In building the model, we initially included various potential confounding factors into a 'base' model using a forward stepwise method; the variables that were statistically significant and finally retained in the 'base' model were age, place of birth, education level, residential radon exposure, past history of lung diseases, any cancer in first-degree relatives, and intake of meat.
For men, the best model included age, place of occupation up to age 18 years, number of moles > =5 mm on the right arm, birthplace, and a history of NMSC.
The variable which predicted the acceptance in univariate logistic regression models included age, place of residence, education, duration of chronic disease, use of the Internet, and the opinion about the usefulness of the Internet in making personal health-related decisions.
The predictors of the acceptance in univariate logistic regression models included age, place of residence, education, admission to hospital due to chronic disease, the use of the Internet, and the opinion about the usefulness of the Internet in making personal health-related decisions.
The predictors included in the model were gender, age, place of residence, education level, number of chronic diseases, duration of the chronic disease, hospitalization related to the chronic disease, Internet use, and opinion about the usefulness of the Internet for making personal health-related decisions.
Models adjusted by age, place of origin, education, smoking habit, family history of gastritis or gastric ulcer, and H. pylori infection.
In all three models, age, gender, place of birth, education, clinical stage (CD4 count or clinical AIDS), were not significantly associated with the probability of receiving an experimental antiretroviral.
Physician- and self-referrals were adjusted for co-variables: PHC model, age, gender, patient's place of residence (rural/urban) and RUB.
The final model for men was adjusted for age, place of residence, highest level of education achieved, year of interview and interviewer.
This model includes the matching variables of age, place of living and the acknowledged predictors in the aetiology of breast cancer from high-income countries.
