Background: Atopic dermatitis (AD) is a common skin disease with a wide range of symptoms. Due to the rapidly changing treatment landscape, regular updates to clinical guidelines are needed. Objective: This study aimed to update the guidelines for the treatment of AD to reflect recent therapeutic advances and evidence-based recommendations. Methods: The Patient characteristics, type of Intervention, Control, and Outcome framework was used to determine 48 questions related to AD management. Evidence was graded, recommendations were determined, and, after 2 voting rounds among the Korean Atopic Dermatitis Association (KADA) council members, consensus was achieved. Results: This guideline provides treatment guidance on advanced systemic treatment modalities for AD. In particular, the guideline offers up-to-date treatment recommendations for biologics and Janus-kinase inhibitors used in the treatment of patients with moderate to severe AD.It also provides guidance on other therapies for AD, along with tailored recommendations for children, adolescents, the elderly, and pregnant or breastfeeding women. Conclusion KADA’s updated AD treatment guidelines incorporate the latest evidence and expert opinion to provide a comprehensive approach to AD treatment. The guidelines will help clinicians optimize patient-specific therapies.

Background: Atopic dermatitis (AD) is a common skin disease with a wide range of symptoms. Due to the rapidly changing treatment landscape, regular updates to clinical guidelines are needed. Objective: This study aimed to update the guidelines for the treatment of AD to reflect recent therapeutic advances and evidence-based practices. Methods: The Patient characteristics, type of Intervention, Control, and Outcome framework was used to determine 48 questions related to AD management. Evidence was graded, recommendations were determined, and, after 2 voting rounds among the Korean Atopic Dermatitis Association (KADA) council members, consensus was achieved. Results: The guidelines provide detailed recommendations on foundational therapies, including the use of moisturizers, cleansing and bathing practices, allergen avoidance, and patient education. Guidance on topical therapies, such as topical corticosteroids and calcineurin inhibitors, is also provided to help manage inflammation and maintain skin barrier function in patients with AD. Additionally, recommendations on conventional systemic therapies, including corticosteroids, cyclosporine, and methotrexate, are provided for managing moderate to severe AD. Conclusion KADA’s updated AD guidelines offer clinicians evidence-based strategies focused on basic therapies, topical therapies, and conventional systemic therapies, equipping them to enhance quality of care and improve patient outcomes in AD management.

Background: In 2006, the Korean Atopic Dermatitis Association (KADA) working group released the diagnostic criteria for Korean atopic dermatitis (AD). Recently, more simplified, and practical AD diagnostic criteria have been proposed. Objective: Based on updated criteria and experience, we studied to develop and share a consensus on diagnostic criteria for AD in Koreans. Materials and Methods: For the diagnostic criteria, a questionnaire was constructed by searching the English-language literature in MEDLINE and the Cochrane Database of Systematic Reviews. A modified Delphi method composed of 3 rounds of email questionnaires was adopted for the consensus process. Fifty-four KADA council members participated in the 3 rounds of votes and expert consensus recommendations were established. Results: Diagnostic criteria for AD include pruritus, eczema with age-specific pattern, and chronic or relapsing history. Diagnostic aids for AD encompass xerosis, immunoglobulin E reactivity, hand–foot eczema, periorbital changes, periauricular changes, perioral changes, nipple eczema, perifollicular accentuation, and personal or family history of atopy. Conclusion This study streamlined and updated the diagnostic criteria for AD in Korea, making them more practicable for use in real-world clinical field.

Background: PM2.5 (particulate matter less than 2.5 μm) causes various health problems. Recent studies suggest that long-term exposure to PM2.5 may have a negative impact on vision. This study examined the effects of long-term exposure to concentrations of PM2.5 exceeding Korean standards on myopia prevalence. Methods: This study was conducted on adults aged 40–69 years. The PM2.5 concentrations were calculated as the 1–5-year moving averages based on the participants' residential areas. The relationships between the PM2.5 levels, categorized by the annual average concentration standard in Korea, and the prevalence of myopia were analyzed using binary logistic regression. The results were evaluated using the 95% confidence interval. Results: PM2.5 concentrations averaged over 1–3 years were not significantly associated with the prevalence of myopia. On the other hand, the prevalence of myopia was significantly higher in areas where the 4–5-year moving average PM2.5 levels exceeded the Korean standards. These findings suggest that long-term exposure to PM2.5 may have a detrimental effect on vision. Conclusions This study revealed the impact of long-term PM2.5 exposure on the prevalence of myopia, highlighting the importance of managing PM2.5 levels. Nevertheless, further cohort studies focusing on adults and in-depth research into the effects of long-term exposure will be necessary.

Background: Humans are exposed to mercury primarily in its highly toxic form, methyl mercury, which is known to have adverse effects on various organs and systems. The negative impact of mercury exposure on the growth, development, and mental health of children, from infancy to adolescence, is well-documented. However, there are no internationally standardized safe limits for mercury exposure. This study investigated the impact of dietary habits and higher body mass index (BMI) on blood mercury levels in adolescents. Methods: This study analyzed the data from the 4th Korean National Environmental Health Survey (KoNEHS) 2018–2020. The focus was on 825 middle and high school students aged 13–18 years, whose blood mercury levels were measured. A survey on dietary and lifestyle habits was also conducted. Blood mercury levels were categorized by geometric median values, and associations with overweight status and seafood consumption were examined using a generalized linear model. Results: The geometric mean blood mercury level for the entire sample was 1.37 μg/L, with levels of 1.31 μg/L in normal-weight individuals and 1.43 μg/L in overweight individuals, showing a statistically significant difference between the two groups. After adjusting for other variables, blood mercury levels were significantly associated with overweight status (estimate: 0.084; p = 0.018; 95% confidence interval [CI]: 0.015–0.153), consumption of large fish and tuna more than once a week (estimate: 0.18; p = 0.001; 95% CI: 0.077–0.284), and consumption of fish once a week or more (estimate: 0.147; p = 0.004; 95% CI: 0.043–0.250). Conclusions In adolescents, a higher BMI and an increased consumption of large fish, tuna, and fish were associated with higher blood mercury levels. Notably, a stronger association was found between large fish consumption and blood mercury levels in the overweight group. These findings suggest the need to moderate seafood consumption and establish more proactive mercury exposure standards for adolescents.

Background: Barriers to treatment with intravenous thrombolysis (IVT) for patients with acute ischemic stroke (AIS) in South Korea remain incompletely characterized. We analyze a nationwide prospective cohort to determine patient-level features associated with delayed presentation and non-treatment of potential IVT-eligible patients. Methods: We identified consecutive patients with AIS from 01/2011 to 08/2023 from a multicenter and prospective acute stroke registry in Korea. Patients were defined as IVT candidates if they presented within 4.5 hours from the last known well, had no lab evidence of coagulopathy, and had National Institute of Health Stroke Scale (NIHSS) ≥ 4. Multivariable generalized linear mixed regression models were used to investigate the associations between their characteristics and the IVT candidates or the use of IVT among the candidates. Results: Among 84,103 AIS patients, 41.0% were female, with a mean age of 69 ± 13 years and presentation NIHSS of 4 [interquartile range, 1–8]. Out of these patients, 13,757 (16.4%) were eligible for IVT, of whom 8,179 (59.5%) received IVT. Female sex (adjusted risk ratio [RR], 0.90; 95% confidence interval [CI], 0.86–0.94) and lower years of education (adjusted RR, 0.90; 95% CI, 0.84–0.97 for 0–3 years, compared to ≥ 13 years) were associated with a decreased likelihood of presenting as eligible for IVT after AIS; meanwhile, young age (adjusted RR, 1.12; 95% CI, 1.01–1.24 for ≤ 44 years, compared to 75–84 years) was associated with an increased likelihood of being an IVT candidate. Among those who were eligible for IVT, only age was significantly associated with the use of IVT (adjusted RR, 1.09; 95% CI, 1.03–1.16 for age 65–74 and adjusted RR, 0.83; 95% CI, 0.76–0.90 for ≥ 85 years, respectively). Conclusion Most patients with AIS present outside IVT eligibility in South Korea, and only 60% of eligible patients were ultimately treated. We identified increased age, female sex and lower education as key features on which to focus interventions for improving IVT utilization.

The utilization of health insurance claims data has expanded significantly, enabling researchers to conduct epidemiological studies on a large scale. This review examines key statistical methods for addressing baseline differences and conducting cohort analyses using Korean National Health Insurance claims data. Propensity score matching and inverse probability of treatment weighting are widely used to mitigate selection bias and enhance causal inference in observational studies. These methods help improve study validity by balancing covariates between treatment and control groups. Additionally, survival analysis techniques, such as the Cox proportional hazards model, are essential for assessing time-toevent outcomes and estimating hazard ratios while accounting for censoring. However, the application of these statistical methods is accompanied by challenges, including unmeasured confounding, instability in weight estimation, and violations of model assumptions.To address these limitations, emerging approaches, such as Doubly robust estimation, machine learning-based causal inference, and the marginal structural model, have gained prominence. These techniques offer greater flexibility and robustness in real-world data analysis. Future research should focus on refining methodologies for integrating highdimensional health datasets and leveraging artificial intelligence to enhance predictive modeling and causal inference. Furthermore, the expansion of international collaborations and the adoption of standardized data models will facilitate large-scale multi-center studies.Ethical considerations, including data privacy and algorithmic transparency, should also be prioritized to ensure responsible data use. Maximizing the utility of health insurance claims data requires interdisciplinary collaboration, methodological advancements, and the implementation of rigorous statistical techniques to support evidence-based healthcare policy and improve public health outcomes.

Background: Barriers to treatment with intravenous thrombolysis (IVT) for patients with acute ischemic stroke (AIS) in South Korea remain incompletely characterized. We analyze a nationwide prospective cohort to determine patient-level features associated with delayed presentation and non-treatment of potential IVT-eligible patients. Methods: We identified consecutive patients with AIS from 01/2011 to 08/2023 from a multicenter and prospective acute stroke registry in Korea. Patients were defined as IVT candidates if they presented within 4.5 hours from the last known well, had no lab evidence of coagulopathy, and had National Institute of Health Stroke Scale (NIHSS) ≥ 4. Multivariable generalized linear mixed regression models were used to investigate the associations between their characteristics and the IVT candidates or the use of IVT among the candidates. Results: Among 84,103 AIS patients, 41.0% were female, with a mean age of 69 ± 13 years and presentation NIHSS of 4 [interquartile range, 1–8]. Out of these patients, 13,757 (16.4%) were eligible for IVT, of whom 8,179 (59.5%) received IVT. Female sex (adjusted risk ratio [RR], 0.90; 95% confidence interval [CI], 0.86–0.94) and lower years of education (adjusted RR, 0.90; 95% CI, 0.84–0.97 for 0–3 years, compared to ≥ 13 years) were associated with a decreased likelihood of presenting as eligible for IVT after AIS; meanwhile, young age (adjusted RR, 1.12; 95% CI, 1.01–1.24 for ≤ 44 years, compared to 75–84 years) was associated with an increased likelihood of being an IVT candidate. Among those who were eligible for IVT, only age was significantly associated with the use of IVT (adjusted RR, 1.09; 95% CI, 1.03–1.16 for age 65–74 and adjusted RR, 0.83; 95% CI, 0.76–0.90 for ≥ 85 years, respectively). Conclusion Most patients with AIS present outside IVT eligibility in South Korea, and only 60% of eligible patients were ultimately treated. We identified increased age, female sex and lower education as key features on which to focus interventions for improving IVT utilization.

The utilization of health insurance claims data has expanded significantly, enabling researchers to conduct epidemiological studies on a large scale. This review examines key statistical methods for addressing baseline differences and conducting cohort analyses using Korean National Health Insurance claims data. Propensity score matching and inverse probability of treatment weighting are widely used to mitigate selection bias and enhance causal inference in observational studies. These methods help improve study validity by balancing covariates between treatment and control groups. Additionally, survival analysis techniques, such as the Cox proportional hazards model, are essential for assessing time-toevent outcomes and estimating hazard ratios while accounting for censoring. However, the application of these statistical methods is accompanied by challenges, including unmeasured confounding, instability in weight estimation, and violations of model assumptions.To address these limitations, emerging approaches, such as Doubly robust estimation, machine learning-based causal inference, and the marginal structural model, have gained prominence. These techniques offer greater flexibility and robustness in real-world data analysis. Future research should focus on refining methodologies for integrating highdimensional health datasets and leveraging artificial intelligence to enhance predictive modeling and causal inference. Furthermore, the expansion of international collaborations and the adoption of standardized data models will facilitate large-scale multi-center studies.Ethical considerations, including data privacy and algorithmic transparency, should also be prioritized to ensure responsible data use. Maximizing the utility of health insurance claims data requires interdisciplinary collaboration, methodological advancements, and the implementation of rigorous statistical techniques to support evidence-based healthcare policy and improve public health outcomes.