Modelling local uncertainty in relations between birth weight and air quality within an urban area: combining geographically weighted regression with geostatistical simulation.

In this study, we combine known methods to present a new approach to assess local distributions of estimated parameters measuring associations between air quality and birth weight in the urban area of Sines (Portugal). To model exposure and capture short-distance variations in air quality, we use a Regression Kriging estimator combining air quality point data with land use auxiliary data. To assess uncertainty of exposure, the Kriging estimator is incorporated in a sequential Gaussian simulation algorithm (sGs) providing a set of simulated exposure maps with similar spatial structural dependence and statistical properties of observed data.

Sexual Dysfunction in Breast Cancer Survivors: Cross-Cultural Adaptation of the Sexual Activity Questionnaire for Use in Portugal.

Introduction: The increasing survivor population of breast cancer has shifted research and practice interests into the impacts of the disease and treatment in quality of life aspects. The lack of tools available in Portuguese to objectively evaluate sexual function led to the development of this study, which aimed to cross-culturally adapt and validate the Sexual Activity Questionnaire for use in Portugal.

Material And Methods: The questionnaire was translated and back-translated, refined following face-to-face interviews with seven breast cancer survivors, and then self-administered by a larger sample at baseline and a fortnight later to test validity and reliability.

A coregionalization model can assist specification of Geographically Weighted Poisson Regression: Application to an ecological study.

The geographical distribution of health outcomes is influenced by socio-economic and environmental factors operating on different spatial scales. Geographical variations in relationships can be revealed with semi-parametric Geographically Weighted Poisson Regression (sGWPR), a model that can combine both geographically varying and geographically constant parameters. To decide whether a parameter should vary geographically, two models are compared: one in which all parameters are allowed to vary geographically and one in which all except the parameter being evaluated are allowed to vary geographically.