Spss statistics version 14

The data for all patients were analyzed through descriptive analysis on the main parameters. Descriptive statistics on study variables used traditional numerical synthesis measurements: mean, standard deviation, median, and maximum and minimum values for the continuous variables, and frequency distributions for the categorical variables. The analysis of variance was used to compare the means of the quantitative variables between patient groups. The main investigated variables were: the patients’ demographic characteristics; stratification by age range; hospitalization; antibiotic therapy; visits; and environmental sanitation. Statistical analysis was conducted using the Statistical Package for the Social Sciences statistical software (SPSS Statistics version 14.0; SPSS Inc., Chicago, IL, USA).

The SPSS Statistics Version 14.0 (SPSS Inc., Chicago, IL, USA) program was used to analyze the data. The statistical methods to be used in the study were determined by first taking into account whether or not the average scores of the scale and subdimensions showed normal distribution. As an indicator of the normality of the data, the skewness and kurtosis coefficient values were taken into consideration and were found to be within normal limits for all subdimensions of both scales. Categorical variables were presented as frequencies and percentages. Continuous variables were expressed as mean ± standard deviation (SD). One-way ANOVA test was used to compare the means of three or more groups while the independent samples t-test, one of the parametric statistical methods, was used to compare the means of two independent groups. Tukey’s multiple comparison test was used to identify the groups from which the differences found using ANOVA came. The independent variables predicting the coronavirus-related health literacy were evaluated with multiple linear regression analysis. The existence of multiple correlations between variables was evaluated with VIF and tolerance. Variables with a tolerance value >0.2 and a VIF value <10 were included in the regression analysis. The significance level was accepted as 0.05.

The patients' age, BMI, systolic BP, diastolic BP, fasting blood glucose, HbA1c, and duration of diabetes were expressed as medians (interquartile range), and differences between groups were compared using the Kruskal-Wallis test. Group comparisons of categorical variables were performed using the chi-square test or, for small cell values, Fisher's exact test. Results of categorical data were summarized using frequencies and percent values. We used P for trend to explore the differences among the patient proportion with DR, CHD, and stroke according to CysC category. Multivariate logistic regression was conducted to estimate odds ratios (OR) with 95% confidence intervals (CI) for the risk of vascular complications. All P values and 95% CI for OR were corrected by Bonferroni's method due to multiple testing. All statistical analyses were performed using SPSS Statistics version 14.0 (SPSS Inc., Chicago, IL, USA). A P value of less than 0.05 was considered statistically significant.