Reducing occurrence of Giardia duodenalis in children living in semiarid regions: impact of a large scale rainwater harvesting initiative.

BACKGROUND: In Brazil, about two million people living in rural semiarid regions were benefited with the construction of rainwater cement cisterns, as an initiative from the program "One Million Cisterns" (P1MC). Nevertheless, few epidemiological studies have been conducted to assess health risks or protection effects associated with consumption of this water source. The aim of this study was to evaluate whether access to rainwater harvesting cisterns is associated with the decrease in the occurrence of Giardia duodenalis infections in children, compared to other children living in households supplied by other water sources. METHODOLOGY/PRINCIPAL FINDINGS: A quasi-experimental study with two concurrent cohorts was developed in two rural municipalities of the semiarid region of Brazil. A sample of 664 children, aged between 4 months and 5 years old, was followed up, of which 332 had access to rainwater cisterns (cistern group) and 332 did not, having water supplied from alternative sources (comparison group). In a period of approximately one year (2010) intestinal parasites were investigated in feces three times. The prevalence of G. duodenalis in children from the cistern group ranged from 4.8 to 10.5%, while the prevalence in the comparison group ranged from 7.6 to 16.7%. Multivariate analysis (GEE) showed a higher risk of G. duodenalis infection in children who did not have access to rainwater cisterns, when compared to children who did (OR 1.72; 95% CI 1.14-2.59). The other variables associated with G. duodenalis infection were: number of rooms per house (OR 0.89; 95% CI 0.80-0.99); family income (OR0.48; 95% CI 0.26-0.88); birth order (OR 1.72; 95% CI 1.17-2.51); preterm children (OR 1.70; 95% CI 1.19-2.43); and improper hand hygiene prior to food preparation (OR 4.78; 95% CI 1.95-11.76). CONCLUSIONS/SIGNIFICANCE: Ownership of a rainwater cistern is associated with a lower prevalence of G. duodenalis infection in children after adjustment for environmental and family-related factors. Nevertheless, the study suggests the necessity to complement physical interventions with actions related to personal and domestic hygiene to enable further reductions in parasite infections affecting mainly the underprivileged populations

Epidemiological characteristics and temporal trends of new leprosy cases in Brazil: 2006 to 2017.

Our study aims to describe trends in new case detection rate (NCDR) of leprosy in Brazil from 2006 to 2017 overall and in subgroups, and to analyze the evolution of clinical and treatment characteristics of patients, with emphasis on cases diagnosed with grade 2 physical disabilities. We conducted a descriptive study to analyze new cases of leprosy registered in the Brazilian Information System for Notificable Diseases (SINAN), from 2006-2017. We calculated the leprosy NCDR per 100,000 inhabitants (overall and for individuals aged < 15 and ≥ 15 years) by sex, age, race/ethnicity, urban/rural areas, and Brazilian regions, and estimated the trends using the Mann-Kendall non-parametric test. We analyzed the distributions of cases according to relevant clinical characteristics over time. In Brazil, there was a sharp decrease in the overall NCDR from 23.4/100,000 in 2006 to 10.3/100,000 in 2017; among children < 15 years, from 6.94 to 3.20/100,000. The decline was consistent in all Brazilian regions and race/ethnicity categories. By 2017, 70.2% of the cases were multibacillary, 30.5% had grade 1 (G1D) or 2 (G2D) physical disabilities at diagnosis and 42.8% were not evaluated at treatment completion/discharge; cases with G2D at diagnosis were mostly detected in urban areas (80%) and 5% of cases died during the treatment (leprosy or other causes). Although the frequency of leprosy NCDR decreased in Brazil from 2006 to 2017 across all evaluated population groups, the large number of cases with multibacillary leprosy, physical disabilities or without adequate evaluation, and among children suggest the need to reinforce timely diagnosis and treatment to control leprosy in Brazil

Modelagem Bayesiana semi-paramétrica via misturas

Exportado OPUSMade available in DSpace on 2019-08-11T14:09:22Z (GMT). No. of bitstreams: 1 tese_nivea_bispo.pdf: 1978490 bytes, checksum: 485f5433861dbc01b527db28236b001e (MD5) Previous issue date: 7A modelagem estatística baseada em misturas nitas de distribuições é uma área de pesquisa em crescente ascensão. Devido à sua exibilidade e ao avanço de métodos computacionais nas duas últimas décadas, esse tipo de modelagem tem se tornado bastante atrativo tanto do ponto de vista prático quanto teórico, pois permite que densidades com estruturas complexas sejam aproximadas usando uma estrutura mais simples.Além disso, os modelos estatísticos baseados em misturas conseguem capturar propriedades especícas dos dados como multimodalidade, assimetria, cauda pesada e heterogeneidade decorrente de fatores não-observados. Há na literatura inúmeros trabalhos sobre modelagem estatística baseada em misturas nitas de distribuições normais e muitos autores mostraram que esse tipo de mistura fornece uma base simples e ecaz para estimação de densidades e modelagem de populações heterogêneas. Entretanto,em problemas práticos onde há a presença de outliers nos dados, a distribuição normal pode ter suas estimativas para média e variância seriamente afetadas. Neste sentido há uma recente propagação de modelos baseados em misturas com componentes não-normais onde as distribuições assumidas para os componentes da mistura são, por exemplo, t de Student, Slash, Skew-Normal, Skew-t, dentre outras. Neste trabalho uma modelagem semi-paramétrica baseada em misturas nitas de distribuições t de Student será introduzida. A especicação do modelo proposto considera estruturas separadas para as modas e o comportamento de cauda, o que exibiliza a estimação de densidades. Além disso, a estrutura de cauda na abordagem apresentada será estimada sem que haja a necessidade de se estimar parâmetros de grau de liberdade, cuja estimação é conhecida por ser difícil e custosa computacionalmente. Uma extensão do modelo no contexto de regressão linear também é apresentada para as situações onde os erros do modelo possuem multimodalidade, assimetria e caudas pesadas. A abordagem proposta é avaliada através de estudos de simulação e aplicações a conjuntos de dados reais, onde um algoritmoMCMCépropostoeimplementadoparaamostrardasdistribuiçõesa posterioriStatistical modeling based on nite mixture distributions is a growing research area. Due to its exibility and the advance of computational methods in the last two decades, this type of modeling has become quite attractive both from a practical and a theoretical point of view, since it allows densities with complex structures to be approximated using a simpler structure. In addition, statistical models based on nite mixtures can capture specic data properties such as multimodality, asymmetry, heavy tail and heterogeneity due to unobserved factors. Numerous studies on statistical modeling based on nite mixtures of normal distributions have been published in the literature, and many authors have shown that this type of mixture provides a simple and eective basis for estimating densities and modeling heterogeneous populations. However, in practical problems where there are outliers in the data, the normal distribution may have its estimates for mean and variance severely aected. In this sense there is a recent propagation of models based on mixtures withnon-normalcomponents where the assumed distributions for the components of the mixture are, for example, Student-t, Slash, Skew-Normal, Skew-t, among others. In this work a semi-parametric model based on nite mixtures of t distributions will be introduced. The proposed model specication considers separate structures for the modes and tail behavior, which makes density estimation more exible. In addition, the tail structure in the presented approach will be estimated without the need to estimate degree of freedom parameters, whose estimation is known to be dicult and computationally costly. An extension of the model in the linear regression context is also presented for situations where model errors have multimodality, asymmetry and heavy tails. The proposed approach is evaluated through simulation studies and applications to real data sets, where an MCMC algorithm is proposed and implemented to sample from the posterior distribution

Diferentes estratégias para modelagem de respostas politômicas ordinais em estudos longitudinais

Exportado OPUSMade available in DSpace on 2019-08-12T09:15:15Z (GMT). No. of bitstreams: 1 disserta__o_final_n_vea.bispo.pdf: 958178 bytes, checksum: 816dfffe746613c894151e9c7adf1dae (MD5) Previous issue date: 22A modelagem de respostas politômicas, em especial as ordinais, tem sido alvo de crescente interesse nos últimos anos, e vem ganhando espaço em pesquisas sobre qualidade de vida, indicadores de condição de saúde, avaliação da proficiência dos alunos em determinadas disciplinas, dentre outras. A sua utilização vai desde os estudos transversais, onde se assume independência entre as observações, até os estudos longitudinais, em que mais de uma resposta do mesmo indivíduo é observada ao longo do tempo. Existem na literatura várias metodologias propostas para modelar dados desta natureza em estudos transversais, sendo a mais usual na prática a que utiliza o modelo de logitos cumulativos, também conhecido como modelo de odds proporcionais devido ao pressuposto do modelo que assume proporcionalidade nas odds, ou seja, o modelo assume que há um crescimento aproximadamente linear das razões de chances para os coeficientes da regressão. Em muitas situações práticas o referido pressuposto pode não ser verificado, tornando desaconselhável a utilização do modelo. Há, contudo, outro modelo que generaliza o de odds proporcionais, conhecido por odds proporcionais parciais, que permite a não proporcionalidade para um subconjunto de covariáveis que violaram o pressuposto de proporcionalidade. Em estudos longitudinais, os modelos usuais - modelos marginais, modelos lineares generalizados mistos e modelos de transição - para análise de dados correlacionados também podem ser utilizados para modelar respostas politômicas. Nesse trabalho a modelagem de respostas politômicas ordinais em estudos longitudinais é discutida sob a ótica dos modelos marginais, modelos lineares generalizados mistos e de transição. A especificação e interpretação dos modelos é ilustrada e discutida através da análise de dois conjuntos de dados reais.The modeling of polytomous responses, especially ordinal, has been the subject of increasing interest in recent years, and has been gaining ground in research on quality of life, health status indicators, assessment of student proficiency, among others. Its use goes from cross-sectional studies, which assume independence among the observations, to longitudinal studies, where more than one response from the same individual is observed over time. There are several methods proposed in the literature for modeling polytomous responses in transversal studies, being the most commonly used the cumulative logits model, also known as proportional odds model due to the assumption of proportionality in odds, i.e., the model assumes that there is an approximately linear increase in odds ratios for the regression coefficients. In many practical situations this assumption is violated. There is, however, another model that generalizes the proportional odds, known as partial proportional odds, which allows no odds proportionality to a subset of covariates that violated that assumption. In longitudinal studies, the conventional models - marginal models, generalized linear mixed models and transition models - for analysis of correlated data can also be used to model polytomous responses. In this work modeling of ordinal polytomous responses in longitudinal studies is discussed from the perspective of marginal, generalized linear mixed models and transition models. The specification and interpretation of the models is illustrated and discussed by analyzing two real data sets

Screening for common mental disorders using the SRQ-20 in Brazil: what are the alternative strategies for analysis?

Submitted by Ana Maria Fiscina Sampaio ([email protected]) on 2018-02-27T17:37:59Z No. of bitstreams: 1 Carmo MB Screening for common mental disorders.....pdf: 241184 bytes, checksum: 960141187acba8d44de88681dcf43725 (MD5)Approved for entry into archive by Ana Maria Fiscina Sampaio ([email protected]) on 2018-02-27T17:52:35Z (GMT) No. of bitstreams: 1 Carmo MB Screening for common mental disorders.....pdf: 241184 bytes, checksum: 960141187acba8d44de88681dcf43725 (MD5)Made available in DSpace on 2018-02-27T17:52:35Z (GMT). No. of bitstreams: 1 Carmo MB Screening for common mental disorders.....pdf: 241184 bytes, checksum: 960141187acba8d44de88681dcf43725 (MD5) Previous issue date: 2017Universidade Federal da Bahia. Instituto de Humanidades, Artes e Ciências. Salvador, BA, BrasilUniversidade Federal da Bahia. Instituto de Humanidades, Artes e Ciências. Salvador, BA, BrasilUniversidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, BrasilUniversidade Federal da Bahia. Instituto de Matemática e Estatística. Departamento de Estatística. Salvador, BA, BrasilUniversidade Federal de Minas Gerais. Departamento de Estatística. Belo Horizonte, MG, BrasilUniversidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, BrasilUniversidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados de Conhecimentos para Saúde. Salvador, BA, BrasilUniversidade Federal da Bahia. Instituto de Matemática e Estatística. Departamento de Estatística. Salvador, BA, BrasilTo analyze the prevalence of common mental disorders (CMD) assessed with the Self Reporting Questionnaire (SRQ-20), using the established cutoff point, and comparing it with the results of a joint correspondence factor analysis (CFA) and cluster analysis and of a latent class analysis (LCA). Methods: A cross-sectional study was carried out in an urban sample of 1,095 women aged 19 to 55 years. Joint CFA-cluster analysis and LCA were used. Results: We found a high prevalence of CMD, regardless of classification method (37.6% when using the cutoff point; 44.4% and 52% for LCA and joint CFA-cluster, respectively). The alternative analysis strategies describe the cases more efficiently when compared to the traditional cutoff method, especially regarding more severe symptoms. Both alternative strategies also provide a description of the SRQ-20 dimensions in their particularities, which may be useful for the planning and implementation of specific actions in a given population. Conclusion: The SRQ-20 cutoff point seems to underestimate the magnitude of CMD among women. The alternative methods of analysis presented herein highlight the different possibilities of using this important instrument of screening for mental health

Multivariate model for the analysis of <i>G. duodenalis</i> infection in children living in Northeast region of Minas Gerais State, Brazil, 2009–2010, according to socioeconomic, sanitary and pregnancy aspects.

a<p>OR =  Odds Ratio.</p

Losses regarding the return of stool samples for parasitological examination.

<p>Losses regarding the return of stool samples for parasitological examination.</p

Characteristics of children from cistern group compared with children from comparison group.

a<p>The children's classification was considered in the first stage (332 children in cistern group and 332 children in comparison group).</p

<i>G. duodenalis</i> infection in children in Northeast region of Minas Gerais State, Brazil, 2009–2010; <b>categorical</b> variables selected in the univariate analysis^a.

a<p>the univariate analysis associated each independent variable with the outcome and was adjusted only by the stage (time of data collection).</p>b<p>OR =  Odds Ratio.</p

<i>G. duodenalis</i> infection in children in Northeast region of Minas Gerais State, Brazil, 2009–2010; continuous variables selected in the univariate analysis^a.

a<p>the univariate analysis associated each independent variable with the outcome and was adjusted by the stage (time of data collection).</p>b<p>OR =  Odds Ratio.</p