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Background: Brazil has one of the highest prevalence of cesarean sections in the world. The private health system is responsible for carrying out most of these surgical procedures. A quality improvement project called Adequate Childbirth Project ("Projeto Parto Adequado"- PPA) was developed to identify models of care for labor and childbirth, which place value on vaginal birth and reduce the frequency of cesarean sections without a clinical indication. This research aims to evaluate the implementation of PPA in private hospitals in Brazil. Method: Evaluative hospital-based survey, carried out in 2017, in 12 private hospitals, including 4,322 women. We used a Bayesian network strategy to develop a theoretical model for implementation analysis. We estimated and compared the degree of implementation of two major driving components of PPA—"Participation of women" and "Reorganization of care" – among the 12 hospitals and according to type of hospital (belonging to a health insurance company or not). To assess whether the degree of implementation was correlated with the rate of vaginal birth data we used the Bayesian Network and compared the difference between the group "Exposed to the PPA model of care" and the group "Standard of care model". Results: PPA had a low degree of implementation in both components "Reorganization of Care" (0.17 – 0.32) and "Participation of Women" (0.21 – 0.34). The combined implementation score was 0.39–0.64 and was higher in hospitals that belonged to a health insurance company. The vaginal birth rate was higher in hospitals with a higher degree of implementation of PPA. Conclusion: The degree of implementation of PPA was low, which reflects the difficulties in changing childbirth care practices. Nevertheless, PPA increased vaginal birth rates in private hospitals with higher implementation scores. PPA is an ongoing quality improvement project and these results demonstrate the need for changes in the involvement of women and the care offered by the provider. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Applied Economics / Economía Aplicada is the property of FEA-RP, Universidade de Sao Paulo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many situations in practice require appropriate specification of operating characteristics under extreme conditions. Typical examples include environmental sciences where studies include extreme temperature, rainfall and river flow to name a few. In these cases, the effect of geographic and climatological inputs are likely to play a relevant role. This paper is concerned with the study of extreme data in the presence of relevant auxiliary information. The underlying model involves a mixture distribution: a generalized Pareto distribution is assumed for the exceedances beyond a high threshold and a non-parametric approach is assumed for the data below the threshold. Thus, the full likelihood including data below and above the threshold is considered in the estimation. The main novelty is the introduction of a regression structure to explain the variation of the exceedances through all tail parameters. Estimation is performed under the Bayesian paradigm and includes model choice. This allows for determination of higher quantiles under each covariate configuration and upper bounds for the data, where appropriate. Simulation results show that the models are appropriate and identifiable. The models are applied to the study of two temperature datasets: maxima in the U.S.A. and minima in Brazil, and compared to other related models. [ABSTRACT FROM AUTHOR]

This paper assesses the effects of non-aqueous fluids (NAF)-associated drill cuttings discharge on shelf break macrobenthic communities in the Campos Basin, off the southeast Brazilian coast, Rio de Janeiro State. Samples were taken with a 0.25-m2 box corer from surrounding two oil and gas wells on three monitoring cruises: before drilling, three months after drilling, and 22 months after drilling. Statistical methodologies used Bayesian geostatistical and analysis of variance models to evaluate the effects of the NAF-associated drill cuttings discharge and to define the impact area. The results indicated that marked variations were not observed in the number of families between cruises, though there were changes in the fauna composition. The changes seen in biological descriptors in both control and background situation areas were not considered significant, showing a temporal homogeneity in means. The impact area presented changes in biological descriptors of communities and trophic structure during the three cruises and such changes were correlated to chemical and physical variables related to the drilling activities, as a result of the mix of drill cuttings and sediment and the anoxic conditions established in the substrate. In that area, three months after drilling, a decrease in diversity and an increase in density, motile deposit-feeders and Pol/Crp ratio, and dominance of opportunistic organisms, such as the capitellid Capitella sp., were observed and, 22 months after drilling, an increase of diversity, reduction of dominance of capitellid polychaete, changes in the fauna composition, and a dominance of opportunistic burrowing and tube-building organisms were observed, indicating an ecological succession process. [ABSTRACT FROM AUTHOR]

This paper describes the use of Bayesian spatial models to develop the concept of a spatial–temporal mask for the purpose of identifying regions in which before and after drilling effects are most clearly defined and from which the consequences of exposure of macrofauna and meiofauna to the release of drilling discharges can be evaluated over time. To determine the effects of drilling fluids and drill-cuttings on the marine benthic community, it is essential to know not only where discharged materials ended up within the possible impact area, but also the chemical concentrations to which biota were exposed during and after drilling. Barium and light hydrocarbons were used as chemical tracers for water-based and non-aqueous-based fluids in a shallow water site in the Campos Basin, off the coast of Brazil. Since the site showed evidence of exposure to waste material from earlier drilling, the analysis needed to take into account the background concentrations of these compounds. Using the Bayesian models, concentrations at unsampled sites were predicted and regions altered and previously contaminated were identified. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate the efficiency indices for 60 Brazilian electricity distribution utilities. These scores are obtained by DEA (Data Envelopment Analysis) and Bayesian Stochastic Frontier Analysis models, two techniques that can reduce the information asymmetry and improve the regulator's skill to compare the performance of the utilities, a fundamental aspect in incentive regulation schemes. In addition, this paper also addresses the problem of identifying outliers and influential observations in deterministic nonparametric DEA models. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Applied Economics / Economía Aplicada is the property of FEA-RP, Universidade de Sao Paulo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Time series models are often used in hydrology and meteorology studies to model streamflows series in order to make forecasting and generate synthetic series which are inputs for the analysis of complex water resources systems. In this paper we introduce a new modeling approach for hydrologic and meteorological time series assuming a continuous distribution for the data, where both the conditional mean and conditional variance parameters are modeled. Bayesian methods using standard MCMC (Markov Chain Monte Carlo Methods) are used to simulate samples for the joint posterior distribution of interest. Two applications to real data sets illustrate the proposed methodology, assuming that the observations come from a normal, a gamma or a beta distribution. A first example is given by a time series of monthly averages of natural streamflows, measured in the year period ranging from 1931 to 2010 in Furnas hydroelectric dam, Brazil. A second example is given with a time series of 313 air humidity data measured in a weather station of Rio Claro, a Brazilian city located in southeastern of Brazil. These applications motivate us to introduce new classes of models to analyze hydrological and meteorological time series. [ABSTRACT FROM AUTHOR]

In the analysis of financial market data, random variations in price movements can share non-trivial statistical properties as observed for the distributions of returns, with possibility of absence of autocorrelations in asset returns, volatility in blocks and presence of asymmetry between rises and falls of stock values. Asset price fluctuations are usually investigated using time series models to obtain inferences about interest rates and future forecasts. In this study we consider the use of existing probability distributions to model run lengths and absolute historical price run returns as an alternative to the use of usual time series models with applications for time series obtained from the NYSE stock exchange for three private banks located in the Brazil in the period from July 19, 2013, to July 19, 2018. We assume discrete Weibull distributions as an alternative to the exponential law commonly used in this type of analysis. Under this modeling approach it is possible to obtain information about the structure of the market such as the probability of the stock market rising and falling daily and the magnitude of the returns. [ABSTRACT FROM AUTHOR]

Background: Mosquito control is currently the main tool available to contain the spread of several arboviruses in Brazil. We have evaluated the association between entomological surveys of female adult Aedes aegypti and the Breteau index (BI) in space and time in a hyperendemic area, and compared the human resources costs required to measure each of these indicators. Methods: Entomological surveys were conducted between 2016 and 2019 in Vila Toninho, a neighborhood in the city of São José do Rio Preto, Brazil. Monthly records of collected mosquito specimens were made and then grouped by season. Results: Our findings showed that adult and immature mosquitoes are more related in time than in space, possibly due to differences in their habitats or in climate variables. Bayesian temporal modeling revealed that an increase in 1 standard deviation in the BI was associated with a 27% increase in the number of adult female mosquitoes when adjusted for climatic conditions. The cost of entomological surveys of adult mosquitoes was found to be 83% lower than the cost of determining the BI when covering the same geographic area. Conclusions: For fine-scale assessments, a simple measure of adult Ae. aegypti abundance may be more realistic than aquatic indicators, but the adult indices are not necessarily the only reliable measure. Surveying adult female mosquitoes has significant potential for optimizing vector control strategies because, unlike the BI, this tool provides an effective indicator for micro-areas within an urban region. It should be noted that the results of the present study may be due to specific features of of the study area, and future studies should analyze whether the patterns found in the study neighborhood are also found in other regions. [ABSTRACT FROM AUTHOR]

Identification of terrestrial carbon (C) sources and sinks is critical for understanding the Earth system as well as mitigating and adapting to climate change resulting from greenhouse gas emissions. Predicting whether a given location will act as a C source or sink using terrestrial ecosystem models (TEMs) is challenging due to net flux being the difference between far larger, spatially and temporally variable fluxes with large uncertainties. Uncertainty in projections of future dynamics, critical for policy evaluation, has been determined using multi-TEM intercomparisons, for various emissions scenarios. This approach quantifies structural and forcing errors. However, the role of parameter error within models has not been determined. TEMs typically have defined parameters for specific plant functional types generated from the literature. To ascertain the importance of parameter error in forecasts, we present a Bayesian analysis that uses data on historical and current C cycling for Brazil to parameterise five TEMs of varied complexity with a retrieval of model error covariance at 1 ∘ spatial resolution. After evaluation against data from 2001–2017, the parameterised models are simulated to 2100 under four climate change scenarios spanning the likely range of climate projections. Using multiple models, each with per pixel parameter ensembles, we partition forecast uncertainties. Parameter uncertainty dominates across most of Brazil when simulating future stock changes in biomass C and dead organic matter (DOM). Uncertainty of simulated biomass change is most strongly correlated with net primary productivity allocation to wood (NPPwood) and mean residence time of wood (MRTwood). Uncertainty of simulated DOM change is most strongly correlated with MRTsoil and NPPwood. Due to the coupling between these variables and C stock dynamics being bi-directional, we argue that using repeat estimates of woody biomass will provide a valuable constraint needed to refine predictions of the future carbon cycle. Finally, evaluation of our multi-model analysis shows that wood litter contributes substantially to fire emissions, necessitating a greater understanding of wood litter C cycling than is typically considered in large-scale TEMs. [ABSTRACT FROM AUTHOR]

Despite all the research done on the first Zika virus (ZIKV) epidemics, it was only after the Brazilian epidemic that the Congenital Zika Syndrome was described. This was made possible due to the large number of babies born with microcephaly in the Northeast region (NE) in a narrow time. We hypothesize that the fivefold difference in the rate of microcephalic neonates between the NE and other regions is partially an effect of the population prior immunity against Dengue viruses (DENV), that cross-react with ZIKV. In this ecological study, we analysed the interaction between dengue fever epidemics from 2001 to 2014 and the 2015/2016 microcephaly epidemic in 400 microregions in Brazil using random-effects models under a Bayesian approach. The estimated effect of the time lag between the most recent large dengue epidemic (>400/100,000 inhabitants) and the microcephaly epidemic ranged from protection (up to 6 years prior) to an increased risk (from 7 to 12 years). This sustained window of protection, larger than described in previous longitudinal studies, is possibly an effect of herd immunity and of multiple exposures to DENV that could boost immunity. [ABSTRACT FROM AUTHOR]

The 37th edition of MaxEnt was held in Brazil, hosting several distinguished researchers and students. The workshop offered four tutorials, nine invited talks, twenty four oral presentations and twenty seven poster presentations. All submissions received their first choice between oral and poster presentations. The event held a celebration to Julio Stern's 60th anniversary and awarded two prizes to young researchers. As customary, the workshop had one free afternoon, in which participants visited the city's surroundings and experienced Brazilian food and traditions. [ABSTRACT FROM AUTHOR]

The aim of this study was to estimate the genetic parameters for the ability of a sheep to rank among the top three in its age category (ABI3) and for its total score (TS) on the judgement championships held in different agricultural fairs in Brazil. Data files included 3180 records referring to 1896 animals for ABI3 and 4383 records referring to 2170 animals for TS. The relationship matrix was composed by 4069 animals. Variance components were estimated fitting threshold and linear animal models for ABI3 and TS, respectively, by Bayesian analysis using the Gibbs sampler. The posterior means of the heritability and repeatability were, respectively, 0.10 ± 0.07 and 0.57 ± 0.11 for ABI3 and 0.12 ± 0.04 and 0.31 ± 0.02 for TS. The low values for the heritabilities related to these traits reveal a strong influence of the environmental factors, as the differential management offered to the animals before and during the fair. The repeatability values for ABI3 suggest high correlation among the animal classifications in different fairs. However, for TS, the observed performance has moderate confidence to be repeated over time. We conclude that although reduced, there is genetic variability for the animal’s performance in agricultural fairs. Nevertheless, due to the low heritability estimates, the expected response to direct selection for ABI3 and TS will be low. [ABSTRACT FROM PUBLISHER]

While South Americans are underrepresented in human genomic diversity studies, Brazil has been a classical model for population genetics studies on admixture. We present the results of the EPIGEN Brazil Initiative, the most comprehensive up-to-date genomic analysis of any Latin-American population. A population-based genomewide analysis of 6,487 individuals was performed in the context of worldwide genomic diversity to elucidate how ancestry, kinship, and inbreeding interact in three populations with different histories from the Northeast (African ancestry: 50%), Southeast, and South (both with European ancestry >70%) of Brazil. We showed that ancestry-positive assortative mating permeated Brazilian history. We traced European ancestry in the Southeast/South to a wider European/Middle Eastern region with respect to the Northeast, where ancestry seems restricted to Iberia. By developing an approximate Bayesian computation framework, we infer more recent European immigration to the Southeast/South than to the Northeast. Also, the observed low Native-American ancestry (6-8%) was mostly introduced in different regions of Brazil soon after the European Conquest. We broadened our understanding of the African diaspora, the major destination of which was Brazil, by revealing that Brazilians display two within-Africa ancestry components: one associated with non-Bantu/western Africans (more evident in the Northeast and African Americans) and one associated with Bantu/eastern Africans (more present in the Southeast/ South). Furthermore, the whole-genome analysis of 30 individuals (42-fold deep coverage) shows that continental admixture rather than local post-Columbian history is the main and complex determinant of the individual amount of deleterious genotypes. [ABSTRACT FROM AUTHOR]

Hansen’s disease (leprosy) elimination has proven difficult in several countries, including Brazil, and there is a need for a mathematical model that can predict control program efficacy. This study applied the Approximate Bayesian Computation algorithm to fit 6 different proposed models to each of the 5 regions of Brazil, then fitted hierarchical models based on the best-fit regional models to the entire country. The best model proposed for most regions was a simple model. Posterior checks found that the model results were more similar to the observed incidence after fitting than before, and that parameters varied slightly by region. Current control programs were predicted to require additional measures to eliminate Hansen’s Disease as a public health problem in Brazil. [ABSTRACT FROM AUTHOR]

Copyright of Tropical Medicine & International Health is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The global spread of HIV-1 has been accompanied by the emergence of genetically distinct viral strains. Over the past two decades subtype C viruses, which predominate in Southern and Eastern Africa, have spread rapidly throughout parts of South America. Phylogenetic studies indicate that subtype C viruses were introduced to South America through a single founder event that occurred in Southern Brazil. However, the external route via which subtype C viruses spread to the South American continent has remained unclear. Methodology/Principal Findings: We used automated genotyping to screen 8,309 HIV-1 subtype C pol gene sequences sampled within the UK for isolates genetically linked to the subtype C epidemic in South America. Maximum likelihood and Bayesian approaches were used to explore the phylogenetic relationships between 54 sequences identified in this screen, and a set of globally sampled subtype C reference sequences. Phylogenetic trees disclosed a robustly supported relationship between sequences from Brazil, the UK and East Africa. A monophyletic cluster comprised exclusively of sequences from the UK and Brazil was identified and dated to approximately the early 1980s using a Bayesian coalescent-based method. A sub-cluster of 27 sequences isolated from homosexual men of UK origin was also identified and dated to the early 1990s. Conclusions: Phylogenetic, demographic and temporal data support the conclusion that the UK was a crucial staging post in the spread of subtype C from East Africa to South America. This unexpected finding demonstrates the role of diffuse international networks in the global spread of HIV-1 infection, and the utility of globally sampled viral sequence data in revealing these networks. Additionally, we show that subtype C viruses are spreading within the UK amongst men who have sex with men. [ABSTRACT FROM AUTHOR]

Methods for modeling and mapping spatial variation in disease risk continue to motivate much research. In particular, spatial analyses provide a useful tool for exploring geographical heterogeneity in health outcomes, and consequently can yield clues as to disease etiology, direct public health management, and generate research hypotheses. This article presents a Bayesian partitioning approach for the analysis of individual level geo-referenced health data. The model makes few assumptions about the underlying form of the risk surface, is data adaptive, and allows for the inclusion of known determinants of disease. The methodology is used to model spatial variation in neonatal mortality in Porto Alegre, Brazil. [ABSTRACT FROM AUTHOR]

Nonlinear adjustment toward long-run price equilibrium relationships in the sugar-ethanol-oil nexus in Brazil is examined. We develop generalized bivariate error correction models that allow for cointegration between sugar, ethanol, and oil prices, where dynamic adjustments are potentially nonlinear functions of the disequilibrium errors. A range of models are estimated using Bayesian Monte Carlo Markov Chain algorithms and compared using Bayesian model selection methods. The results suggest that the long-run drivers of Brazilian sugar prices are oil prices and that there are nonlinearities in the adjustment processes of sugar and ethanol prices to oil price but linear adjustment between ethanol and sugar prices. [ABSTRACT FROM AUTHOR]

In this work we present a Bayesian analysis in linear regression models with spatially varying coefficients for modeling and inference in spatio-temporal processes. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial spatial heterogeneity. We describe for this model how to make inference about the regression coefficients and response processes under two scenarios: when the explanatory processes are known throughout the study region, and when they are known only at the sampling locations. Using a simulation experiment we investigate how parameter inference and interpolation performance are affected by some features of the data and prior distribution that is used. The proposed methodology is used to model the dataset on PM10 levels in the metropolitan region of Rio de Janeiro presented in Paez and Gamerman (2003). [ABSTRACT FROM AUTHOR]

This article proposes the use of the Bayesian reference analysis to estimate the parameters of the generalized normal linear regression model. It is shown that the reference prior led to a proper posterior distribution, while the Jeffreys prior returned an improper one. The inferential purposes were obtained via Markov Chain Monte Carlo (MCMC). Furthermore, diagnostic techniques based on the Kullback–Leibler divergence were used. The proposed method was illustrated using artificial data and real data on the height and diameter of Eucalyptus clones from Brazil. [ABSTRACT FROM AUTHOR]

Background: Visceral leishmaniasis (VL) is a neglected tropical disease of public health relevance in Brazil. To prioritize disease control measures, the Secretaria de Vigilância em Saúde of Brazil's Ministry of Health (SVS/MH) uses retrospective human case counts from VL surveillance data to inform a municipality-based risk classification. In this study, we compared the underlying VL risk, using a spatiotemporal explicit Bayesian hierarchical model (BHM), with the risk classification currently in use by the Brazil's Ministry of Health. We aim to assess how well the current risk classes capture the underlying VL risk as modelled by the BHM.Methods: Annual counts of human VL cases and the population at risk for all Brazil's 5564 municipalities between 2004 and 2014 were used to fit a relative risk BHM. We then computed the predicted counts and exceedence risk for each municipality and classified them into four categories to allow comparison with the four risk categories by the SVS/MH.Results: Municipalities identified as high-risk by the model partially agreed with the current risk classification by the SVS/MH. Our results suggest that counts of VL cases may suffice as general indicators of the underlying risk, but can underestimate risks, especially in areas with intense transmission.Conclusion: According to our BHM the SVS/MH risk classification underestimated the risk in several municipalities with moderate to intense VL transmission. Newly identified high-risk areas should be further evaluated to identify potential risk factors and assess the needs for additional surveillance and mitigation efforts. [ABSTRACT FROM AUTHOR]