Background: Children, adolescents, and young adults (CAYAs) with severe illnesses require intensive treatment, often relying on medical devices and advanced medical services.Modern medical technology has improved the lifespans of these patients. In addition, CAYAs represent a vulnerable group, resulting in a significant caregiving burden on the entire family.This study examined patterns of medical utilization following diagnosis of a life-limiting condition (LLC). Methods: We establish a cohort of 176,236 CAYAs who were first diagnosed with an LLC using National Health Insurance data between 2011 and 2013. Patients diagnosed with an LLC within the 3 years preceding this period and those who had died were excluded, and only those receiving care at a general medical hospital were included. In total, 25,410,411 claims for medical expenses, outpatient visits, and lengths of stay for medical utilization over the approximately 10 years up to 2020 were investigated (2.3% inpatients, 97.7% outpatients). Results: The average annual medical utilization per LLC patient among CAYAs following initial diagnosis included medical expenses of $1,163, 16.8 outpatient visits, and 18.7 days of admission. Among inpatients, cancer patients averaged $5,340 for total medical expenses and 21.0 days of admission, while non-cancer patients averaged $3,013 and 18.1 days, respectively. The overall average medical expenses during the first year following diagnosis of an LLC were $3,012, whereas for cancer patients they were $5,962. In addition, there was a sharp increase in total medical expenses as death approached, particularly in the last month of life, with a considerable proportion attributable to critical-care treatments. Conclusion Our investigation into medical utilization by CAYAs with an LLC in Korea provides a foundation for healthcare policy development. Timely treatment at each stage and tailored policies that take into account the heterogeneity among diseases are of paramount importance.

Background: Children, adolescents, and young adults (CAYAs) with severe illnesses require intensive treatment, often relying on medical devices and advanced medical services.Modern medical technology has improved the lifespans of these patients. In addition, CAYAs represent a vulnerable group, resulting in a significant caregiving burden on the entire family.This study examined patterns of medical utilization following diagnosis of a life-limiting condition (LLC). Methods: We establish a cohort of 176,236 CAYAs who were first diagnosed with an LLC using National Health Insurance data between 2011 and 2013. Patients diagnosed with an LLC within the 3 years preceding this period and those who had died were excluded, and only those receiving care at a general medical hospital were included. In total, 25,410,411 claims for medical expenses, outpatient visits, and lengths of stay for medical utilization over the approximately 10 years up to 2020 were investigated (2.3% inpatients, 97.7% outpatients). Results: The average annual medical utilization per LLC patient among CAYAs following initial diagnosis included medical expenses of $1,163, 16.8 outpatient visits, and 18.7 days of admission. Among inpatients, cancer patients averaged $5,340 for total medical expenses and 21.0 days of admission, while non-cancer patients averaged $3,013 and 18.1 days, respectively. The overall average medical expenses during the first year following diagnosis of an LLC were $3,012, whereas for cancer patients they were $5,962. In addition, there was a sharp increase in total medical expenses as death approached, particularly in the last month of life, with a considerable proportion attributable to critical-care treatments. Conclusion Our investigation into medical utilization by CAYAs with an LLC in Korea provides a foundation for healthcare policy development. Timely treatment at each stage and tailored policies that take into account the heterogeneity among diseases are of paramount importance.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Background: Children, adolescents, and young adults (CAYAs) with severe illnesses require intensive treatment, often relying on medical devices and advanced medical services.Modern medical technology has improved the lifespans of these patients. In addition, CAYAs represent a vulnerable group, resulting in a significant caregiving burden on the entire family.This study examined patterns of medical utilization following diagnosis of a life-limiting condition (LLC). Methods: We establish a cohort of 176,236 CAYAs who were first diagnosed with an LLC using National Health Insurance data between 2011 and 2013. Patients diagnosed with an LLC within the 3 years preceding this period and those who had died were excluded, and only those receiving care at a general medical hospital were included. In total, 25,410,411 claims for medical expenses, outpatient visits, and lengths of stay for medical utilization over the approximately 10 years up to 2020 were investigated (2.3% inpatients, 97.7% outpatients). Results: The average annual medical utilization per LLC patient among CAYAs following initial diagnosis included medical expenses of $1,163, 16.8 outpatient visits, and 18.7 days of admission. Among inpatients, cancer patients averaged $5,340 for total medical expenses and 21.0 days of admission, while non-cancer patients averaged $3,013 and 18.1 days, respectively. The overall average medical expenses during the first year following diagnosis of an LLC were $3,012, whereas for cancer patients they were $5,962. In addition, there was a sharp increase in total medical expenses as death approached, particularly in the last month of life, with a considerable proportion attributable to critical-care treatments. Conclusion Our investigation into medical utilization by CAYAs with an LLC in Korea provides a foundation for healthcare policy development. Timely treatment at each stage and tailored policies that take into account the heterogeneity among diseases are of paramount importance.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Background: Children, adolescents, and young adults (CAYAs) with severe illnesses require intensive treatment, often relying on medical devices and advanced medical services.Modern medical technology has improved the lifespans of these patients. In addition, CAYAs represent a vulnerable group, resulting in a significant caregiving burden on the entire family.This study examined patterns of medical utilization following diagnosis of a life-limiting condition (LLC). Methods: We establish a cohort of 176,236 CAYAs who were first diagnosed with an LLC using National Health Insurance data between 2011 and 2013. Patients diagnosed with an LLC within the 3 years preceding this period and those who had died were excluded, and only those receiving care at a general medical hospital were included. In total, 25,410,411 claims for medical expenses, outpatient visits, and lengths of stay for medical utilization over the approximately 10 years up to 2020 were investigated (2.3% inpatients, 97.7% outpatients). Results: The average annual medical utilization per LLC patient among CAYAs following initial diagnosis included medical expenses of $1,163, 16.8 outpatient visits, and 18.7 days of admission. Among inpatients, cancer patients averaged $5,340 for total medical expenses and 21.0 days of admission, while non-cancer patients averaged $3,013 and 18.1 days, respectively. The overall average medical expenses during the first year following diagnosis of an LLC were $3,012, whereas for cancer patients they were $5,962. In addition, there was a sharp increase in total medical expenses as death approached, particularly in the last month of life, with a considerable proportion attributable to critical-care treatments. Conclusion Our investigation into medical utilization by CAYAs with an LLC in Korea provides a foundation for healthcare policy development. Timely treatment at each stage and tailored policies that take into account the heterogeneity among diseases are of paramount importance.

The emergence of the omicron variant and its sub-lineages has necessitated vaccine updates for coronavirus disease 2019. In September 2023, the U.S. Food and Drug Administration approved an updated BNT162b2 vaccine targeting the omicron XBB.1.5 variant, which was initiated in Korea in October 2024. This study demonstrates the adverse events reported through active nationwide surveillance after XBB.1.5 vaccination in Korea. Since October 19, 2023, the Korea Disease Control and Prevention Agency has conducted daily Short Message Service surveys to collect data on health issues, fever, vaccination site reactions, systemic symptoms, impact on daily life, and healthcare visits. Among 20,180 respondents, 27.9% reported health issues. Adverse reactions peaked on day 1 (28.7%), including pain at the vaccination site, muscle pain, fatigue, and fever. These findings elucidate the short-term safety of the XBB.1.5 vaccine and support its co-administration with the influenza vaccine, reducing vaccine hesitancy and achieving herd immunity.

The emergence of the omicron variant and its sub-lineages has necessitated vaccine updates for coronavirus disease 2019. In September 2023, the U.S. Food and Drug Administration approved an updated BNT162b2 vaccine targeting the omicron XBB.1.5 variant, which was initiated in Korea in October 2024. This study demonstrates the adverse events reported through active nationwide surveillance after XBB.1.5 vaccination in Korea. Since October 19, 2023, the Korea Disease Control and Prevention Agency has conducted daily Short Message Service surveys to collect data on health issues, fever, vaccination site reactions, systemic symptoms, impact on daily life, and healthcare visits. Among 20,180 respondents, 27.9% reported health issues. Adverse reactions peaked on day 1 (28.7%), including pain at the vaccination site, muscle pain, fatigue, and fever. These findings elucidate the short-term safety of the XBB.1.5 vaccine and support its co-administration with the influenza vaccine, reducing vaccine hesitancy and achieving herd immunity.