• Health Policy and Management
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• v.26 no.1
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• pp.30-38
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• 2016

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.

• Health Policy and Management
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• v.26 no.4
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• pp.271-278
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• 2016

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.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.291-298
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• 2017

This study is to examine the status and trends of regional elderly group(pre-elderly and elderly segment)'s suicide rates and socio-economic factors, to analyze the relevance of suicide rates and socio-economic factors, and to understand differential factors of elderly group's suicide rates between metropolitan areas and small-medium sized areas. For this, analysis on relevance between socio-economic factors and elderly group's suicide rates has been done using regional statistical data for the past ten years(2006~2015) by National Statistical office. According to research findings, Socio-economic factors such as welfare budget ratio, divorce rates, economic activity rates, and financial independent rates have shown different effects on elderly group's suicide rates by age range and regional characteristics. Based on these analyses, this study suggested policy tasks for alleviating suicide rates of elderly group.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.2895-2899
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• 2016

Background: Increasing breast cancer (BC) incidence rates have been reported from India; causal factors for this increased incidence are not understood and diagnosis is mostly in advanced stages. Trivandrum exhibits the highest BC incidence rates in India. This study aimed to estimate trends in incidence by age from 2005-2014, to predict rates through 2020 and to assess the stage at diagnosis of BC in Trivandrum. Materials and Methods: BC cases were obtained from the Population Based Cancer Registry, Trivandrum. Distribution of stage at diagnosis and incidence rates of BC [Age-specific (ASpR), crude (CR) and age-standardized (ASR)] are described and employed with a joinpoint regression model to estimate average annual percent changes (AAPC) and a Bayesian model to estimate predictive rates. Results: BC accounts for 31% (2681/8737) of all female cancers in Trivandrum. Thirty-five percent (944/2681) are <50 years of age and only 9% present with stage I disease. Average age increased from 53 to 56.4 years (p=0.0001), CR (per $10^5$ women) increased from 39 (ASR: 35.2) to 55.4 (ASR: 43.4), AAPC for CR was 5.0 (p=0.001) and ASR was 3.1 (p=0.001). Rates increased from 50 years. Predicted ASpR is 174 in 50-59 years, 231 in > 60 years and overall CR is 80 (ASR: 57) for 2019-20. Conclusions: BC, mostly diagnosed in advanced stages, is rising rapidly in South India with large increases likely in the future; particularly among post-menopausal women. This increase might be due to aging and/or changes in lifestyle factors. Reasons for the increased incidence and late stage diagnosis need to be studied.

• Journal of the Korean Geographical Society
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• v.39 no.1
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• pp.88-101
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• 2004

This paper aims to examin the channel of waste collection, as well as to clarify the relationship between collection types and regional characteristics. According to the analysis results, a kind of used paper and old metal are mainly collected in regions which have a large population size, high population increasing rates, high population composition rates of secondary industry and high degree of self-supporting in public finance. On the other hand, in regions which have a small population size, low population increasing rates or a decrease in population, and high population composition rates of primary industry and rates of agricultural household, there are high collection rates for others recyclable wastes, a kind of scrap iron, used paper, and used bottle. These results show that the collection types of recyclable domestic wastes are different depend on industry developed degree in region.

• The Journal of Health Technology Assessment
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• v.6 no.2
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• pp.100-105
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• 2018

Objectives: The purpose of this research is to investigate regional variations of mastication difficulty rates in the elderly over 65 in 229 primary autonomous districts in Korea and explore relevant characteristics. For the research data, local community health survey conducted in 2015 and data from Ministry of the Interior and Safety, Ministry of Health & Welfare, National Health Insurance Service and Statistics Korea were used. Methods: Frequency analysis was conducted for general characteristics and mastication difficulty rates of each region, and distribution of mastication difficulty rates was displayed for each town, city and province by mapping them. Extremal quotient (EQ), coefficient of variation (CV) values were calculated for regional variations, and logistic regression analysis was performed to reveal the relationship between each independent variable and mastication difficulty rates. Results: The average of standardized mastication difficulty rate was 46.78%, and regional variations were significant with EQ 3.46, CV 0.18. Characteristic factors that have significant effects on mastication difficulty rate included sex ratio, elderly population and the number of dentists per 10000 people. Conclusion: As a result, there were variations among mastication difficulty rates in 229 primary local governments across the country, and the distribution of health care resources by the characteristics of the local environment by region affected mastication difficulty rates. Accordingly, it is required to provide political supports to overcome regional inequality of oral health levels and develop cooperative system between local governments and local dentists.

• Health Policy and Management
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• v.24 no.2
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• pp.143-152
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• 2014

Background: Previous studies showed that the characteristics of population and regions were related to the suicide rates. This study purposed to analyze the relationships between regional factors and suicide rates with spatial analysis model. Methods: This is a cross sectional study based on the statistics of 2011 which was extracted from the 229 City Gun Gu administrative districts in Korea. Cause of death statistics on each district was used to produce the age-, sex-adjusted mortality rates resulting from suicide. Regional characteristics were measured by the number of doctors engaged in medical institutions per 1,000 population, divorced people's rate per 1,000 population, number of marriages per 1,000 population, and percent of welfare budget in general accounting. Statistical analysis was performed by using SAS ver. 9.3 and ArcGIS ver. 10.2 was used for geographically weighted regression (GWR). Results: In ordinary least square (OLS) regression, divorced people's rate per 1,000 population had a significant positive relationship with the standardized mortality rate per 100,000 population. Marriages per 1,000 population and the proportion of welfare budget in the general accounting had significant negative relationships with the mortality rates. Meanwhile, GWR provided that the directions of variable, divorced people's rate per 1,000 population, were varied depending on regions. The adjusted $R^2$ was improved from the 0.32 in OLS to the 0.46 in GWR. Conclusion: Results of GWR showed that regional factors had different effects on the suicide rates depending on locations. It suggested that policy interventions for reducing the suicide rate should consider the regional characteristics in obtaining policy objectives.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.149-151
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• 2018

The purpose of this paper is to visualize and to analyze differences of regional mortality rates by major causes of death. We use causes of death statistics from KOSIS and compare regional mortality rates divided by national mortality rates by three causes of death. To do this, we define regional mortality ratio and regional age-standardized mortality ratio, and visualized by choropleth map using R. As a result, In case of neoplasm, there was no significant difference by region. In case of circulatory system, Ulsan, Daegu, Busan and Gyungnam showed relatively high regional age-standardized mortality ratio. In case of respiratory system, the ratios were in order of Gangwon, Sejong, and Chungbuk.

• KDI Journal of Economic Policy
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• v.37 no.4
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• pp.55-74
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• 2015

Using the Census on Establishments collected by Statistics Korea, we analyze how the patterns of job creation and destruction differ across counties (si-gun-gu). We measure aggregate employment changes due to establishment startups, expansions, contractions, and shutdowns for each county and quantify the role of such reallocations in explaining variation in employment growth across counties. Overall we find that both rates of net entry and job creation play an important role in explaining differences in net job creation rates across regions. Moreover, counties with high employment growth rates also tend to have high exit and job destruction rates, which suggests that an active process of job reallocation is a key source of regional employment growth.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.41-50
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• 2024

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.