Background: Mid-to-long-term hospitalization (MLTH) can threaten the household economy with high medical costs and loss of income. Therefore, it could increase the catastrophic health expenditure (CHE), measured as the ratio of medical expenses to the ability to pay. This study aimed to determine the effect of MLTH on the incidence of CHE and the mediating effect of earned income reduction rate (EIRR). Methods: We used 2015 to 2017 data from the Korean Welfare Panel Study and selected households with earned income through work. The final samples were 1671 households in the database. This study applied three-step regression analyses for estimating mediation effects. Results: First, MLTH affected CHE increases; second, MLTH increased EIRR; third, both EIRR and MLTH increased CHE at the same time. Additionally, the bootstrapping results were 0.364 to 0.644 in the 95% confidence interval, which suggested that EIRR mediated the effects of MLTH on CHE. Conclusions: Previous studies have only focused on medical costs when interpreting CHE; however, it is also essential to recognize that the MLTH can have a negative effect on the EIRR. This study contributed to the literature by giving another insight into interpreting the cause of CHE, focusing on income loss factors.

Background Low-density lipoprotein cholesterol is an important marker highly associated with cardiovascular disease. Since the direct measurement of it is inefficient in terms of cost and time, it is common to estimate through the Friedewald equation developed about 50 years ago. However, various limitations exist since the Friedewald equation was not designed for Koreans. This study proposes a new low-density lipoprotein cholesterol estimation equation for South Koreans using nationally approved statistical data. Methods This study used data from the Korean National Health and Nutrition Examination Survey from 2009 to 2019. The 18,837 subjects were used to develop the equation for estimating low-density lipoprotein cholesterol. The subjects included individuals with low-density lipoprotein cholesterol levels directly measured among those with high-density lipoprotein cholesterol, triglycerides, and total cholesterol measured. We compared twelve equations developed in the previous studies and the newly proposed equation (model 1) developed in this study with the actual low-density lipoprotein cholesterol value in various ways. Results The low-density lipoprotein cholesterol value estimated using the estimation formula and the actual low-density lipoprotein cholesterol value were compared using the root mean squared error. When the triglyceride level was less than 400 mg/dL, the root mean squared of the model 1 was 7.96, the lowest compared to other equations, and the model 2 was 7.82. The degree of misclassification was checked according to the NECP ATP III 6 categories. As a result, the misclassification rate of the model 1 was the lowest at 18.9%, and Weighted Kappa was the highest at 0.919 (0.003), which means it significantly reduced the underestimation rate shown in other existing estimation equations. Root mean square error was also compared according to the change in triglycerides level. As the triglycerides level increased, the root mean square error showed an increasing trend in all equations, but it was confirmed that the model 1 was the lowest compared to other equations. Conclusion The newly proposed low-density lipoprotein cholesterol estimation equation showed significantly improved performance compared to the 12 existing estimation equations. The use of representative samples and external verification is required for more sophisticated estimates in the future.