• Journal of the Korean Society of School Health
• /
• v.22 no.2
• /
• pp.25-36
• /
• 2009

Purpose: The purpose of this study was to estimate income-related health inequalities among adolescent population across regions in Korea. Methods: Data of 8,456 adolescents from 1998, 2001, 2005, 2007 Korean Health and Nutrition Examination Survey were used for the analysis. True health status was proxied by self-rated health and overweight status. Per capita income was computed from household monthly average income adjusted by consumer price with base year 2005. Adolescent health inequalities were estimated by Concentration Index (CI) across income and space. Results: Ill health score was related with age (p<0.0001), gender (p=0.0155) and income (p<0.0001). Negative relationship between income and ill-health indicated that higher income group tended to enjoy better health and less overweight. These evidences suggested ill health were accumulated on the economically disadvantaged adolescents. The size of health inequalities (ill-health score) were estimated as CI=-0.057 and CI=-0.030 across income groups and regions, respectively. Comparable measures of within region health disparities were also observed. Conclusion: Since health disparity among adolescent population was small compared to adult population, lessening adolescent health inequality could be a helpful way of mitigating health disparities in later stage. Considering life stage of adolescents, school system and local communities could play important roles toward adolescent health distribution. Although health disparity between regions existed, health disparity within a region should not be neglected.

• Korean Journal of Health Education and Promotion
• /
• v.18 no.3
• /
• pp.11-36
• /
• 2001

The purpose of this study is to analyze health behavior by comparing the difference between self-perceived health status and health examination results. The study subjects consist of 7,702 people aged over 20, surveyed by Health Interview survey, Health Examination survey, Dietary Life survey, Health Consciousness and Behavior survey. Data used in the study are drawn from raw data from a 1998 National Health and Nutrition survey. General characteristics variables are sex, age, education level, residential area, marital status, occupation, and living standard while dichotomous variables, ‘not healthy’ and ‘healthy’ are used to measure self-perceived health status. Variables for health examination results are high blood pressure, high cholesterol, diabetes, liver diseases, liver inflammation, kidney diseases, normal weight, regular diet, optimum sleeping time(7-8 hours), regular health examination and health behavior practice group. Major findings of the study are as follows: 1) Analysis of self-perceived health status and health behavior by disease: Variables significantly correlated with high self-perceived health status have strong associations with high health behavior practice, which supports the hypothesis that as one has high self-perceived health status, one is more likely to practice health promoting behavior. The results of analysis of health behavior differences by dividing subjects into two categories, ‘cases of illness’ and ‘cases of no illness’ indicate that drinking, sleeping time, health examination are significant variables (p〈0.001, 0.05) whereas smoking, weight control, regular exercise, regular diet are not significant. 2) Analysis of disparity patterns between self-perceived health status and health examination: The hypothesis that health behaviors would be different according to the disparity pattern between self-perceived health status and health examination is supported as a result of χ2 test. Among Type I : Self-perceived health status is high and actual health status is good (no disease) Type II: Self-perceived health status is high and actual health status is poor(have disease) Type III: Self-perceived health status is low and actual health status is good(no disease) Type IN: Self-perceived health status is low and actual health status is poor(have disease) Type I and Type IV show no disparity, Type I shows the highest health promoting behavior whereas Type IV shows the lowest health promoting behavior. Type II, and III, compared to Type I, practise lower health promoting behavior. Multi-logistics regression analysis was conducted to find out the degree of impact on health behavior. Independent variables are general characteristics, self-perceived health status and health examination result and presence of illness, while the dependent variable is health promoting behavior. The analysis of the impact of self-perceived health status on the health promoting behavior shows that smoking, drinking, weight control, regular exercise, health examination practice, and/or regular diet are significantly correlated to self-perceived health status. High self-perceived health status is inversely related to high health promoting behavior. This finding supports the hypothesis that the higher one perceives one's health, the more likely one is to practice health promoting behavior. On the contrary, the presence of illness has little impact on health promoting behavior. 3) Multiple logistics analysis on how disparity patterns between self-perceived health status and health examination affect health behavior: The results of multiple logistics analysis made on health behavior variables compared to the standard variable are as follows: When analyzed on the standard of Type I, smoking is a significant risk factor for the Type IV. In case of drinking, all the patterns show a high probability of relative risk ratio. With regard to weight control, it is a risk factor for Type II while all the patterns show high probability of not practising when analyzed on the standard of type IV. Type III and IV show high probability of not doing regular exercise while Type IV, shows a high probability of not taking appropriate sleeping time. When analyzed on the standard of type IV, all the patterns show a high probability of not taking health examinations. Type III and IV show a high probability of not having regular meals. As for overall health promoting behavior, Type III and IV show a high relative risk ratio. These two groups have low self-perceived health status. It implies that self-perceived health status has significant impact on health promoting behavior. This is also supported by the fact that Type I with high self-perceived health status and no illness shows a high practice rate of health promoting behavior. Types II and III the groups with high disparity between self-perceived health status and health examination results, show a low practice rate of health promoting behavior when compared to Type I. Type IV, that is the group with low self-perceived health status and actual illness, shows the lowest practice of health promoting behavior. It is highly probable that this type proves to be the poorest health group.

• Korean Journal of Social Welfare
• /
• v.60 no.4
• /
• pp.231-251
• /
• 2008

Academic interest has been drastically increased for the health disparities due to socioeconomic factors. For those who have a chronic illness such as diabetes, various psychosocial barriers related to illness management might affect and aggravate this disparity. This study focused on illness management barriers experienced by people with diabetes, and examined the differences by their Medicaid beneficiary status. The between group differences in illness management barriers, family support and health status were examined as well as the association between illness management barriers and health status. The sample of this study consists of 144 community dwelling adults who have diabetes, recruited from an outpatient diabetes clinic and a community welfare center. Medicaid beneficiaries reported poorer health status, and experienced more illness management barriers compared to their counterparts. Statistically significant differences were observed in barriers due to mental health, financial status, and lack of support. The between group difference in health status remained significant after controlling for the effect of demographic characteristics and illness related factors. Social work practitioners working with this population should address these illness management barriers to reduce socioeconomic health disparity.

• The Korean Journal of Health Service Management
• /
• v.6 no.4
• /
• pp.13-25
• /
• 2012

There is growing evidence that substance use such as tobacco or alcohol consumption influences health disparity among adolescents. Previous research papers have shown an inconsistency in the relationship between socio-economic status (SES) and substance use in adolescents. However, little is known about socio-economic differences in unhealthy behaviors among Korean adolescents. The purpose of the present study is to explore associations between SES and substance use in Korean adolescents. The analysis was performed using data from the 2009 Korean Youth Risk Behavior Web-based Survey (YRBS), which included a nationally representative sample of middle and high school students. Drinking/heavy drinking and smoking/daily smoking behavior indices were used for dependent variables, and perceived economic status, family affluence score, parents' education were used for independent variables. Chi-square test were used to compare tobacco and alcohol consumption among 3 SES groups. Logistic regression models were used to identify statistically significant socio-economic factors after adjusting other covariates. Higher perceived economic status and higher family affluence were associated with higher rates of smoking, daily smoking, drinking, and heavy drinking, while lower level of parents' education was related to higher use of tobacco and alcohol. Socio-economic status significantly influences health behaviors in adolescents, and it may consequently affect health disparity in their adulthood. Therefore, there is a need of continuous monitoring and follow-up research of health disparity among adolescents.

• Korean Journal of Occupational Health Nursing
• /
• v.17 no.2
• /
• pp.191-201
• /
• 2008

Purpose: The purpose of this study were to investigate the health status and to identify the influencing factors on health status by gender among service workers. Method: Data were collected from the self-reported survey of 781 service workers in sales, food & lodging industry. The data were analyzed by descriptive statistics and multiple linear regression analysis using SPSS Win 10.0. Result: The influencing factors of male workers' health status were job satisfaction, standing hour during work time, physical work environment, job demand and family support. The influencing factors of female workers' health status were job satisfaction, emotional labor, physical work environment, regular exercise, standing hour during work time, social support and age. Conclusion: Gender specific occupational Health program for service workers should be developed in consideration of these influencing factors.

• Journal of the Korean Society of School Health
• /
• v.20 no.1
• /
• pp.63-75
• /
• 2007

Purpose: With economic development and prolonged longevity, the level of health and health disparities have became growing concerns for individual and society as well. Since youth's health status are influenced by households' socioeconomic status and associated with heath status in later stage of life, assessing health inequality in the youth is a significant step toward lessening health disparity and promoting health. We measured health inequality in high school students and decomposed it into health factors. Methods: The subjects included 3,787 high school students of 12th graders from the Korea Education and Employment Panel (KEEP) in 2004. True health status was assumed as a latent variable and estimated by ordered logistic regression model. The predicted health was used as a measure of individual health after rPSraling to [0,1] interval. Total health inequality was then measured by Gini coefficient and was decomposed into health factors. Results: Health inequality in high school students was observed. Of total health inequality, 44% was explained by biological factors such as body mass index (BMI) (32.5%) and gender (13.5%). Behavioral factors such as smoking, drinking, physical activity, hours in bed and hours of computer ussge added to 11.7%. Household income and work experiences explained 5.6% and 8.8%, respectively. School satisfaction explained 14.6%. Other school related factors such as self-assessed achievement and experience of being bullied accounted for 15.5%. Conclusion: Among the health factors, biological factor was the most important contributor in health disparity. Other factors such as health behaviors, socioeconomic factors, school satisfaction and school related factors exhibited somewhat similar magnitude. For policy purposes, it is recommended to look into modifiable factors depending BM, gender and school surroundings.

• Journal of Preventive Medicine and Public Health
• /
• v.45 no.2
• /
• pp.113-121
• /
• 2012

Objectives: This study examined the trends in gender disparity in the self-rated health of people aged 25 to 64 in South Korea, a rapidly changing society, with specific attention to socio-structural inequality. Methods: Representative sample data were obtained from six successive, nationwide Social Statistics Surveys of the Korean National Statistical Office performed during 1992 to 2010. Results: The results showed a convergent trend in poor self-rated health between genders since 1992, with a sharper decline in gender disparity observed in younger adults (aged 25 to 44) than in older adults (aged 45 to 64). The diminishing gender gap seemed to be attributable to an increase in women;s educational attainment levels and to their higher status in the labor market. Conclusions: The study indicated the importance of equitable social opportunities for both genders for understanding the historical trends in the gender gap in the self-reported health data from South Korea.

• Research in Community and Public Health Nursing
• /
• v.29 no.1
• /
• pp.21-32
• /
• 2018

Purpose: This study attempted to identify regional disparities of self-rated health among Korean wage workers and to investigate the influencing factors on them. Methods: The study subjects were 25,069 workers in 16 regions who were extracted from the 2014 Korean Working Condition Survey (KWCS). A multilevel analysis was conducted by building hierarchical data at individual and regional level. Results: In this study, 'financial autonomy rate' and 'current smoking rate' were identified as regional factors influencing the workers' self-rated health. When the socio-demographic and occupational factors of the workers were controlled, 'current smoking rate', a health policy factor, explained the regional disparity of workers' health status. Conclusion: We found that the health status of workers can be affected by the health behavior level of the whole population in their residential area. In order to improve the health status of working population and to alleviate their regional health inequalities, it is necessary to strengthen macro and structural level interventions.

• Health Policy and Management
• /
• v.26 no.1
• /
• pp.12-23
• /
• 2016

Background: Many studies have explained regional disparities in health by socioeconomic status and healthcare resources, focusing on differences between urban and rural area. However some cities in Korea have the highest cardiovascular mortality, even though they have sufficient healthcare resources. So this study aims to confirm three hypotheses. (1) There are also regional health disparities between cities not only between urban and rural area. (2) It has different regional risk factors affecting cardiovascular mortality whether it is urban or rural area. (3) Besides socioeconomic and healthcare resources factors, there are remnant factors that affect regional cardiovascular mortality such as health behavior and physical environment. Methods: The subject of this study is 227 local authorities (si, gun, and gu). They were categorized into city (gu and si consisting of urban area) and non-city (gun consisting of rural area), and the city group was subdivided into 3 parts to reflect relative different city status: city 1 (Seoul, Gyeonggi cities), city 2 (Gwangyeoksi cities), and city 3 (other cities). We compared their mortalities among four groups by using analysis of variance analysis. And we explored what had contributed to it in whole authorities, city and non-city group by using multiple regression analysis. Results: Cardiovascular mortality is highest in city 2 group, lowest in city 1 group and middle in non-city group. Socioeconomic status and current smoking significantly increase mortality regardless of group. Other than those things, in city, there are some factors associated with cardiovascular mortality: walking practice(-), weight control attempt(-), deficiency of sports facilities(+), and high rate of factory lot(+). In non-city, there are other factors different from those of city: obesity prevalence(+), self-perceiving obesity(-), number of public health institutions(-), and road ratio(-). Conclusion: To reduce cardiovascular mortality and it's regional disparities, we need to consider differentiated approach, respecting regional character and different risk factors. Also, it is crucial to strengthen local government's capacity for practicing community health policy.

• Journal of Korean society of Dental Hygiene
• /
• v.24 no.1
• /
• pp.17-26
• /
• 2024

Objectives: This study aimed to analyze the relationship between the socioeconomic status and oral health of adults. Methods: Data from the 7th Korea National Health and Nutrition Examination Survey (2016-2018) were analyzed, and 13,199 adults aged 19 years or older were selected as study subjects. Various oral health indicators were used to analyze the effect of socioeconomic status on oral health. Disparities in oral health according to socioeconomic status were analyzed using the complex sample chi-squared test and multiple logistic regression analysis. Results: A statistically significant difference was observed between income level, medical aid, and all oral health indicators, which indicated that the lower the income level, the lower the oral health level (p<0.001). Furthermore, all oral health indicators displayed statistically significant differences, with the exception of the prevalence of dental caries and education level. The lower the education level, the lower the oral health level (p<0.001). Therefore, the oral health level of adults presented significant differences according to different socioeconomic status indicators. Conclusions: To prevent oral health inequalities, the government and local governments need to intervene not only in the field of health care but also in the social determinants. Additionally, concerted efforts should be made to eliminate oral health disparities by improving policies and systems.