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For the hierarchical analyses the independent variables were adjusted at each block after bivariate analysis, considering the theoretical framework.
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Another interesting finding is the subtle discrepancy between meeting places as risk factors for STI and for HIV diagnosis in the bivariate analysis, considering only the results which reached borderline statistical significance.
Therefore, the bivariate analysis was done considering the data as a pool across studies to estimate associations rather than accounting for the correlation between studies as done for the shedding outcome.
We used logistic regression to explore factors associated with a child having caries and independent factors with p < 0.10 in the bivariate analysis were considered for the model.
To reduce excess number of variables in the final model, only those variables with P < 0.2 in the bivariate analysis were considered in the multivariate analysis, along with variables that were well known predictors such as maternal education and residence.
Variables that presented p ≤ 0.20 in bivariate analysis were considered for the multivariate multilevel analysis.
Variables with a P value < 0.1 in the bivariate analysis were considered for inclusion in the multivariate model.
Variables in the bivariate analysis were considered eligible for inclusion in the regression modeling if they were significant at p ≤ 0.10, or if prior evidence or theoretical considerations supported their inclusion.
The independent variables that were significantly associated (p < 0.05) with the outcome variables in the bivariate analysis were considered as possible contributory factors and entered into the regression models.
Variables with P < 0.10 in bivariate analysis were considered for inclusion in the models.
All variables with p value of less than 0.02 in the bivariate analysis were considered for multivariate analysis.
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