Causas do declínio da desnutrição infantil no Brasil, 1996-2007

OBJETIVO: Estabelecer a evolução da prevalência de desnutrição na população brasileira de crianças menores de cinco anos de idade entre 1996 e 2007 e identificar os principais fatores responsáveis por essa evolução.MÉTODOS: Os dados analisados procedem de inquéritos "Demographic Health Surveys" realizados no Brasil em 1996 e 2006/7 em amostras probabilísticas de cerca de 4 mil crianças menores de cinco anos. A identificação dos fatores responsáveis pela variação temporal da prevalência da desnutrição (altura-para-idade inferior a -2 escores z; padrão OMS 2006) considerou mudanças na distribuição de quatro determinantes potenciais do estado nutricional. Modelagem estatística da associação independente entre determinante e risco de desnutrição em cada inquérito e cálculo de frações atribuíveis parciais foram utilizados para avaliar a importância relativa de cada fator na evolução da desnutrição infantil. RESULTADOS: A prevalência da desnutrição foi reduzida em cerca de 50%: de 13,5% (IC 95%: 12,1%;14,8%) em 1996 para 6,8% (5,4%;8,3%) em 2006/7. Dois terços dessa redução poderiam ser atribuídos à evolução favorável dos quatro fatores estudados: 25,7% ao aumento da escolaridade materna; 21,7% ao crescimento do poder aquisitivo das famílias; 11,6% à expansão da assistência à saúde e 4,3% à melhoria nas condições de saneamento.CONCLUSÕES: A taxa anual de declínio de 6,3% na proporção de crianças com déficits de altura-para-idade indica que em cerca de mais dez anos a desnutrição infantil poderia deixar de ser um problema de saúde pública no Brasil. A conquista desse resultado dependerá da manutenção das políticas econômicas e sociais que têm favorecido o aumento do poder aquisitivo dos mais pobres e de investimentos públicos que permitam completar a universalização do acesso da população brasileira aos serviços essenciais de educação, saúde e saneamento

Causas do declínio acelerado da desnutrição infantil no Nordeste do Brasil (1986-1996-2006)

OBJETIVO: Descrever a variação temporal na prevalência de desnutrição infantil na região Nordeste do Brasil, em dois períodos sucessivos, identificando os principais fatores responsáveis pela evolução observada em cada período. MÉTODOS: Os dados analisados provêm de amostras probabilísticas da população de crianças menores de cinco anos estudadas por inquéritos domiciliares do programa Demographic Health Surveys realizados em 1986 (n=1.302), 1996 (n=1.108) e 2006 (n=950). A identificação dos fatores responsáveis pela variação na prevalência da desnutrição (altura para idade < -2 z) levou em conta mudanças na freqüência de cinco determinantes potenciais do estado nutricional, modelagens estatísticas da associação independente entre determinante e risco de desnutrição no início de cada período e cálculo de frações atribuíveis. RESULTADOS: A prevalência da desnutrição foi reduzida em um terço de 1986 a 1996 (de 33,9 por cento para 22,2 por cento ) e em quase três quartos de 1996 a 2006(de 22,2 por cento para 5,9 por cento ). Melhorias na escolaridade materna e na disponibilidade de serviços de saneamento foram particularmente importantes para o declínio da desnutrição no primeiro período, enquanto no segundo período foram decisivos o aumento do poder aquisitivo das famílias mais pobres e, novamente, a melhoria da escolaridade materna. CONCLUSÕES: A aceleração do declínio da desnutrição do primeiro para o segundo período foi consistente com a aceleração de melhorias em escolaridade materna, saneamento, assistência à saúde e antecedentes reprodutivos e, sobretudo, com o excepcional aumento do poder aquisitivo familiar, observado apenas no segundo período. Mantida a taxa de declínio observada entre 1996 e 2006, o problema da desnutrição infantil na região Nordeste poderia ser considerado controlado em menos de dez anos. ) Para se chegar a este resultado será preciso manter o aumento do poder aquisitivo dos mais pobres e assegurar investimentos públicos para completar a universalização do acesso a serviços essenciais de educação, saúde e saneamento

Spatial pattern detection modeling of thrips (Thrips tabaci) on onion fields

Onion (Allium cepa) is one of the most cultivated and consumed vegetables in Brazil and its importance is due to the large laborforce involved. One of the main pests that affect this crop is the Onion Thrips (Thrips tabaci), but the spatial distribution of this insect, although important, has not been considered in crop management recommendations, experimental planning or sampling procedures. Our purpose here is to consider statistical tools to detect and model spatial patterns of the occurrence of the onion thrips. In order to characterize the spatial distribution pattern of the Onion Thrips a survey was carried out to record the number of insects in each development phase on onion plant leaves, on different dates and sample locations, in four rural properties with neighboring farms under different infestation levels and planting methods. The Mantel randomization test proved to be a useful tool to test for spatial correlation which, when detected, was described by a mixed spatial Poisson model with a geostatistical random component and parameters allowing for a characterization of the spatial pattern, as well as the production of prediction maps of susceptibility to levels of infestation throughout the area.

Body Mass Index, Cigarette Smoking, and Alcohol Consumption and Cancers of the Oral Cavity, Pharynx, and Larynx: Modeling Odds Ratios in Pooled Case-Control Data

study-specific results, their findings should be interpreted with caution

The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) - Part 2: Model sensitivity to the biomass burning

We describe an estimation technique for biomass burning emissions in South America based on a combination of remote-sensing fire products and field observations, the Brazilian Biomass Burning Emission Model (3BEM). For each fire pixel detected by remote sensing, the mass of the emitted tracer is calculated based on field observations of fire properties related to the type of vegetation burning. The burnt area is estimated from the instantaneous fire size retrieved by remote sensing, when available, or from statistical properties of the burn scars. The sources are then spatially and temporally distributed and assimilated daily by the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) in order to perform the prognosis of related tracer concentrations. Three other biomass burning inventories, including GFEDv2 and EDGAR, are simultaneously used to compare the emission strength in terms of the resultant tracer distribution. We also assess the effect of using the daily time resolution of fire emissions by including runs with monthly-averaged emissions. We evaluate the performance of the model using the different emission estimation techniques by comparing the model results with direct measurements of carbon monoxide both near-surface and airborne, as well as remote sensing derived products. The model results obtained using the 3BEM methodology of estimation introduced in this paper show relatively good agreement with the direct measurements and MOPITT data product, suggesting the reliability of the model at local to regional scales.

Adaptive Approach for a Maximum Entropy Algorithm in Ecological Niche Modeling

This paper presents an Adaptive Maximum Entropy (AME) approach for modeling biological species. The Maximum Entropy algorithm (MaxEnt) is one of the most used methods in modeling biological species geographical distribution. The approach presented here is an alternative to the classical algorithm. Instead of using the same set features in the training, the AME approach tries to insert or to remove a single feature at each iteration. The aim is to reach the convergence faster without affect the performance of the generated models. The preliminary experiments were well performed. They showed an increasing on performance both in accuracy and in execution time. Comparisons with other algorithms are beyond the scope of this paper. Some important researches are proposed as future works.

Different Approaches for Modeling Grouped Survival Data: A Mango Tree Study

Interval-censored survival data, in which the event of interest is not observed exactly but is only known to occur within some time interval, occur very frequently. In some situations, event times might be censored into different, possibly overlapping intervals of variable widths; however, in other situations, information is available for all units at the same observed visit time. In the latter cases, interval-censored data are termed grouped survival data. Here we present alternative approaches for analyzing interval-censored data. We illustrate these techniques using a survival data set involving mango tree lifetimes. This study is an example of grouped survival data.

Spatio-temporal modeling of agricultural yield data with an application to pricing crop insurance contracts

This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Parana (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited.

Molecular modeling and QSAR studies of a set of indole and benzimidazole derivatives as H(4) receptor antagonists

Histamine is an important biogenic amine, which acts with a group of four G-protein coupled receptors (GPCRs), namely H(1) to H(4) (H(1)R - H(4)R) receptors. The actions of histamine at H(4)R are related to immunological and inflammatory processes, particularly in pathophysiology of asthma, and H(4)R ligands having antagonistic properties could be helpful as antiinflammatory agents. In this work, molecular modeling and QSAR studies of a set of 30 compounds, indole and benzimidazole derivatives, as H(4)R antagonists were performed. The QSAR models were built and optimized using a genetic algorithm function and partial least squares regression (WOLF 5.5 program). The best QSAR model constructed with training set (N = 25) presented the following statistical measures: r (2) = 0.76, q (2) = 0.62, LOF = 0.15, and LSE = 0.07, and was validated using the LNO and y-randomization techniques. Four of five compounds of test set were well predicted by the selected QSAR model, which presented an external prediction power of 80%. These findings can be quite useful to aid the designing of new anti-H(4) compounds with improved biological response.

Multilevel analysis of hepatitis A infection in children and adolescents: a household survey in the Northeast and Central-west regions of Brazil

Background The objectives were to estimate the prevalence of hepatitis A among children and adolescents from the Northeast and Midwest regions and the Federal District of Brazil and to identify individual-, household- and area-levels factors associated with hepatitis A infection. Methods This population-based survey was conducted in 20042005 and covered individuals aged between 5 and 19 years. A stratified multistage cluster sampling technique with probability proportional to size was used to select 1937 individuals aged between 5 and 19 years living in the Federal capital and in the State capitals of 12 states in the study regions. The sample was stratified according to age (59 and 10- to 19-years-old) and capital within each region. Individual- and household-level data were collected by interview at the home of the individual. Variables related to the area were retrieved from census tract data. The outcome was total antibodies to hepatitis A virus detected using commercial EIA. The age distribution of the susceptible population was estimated using a simple catalytic model. The associations between HAV infection and independent variables were assessed using the odds ratio and corrected for the random design effect and sampling weight. Multilevel analysis was performed by GLLAMM using Stata 9.2. Results The prevalence of hepatitis A infection in the 59 and 1019 age-group was 41.5 and 57.4, respectively for the Northeast, 32.3 and 56.0, respectively for the Midwest and 33.8 and 65.1 for the Federal District. A trend for the prevalence of HAV infection to increase according to age was detected in all sites. By the age of 5, 31.5 of the children had already been infected with HAV in the Northeast region compared with 20.0 in the other sites. By the age of 19 years, seropositivity was 70 in all areas. The curves of susceptible populations differed from one area to another. Multilevel modeling showed that variables relating to different levels of education were associated with HAV infection in all sites. Conclusion The study sites were classified as areas with intermediate endemicity area for hepatitis A infection. Differences in age trends of infection were detected among settings. This multilevel model allowed for quantification of contextual predictors of hepatitis A infection in urban areas.

A multilevel model for the study of breastfeeding determinants in Brazil

The benefits of breastfeeding for the children`s health have been highlighted in many studies. The innovative aspect of the present study lies in its use of a multilevel model, a technique that has rarely been applied to studies on breastfeeding. The data reported were collected from a larger study, the Family Budget Survey-Pesquisa de Orcamentos Familiares, carried out between 2002 and 2003 in Brazil that involved a sample of 48 470 households. A representative national sample of 1477 infants aged 0-6 months was used. The statistical analysis was performed using a multilevel model, with two levels grouped by region. In Brazil, breastfeeding prevalence was 58%. The factors that bore a negative influence on breastfeeding were over four residents living in the same household [odds ratio (OR) = 0.68, 90% confidence interval (CI) = 0.51-0.89] and mothers aged 30 years or more (OR = 0.68, 90% CI = 0.53-0.89). The factors that positively influenced breastfeeding were the following: higher socio-economic levels (OR = 1.37, 90% CI = 1.01-1.88), families with over two infants under 5 years (OR = 1.25, 90% CI = 1.00-1.58) and being a resident in rural areas (OR = 1.25, 90% CI = 1.00-1.58). Although majority of the mothers was aware of the value of maternal milk and breastfed their babies, the prevalence of breastfeeding remains lower than the rate advised by the World Health Organization, and the number of residents living in the same household along with mothers aged 30 years or older were both factors associated with early cessation of infant breastfeeding before 6 months.

MODELING VERY LONG BASELINE INTERFEROMETRIC IMAGES WITH THE CROSS-ENTROPY GLOBAL OPTIMIZATION TECHNIQUE

We present a new technique for obtaining model fittings to very long baseline interferometric images of astrophysical jets. The method minimizes a performance function proportional to the sum of the squared difference between the model and observed images. The model image is constructed by summing N(s) elliptical Gaussian sources characterized by six parameters: two-dimensional peak position, peak intensity, eccentricity, amplitude, and orientation angle of the major axis. We present results for the fitting of two main benchmark jets: the first constructed from three individual Gaussian sources, the second formed by five Gaussian sources. Both jets were analyzed by our cross-entropy technique in finite and infinite signal-to-noise regimes, the background noise chosen to mimic that found in interferometric radio maps. Those images were constructed to simulate most of the conditions encountered in interferometric images of active galactic nuclei. We show that the cross-entropy technique is capable of recovering the parameters of the sources with a similar accuracy to that obtained from the very traditional Astronomical Image Processing System Package task IMFIT when the image is relatively simple (e. g., few components). For more complex interferometric maps, our method displays superior performance in recovering the parameters of the jet components. Our methodology is also able to show quantitatively the number of individual components present in an image. An additional application of the cross-entropy technique to a real image of a BL Lac object is shown and discussed. Our results indicate that our cross-entropy model-fitting technique must be used in situations involving the analysis of complex emission regions having more than three sources, even though it is substantially slower than current model-fitting tasks (at least 10,000 times slower for a single processor, depending on the number of sources to be optimized). As in the case of any model fitting performed in the image plane, caution is required in analyzing images constructed from a poorly sampled (u, v) plane.

Site-directed mutagenesis and structural modeling of Coq10p indicate the presence of a tunnel for coenzyme Q6 binding

Coq10p is a protein required for coenzyme Q function, but its specific role is still unknown. It is a member of the START domain superfamily that contains a hydrophobic tunnel implicated in the binding of lipophilic molecules. We used site-directed mutagenesis, statistical coupling analysis and molecular modeling to probe structural determinants in the Coq10p putative tunnel. Four point mutations were generated (coq10-K50E, coq10-L96S, coq10-E105K and coq10-K162D) and their biochemical properties analysed, as well as structural consequences. Our results show that all mutations impaired Coq10p function and together with molecular modeling indicate an important role for the Coq10p putative tunnel. (C) 2010 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Regression Models with Heteroscedasticity using Bayesian Approach

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.

MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modi. cations in their intrinsic organization. For instance, the network of transportation routes between cities and towns ( hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways ( paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation ( in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.