This study investigated lung cancer risk in people infected with tuberculosis (TB) compared to the general population and evaluated factors associated with lung cancer in TB-infected individuals. Mandatory reported TB infection case data in Gyeonggi Province, South Korea (2010 to 2016) were obtained and linked with medical usage and health screening data from the National Health Information Database. Lung cancer incidence in patients with TB was compared to that in the general population using standardized incidence ratio (SIR), adjusted for age and sex. Lung cancer risk factors in patients with TB were studied using the Cox proportional hazards model. By April 2022, 1.26% (n = 444) of 35,140 patients developed lung cancer after TB diagnosis. Compared to the incidence in the general population, increased lung cancer risk in people with TB was observed (SIR: 2.04, 95% CI: 1.85-2.23). Multivariate analysis showed increased lung cancer in TB-infected individuals, associated with being male (hazard ratio [HR]: 2.24, 95% CI: 1.65-3.04), 1-year increase of age (HR: 1.09, 95% CI: 1.08-1.10), ever smoking (HR: 1.42, 95% CI: 1.02-1.97), and amount of daily smoking with one pack or more (HR: 2.17, 95% CI: 1.63-2.89). Increased lung cancer risk was noted in patients with TB compared to the general population, and sex, age, and smoking were factors associated with lung cancer in patients with TB.

This study investigated lung cancer risk in people infected with tuberculosis (TB) compared to the general population and evaluated factors associated with lung cancer in TB-infected individuals. Mandatory reported TB infection case data in Gyeonggi Province, South Korea (2010 to 2016) were obtained and linked with medical usage and health screening data from the National Health Information Database. Lung cancer incidence in patients with TB was compared to that in the general population using standardized incidence ratio (SIR), adjusted for age and sex. Lung cancer risk factors in patients with TB were studied using the Cox proportional hazards model. By April 2022, 1.26% (n = 444) of 35,140 patients developed lung cancer after TB diagnosis. Compared to the incidence in the general population, increased lung cancer risk in people with TB was observed (SIR: 2.04, 95% CI: 1.85-2.23). Multivariate analysis showed increased lung cancer in TB-infected individuals, associated with being male (hazard ratio [HR]: 2.24, 95% CI: 1.65-3.04), 1-year increase of age (HR: 1.09, 95% CI: 1.08-1.10), ever smoking (HR: 1.42, 95% CI: 1.02-1.97), and amount of daily smoking with one pack or more (HR: 2.17, 95% CI: 1.63-2.89). Increased lung cancer risk was noted in patients with TB compared to the general population, and sex, age, and smoking were factors associated with lung cancer in patients with TB.

This study investigated lung cancer risk in people infected with tuberculosis (TB) compared to the general population and evaluated factors associated with lung cancer in TB-infected individuals. Mandatory reported TB infection case data in Gyeonggi Province, South Korea (2010 to 2016) were obtained and linked with medical usage and health screening data from the National Health Information Database. Lung cancer incidence in patients with TB was compared to that in the general population using standardized incidence ratio (SIR), adjusted for age and sex. Lung cancer risk factors in patients with TB were studied using the Cox proportional hazards model. By April 2022, 1.26% (n = 444) of 35,140 patients developed lung cancer after TB diagnosis. Compared to the incidence in the general population, increased lung cancer risk in people with TB was observed (SIR: 2.04, 95% CI: 1.85-2.23). Multivariate analysis showed increased lung cancer in TB-infected individuals, associated with being male (hazard ratio [HR]: 2.24, 95% CI: 1.65-3.04), 1-year increase of age (HR: 1.09, 95% CI: 1.08-1.10), ever smoking (HR: 1.42, 95% CI: 1.02-1.97), and amount of daily smoking with one pack or more (HR: 2.17, 95% CI: 1.63-2.89). Increased lung cancer risk was noted in patients with TB compared to the general population, and sex, age, and smoking were factors associated with lung cancer in patients with TB.

Background/Aims: To investigate a reported outbreak of presumed hepatitis E virus (HEV) infection in a Korean food manufacturing facility and to explore the association between anti-HEV immunoglobulin M (IgM) positivity and coronavirus disease 2019 (COVID-19) infection or vaccination. Methods: Twenty-four cases of anti-HEV IgM positivity were reported among 646 workers at the facility in 2022. An epidemiological investigation was conducted, comprising HEV-RNA testing of blood and environmental samples, analysis of group meal records, and an association between anti-HEV IgM positivity and confirmed COVID-19 infection or vaccination. Results: All 24 patients were asymptomatic, with cases spread sporadically across the facility. HEV RNA was not detected in the serum or environmental samples. Four out of 340 meals (1.2%) showed a significantly higher proportion of anti-HEV positivity in each meal intake group than in the non-intake group on certain days. Although the cumulative rate of COVID-19 infection showed no difference, the anti-HEV IgM positive group showed significantly higher proportions of >2 doses of COVID-19 vaccination (83.3% vs 48.7%, p=0.021), vaccination within 90 days (45.8% vs 19.7%, p=0.008), and having the Moderna vaccine administered as the last vaccine (75.0% vs 14.5%, p<0.001) than those of the anti-HEV negative group. In four multivariable models, three or more COVID-19 vaccinations and the Moderna vaccine as the last vaccine were consistently associated with anti-HEV IgM positivity, while the specific day group meal intake was also a significant factor. Conclusions This epidemiological investigation showed that anti-HEV IgM positivity may occur as a false-positive result related to COVID-vaccination over three times and use of the Moderna vaccine, although a portion of true HEV infection may not be excluded.

OBJECTIVES: We compared the viral cycle threshold (Ct) values of infected patients to better understand viral kinetics by vaccination status during different periods of variant predominance in Gyeonggi Province, Korea. METHODS: We obtained case-specific data from the coronavirus disease 2019 (COVID-19) surveillance system, Gyeonggi in-depth epidemiological report system, and Health Insurance Review & Assessment Service from January 2020 to January 2022. We defined periods of variant predominance and explored Ct values by analyzing viral sequencing test results. Using a generalized additive model, we performed a nonlinear regression analysis to determine viral kinetics over time. RESULTS: Cases in the Delta variant’s period of predominance had higher viral shedding patterns than cases in other periods. The temporal change of viral shedding did not vary by vaccination status in the Omicron-predominant period, but viral shedding decreased in patients who had completed their third vaccination in the Delta-predominant period. During the Delta-predominant and Omicron-predominant periods, the time from symptom onset to peak viral shedding based on the E gene was approximately 2.4 days (95% confidence interval [CI], 2.2 to 2.5) and 2.1 days (95% CI, 2.0 to 2.1), respectively. CONCLUSIONS In one-time tests conducted to diagnose COVID-19 in a large population, although no adjustment for individual characteristics was conducted, it was confirmed that viral shedding differed by the predominant strain and vaccination history. These results show the value of utilizing hundreds of thousands of test data produced at COVID-19 screening test centers.

Purpose: The model for end-stage liver disease (MELD) 3.0 has recently been suggested for determining liver allocation. We aimed to apply MELD 3.0 to a Korean population and to discover differences between patients with and without hepatocellular carcinoma (HCC). Materials and Methods: This study is a retrospective study of 2203 patients diagnosed with liver cirrhosis at Severance Hospital between 2016–2022. Harrell’s concordance index was used to validate the ability of MELD scores to predict 90-day survival. Results: During a mean follow-up of 12.9 months, 90-day survival was 61.9% in all patients, 50.4% in the HCC patients, and 74.8% in the non-HCC patients. Within the HCC patients, the concordance index for patients on the waitlist was 0.653 using MELD, which increased to 0.753 using MELD 3.0. Among waitlisted patients, the 90-day survival of HCC patients was worse than that of non-HCC patients with MELD scores of 31–37 only (69.7% vs. 30.0%, p=0.001). Applying MELD 3.0, the 90-day survival of HCC patients was worse than that of non-HCC patients across a wider range of MELD 3.0 scores, compared to MELD, with MELD 3.0 scores of 21–30 and 31–37 (82.0% vs. 72.5% and 72.3% vs. 24.3%, p=0.02 and p<0.001, respectively). Conclusion MELD 3.0 predicted 90-day survival of the HCC patients more accurately than original MELD score; however, the disparity between HCC and non-HCC patients increased, particularly in patients with MELD scores of 21–30. Therefore, a novel exception score is needed or the current exception score system should be modified.

Mendelian randomization (MR) uses genetic variation as a natural experiment to investigate the causal effects of modifiable risk factors (exposures) on outcomes. Two-sample Mendelian randomization (2SMR) is widely used to measure causal effects between exposures and outcomes via genome-wide association studies. 2SMR can increase statistical power by utilizing summary statistics from large consortia such as the UK Biobank. However, the first-order term approximation of standard error is commonly used when applying 2SMR. This approximation can underestimate the variance of causal effects in MR, which can lead to an increased false-positive rate. An alternative is to use the second-order approximation of the standard error, which can considerably correct for the deviation of the first-order approximation. In this study, we simulated MR to show the degree to which the first-order approximation underestimates the variance. We show that depending on the specific situation, the first-order approximation can underestimate the variance almost by half when compared to the true variance, whereas the second-order approximation is robust and accurate.