Women with HIV: gender violence and suicidal ideation

OBJECTIVE To analyze the relationship between gender violence and suicidal ideation in women with HIV. METHODS A cross-sectional study with 161 users of specialized HIV/AIDS care services. The study investigated the presence of gender violence through the Brazilian version of the World Health Organization Violence against Women instrument, and suicidal ideation through the Suicidal Ideation Questionnaire. Statistical analyses were performed with the SPSS software, using the Chi-square test and Poisson multiple regression model. RESULTS Eighty-two women with HIV reported suicidal ideation (50.0%), 78 (95.0%) of who had suffered gender violence. Age at first sexual intercourse < 15 years old, high number of children, poverty, living with HIV for long, and presence of violence were statistically associated with suicidal ideation. Women who suffered gender violence showed 5.7 times more risk of manifesting suicidal ideation. CONCLUSIONS Women with HIV showed a high prevalence to gender violence and suicidal ideation. Understanding the relationship between these two grievances may contribute to the comprehensive care of these women and implementation of actions to prevent violence and suicide.

Influence of the support offered to breastfeeding by maternity hospitals

ABSTRACT OBJECTIVE To evaluate whether the support offered by maternity hospitals is associated with higher prevalences of exclusive and predominant breastfeeding. METHODS This is a cross-sectional study including a representative sample of 916 infants less than six months who were born in maternity hospitals, in Ribeirao Preto, Sao Paulo, Southeastern Brazil, 2011. The maternity hospitals were evaluated in relation to their fulfillment of the Ten Steps to Successful Breastfeeding. Data were collected regarding breastfeeding patterns, the birth hospital and other characteristics. The individualized effect of the study factor on exclusive and predominant breastfeeding was analyzed using Poisson multiple regression with robust variance. RESULTS Predominant breastfeeding tended to be more prevalent when the number of fulfilled steps was higher (p of linear trend = 0.057). The step related to not offering artificial teats or pacifiers to breastfed infants and that related to encouraging the establishment of breastfeeding support groups were associated, respectively, to a higher prevalence of exclusive (PR = 1.26; 95%CI 1.04;1.54) and predominant breastfeeding (PR = 1.55; 95%CI 1.01;2.39), after an adjustment was performed for confounding variables. CONCLUSIONS We observed a positive association between support offered by maternity hospitals and prevalences of exclusive and predominant breastfeeding. These results can be useful to other locations with similar characteristics (cities with hospitals that fulfill the Ten Steps to Successful Breastfeeding) to provide incentive to breastfeeding, by means of promoting, protecting and supporting breastfeeding in maternity hospitals.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Predicting Xerostomia induced by IMRT treatments: A logistic regression approach

Radiotherapy is one of the main treatments used against cancer. Radiotherapy uses radiation to destroy cancerous cells trying, at the same time, to minimize the damages in healthy tissues. The planning of a radiotherapy treatment is patient dependent, resulting in a lengthy trial and error procedure until a treatment complying as most as possible with the medical prescription is found. Intensity Modulated Radiation Therapy (IMRT) is one technique of radiation treatment that allows the achievement of a high degree of conformity between the area to be treated and the dose absorbed by healthy tissues. Nevertheless, it is still not possible to eliminate completely the potential treatments’ side-effects. In this retrospective study we use the clinical data from patients with head-and-neck cancer treated at the Portuguese Institute of Oncology of Coimbra and explore the possibility of classifying new and untreated patients according to the probability of xerostomia 12 months after the beginning of IMRT treatments by using a logistic regression approach. The results obtained show that the classifier presents a high discriminative ability in predicting the binary response “at risk for xerostomia at 12 months”

Local Fractional Variational Iteration Method for Local Fractional Poisson Equations in Two Independent Variables

The local fractional Poisson equations in two independent variables that appear in mathematical physics involving the local fractional derivatives are investigated in this paper. The approximate solutions with the nondifferentiable functions are obtained by using the local fractional variational iteration method.

Double coverage and demand for health care: Evidence from quantile regression

An individual experiences double coverage when he bene ts from more than one health insurance plan at the same time. This paper examines the impact of such supplementary insurance on the demand for health care services. Its novelty is that within the context of count data modelling and without imposing restrictive parametric assumptions, the analysis is carried out for di¤erent points of the conditional distribution, not only for its mean location. Results indicate that moral hazard is present across the whole outcome distribution for both public and private second layers of health insurance coverage but with greater magnitude in the latter group. By looking at di¤erent points we unveil that stronger double coverage e¤ects are smaller for high levels of usage. We use data for Portugal, taking advantage of particular features of the public and private protection schemes on top of the statutory National Health Service. By exploring the last Portuguese Health Survey, we were able to evaluate their impacts on the consumption of doctor visi

Morbidity and survival in advanced AIDS in Rio de Janeiro, Brazil

Opportunistic diseases (OD) are the most common cause of death in AIDS patients. To access the incidence of OD and survival in advanced immunodeficiency, we included 79 patients with AIDS treated at Hospital Evandro Chagas (FIOCRUZ) from September 1997 to December 1999 with at least one CD4 count <=100 cells/mm³. The incidence of OD was analyzed by Poisson's regression, and survival by Kaplan Meier and Cox analysis, considering a retrospective (before CD4 <=100 cells/mm³) and a prospective (after CD4 <=100 cells/mm³) period, and controlling for demographic, clinical and laboratory characteristics. The confidence interval estipulated was 95%. Mean follow-up period was 733 days (CI = 683-782). During the study 9 (11.4%) patients died. Survival from AIDS diagnosis was a mean of 2589 days (CI = 2363-2816) and from the date of the CD4 count CD4 <=100 cells/mm³ was a mean of 1376 (CI = 1181-1572) days. Incidence of OD was 0.51 pp/y before CD4 <= 100 cells/mm³ and 0.29 pp/y after CD4 <= 100 cells/mm³. A lower number of ODs before CD4 < 100 cells/mm³ was associated with lower incidence rates after CD4 <= 100 cells/mm³. AIDS diagnosis based on CD4+ counts <= 200 cells/mm³ was associated with lower incidence rates after CD4 <= 100 cells/mm³. Baseline CD4 counts above 50 cells/mm³ (HR = 0.13) and restoration of baseline CD4+ counts above 100 cells/mm³ (HR = 0.16) were associated with a lower risk of death. Controling both variables, only restoration of baseline counts was statistically significant (HR = 0.22, p = 0.04). We found a very low incidence of OD and long survival after CD4 < 100 cells/mm³. Survival was significantly associated with restoration of baseline CD4 counts above 100 cells/mm³.

Local Fractional Variational Iteration Method for Local Fractional Poisson Equations in Two Independent Variables

The local fractional Poisson equations in two independent variables that appear in mathematical physics involving the local fractional derivatives are investigated in this paper. The approximate solutions with the nondifferentiable functions are obtained by using the local fractional variational iteration method.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.

Investigating the use of dasymetric techniques for assessing employment containment in Melbourne, Australia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Analysing and visualising areal crime data. A case study of residential burglary in San Francisco, USA

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Um alisador de máxima verosimilhança local (MVL) com o modelo de regressão de Poisson, para análise de regressão de dados de contagem

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.

Análise espacial dos resultados eleitorais em Portugal – Aplicação do Geographically Weighted Regression

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.