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We performed a bivariate analysis, using the variables of exposure and potential confounders; and a multivariate analysis, including those independent variables which yielded a statistical significance of less than 0.2 in the bivariate analysis.
Results were similar when we repeated the bivariate analysis using the spoligotyping method to identify M. bovis disease.
Categorical variables were described in the univariate and bivariate analysis using the overall number of cases (n) and the percentage of each category.
The association between awareness of EC and the explanatory variables were first assessed in bivariate analysis using the Chi-square test.
Bivariate analysis using the Student's t test was conducted to compare the south and north regions of Taiwan; a two-sided p value < 0.05 was considered statistically significant.
The basic characteristics of the two groups of children were compared by bivariate analysis using the t-test, the Fisher exact test, or the chi-square test, depending on variable type.
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The variables used in the bivariate analysis using chi-square were the same variables used in the logistic regression analysis.
Predictors of long-lasting relapsing or lingering RMSP related to explanatory variables were assessed in bivariate analysis using recovery as the referent category.
Frequency tabulations were used to describe the data, followed by the bivariate analysis using Chi-square tests and contingency table analyses to examine the association of all potential determinants on infant mortality without adjusting for other covariates.
Bivariate analysis, using chi-square test as the test of trend, was conducted to compare the prevalence of central obesity among the study variables.
Bivariate analysis using χ test was used to investigate the relationship between the independent variables and the categorical outcome variable, with detection of significant differences at p<0.05.
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