For the multiple logistical regression models, gender, age, mental health score and emotional role function were significant predictors of vitality in all four models.
In univariable models gender, age, X-ray and microscopy were associated with a diagnosis of TB by culture.
We performed multivariate analysis with all available predictors of hospital mortality included in the models (gender, age, APACHE II, mechanical ventilation, surgical admission and diagnosis type).
We will also use two multiple linear regression models for pain and disability as dependent variables and in both models, gender, age, pain, catastrophizing, fear avoidance, days taken off at work and time evolution will be employed as independent variables.
In addition, we performed multivariate analysis where we adjusted for all available predictors of hospital mortality included in the models (gender, age, APACHE II, mechanical ventilation, surgical admission and diagnosis type) determined by backward elimination of non-significant variables.
The following independent variables were included in all models: gender, age, marital status, professional role, ward of activity, scientific journals as the source of information, and need of additional information about influenza A/H1N1.
In the Cox proportional hazard model, gender, age, N stage, and initial surgery were included.
The independent variables were entered stepwise into the model: gender, age, interaction between gender and age, and finally, BMI.
In the multivariable model, gender, age, education, household type, residence, season, province and travel were significant risk factors of being a case of AGI.
The following socio-demographic variables were included in each model: gender, age, marital status, household income, highest education, immigration status, primary language, and ethnicity.
After an initial fit of a full model, gender, age, and educational level were clearly not significant predictors in the model (respective P-values were 0.96, 0.65, and 0.81; Wald's test).
