This paper presents a cross-sectional study based on the cause of death statistics in 2011 extracted from all 229 local governments in South Korea. The standardised hypertensive disease mortality rate (SHDMR) was defined by age-and sex-adjusted mortality by hypertensive diseases distinguished by International Classification of Disease-10 (ICD-10). Variables taken into account were the number of doctors per 100,000 persons, the proportion with higher education (including university students and high school graduates), the number of recipients of basic livelihood support per 100,000 persons, the annual national health insurance premium per capita and the proportion of persons classified as high-risk drinkers. Ordinary least square (OLS) regression and geographically weighted regression (GWR) were applied to identify the potential associations. The statistical analysis was conducted with SAS ver. 9.3, while ArcGIS ver. 10.0 was utilised for the spatial analysis. The OLS results showed that the number of basic livelihood recipients per 100,000 persons had a significant positive association with the SHDMR, and the proportion with higher education had a significant negative one. GWR coefficients varied depending on region investigated and some regional variables had various directions. GWR showed higher adjusted R 2 than that of OLS. It was found that the SHDMR was affected by socio-economic status, but as the effects observed were not consistent in all regions of the country, the development of health policies will need to consider the potential for regional variation.
Background: This study purposed to analyze the relationship between spatial distribution of Diabetes prevalence rates and regional variables. Methods: The unit of analysis was administrative districts of city•gun•gu. Dependent variable was the age-and sex-adjusted diabetes prevalence rates and regional variables were selected to represent three aspects: demographic and socioeconomic factor, health and medical factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis, geographically weighted regression (GWR) was applied for the spatial analysis. Results: Analysis results showed that age-and sex-adjusted diabetes prevalence rates were varied depending on regions. OLS regression showed that diabetes prevalence rates had significant relationships with percent of population over age 65 and financial independence rate. In GWR, the effects of regional variables were not consistent. These results provide information to health policy makers. Conclusion: Regional characteristics should be considered in allocating health resources and developing health related programs for the regional disease management.
Background: Previous studies showed differences in healthcare utilization among insurance types. This study aimed to analyze the difference in healthcare utilization for percutaneous transluminal coronary angioplasty inpatients by insurance types after controlling factors affecting healthcare utilization using propensity score matching (PSM).
Methods:The 2011 national inpatient sample based on health insurance claims data was used for analysis. PSM was used to control factors influencing healthcare utilization except insurance types. Length of stay and total charges were used as healthcare utilization variables. Patients were divided into National Health Insurance (NHI) and Medical Aid (MA) patients. Factors representing inpatients (gender, age, admission sources, and Elixhauser comorbidity index) and hospitals (number of doctors, number of beds, and location of hospitals) were used as covariates in PSM. Results: Tertiary hospitals didn't show significant difference in length of stay and total charges after PSM between two insurance types. However, MA patients showed significantly longer length of stay than that of NHI patients after PSM in general hospitals. Multivariate regression analysis provided that admission sources, Elixhauser comorbidity index, insurance types, number of doctors, and location of hospitals (province) had significant influences on the length of stay in general hospitals. Conclusion: Study results provided evidences that healthcare utilization was differed by insurance types in general hospitals. Health policy makers will need to prepare interventions to influence the healthcare utilization differences between insurance types.
Background: This study purposed to analyze the relationship between regional obesity rates and regional variables. Methods: Data was collected from the Korean Statistical Information Service (KOSIS) and Community Health Survey in 2012. The units of analysis were administrative districts such as city, county, and district. The dependent variable was the age-sex adjusted regional obesity rates. The independent variables were selected to represent four aspects of regions: health behaviour factor, psychological factor, socio-economic factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis model, this study applied geographically weighted regression (GWR) analysis to calculate the regression coefficients for each region. Results: The OLS results showed that there were significant differences in regional obesity rates in high-risk drinking, walking, depression, and financial independence. The GWR results showed that the size of regression coefficients in independent variables was differed by regions.
Conclusion:Our results can help in providing useful information for health policy makers. Regional characteristics should be considered when allocating health resources and developing health-related programs.
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