Background/Aims: The 2030 hepatitis C virus (HCV) elimination targets of the World Health Organization are an 80% reduction in incidence and 65% reduction in mortality compared to the 2015 rates. However, information on the nationwide incidence and treatment rates of HCV infection are limited. We aimed to investigate the nationwide incidence and status of the care cascade for HCV infection in Korea.Methods: This study used data from the Korea Disease Control and Prevention Agency linked with the data of the Korea National Health Insurance Service. Linkage to care was defined as visiting hospitals twice or more due to HCV infection within 1.5 years of the index date. The treatment rate was the number who had been prescribed antiviral medication within 1.5 years from the index date out of patients newly diagnosed with HCV. Results:The new HCV infection rate was 17.2 per 100,000 person-years (n=8,810) in 2019. The number of new HCV infections was the highest in patients aged 50 to 59 years (n=2,480), and the new HCV infection rate significantly increased with age (p<0.001). Among newly infected patients with HCV, the linkage to care rate was 78.2% (78.2% men, 78.2% women) and the treatment rate was 58.1% (56.8% men, 59.3% women) within 1.5 years. Conclusions:The new HCV infection rate was 17.2 per 100,000 person-years in Korea. It is necessary to continuously monitor the incidence and care cascade of HCV to establish proper strategies to reach the goal of HCV elimination by 2030.

Background Advanced cancers are associated with more severe symptoms and greater impairment. Although most patients with metastatic cancer would benefit from rehabilitation, few patients receive appropriate rehabilitation therapy. We explored the use of rehabilitation therapy by cancer patients. Our data represented the entire population of Korea. The analyses were performed according to cancer type and stage. Methods We extracted rehabilitation utilization data of patients newly diagnosed with cancer in the period of 2011–2015 from the Korea Central Cancer Registry, which is linked to the claims database of the National Health Insurance Service (n = 958,928). Results The utilisation rate increased during the study period, from 6.0% (11,504) of 192,835 newly diagnosed patients in 2011 to 6.8% (12,455) of 183,084 newly diagnosed patients in 2015. Patients with central nervous system (28.4%) and bone (27.8%) cancer were most likely to undergo physical rehabilitation. The rehabilitation rate was higher in patients with metastatic than localised or regional cancer (8.7% vs. 5.3% vs. 5.5%). Conclusion This claims-based study revealed that rehabilitation therapy for cancer patients is underutilised in Korea. Although patients with metastasis underwent more intensive rehabilitation than patients with early stage cancer, those without brain and bone tumours (the treatment of which is covered by insurance) were less likely to use rehabilitation services. Further efforts to improve the use of rehabilitation would improve the outcomes of cancer patients.

For a chi-squared test, which is a statistical method used to test the independence of a contingency table of two factors, the expected frequency of each cell must be greater than 5. The percentage of cells with an expected frequency below 5 must be less than 20% of all cells. However, there are many cases in which the regional expected frequency is below 5 in general small area studies. Even in large-scale surveys, it is difficult to forecast the expected frequency to be greater than 5 when there is small area estimation with subgroup analysis. Another statistical method to test independence is to use the Bayes factor, but since there is a high ratio of data dependency due to the nature of the Bayesian approach, the low expected frequency tends to decrease the precision of the test results. To overcome these limitations, we will borrow information from areas with similar characteristics and pool the data statistically to propose a pooled Bayes test of independence in target areas. Jo et al. (2021) suggested hierarchical Bayesian pooling models for small area estimation of categorical data, and we will introduce the pooled Bayes factors calculated by expanding their restricted pooling model. We applied the pooled Bayes factors using bone mineral density and body mass index data from the Third National Health and Nutrition Examination Survey conducted in the United States and compared them with chi-squared tests often used in tests of independence.

Background/Aims Noninvasive methods have become increasingly critical in the diagnosis of fibrosis in chronic liver diseases. Herein, we compared the diagnostic performance of serum Mac2 binding protein glycosylation isomer (M2BPGi) and other serological panels for fibrosis in patients with nonalcoholic fatty liver disease (NAFLD) and proposed an improved two-step diagnostic algorithm for advanced fibrosis. Methods We enrolled 231 patients diagnosed with NAFLD who underwent a liver biopsy. We subsequently evaluated the diagnostic performance of serological panels, including serum M2BPGi, a fibrosis index based on four factors (FIB-4), aspartate aminotransferase-to-platelet ratio index (APRI), and NAFLD fibrosis score (NFS), in predicting the stage of liver fibrosis. We then constructed a two-step algorithm to better differentiate advanced fibrosis. Results The areas under the receiver operating characteristic curves of serum M2BPGi, FIB-4, APRI, and NFS for advanced fibrosis (≥F3) were 0.823, 0.858, 0.779, and 0.827, respectively. To reduce the performance of unnecessary liver biopsy, we propose a two-step algorithm using FIB-4 as an initial diagnostic tool and serum M2BPGi (≥0.6) as an additional diagnostic method for patients classified as intermediate (23%). Using the proposed algorithm, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.812, 0.814, 0.814, 0.600, and 0.927, respectively. Conclusions Serum M2BPGi is a simple and effective test for advanced fibrosis in patients with NAFLD. Application of the two-step algorithm based on FIB-4 and M2BPGi proposed here can improve diagnostic performance and reduce unnecessary tests, making diagnosis easily accessible, especially in primary medical centers.

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