Regional Adolescent Obesity and Overweight Status in Korea from 2018-2019: Comparison between Two Data Sources.
Dong Hee RyuSu-Jin LeePublished in: Healthcare (Basel, Switzerland) (2021)
Difference in the regional adolescent obesity level may cause a notable health inequality between regions since it significantly affects adulthood health status. This study examined 2018 and 2019 regional obesity and overweight status of adolescents aged 12 to 18 by comparing two cross sectional population-based data sources, the Korea Youth Risk Behavior Web-based Survey (KYRBS) and the National Student Health Examination (NSHE). Prevalence was estimated by calculating weighted percentages and 95% confidence intervals. Correlations in the relative rankings of each municipality were determined by computing Spearman correlation coefficients ( r s ), and prevalence discrepancies between the data sources were visualized by simple correlation graphs. The geographical distributions of adolescent obesity and overweight status showed no perfect concordances between the data sources regardless of sexes and survey years. For adolescent obesity status, there were significant difference between the least and the most obese regions and r s levels were fair to good with p -values less than 0.05, but the correlation graphs indicated body mass index (BMI) underreporting in the KYRBS. For adolescent overweight status, no significant similarities were defined between the data. These results can be used as a basis for the establishment of related policies.
Keyphrases
- weight loss
- weight gain
- young adults
- mental health
- body mass index
- metabolic syndrome
- insulin resistance
- bariatric surgery
- physical activity
- electronic health record
- cross sectional
- type diabetes
- public health
- big data
- high fat diet induced
- healthcare
- drinking water
- risk factors
- magnetic resonance
- adipose tissue
- climate change
- magnetic resonance imaging
- computed tomography
- quality improvement
- data analysis
- health information
- skeletal muscle
- machine learning
- human health