38 resultados para cross-sectional data


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This article analyzes food insecurity and hunger in Brazilian families with children under five years of age. This was a nationally representative cross-sectional study using data from the National Demographic and Health Survey on Women and Children (PNDS-2006), in which the outcome variable was moderate to severe food insecurity, measured by the Brazilian Food Insecurity Scale (EBIA). Prevalence estimates and prevalence ratios were generated with 95% confidence intervals. The results showed a high prevalence of moderate to severe food insecurity, concentrated in the North and Northeast regions (30.7%), in economic classes D and E (34%), and in beneficiaries of conditional cash transfer programs (36.5%). Multivariate analysis showed that the socioeconomic relative risks (beneficiaries of conditional cash transfers), regional relative risks (North and Northeast regions), and economic relative risks (classes D and E) were 1.8, 2.0 and 2.4, respectively. Aggregation of the three risks showed 48% of families with moderate to severe food insecurity, meaning that adults and children were going hungry during the three months preceding the survey.

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PURPOSE: To evaluate the prevalence of pterygium in a population-based sample at Botucatu City - São Paulo State, Brazil. METHODS: A population-based cross-sectional study with randomized clustered sampling of households was conducted in the urban area of the Botucatu City -São Paulo State, Brazil and 85.1% of the intended sample was evaluated. All participants were submitted to ophthalmologic examination and the data were statistically analyzed. RESULTS: The prevalence of pterygium lesion in Botucatu City was 8.12% (7.0% < CI < 9.2%), affecting mainly males (10.4% males X 6.5% females - 8.5% < CI < 12.3% for males and 5.1% < CI < 7.8% for females) with 49.6 ± 14.9 years old in average; 32.18% of the pterygium carriers aged between 40 and 50 years. CONCLUSIONS: The prevalence of pterygium at Botucatu is 8.12%, affecting most frequently 40-50 year-old males.

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OBJECTIVE: To characterize the behavior of premature newborns in the first year of chronological age. METHODS: This is a cross-sectional descriptive study, bound to a longitudinal study titled: Comparison of visual behavior on the first quarter of year of life of premature nursling born at two maternities of Recife/PE. The sample was composed by 52 premature newborns selected from June, 2007 to June, 2008 from the Maternity of the Federal University of Pernambuco (UFPE). Biological, socioeconomic and demographic data was collected through medical records and interviews with progeny. Newborns were evaluated by the Assessment Guide of Visual Ability in Infants. RESULTS: Most of the newborns were male at a gestational period between 33 weeks and 36 weeks and 6 days, showed a good visual behavior development for the age researched, and most of the families showed good socioeconomical and demographic profile. Besides, it was possible to detect ocular signs in 19% of sample, that were referred to an Ophthalmology Service. CONCLUSION: This study results point out the method like an important key in the early detection and visual screening for premature nursling since the first month of life and it led us to believe that clinical view for occupational therapy intervention must be focused not only on biological risks but also at the influence environment in newborn performance.

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PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

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Universidade Estadual de Campinas . Faculdade de Educação Física

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Universidade Estadual de Campinas. Faculdade de Educação Física

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Universidade Estadual de Campinas . Faculdade de Educação Física