OBJECTIVES: Humidifier disinfectants (HDs) were sold in Korea from 1994 until their recall in 2011. We examined the incidence patterns of 8 respiratory diseases before and after the HD recall and estimated the attributable risk in the Korean population. METHODS: Using National Health Insurance data from 2002 to 2019, we performed age–cohort–period and differences-in-diffference analyses (comparing periods before vs. after the recall) to estimate the population-attributable fraction and the excess number of episodes. The database comprised 51 million individuals (99% of the Korean population). The incidence of 8 diseases—acute upper respiratory infection (AURI), acute lower respiratory infection (ALRI), asthma, pneumonia, chronic sinusitis (CS), interstitial lung disease (ILD), bronchiectasis, and chronic obstructive pulmonary disease (COPD)—was defined by constructing episodes of care based on patterns of medical care and the clinical characteristics of each disease. RESULTS: The relative risks (RRs) for AURI, ALRI, asthma, pneumonia, CS, and ILD were elevated among younger individuals (with an RR as high as 82.18 for AURI in males), whereas chronic conditions such as bronchiectasis, COPD, and ILD showed higher RRs in older individuals. During the HD exposure period, the population-attributable risk percentage ranged from 4.6% for bronchiectasis to 25.1% for pneumonia, with the excess number of episodes ranging from 6,218 for ILD to 3,058,861 for CS. Notably, females of reproductive age (19-44 years) experienced 1.1-9.2 times more excess episodes than males. CONCLUSIONS This study provides epidemiological evidence that inhalation exposure to HDs affects the entire respiratory tract and identifies vulnerable groups.

OBJECTIVES: Humidifier disinfectants (HDs) were sold in Korea from 1994 until their recall in 2011. We examined the incidence patterns of 8 respiratory diseases before and after the HD recall and estimated the attributable risk in the Korean population. METHODS: Using National Health Insurance data from 2002 to 2019, we performed age–cohort–period and differences-in-diffference analyses (comparing periods before vs. after the recall) to estimate the population-attributable fraction and the excess number of episodes. The database comprised 51 million individuals (99% of the Korean population). The incidence of 8 diseases—acute upper respiratory infection (AURI), acute lower respiratory infection (ALRI), asthma, pneumonia, chronic sinusitis (CS), interstitial lung disease (ILD), bronchiectasis, and chronic obstructive pulmonary disease (COPD)—was defined by constructing episodes of care based on patterns of medical care and the clinical characteristics of each disease. RESULTS: The relative risks (RRs) for AURI, ALRI, asthma, pneumonia, CS, and ILD were elevated among younger individuals (with an RR as high as 82.18 for AURI in males), whereas chronic conditions such as bronchiectasis, COPD, and ILD showed higher RRs in older individuals. During the HD exposure period, the population-attributable risk percentage ranged from 4.6% for bronchiectasis to 25.1% for pneumonia, with the excess number of episodes ranging from 6,218 for ILD to 3,058,861 for CS. Notably, females of reproductive age (19-44 years) experienced 1.1-9.2 times more excess episodes than males. CONCLUSIONS This study provides epidemiological evidence that inhalation exposure to HDs affects the entire respiratory tract and identifies vulnerable groups.

OBJECTIVES: Humidifier disinfectants (HDs) were sold in Korea from 1994 until their recall in 2011. We examined the incidence patterns of 8 respiratory diseases before and after the HD recall and estimated the attributable risk in the Korean population. METHODS: Using National Health Insurance data from 2002 to 2019, we performed age–cohort–period and differences-in-diffference analyses (comparing periods before vs. after the recall) to estimate the population-attributable fraction and the excess number of episodes. The database comprised 51 million individuals (99% of the Korean population). The incidence of 8 diseases—acute upper respiratory infection (AURI), acute lower respiratory infection (ALRI), asthma, pneumonia, chronic sinusitis (CS), interstitial lung disease (ILD), bronchiectasis, and chronic obstructive pulmonary disease (COPD)—was defined by constructing episodes of care based on patterns of medical care and the clinical characteristics of each disease. RESULTS: The relative risks (RRs) for AURI, ALRI, asthma, pneumonia, CS, and ILD were elevated among younger individuals (with an RR as high as 82.18 for AURI in males), whereas chronic conditions such as bronchiectasis, COPD, and ILD showed higher RRs in older individuals. During the HD exposure period, the population-attributable risk percentage ranged from 4.6% for bronchiectasis to 25.1% for pneumonia, with the excess number of episodes ranging from 6,218 for ILD to 3,058,861 for CS. Notably, females of reproductive age (19-44 years) experienced 1.1-9.2 times more excess episodes than males. CONCLUSIONS This study provides epidemiological evidence that inhalation exposure to HDs affects the entire respiratory tract and identifies vulnerable groups.

OBJECTIVES: Humidifier disinfectants (HDs) were sold in Korea from 1994 until their recall in 2011. We examined the incidence patterns of 8 respiratory diseases before and after the HD recall and estimated the attributable risk in the Korean population. METHODS: Using National Health Insurance data from 2002 to 2019, we performed age–cohort–period and differences-in-diffference analyses (comparing periods before vs. after the recall) to estimate the population-attributable fraction and the excess number of episodes. The database comprised 51 million individuals (99% of the Korean population). The incidence of 8 diseases—acute upper respiratory infection (AURI), acute lower respiratory infection (ALRI), asthma, pneumonia, chronic sinusitis (CS), interstitial lung disease (ILD), bronchiectasis, and chronic obstructive pulmonary disease (COPD)—was defined by constructing episodes of care based on patterns of medical care and the clinical characteristics of each disease. RESULTS: The relative risks (RRs) for AURI, ALRI, asthma, pneumonia, CS, and ILD were elevated among younger individuals (with an RR as high as 82.18 for AURI in males), whereas chronic conditions such as bronchiectasis, COPD, and ILD showed higher RRs in older individuals. During the HD exposure period, the population-attributable risk percentage ranged from 4.6% for bronchiectasis to 25.1% for pneumonia, with the excess number of episodes ranging from 6,218 for ILD to 3,058,861 for CS. Notably, females of reproductive age (19-44 years) experienced 1.1-9.2 times more excess episodes than males. CONCLUSIONS This study provides epidemiological evidence that inhalation exposure to HDs affects the entire respiratory tract and identifies vulnerable groups.

Background: A healthcare system’s collapse due to a pandemic, such as the coronavirus disease 2019 (COVID-19), can expose healthcare workers (HCWs) to various mental health problems. This study aimed to investigate the impact of the COVID-19 pandemic on the depression and anxiety of HCWs. Methods: A nationwide questionnaire-based survey was conducted on HCWs who worked in healthcare facilities and public health centers in Korea in December 2020. Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) were used to measure depression and anxiety. To investigate factors associated with depression and anxiety, stepwise multiple logistic regression analysis was performed. Results: A total of 1,425 participating HCWs were included. The mean depression score (PHQ-9) of HCWs before and after COVID-19 increased from 2.37 to 5.39, and the mean anxiety score (GAD-7) increased from 1.41 to 3.41. The proportion of HCWs with moderate to severe depression (PHQ-9 ≥ 10) increased from 3.8% before COVID-19 to 19.5% after COVID-19, whereas that of HCWs with moderate to severe anxiety (GAD-7 ≥ 10) increased from 2.0% to 10.1%. In our study, insomnia, chronic fatigue symptoms and physical symptoms after COVID-19, anxiety score (GAD-7) after COVID-19, living alone, and exhaustion were positively correlated with depression. Furthermore, post-traumatic stress symptoms, stress score (Global Assessment of Recent Stress), depression score (PHQ-9) after COVID-19, and exhaustion were positively correlated with anxiety. Conclusion In Korea, during the COVID-19 pandemic, HCWs commonly suffered from mental health problems, including depression and anxiety. Regularly checking the physical and mental health problems of HCWs during the COVID-19 pandemic is crucial, and social support and strategy are needed to reduce the heavy workload and psychological distress of HCWs.

OBJECTIVES: This study investigated the associations between several obesity-related anthropometric indices and mortality in middle-aged and elderly populations to compare the indices’ predictive ability with that of the body mass index (BMI). METHODS: We analyzed data on 12 indices calculated from 19,805 community-based cohort participants (average age, 63.27 years; median follow-up, 13.49 years). Each index was calculated using directly measured values of height, weight, waist circumference (WC), and hip circumference (HC). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each index using Cox regression and evaluated mortality prediction with the Harrell concordance index (c-index). RESULTS: Adding anthropometric indices to the basic mortality model (c-index, 0.7723; 95% CI, 0.7647 to 0.7799) significantly increased the predictive power of BMI (c-index, 0.7735; 95% CI, 0.7659 to 0.7811), a body shape index (ABSI; c-index, 0.7735; 95% CI, 0.7659 to 0.7810), weight-adjusted waist index (WWI; c-index, 0.7731; 95% CI, 0.7656 to 0.7807), and waist to hip index (WHI; c-index, 0.7733; 95% CI, 0.7657 to 0.7809). The differences between the BMI model and the other 3 models were not statistically significant. CONCLUSIONS In predicting all-cause mortality, the ABSI, WWI, and WHI models based on WC or HC had stronger predictive power than conventional risk factors but were not significantly different from the BMI model.

OBJECTIVES: This study investigated the associations between several obesity-related anthropometric indices and mortality in middle-aged and elderly populations to compare the indices’ predictive ability with that of the body mass index (BMI). METHODS: We analyzed data on 12 indices calculated from 19,805 community-based cohort participants (average age, 63.27 years; median follow-up, 13.49 years). Each index was calculated using directly measured values of height, weight, waist circumference (WC), and hip circumference (HC). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each index using Cox regression and evaluated mortality prediction with the Harrell concordance index (c-index). RESULTS: Adding anthropometric indices to the basic mortality model (c-index, 0.7723; 95% CI, 0.7647 to 0.7799) significantly increased the predictive power of BMI (c-index, 0.7735; 95% CI, 0.7659 to 0.7811), a body shape index (ABSI; c-index, 0.7735; 95% CI, 0.7659 to 0.7810), weight-adjusted waist index (WWI; c-index, 0.7731; 95% CI, 0.7656 to 0.7807), and waist to hip index (WHI; c-index, 0.7733; 95% CI, 0.7657 to 0.7809). The differences between the BMI model and the other 3 models were not statistically significant. CONCLUSIONS In predicting all-cause mortality, the ABSI, WWI, and WHI models based on WC or HC had stronger predictive power than conventional risk factors but were not significantly different from the BMI model.

OBJECTIVES: This study investigated the associations between several obesity-related anthropometric indices and mortality in middle-aged and elderly populations to compare the indices’ predictive ability with that of the body mass index (BMI). METHODS: We analyzed data on 12 indices calculated from 19,805 community-based cohort participants (average age, 63.27 years; median follow-up, 13.49 years). Each index was calculated using directly measured values of height, weight, waist circumference (WC), and hip circumference (HC). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each index using Cox regression and evaluated mortality prediction with the Harrell concordance index (c-index). RESULTS: Adding anthropometric indices to the basic mortality model (c-index, 0.7723; 95% CI, 0.7647 to 0.7799) significantly increased the predictive power of BMI (c-index, 0.7735; 95% CI, 0.7659 to 0.7811), a body shape index (ABSI; c-index, 0.7735; 95% CI, 0.7659 to 0.7810), weight-adjusted waist index (WWI; c-index, 0.7731; 95% CI, 0.7656 to 0.7807), and waist to hip index (WHI; c-index, 0.7733; 95% CI, 0.7657 to 0.7809). The differences between the BMI model and the other 3 models were not statistically significant. CONCLUSIONS In predicting all-cause mortality, the ABSI, WWI, and WHI models based on WC or HC had stronger predictive power than conventional risk factors but were not significantly different from the BMI model.

OBJECTIVES: This study investigated the associations between several obesity-related anthropometric indices and mortality in middle-aged and elderly populations to compare the indices’ predictive ability with that of the body mass index (BMI). METHODS: We analyzed data on 12 indices calculated from 19,805 community-based cohort participants (average age, 63.27 years; median follow-up, 13.49 years). Each index was calculated using directly measured values of height, weight, waist circumference (WC), and hip circumference (HC). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each index using Cox regression and evaluated mortality prediction with the Harrell concordance index (c-index). RESULTS: Adding anthropometric indices to the basic mortality model (c-index, 0.7723; 95% CI, 0.7647 to 0.7799) significantly increased the predictive power of BMI (c-index, 0.7735; 95% CI, 0.7659 to 0.7811), a body shape index (ABSI; c-index, 0.7735; 95% CI, 0.7659 to 0.7810), weight-adjusted waist index (WWI; c-index, 0.7731; 95% CI, 0.7656 to 0.7807), and waist to hip index (WHI; c-index, 0.7733; 95% CI, 0.7657 to 0.7809). The differences between the BMI model and the other 3 models were not statistically significant. CONCLUSIONS In predicting all-cause mortality, the ABSI, WWI, and WHI models based on WC or HC had stronger predictive power than conventional risk factors but were not significantly different from the BMI model.

This report presents the latest statistics on the stroke population in South Korea, sourced from the Clinical Research Collaborations for Stroke in Korea-National Institute for Health (CRCS-K-NIH), a comprehensive, nationwide, multicenter stroke registry. The Korean cohort, unlike western populations, shows a male-to-female ratio of 1.5, attributed to lower risk factors in Korean women. The average ages for men and women are 67 and 73 years, respectively.Hypertension is the most common risk factor (67%), consistent with global trends, but there is a higher prevalence of diabetes (35%) and smoking (21%). The prevalence of atrial fibrillation (19%) is lower than in western populations, suggesting effective prevention strategies in the general population. A high incidence of large artery atherosclerosis (38%) is observed, likely due to prevalent intracranial arterial disease in East Asians and advanced imaging techniques.There has been a decrease in intravenous thrombolysis rates, from 12% in 2017–2019 to 10% in 2021, with no improvements in door-to-needle and door-to-puncture times, worsened by the coronavirus disease 2019 pandemic. While the use of aspirin plus clopidogrel for noncardioembolic stroke and direct oral anticoagulants for atrial fibrillation is well-established, the application of direct oral anticoagulants for non-atrial fibrillation cardioembolic strokes in the acute phase requires further research. The incidence of early neurological deterioration (13%) and the cumulative incidence of recurrent stroke at 3 months (3%) align with global figures. Favorable outcomes at 3 months (63%) are comparable internationally, yet the lack of improvement in dependency at 3 months highlights the need for advancements in acute stroke care.