Methods: Data from approximately 230,000 adults aged ≥19 years who participated annually in the Korea Community Health Survey between 2015 and 2024 were analyzed. Obesity was defined as a body mass index ≥25 kg/m2 based on self-reported height and weight. Annual trends and regional distributions in obesity prevalence were assessed with descriptive statistics, and associations with sociodemographic characteristics were examined using multivariable logistic regression models. All analyses incorporated the complex sampling design and survey weights, and were performed using SAS version 9.4.
Results: In 2024, obesity prevalence among Korean adults was 34.4%, and had increased steadily over the past decade. The prevalence was greater in men (41.4%) than in women (23.0%). Among men, the prevalence of obesity was highest in those aged 30–49 years, while among women, it was higher among those aged ≥60 years. Jeonnam and Jeju had the highest prevalence (36.8%), whereas Sejong had the lowest prevalence (29.1%).
Conclusions: The prevalence of obesity among Korean adults increased steadily over the past decade, with substantial differences by sex, age, and region. Continuous monitoring and targeted prevention and management strategies are needed to address population-specific characteristics.
Methods: This study evaluated the achievements and limitations of the project drawing on the implementation results of the pilot and main phase, on-site assessments, and international case studies.
Results: The project achieved key milestones through the establishment of an operational framework, the development of a registry management system, and links it with related institutional systems to facilitate data collection and analysis. However, certain challenges remain, including the limited amount of data coverage due to institutional workload and lack of dedicated personnel, the need for standardized registration criteria, and the expansion of linked administrative and clinical data.
Conclusions: Moving forward, the KDCA aims to establish a systematic and standardized data collection foundation to secure reliable, large-scale datasets and strengthen the institutional framework for data utilization, contributing to the development of evidence-based rare disease management policies.





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