*2.3. Statistical Analysis*

We conducted statistical analyses using R version 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria). All the results of quantitative variables were reported by mean (M), standard deviation (SD), or frequency (%) (Table 1). To determine the role of sociodemographic and health-related factors on healthcare utilization avoidance, differences in socio-demographics and health-related factors were compared with the healthcare utilization avoidance using the chi-square statistics (Table 2). The logit model for regression analyzed the associations between sociodemographic factors (e.g., gender, age, family size, education, marital status, income, and employment) and health-related factors (i.e., subjective health and presence of underlying disease) toward one's avoidance of healthcare utilization. Confounding factors were explored by comparing the differences between the adjusted odds ratio (aOR) in multivariate analysis and the crude odds ratio (OR) in a bivariate analysis of each independent variable on healthcare utilization avoidance (Table 3). Additionally, to examine the moderating effect of gender and the presence of an underlying disease, the same logit model for regression was performed among subgroup participants along with gender (Table 4) and the presence of underlying disease (Table 5).

**Table 2.** Chi-square statistics for variables related to healthcare utilization avoidance.



**Table 2.** *Cont.*

**Table 3.** Influencing factors associated with healthcare utilization avoidance (*n* = 1000).



**Table 3.** *Cont.*

**Table 4.** Influencing factors associated with healthcare utilization avoidance among subgroup participants along with gender.



**Table 4.** *Cont.*

**Table 5.** Influencing factors associated with the avoidance of healthcare utilization among subgroup participants according to the presence of an underlying disease.



**Table 5.** *Cont.*

#### **3. Results**
