No abstract

Governments and also local councils create and enforce their own regional public health care plans for the problem of overweight and obesity in the population. Public sports facilities can help these plans. In this paper, we investigated the contribution of public sports facilities to the reduction of the obesity of local residents. We used the data obtained from the Fifth Korea National Health and Nutrition Examination Surveys; and measured the degree of obesity using body mass index (BMI). We conducted various spatial regression analyses including the global Moran's I test and local indicators of spatial autocorrelation analysis finding that there exists spatial dependence in the error term of spatial regression model for BMI. However, we also observed that the number of local public sports facilities is not significantly related to local BMI. This result can be caused by the low utilization ratio and an unbalanced spatial distribution of local public sports facilities. Based on our findings, we suggest that local councils need to improve the quality of public sports facilities encouraging the establishment of preferred types of pubic sports facilities.

Aim It is important to identify differences in nutrient patterns by region so that local governments can be better equipped to design effective public health plans in their administrative areas. The aim of this study was to identify the nutrient patterns of Korean adults living in various regions with different dietary cultures. We explored if there are regional associations with nutritional patterns along with other socio‐demographic characteristics. Methods We utilised data for 17 415 adults who participated in the 2010–2012 Korean National Health and Nutrition Examination Survey (KNHANES V). First, we reduced 18 nutrient intake variables to nutrient patterns via exploratory factor analysis. Next, we used multiple linear regression analysis to identify the associations between nutrient patterns and geodemographic characteristics. Results Five nutrient patterns were identified: ‘Meat‐Related Nutrients', ‘Minerals', ‘Fibre/Vitamin C', ‘Vitamin A' and ‘Retinol'. Nutrient patterns in the Honam and Jeju region were significantly different from those in the Capital region. Age, gender, household income, education level and number of family members were significantly associated with the identified nutrient patterns. Conclusions We confirmed that there are differences in nutrient patterns by region. Our results suggest that the Honam and Jeju region should implement different regional public health plans from those in the Capital region.

ObjectivesThe purpose of this study is to find out the relationship between parents' heights and predicted height of the children who had been treated in the growth clinic of oriental medical hospital. MethodsThe study was conducted with 253 children who visited Oriental Medical Hospital from July 2010 to June 2012. They were analyzed by reviewing the children's chart and correlation analysis to find out the relationship between the two heights. ResultsIn distribution of the sex and the age, sex were similar, but boys who came to the clinic were averagely younger than the girls. In predicting adult height by TW3 method and mean parent's height, correlation in the girls was higher than the boys, especially the girls after their first menstruation. Parents' heights were related to both the boys and the girls, but mother's height was more closely related. Predicted heights of the boys before secondary sex characteristics were correlated with the child's height, but rather correlated with parent's both heights after secondary sexual character and found to be more relevant to father's height. The girls' predicted heights before their menstruation were not correlated with father's height, but with mother's. Their heights after their first periods were correlated with parents' both heights, but more correlated with father's height. ConclusionsThis study helps set proper periods and goals of growth treatment based on the correlation between parents' height and predicted adult height according to TW3 method.