Spss version 24 2016

All questionnaires and laboratory information were coded and double entered into an Excel sheet. Data were checked for errors, including missing values, before analysis. Data were analyzed by SPSS version 24, 2016 (SPSS, Chicago, IL). Descriptive and inferential statistics were used for analysis; general characteristics of participants were described by means, standard deviations, and percentages. Logistic regression was used to detect the associations between independent variables (general characteristics, risk behaviors, and sexual behaviors) and the dependent variable, HBV infection, at the α = 0.05 significance level. The “ENTER” method was chosen in the logistic regression model, and statistically nonsignificant variables were excluded from the model; therefore, only statistically significant variables were included in the interpretation.

Microsoft Excel was used for double entry and data validation. Data were analyzed by SPSS version 24, 2016 (SPSS, Chicago, IL). Both descriptive and inferential statistics were used to describe the general characteristics of participants and to test the associations between variables. Chi-square test or Fisher’s exact test was used to detect the associations of clinical features with the serotype of HFMD. Logistic regression was used to identify the factors associated with HFMD. The “ENTER” mode was used to detect associations in both the univariate and multivariable steps. All significant variables in the univariate analysis were subsequently used in the multivariable analysis to establish the final fixed model. The alpha was set at 0.05 in both the univariate and multivariable analyses (Additional file 2).

Data were coded and cleaned before transferring into SPSS version 24, 2016 (SPSS, Chicago, IL) for analysis. All independent variables were categorized properly and checked for the completion before further analysis. Dependent variable was classified according to the levels of QOL. Descriptive statistics were used to describe the general characteristics of the participants. Continuous data with normal distributions are presented as the mean and SD. For continuous data with distribution in skewness form, the median and IQR are presented. Percentages were used to present all categorical data. Chi-square tests and logistic regression were used to detect the associations between variables at the significance level of α = 0.05. In the univariate and multivariate logistic regression models, two categories of QOL were classified as the dependent variable: poor-to-moderate, and good QOL. The mode of “ENTER” was used to select the variable into the model. The pseudo R² and the Hosmer–Lemeshow chi-square were determined for fitting the final model.