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This study adopts a spatio-statistical approach to quantify and model associations between noise pollution levels and landscape metrics of land categories (built-up structures and urban green covers).
With the addition of health and demographic covariates to the model, associations between race/ethnicity and PWB score became attenuated for blacks and Asians (beta: - 0.63, p = 0.06; beta: - 0.68, p = 0.02, respectively) and, to a lesser extent, for Hispanic women (beta: - 1.06, p = 0.0003).
We described rates of loss to follow-up according to migrant status by Kaplan-Meier estimates, and used Poisson regression to model associations between migrant status and loss to follow-up controlling for potential confounders identified a priori.
Kaplan-Meier survival curves were generated to describe time to mortality and loss to follow-up, and Cox regression used to model associations between age and mortality and loss to follow-up.
By using univariate logistic regression model, associations between all above-mentioned variables and CS were displayed.
We thus found it meaningful to model associations between HRQOL and depressive symptoms.
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Primary multivariable model: association between multiple variables and survival to discharge.
In two-pollutant models, associations between respiratory ERV and NO2 were more consistent than those for other pollutants.
In the first stage, we modeled associations between frustration and physician and practice characteristics, which included all physician and practice variables from the bivariable analyses.
Using uni- and multivariable logistic regression models, association between selected factors and development delays were assessed.
First, the models (basic models) including associations between family background, school career and educational level were fitted.
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