19%) higher mortality. Income inequality and income segregation are long-standing hallmarks of large Brazilian cities. Risk factors related to the socioeconomic context affected the course of the pandemic in the country and contributed to high mortality rates. Pre-existing social vulnerabilities were critical factors in the aggravation of COVID-19, as supported by the observed associations in this study., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Sousa Filho et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The outbreak of COVID-19 has led to there being a worldwide socio-economic crisis, with major impacts on developing countries. Understanding the dynamics of the disease and its driving factors, on a small spatial scale, might support strategies to control infections. This paper explores the impact of the COVID-19 on neighborhoods of Recife, Brazil, for which we examine a set of drivers that combines socio-economic factors and the presence of non-stop services. A three-stage methodology was conducted by conducting a statistical and spatial analysis, including clusters and regression models. COVID-19 data were investigated concerning ten dates between April and July 2020. Hotspots of the most affected regions and their determinant effects were highlighted. We have identified that clusters of confirmed cases were carried from a well-developed neighborhood to socially deprived areas, along with the emergence of hotspots of the case-fatality rate. The influence of age-groups, income, level of education, and the access to essential services on the spread of COVID-19 was also verified. The recognition of variables that influence the spatial spread of the disease becomes vital for pinpointing the most vulnerable areas. Consequently, specific prevention actions can be developed for these places, especially in heterogeneous cities., Competing Interests: The authors have declared that no competing interests exist.

Serological databases represent an important source of information to perceive COVID-19 impact on health professionals involved in combating the disease. This paper describes SerumCovid, a COVID-19 serological database focused on the diagnosis of health professionals, providing a preliminary analysis to contribute to the understanding of the antibody response to the SARS-CoV-2. The study population comprises 321 samples from 236 healthcare and frontline workers fighting COVID-19 in Vitória de Santo Antão, Brazil. Samples were collected from at least six days of symptoms to more than 100 days. The used immunoenzymatic assays were Euroimmun Anti-SARS-CoV-2 ELISA IgG and IgA. The most common gender in SerumCovid is female, while the most common age group is between 30 and 39 years old. However, no statistical differences were observed in either genders or age categories. The most reported symptoms were fatigue, headaches, and myalgia. Still, some subjects presented positive results for IgA after 130 days. Based on a temporal analysis, we have not identified general patterns as subjects presented high and low values of IgA and IgG with different evolution trends. Unexpectedly, for subjects with both serological tests, the outcome of IgA and IgG tests were the same (either positive or negative) for more than 80% of the samples. Therefore, SerumCovid helps better understand how COVID-19 affected healthcare and frontline workers, which increases knowledge about the infection and enables direct prevention actions., Competing Interests: The authors have declared that no competing interests exist.

Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that the fraction of reported cases varies across all countries. For example, the reported incidence fraction for the United States of America varies from 0.3 to 0.6, while for Brazil it varies from 0.2 to 0.4., Competing Interests: The authors have declared that no competing interests exist.

The global impact of coronavirus disease 2019 (COVID-19) is unprecedented, and many control and prevention measures have been implemented to test for and trace COVID-19. However, invisible-spreaders, who are associated with nucleic acid detection and asymptomatic infections, have received insufficient attention in the current COVID-19 control efforts. In this paper, we analyze the time series infection data for Italy, Germany, Brazil, India and Sweden since the first wave outbreak to address the following issues through a series of experiments. We conclude that: 1) As of June 1, 2020, the proportion of invisible-spreaders is close to 0.4% in Sweden, 0.8% in early Italy and Germany, and 0.4% in the middle and late stages. However, in Brazil and India, the proportion still shows a gradual upward trend; 2) During the spread of this pandemic, even a slight increase in the proportion of invisible-spreaders could have large implications for the health of the community; and 3) On resuming work, the pandemic intervention measures will be relaxed, and invisible-spreaders will cause a new round of outbreaks., Competing Interests: The authors have declared that no competing interests exist.

This paper analyses the influence of the Covid-19 coverage by the social media upon the shape of the sovereign yield curves of the five major developing countries, namely Federative Republic of B razil, Russian Federation, Republic of India, People's Republic of China, and the Republic of South Africa (BRICS). The coherenc e between the level, slope, and the curvature of the sovereign yield term structures and the Covid-19 medi a coverage is found to vary between low and high ranges, depending on the phases of the pandemic. The empirical estimations of the yield-curve factors a re performed by means of the Diebold-Li modified version of the Nelson-Siegel model. The intervals of low coherence reveal the capacity of the two latent factors, level and slope, to be used for creating cross-factor diversification strategies, workable under crisis conditions, as evidenced on the example of the ongoing pandemic. Diverse coherence patterns are reported on a per-country basis, highlighting a promising potential of sovereign debt investments for designing cross-country and cross-factor fixed-income strategies, capable of hedging downside risks., Competing Interests: The authors have declared that no competing interests exist.

The aim of this study was to examine aspects of children's health literacy; the information sources they were accessing, their information preferences, their perceived understanding of and their reported information needs in relation to COVID-19. An online survey for children aged 7-12 years of age and parent/caregivers from the UK, Sweden, Brazil, Spain, Canada and Australia was conducted between 6th of April and the 1st of June 2020. The surveys included demographic questions and both closed and open questions focussing on access to and understanding of COVID-19 information. Descriptive statistics and qualitative content analysis procedures were conducted. The findings show that parents are the main source of information for children during the pandemic in most countries (89%, n = 347), except in Sweden where school was the main source of information. However, in many cases parents chose to shield, filter or adapt their child's access to information about COVID-19, especially in relation to the death rates within each country. Despite this, children in this study reported knowing that COVID-19 was deadly and spreads quickly. This paper argues for a community rather than individual approach to addressing children's health literacy needs during a pandemic., Competing Interests: The authors have declared that no competing interests exist.

In this paper, we provide a retrospective cohort study with patients that have been hospitalized for general or intensive care unit admission due to COVID-19, between March 3 and July 29, 2020, in the state of Bahia, Brazil. We aim to correlate those patients' demographics, symptoms and comorbidities, with the risk of mortality from COVID-19, length of hospital stay, and time from diagnosis to definitive outcome. On the basis of a dataset provided by the Health Secretary of the State of Bahia, we selected 3,896 hospitalized patients from a total of 154,868 COVID-19 patients that included non-hospitalized patients and patients with invalid registration in the dataset. Then, we statistically analyzed whether there was a significant correlation between the patient record data and the COVID-19 pandemic, and our main findings reinforced by the use of a multivariable logistic regression were that older age (Odds Ratio [OR] = 1.03, 95% Confidence Interval [CI] = 1.03-1.04, p-value (p) <0.001), an initial symptom of shortness of breath (OR = 1.88, 95% CI = 1.60-2.20, p < 0.001), and the presence of comorbidities, mainly chronic kidney disease (OR = 2.41, 95% CI = 1.67-3.48, p < 0.001) are related to an increased risk of mortality from COVID-19. On the other hand, sore throat (OR = 0.74, 95% CI = 0.58-0.95, p = 0.02) and length of hospital stay (OR = 0.96, 95% CI = 0.58-0.95, p < 0.001) are more related to a reduced risk of mortality from COVID-19. Moreover, a multivariable linear regression conducted with statistically significant variables (p < 0.05) showed that age (OR = 0.97, 95% CI = 0.95-0.98, p < 0.001) and time from diagnosis to definitive outcome (OR = 1.67, 95% CI = 1.64-1.71, p < 0.001) are associated with the length of hospital stay., Competing Interests: The authors have declared that no competing interests exist.

Mobile geolocation data is a valuable asset in the assessment of movement patterns of a population. Once a highly contagious disease takes place in a location the movement patterns aid in predicting the potential spatial spreading of the disease, hence mobile data becomes a crucial tool to epidemic models. In this work, based on millions of anonymized mobile visits data in Brazil, we investigate the most probable spreading patterns of the COVID-19 within states of Brazil. The study is intended to help public administrators in action plans and resources allocation, whilst studying how mobile geolocation data may be employed as a measure of population mobility during an epidemic. This study focuses on the states of São Paulo and Rio de Janeiro during the period of March 2020, when the disease first started to spread in these states. Metapopulation models for the disease spread were simulated in order to evaluate the risk of infection of each city within the states, by ranking them according to the time the disease will take to infect each city. We observed that, although the high-risk regions are those closer to the capital cities, where the outbreak has started, there are also cities in the countryside with great risk. The mathematical framework developed in this paper is quite general and may be applied to locations around the world to evaluate the risk of infection by diseases, in special the COVID-19, when geolocation data is available. [ABSTRACT FROM AUTHOR]

Aim: To test the association between sociodemographic and social characteristics with COVID-19 cases and deaths in small and large Brazilian cities. Methods: This ecological study included COVID-19 data available in State Health Secretaries (managed by brasil.io API) and three national databases (IBGE, DATASUS and Embrapa). Temporal spread of COVID-19 in Brazil during the first year considered as outcome: a) days until 1st case in each city since 1st in the country; b) days until 1,000 cases/100,000 inhabitants since 1st case in each city; c) days until 1st death until 50 deaths/100,000 inhabitants. Covariates included geographic region, city social and environmental characteristics, housing conditions, job characteristics, socioeconomic and inequalities characteristics, and health services and coverage. The analysis were stratified by city size into small (<100,000 inhabitants) and large cities (≥100,00 inhabitants). Multiple linear regressions were performed to test associations of all covariates to adjust to potential confounders. Results: In small cities, the first cases were reported after 82.2 days and 1,000 cases/100,000 were reported after 117.8 days, whereas in large cities these milestones were reported after 32.1 and 127.7 days, respectively. For first death, small and large cities took 121.6 and 36.0 days, respectively. However, small cities were associated with more vulnerability factors to first case arrival in 1,000 cases/100,000 inhabitants, first death and 50 deaths/100,000 inhabitants. North and Northeast regions positively associated with faster COVID-19 incidence, whereas South and Southeast were least. Conclusion: Social and built environment characteristics and inequalities were associated with COVID-19 cases spread and mortality incidence in Brazilian cities. [ABSTRACT FROM AUTHOR]

Background: Accurate estimates of the COVID-19 pandemic's indirect impacts are crucial, especially in low- and middle-income countries. This study aims to update estimates of excess maternal deaths in Brazil during the first two years of the COVID-19 pandemic. Methods: This was an exploratory mixed ecological study using the counterfactual approach. The observed maternal deaths were gathered from the Mortality Information System (SIM) for the period between March 2015 and February 2022. Expected deaths from March 2020 to February 2022 were estimated using quasipoisson generalized additive models, considering quadrimester, age group, and their interaction as predictor variables. Analyses were performed in R version 4.1.2, RStudio, version 2023.03.1+446 and carried out with support from the "mgcv" and "plot_model" libraries. Results: A total of 5,040 maternal deaths were reported, with varying excess mortality across regions and age groups, resulting in 69% excess maternal mortality throughout Brazil during the first two years of the pandemic. The Southeast region had 50% excess mortality throughout the first two years and 76% excess in the second year. The North region had 69% excess mortality, increasing in the second year, particularly among women aged 20–34. The Northeast region showed 80% excess mortality, with a significant increase in the second year, especially among women aged 35–49. The Central-West region had 75% excess mortality, higher in the second year and statistically significant among women aged 35–49. The South region showed 117% excess mortality, reaching 203% in the second year among women aged 20–34, but no excess mortality in the 10–19 age category. Conclusions: Over two years, Brazil saw a significant impact on maternal excess deaths, regardless of region and pandemic year. The highest peak occurred between March and June 2021, emphasizing the importance of timely and effective epidemic responses to prevent avoidable deaths and prepare for new crises. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has had a negative impact on socioeconomic and public health conditions of the population. Aim: To measure the temporal evolution of COVID-19 cases in cities near the countryside outside metropolitan areas of northeastern Brazil and the impact of the primary care organization in its containment. Methods: This is a time-series study, based on the first three months of COVID-19 incidence in northeastern Brazil. Secondary data were used, the outcome was number of COVID-19 cases. Independent variables were time, coverage and quality score of basic health services, and demographic, socioeconomic and social isolation variables. Generalizable Linear Models with first order autoregression were applied. Results: COVID-19 spreads heterogeneously in cities near the countryside of Northeastern Brazilian cities, showing associations with the city size, socioeconomic and organizational indicators of services. The Family Health Strategy seems to mitigate the speed of progression and burden of the disease, in addition to measures such as social isolation and closure of commercial activities. Conclusion: The spread of COVID-19 reveals multiple related factors, which require coordinated intersectoral actions in order to mitigate its problems, especially in biologically and socially vulnerable populations. [ABSTRACT FROM AUTHOR]

Background: The emergence of SARS-CoV-2 variants led to subsequent waves of COVID-19 worldwide. In many countries, the second wave of COVID-19 was marked by record deaths, raising the concern that variants associated with that wave might be more deadly. Our aim was to compare outcomes of critically-ill patients of the first two waves of COVID-19. Methods: This retrospective cohort included critically-ill patients admitted between March-June 2020 and April-July 2021 in the largest academic hospital in Brazil, which has free-access universal health care system. We compared admission characteristics and hospital outcomes. The main outcome was 60‐day survival and we built multivariable Cox model based on a conceptual causal diagram in the format of directed acyclic graph (DAG). Results: We included 1583 patients (1315 in the first and 268 in the second wave). Patients in the second wave were younger, had lower severity scores, used prone and non-invasive ventilatory support more often, and fewer patients required mechanical ventilation (70% vs 80%, p<0.001), vasopressors (60 vs 74%, p<0.001), and dialysis (22% vs 37%, p<0.001). Survival was higher in the second wave (HR 0.61, 95%CI 0.50–0.76). In the multivariable model, admission during the second wave, adjusted for age, SAPS3 and vaccination, was not associated with survival (aHR 0.85, 95%CI 0.65–1.12). Conclusions: In this cohort study, patients with COVID-19 admitted to the ICU in the second wave were younger and had better prognostic scores. Adjusted survival was similar in the two waves, contrasting with record number of hospitalizations, daily deaths and health system collapse seen across the country in the second wave. Our findings suggest that the combination of the burden of severe cases and factors such as resource allocation and health disparities may have had an impact in the excess mortality found in many countries in the second wave. [ABSTRACT FROM AUTHOR]

Since the first case of COVID-19, Brazil has undergone infection waves with distinct characteristics. The description of new variants has alerted the emergence of more contagious or virulent viruses. The variant of concern Gamma emerged in Brazil and caused an epidemic wave, but its spread outside the country was limited. We report the clinical-epidemiological profile of hospitalized patients with COVID-19 by comparing two periods. A retrospective cohort study was performed. The primary outcome was to assess individuals with COVID-19 admitted in wards and intensive care units at the academic hospital of the Federal University of Parana (CHC-UFPR) between March 2020 and July 2021, correlating demographic, clinical-epidemiologic, and survival data with the most prevalent viral variant found in each period. We used Kaplan-Meier analysis to estimate the probability of survival and ROC curves to evaluate laboratory tests to find a cutoff point for poor outcomes. Data from 2,887 individuals were analyzed, 1,495 and 1,392 from the first and second periods, respectively. Hospitalization predominated among males in both periods, and the median age was significantly lower in the second one. The frequency of comorbidities was similar. Various demographic factors, clinical assessments, and laboratory tests were examined in relation to greater severity. When comparing the two periods, we observed predominance of the Wild virus during the first wave and the Gamma variant during the second, with no significant difference in outcomes. The findings suggest that despite the association of many factors with increased severity, the temporal variation between the two periods did not result in a notable divergence in the measured outcomes. The COVID-19 pandemic has lasted for a long time, with periods marked by peaks of cases, often caused by the emergence of viral variants, resulting in higher infection rates and rapid dissemination but, for variant Gamma, no apparent greater virulence. [ABSTRACT FROM AUTHOR]

Background: Knowledge about the epidemiology and risk factors surrounding COVID-19 contributes to developing better health strategies to combat the disease. Objective: This study aimed to establish a survival analysis and identify the risk factors for patients with COVID-19 in an upper middle-income city in Brazil. Methods: A retrospective cohort study was conducted with 280 hospitalized patients with COVID-19. The eCOVID platform provided data to monitor COVID-19 cases and help the communication between professionals. Results: Age ≥ 65 years was associated with decreased survival (54.8%), and females had a lower survival rate than males (p = 0.01). Regarding risk factors, urea concentration (p<0.001), hospital length of stay (p = 0.002), oxygen concentration (p = 0.005), and age (p = 0.02) were associated with death. Conclusion: Age, hospital length of stay, high blood urea concentration, and low oxygen concentration were associated with death by COVID-19 in the studied population. These findings corroborate with studies conducted in research centers worldwide. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic was severely aggravated in Brazil due to its politicization by the country's federal government. However, the impact of diffuse political forces on the fatality of an epidemic is notoriously difficult to quantify. Here we introduce a method to measure this effect in the Brazilian case, based on the inhomogeneous distribution throughout the national territory of political support for the federal government. This political support is quantified by the voting rates in the last general election in Brazil. This data is correlated with the fatality rates by COVID-19 in each Brazilian state as the number of deaths grows over time. We show that the correlation between fatality rate and political support grows as the government's misinformation campaign is developed. This led to the dominance of such political factor for the pandemic impact in Brazil in 2021. Once this dominance is established, this correlation allows for an estimation of the total number of deaths due to political influence as 350±70 thousand up to the end of 2021, corresponding to (57±11)% of the total number of deaths. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic in Brazil has been showing a pattern of distribution of related deaths associated with individual socioeconomic status (SES). However, little is known about the role of SES in the distribution of the mortality rate in different population, from an ecological perspective. Objective: The objective of this study was to evaluate the role of socioeconomic factors in the distribution of the COVID-19-related mortality rate among Brazilian municipalities in 2020. Methods: We conducted a retrospective, cross-sectional, observational, population-wide, and ecological study, using data of COVID-19-related deaths from the Influenza Epidemiological Surveillance Information System database (SIVEP-Gripe) and SES from the Social Vulnerability Index (SVI), the Human Development Index (HDI), the Geographic Index of the Socioeconomic Context and Social Studies (GeoSES), and 2010 Demographic Census (IBGE/Brazil). We computed crude, age- and sex-standardized, and the latter offset by the time of exposure to the epidemic mortality rates. To determine socioeconomic factors associated with mortality rates we used log-linear models with state codes as a random effect and Haversine variance-covariance matrix. Results: 191,528 deaths were related to COVID-19 and distributed in 4,928 (88.55%) Brazilian municipalities. Whatever the socioeconomic indexes used, the R2 were very small to explain SMRT. Consistent across all socioeconomic indexes used, high-income, more educated, and well infrastructure municipalities generally had higher mortality rates. Conclusion: Excluding the effect of demographic structure and pandemic timing from mortality rates, the contribution of SES to explain differences in COVID-19-related mortality rates among municipalities in Brazil became very low. The impact of SES on COVID-19-related mortality may vary across levels of aggregation. Urban infrastructure, which includes mobility structures, more complex economic activities and connections, may have influenced the average municipal death rate. [ABSTRACT FROM AUTHOR]

Purpose: Medical residents' mental health is currently an issue of concern for medical educators worldwide. The COVID-19 pandemic has raised the greatest concerns given the psychological effects of this scenario on medical residents on the frontlines of the pandemic. To assess the psychological impact of the COVID-19 pandemic on physicians in residency training, the collective symptoms of burnout, depression and anxiety are used to identify the residents' beliefs and clinical practices related to COVID-19 patients and their behaviors concerning disease prevention. Method: This observational study involved 3071 medical residents from all regions of Brazil. An online questionnaire assessed the presence of burnout using the Oldenburg Burnout Inventory, depressive symptoms using the Patient Health Questionaire-9, anxiety symptoms using the Generalized Anxiety Disorder-7, and COVID-19 Impact Questions to assess the residents' beliefs and clinical practices related to COVID-19 patients. Exploratory analyses, logistic regression and multinomial regression analysis were performed in this investigation. Results: Moderate and severe depressive symptoms were the most common (67.7%) followed by anxiety symptoms (52.8%) and burnout (48.6%). The difference between residents with or without contact with COVID-19 patients was significant increased when analyzing different aspects of clinical practice, behavior, substance use and mental health. Conclusions: These results suggest an increase in depression and anxiety symptoms among medical residents dealing with COVID-19, upstaging previous concerns about medical residents' mental health. The prevalence of burnout is similar to that of a nonpandemic scenario. Considering the severity of the pandemic scenario and the overburden of healthcare services, medical residents' mental health deserves special care. [ABSTRACT FROM AUTHOR]

Background: COVID-19, SARS and MERS are diseases that present an important health burden worldwide. This situation demands resource allocation to the healthcare system, affecting especially middle- and low-income countries. Thus, identifying the main cost drivers is relevant to optimize patient care and resource allocation. Objective: To systematically identify and summarize the current status of knowledge on direct medical hospitalization costs of SARS, MERS, or COVID-19 in Upper-Middle-Income Countries. Methods: We conducted a systematic review across seven key databases (PubMed, EMBASE, BVS Portal, CINAHL, CRD library, MedRxiv and Research Square) from database inception to February 2021. Costs extracted were converted into 2021 International Dollars using the Purchasing Power Parity-adjusted. The assessment of quality was based on the protocol by the BMJ and CHEERS. PROSPERO 2020: CRD42020225757. Results: No eligible study about SARS or MERS was recovered. For COVID-19, five studies presented cost analysis performed in Brazil, China, Iran, and Turkey. Regarding total direct medical costs, the lowest cost per patient at ward was observed in Turkey ($900.08), while the highest in Brazil ($5,093.38). At ICU, the lowest was in Turkey ($2,984.78), while the highest was in China ($52,432.87). Service care was the most expressive (58% to 88%) cost driver of COVID-19 patients at ward. At ICU, there was no consensus between service care (54% to 87%) and treatment (72% to 81%) as key burdens of total cost. Conclusion: Our findings elucidate the importance of COVID-19 on health-economic outcomes. The marked heterogeneity among studies leaded to substantially different results and made challenging the comparison of data to estimate pooled results for single countries or regions. Further studies concerning cost estimates from standardized analysis may provide clearer data for a more substantial analysis. This may help care providers and policy makers to organize care for patients in the most efficient way. [ABSTRACT FROM AUTHOR]

This article proposes a study of the SARS-CoV-2 virus spread and the efficacy of public policies in Brazil. Using both aggregated (from large Internet companies) and fine-grained (from Departments of Motor Vehicles) mobility data sources, our work sheds light on the effect of mobility on the pandemic situation in the Brazilian territory. Our main contribution is to show how mobility data, particularly fine-grained ones, can offer valuable insights into virus propagation. For this, we propose a modification in the SENUR model to add mobility information, evaluating different data availability scenarios (different information granularities), and finally, we carry out simulations to evaluate possible public policies. In particular, we conduct a case study that shows, through simulations of hypothetical scenarios, that the contagion curve in several Brazilian cities could have been milder if the government had imposed mobility restrictions soon after reporting the first case. Our results also show that if the government had not taken any action and the only safety measure taken was the population's voluntary isolation (out of fear), the time until the contagion peak for the first wave would have been postponed, but its value would more than double. [ABSTRACT FROM AUTHOR]

The emergence of the COVID-19 pandemic resulted in an unprecedented need for RT-qPCR-based molecular diagnostic testing, placing a strain on the supply chain and the availability of commercially available PCR testing kits and reagents. The effect of limited molecular diagnostics-related supplies has been felt across the globe, disproportionally impacting molecular diagnostic testing in developing countries where acquisition of supplies is limited due to availability. The increasing global demand for commercial molecular diagnostic testing kits and reagents has made standard PCR assays cost prohibitive, resulting in the development of alternative approaches to detect SARS-CoV-2 in clinical specimens, circumventing the need for commercial diagnostic testing kits while mitigating the high-demand for molecular diagnostics testing. The timely availability of the complete SARS-CoV-2 genome in the beginning of the COVID-19 pandemic facilitated the rapid development and deployment of specific primers and standardized laboratory protocols for the molecular diagnosis of COVID-19. An alternative method offering a highly specific manner of detecting and genotyping pathogens within clinical specimens is based on the melting temperature differences of PCR products. This method is based on the melting temperature differences between purine and pyrimidine bases. Here, RT-qPCR assays coupled with a High Resolution Melting analysis (HRM-RTqPCR) were developed to target different regions of the SARS-CoV-2 genome (RdRp, E and N) and an internal control (human RNAse P gene). The assays were validated using synthetic sequences from the viral genome and clinical specimens (nasopharyngeal swabs, serum and saliva) of sixty-five patients with severe or moderate COVID-19 from different states within Brazil; a larger validation group than that used in the development to the commercially available TaqMan RT-qPCR assay which is considered the gold standard for COVID-19 testing. The sensitivity of the HRM-RTqPCR assays targeting the viral N, RdRp and E genes were 94.12, 98.04 and 92.16%, with 100% specificity to the 3 SARS-CoV-2 genome targets, and a diagnostic accuracy of 95.38, 98.46 and 93.85%, respectively. Thus, HRM-RTqPCR emerges as an attractive alternative and low-cost methodology for the molecular diagnosis of COVID-19 in restricted-budget laboratories. [ABSTRACT FROM AUTHOR]

In Dec 2020 Brazil became one of the worldwide epicenters of the COVID-19 pandemic with more than 7.2M reported cases. Brazil has a large territory with unequal distribution of healthcare resources including physicians. Resource limitation has been one of the main factors hampering Brazil's response to the COVID-19 crisis. Telemedicine has been an effective approach for COVID-19 management as it allows to reduce the risk of cross-contamination and provides support to remote rural locations. Here we present the analyses of teleconsultations from a countrywide telemedicine service (TelessáudeRS-UFRGS, TRS), that provides physician-to-physician remote support during the COVID-19 pandemic in Brazil. We performed a descriptive analysis of the teleconsultation incoming calls and a text analysis from the call transcripts. Our findings indicate that TRS teleconsultations in Brazil experienced an exponential increment of 802.% during a period of 6 days, after the first death due to COVID-19 was reported. However, the number of teleconsultations cases decreased over time, despite the number of reported COVID-19 cases continuously increasing. The results also showed that physicians in low-income municipalities, based on GDP per capita, are less likely to consult the telemedicine service despite facing higher rates of COVID-19 cases. The text analysis of call transcripts from medical teleconsultations showed that the main concern of physicians were "asymptomatic" patients. We suggest an immediate reinforcement of telehealth services in the regions of lower income as a strategy to support COVID-19 management. [ABSTRACT FROM AUTHOR]

This work presents a practical proposal for estimating health system utilization for COVID-19 cases. The novel methodology developed is based on the dynamic model known as Susceptible, Infected, Removed and Dead (SIRD). The model was modified to focus on the healthcare system dynamics, rather than modeling all cases of the disease. It was tuned using data available for each Brazilian state and updated with daily figures. A figure of merit that assesses the quality of the model fit to the data was defined and used to optimize the free parameters. The parameters of an epidemiological model for the whole of Brazil, comprising a linear combination of the models for each state, were estimated considering the data available for the 26 Brazilian states. The model was validated, and strong adherence was demonstrated in most cases. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the discourses of patients who were diagnosed with multidrug-resistant tuberculosis, the perception of why they acquired this health condition and barriers to seeking care in a priority city in Brazil during the COVID-19 pandemic. This was an exploratory qualitative study, which used the theoretical-methodological framework of the Discourse Analysis of French matrix, guided by the Consolidated Criteria for Reporting Qualitative Research. The study was conducted in Ribeirão Preto, São Paulo, Brazil. Seven participants were interviewed who were undergoing treatment at the time of the interview. The analysis of the participants' discourses allowed the emergence of four discursive blocks: (1) impact of the social determinants in the development of multidrug-resistant tuberculosis, (2) barriers to seeking care and difficulties accessing health services, (3) perceptions of the side effects and their impact on multidrug-resistant tuberculosis treatment, and (4) tuberculosis and COVID-19: a necessary dialogue. Through discursive formations, these revealed the determinants of multidrug-resistant tuberculosis. Considering the complexity involved in the dynamics of multidrug-resistant tuberculosis, advancing in terms of equity in health, that is, in reducing unjust differences, is a challenge for public policies, especially at the current moment in Brazil, which is of accentuated economic, political and social crisis. The importance of psychosocial stressors and the lack of social support should also be highlighted as intermediary determinants of health. The study has also shown the situation of COVID-19, which consists of an important barrier for patients seeking care. Many patients reported fear, insecurity and worry with regard to returning to medical appointments, which might contribute to the worsening of tuberculosis in the scenario under study. [ABSTRACT FROM AUTHOR]

As of mid-August 2020, Brazil was the country with the second-highest number of cases and deaths by the COVID-19 pandemic, but with large regional and social differences. In this study, using data from the Brazilian Ministry of Health, we analyze the spatial patterns of infection and mortality from Covid-19 across small areas of Brazil. We apply spatial autoregressive Bayesian models and estimate the risks of infection and mortality, taking into account age, sex composition of the population and other variables that describe the health situation of the spatial units. We also perform a decomposition analysis to study how age composition impacts the differences in mortality and infection rates across regions. Our results indicate that death and infections are spatially distributed, forming clusters and hotspots, especially in the Northern Amazon, Northeast coast and Southeast of the country. The high mortality risk in the Southeast part of the country, where the major cities are located, can be explained by the high proportion of the elderly in the population. In the less developed areas of the North and Northeast, there are high rates of infection among young adults, people of lower socioeconomic status, and people without access to health care, resulting in more deaths. [ABSTRACT FROM AUTHOR]

Objective: To study the profile of hospitalizations due to COVID-19 in the Unified Health System (SUS) in Brazil and to identify factors associated with in-hospital mortality related to the disease. Methods: Cross-sectional study, based on secondary data on COVID-19 hospitalizations that occurred in the SUS between late February through June. Patients aged 18 years or older with primary or secondary diagnoses indicative of COVID-19 were included. Bivariate analyses were performed and generalized linear mixed models (GLMM) were estimated with random effects intercept. The modeling followed three steps, including: attributes of the patients; elements of the care process; and characteristics of the hospital and place of hospitalization. Results: 89,405 hospitalizations were observed, of which 24.4% resulted in death. COVID-19 patients hospitalized in the SUS were predominantly male (56.5%) with a mean age of 58.9 years. The length of stay ranged from less than 24 hours to 114 days, with a mean of 6.9 (±6.5) days. Of the total number of hospitalizations, 22.6% reported ICU use. The odds on in-hospital death were 16.8% higher among men than among women and increased with age. Black individuals had a higher likelihood of death. The behavior of the Charlson and Elixhauser indices was consistent with the hypothesis of a higher risk of death among patients with comorbidities, and obesity had an independent effect on increasing this risk. Some states, such as Amazonas and Rio de Janeiro, had a higher risk of in-hospital death from COVID-19. The odds on in-hospital death were 72.1% higher in municipalities with at least 100,000 inhabitants, though being hospitalized in the municipality of residence was a protective factor. Conclusion: There was broad variation in COVID-19 in-hospital mortality in the SUS, associated with demographic and clinical factors, social inequality, and differences in the structure of services and quality of health care. [ABSTRACT FROM AUTHOR]

Background: Given the severity and scope of the current COVID-19 pandemic, it is critical to determine predictive features of COVID-19 mortality and medical resource usage to effectively inform health, risk-based physical distancing, and work accommodation policies. Non-clinical sociodemographic features are important explanatory variables of COVID-19 outcomes, revealing existing disparities in large health care systems. Methods and findings: We use nation-wide multicenter data of COVID-19 patients in Brazil to predict mortality and ventilator usage. The dataset contains hospitalized patients who tested positive for COVID-19 and had either recovered or were deceased between March 1 and June 30, 2020. A total of 113,214 patients with 50,387 deceased, were included. Both interpretable (sparse versions of Logistic Regression and Support Vector Machines) and state-of-the-art non-interpretable (Gradient Boosted Decision Trees and Random Forest) classification methods are employed. Death from COVID-19 was strongly associated with demographics, socioeconomic factors, and comorbidities. Variables highly predictive of mortality included geographic location of the hospital (OR = 2.2 for Northeast region, OR = 2.1 for North region); renal (OR = 2.0) and liver (OR = 1.7) chronic disease; immunosuppression (OR = 1.7); obesity (OR = 1.7); neurological (OR = 1.6), cardiovascular (OR = 1.5), and hematologic (OR = 1.2) disease; diabetes (OR = 1.4); chronic pneumopathy (OR = 1.4); immunosuppression (OR = 1.3); respiratory symptoms, ranging from respiratory discomfort (OR = 1.4) and dyspnea (OR = 1.3) to oxygen saturation less than 95% (OR = 1.7); hospitalization in a public hospital (OR = 1.2); and self-reported patient illiteracy (OR = 1.1). Validation accuracies (AUC) for predicting mortality and ventilation need reach 79% and 70%, respectively, when using only pre-admission variables. Models that use post-admission disease progression information reach accuracies (AUC) of 86% and 87% for predicting mortality and ventilation use, respectively. Conclusions: The results highlight the predictive power of socioeconomic information in assessing COVID-19 mortality and medical resource allocation, and shed light on existing disparities in the Brazilian health care system during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The current outbreak of the coronavirus disease 2019 (COVID-19) is an unprecedented example of how fast an infectious disease can spread around the globe (especially in urban areas) and the enormous impact it causes on public health and socio-economic activities. Despite the recent surge of investigations about different aspects of the COVID-19 pandemic, we still know little about the effects of city size on the propagation of this disease in urban areas. Here we investigate how the number of cases and deaths by COVID-19 scale with the population of Brazilian cities. Our results indicate small towns are proportionally more affected by COVID-19 during the initial spread of the disease, such that the cumulative numbers of cases and deaths per capita initially decrease with population size. However, during the long-term course of the pandemic, this urban advantage vanishes and large cities start to exhibit higher incidence of cases and deaths, such that every 1% rise in population is associated with a 0.14% increase in the number of fatalities per capita after about four months since the first two daily deaths. We argue that these patterns may be related to the existence of proportionally more health infrastructure in the largest cities and a lower proportion of older adults in large urban areas. We also find the initial growth rate of cases and deaths to be higher in large cities; however, these growth rates tend to decrease in large cities and to increase in small ones over time. [ABSTRACT FROM AUTHOR]

Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease. [ABSTRACT FROM AUTHOR]