Factors influencing use of conventional and traditional Korean medicine-based health services: a nationwide cross-sectional study

Abstract

Abstract Background In Korea, conventional medicine (CM) and traditional Korean medicine (KM) are run as a dual healthcare system; however, the backgrounds and characteristics of the users of both medical services have not yet been compared. This study aimed to identify the differences in factors determining the use of CM and KM health services. Methods A secondary data analysis of a nationwide cross-sectional survey was conducted in this study. The Survey on the Experience with Healthcare Services 2017 asked participants about their most recent outpatient visit to a health service. Initially, a descriptive analysis was performed on respondents who visited the CM or KM health service in the last 12 months. Then, logistic regression analysis using Andersen’s behavioral model was performed, to identify the factors affecting health service selection, by classifying demographic variables into predisposing, enabling, and need factors. Respondents who replied they did not frequently use CM/KM and those with missing data were excluded. Results Of the total 11,098 respondents, 7,116 (64.1%) reported to have used CM/KM: 2,034 (18.3%), 4,475 (40.3%), and 607 (5.5%) for hospital CM, clinic CM, and KM, respectively. In logistic regression analysis, of the 2,723 (24.5%) respondents analyzed, 822 (7.4%) went to a hospital, 1,689 (15.2%) to a clinic, and 212 (1.9%) opted for KM service. Respondents with a higher number of chronic diseases were less likely to use KM (one disease, odds ratio: 0.52, 95% confidence interval: 0.36–0.76; two diseases: 0.51, 0.31–0.85; three to five diseases: 0.26, 0.10–0.69). Respondents with a high income were likely to go to the hospital (4Q vs. 1Q: 1.92, 1.35–2.72) and less likely to go to the clinic (4Q vs. 1Q: 0.49, 0.35–0.68). Conclusions Significant differences were observed on the enabling factor (income) for CM and need factors (number of chronic diseases) for KM. Our analysis suggests that through the healthcare policy, we should consider stratifying user backgrounds and needs for each medical service.