The rapid increase in out-of-pocket expenditures regressively raises the issue of equity in medical access opportunities according to income class and negatively affects public health. Factors related to out-of-pocket expenses have been analyzed in previous studies using an ordinary regression model (Ordinary Least Squares [OLS]). However, as OLS assumes equal error variance, it does not consider spatial variation due to spatial heterogeneity and dependence. Accordingly, this study presents a spatial analysis of outpatient out-of-pocket expenses from 2015 to 2020, targeting 237 local governments nationwide, excluding islands and island regions. R (version 4.1.1) was used for statistical analysis, and QGIS (version 3.10.9), GWR4 (version 4.0.9), and Geoda (version 1.20.0.10) were used for the spatial analysis. As a result, in OLS, it was found that the aging rate and number of general hospitals, clinics, public health centers, and beds had a positive (+) significant effect on outpatient out-of-pocket expenses. The Geographically Weighted Regression (GWR) suggests regional differences exist concerning out-of-pocket payments. As a result of comparing the OLS and GWR models through the Adj. R² and Akaike’s Information Criterion indices, the GWR model showed a higher fit. This study provides public health professionals and policymakers with insights that could inform effective regional strategies for appropriate out-of-pocket cost management.
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