• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7133-7136
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• 2013

Background: We studied Surveillance, Epidemiology and End Results (SEER) breast cancer data of Georgia USA to analyze the impact of socio-economic factors on the disparity of breast cancer treatment outcome. Materials and Methods: This study explored socio-economic, staging and treatment factors that were available in the SEER database for breast cancer from Georgia registry diagnosed in 2004-2009. An area under the receiver operating characteristic curve (ROC) was computed for each predictor to measure its discriminatory power. The best biological predictors were selected to be analyzed with socio-economic factors. Survival analysis, Kolmogorov-Smirnov 2-sample tests and Cox proportional hazard modeling were used for univariate and multivariate analyses of time to breast cancer specific survival data. Results: There were 34,671 patients included in this study, 99.3% being females with breast cancer. This study identified race and education attainment of county of residence as predictors of poor outcome. On multivariate analysis, these socio-economic factors remained independently prognostic. Overall, race and education status of the place of residence predicted up to 10% decrease in cause specific survival at 5 years. Conclusions: Socio-economic factors are important determinants of breast cancer outcome and ensuring access to breast cancer treatment may eliminate disparities.

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

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.159-163
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• 2013

Background: This study used Surveillance, Epidemiology and End Results (SEER) pancreatic cancer data to identify predictive models and potential socio-economic disparities in pancreatic cancer outcome. Materials and Methods: For risk modeling, Kaplan Meier method was used for cause specific survival analysis. The Kolmogorov-Smirnov's test was used to compare survival curves. The Cox proportional hazard method was applied for multivariate analysis. The area under the ROC curve was computed for predictors of absolute risk of death, optimized to improve efficiency. Results: This study included 58,747 patients. The mean follow up time (S.D.) was 7.6 (10.6) months. SEER stage and grade were strongly predictive univariates. Sex, race, and three socio-economic factors (county level family income, rural-urban residence status, and county level education attainment) were independent multivariate predictors. Racial and socio-economic factors were associated with about 2% difference in absolute cause specific survival. Conclusions: This study s found significant effects of socio-economic factors on pancreas cancer outcome. These data may generate hypotheses for trials to eliminate these outcome disparities.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2567-2570
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• 2013

Background: This study analyzed Surveillance, Epidemiology and End Results (SEER) data to assess if socio-economic factors (SEFs) impact on endometrial cancer survival. Materials and Methods: Endometrial cancer patients treated from 2004-2007 were included in this study. SEER cause specific survival (CSS) data were used as end points. The areas under the receiver operating characteristic (ROC) curve were computed for predictors. Time to event data were analyzed with Kaplan-Meier method. Univariate and multivariate analyses were used to identify independent risk factors. Results: This study included 64,710 patients. The mean follow up time (S.D.) was 28.2 (20.8) months. SEER staging (ROC area of 0.81) was the best pretreatment predictor of CSS. Histology, grade, race/ethnicity and county level family income were also significant pretreatment predictors. African American race and low income neighborhoods decreased the CSS by 20% and 3% respectively at 5 years. Conclusions: This study has found significant endometrial survival disparities due to SEFs. Future studies should focus on eliminating socio-economic barriers to good outcomes.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.4
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• pp.729-742
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• 2016

Socio-economic polarization in Korea partly due to recent globalization and industrial restructuring could reduce social mobility significantly through passing down educational achievement to one's children. Under the notion that residential segregation is geographical frame for the reproduction of educational inequality, this research investigates residential segregation by educational attainment and neighborhood disparity with a case study of Seoul. The statistical analyses employed local segregation measures such as Location Quotient and Local Moran's I and a variety of variables that reflect neighborhood characteristics. As a result, it found that there are sharp and clear contracts between low- and high-educational group concentrations/clusters particularly in terms of housing characteristics and educational facilities. This results provide some evidences that support the arguments about the causes of residential segregation by class in Korean Cities.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.1
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• pp.86-98
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• 2013

This study began with the fact that the relationship between eductional environments and housing price needs to be understood in the context of the structuring of socio-spatial disparity. In other words, this paper focuses on the fact that the eduction with public features and functions plays a role of housing price determination and the rising price is privatized only to cause socio-spatial inequality. The study first examines how the education factors determine the housing price and cause increasing social inequality in Seoul at the macro level. It also carried out more detailed quantitative analysis on the relationship between educational environment factors and housing price with the case study of Yangcheon-gu, Seoul. This study found out that the close relationship between educational environment, housing price and social disparity at various spatial scales. It also figured out the the educational environment factors play an important role of housing price determination as much as material features per se. This means that the relationship between education, housing price and inequality needs to be dealt with not just socially but also in spatial perspective. In addition, the housing price determination is not just technical research but an social science issue in the context of rising socio-spatial disparity. This study is of only significance as a starting point of promising related researches in the future and much more efforts will be needed.

• Journal of the Korean association of regional geographers
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• v.12 no.5
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• pp.557-570
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• 2006

This study attempts to analyze the spatial disparity between supply and the demand of volunteering activities in Daegu, South Korea. The spatial disparity of volunteering activities has been strongly influenced by the residental disparity in terms of socio-economic level. The ratio of volunteers is mostly determined by income and education level that strongly impact the spatial distribution of residential area. The regional difference of ratio of volunteers is greater among Gu districts than in Dong level. The recipients for the volunteering activities have been likely to reside in a particular area within a Gu district; so, regional difference of the recipients was severe in dong level rather than Gu level. It is strongly recommended to exchange volunteers among regions to reduce the spatial disparity between the volunteers and the recipients.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4091-4094
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• 2014

Background: This study used the receiver operating characteristic curve (ROC) to analyze Surveillance, Epidemiology and End Results (SEER) bronchioaveolar carcinoma data to identify predictive models and potential disparity in outcomes. Materials and Methods: Socio-economic, staging and treatment factors were assessed. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict cause specific survival. The area under the ROC was computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate modeling errors. Risk of cause specific death was computed for the predictors for comparison. Results: There were 7,309 patients included in this study. The mean follow up time (S.D.) was 24.2 (20) months. Female patients outnumbered male ones 3:2. The mean (S.D.) age was 70.1 (10.6) years. Stage was the most predictive factor of outcome (ROC area of 0.76). After optimization, several strata were fused, with a comparable ROC area of 0.75. There was a 4% additional risk of death associated with lower county family income, African American race, rural residency and lower than 25% county college graduate. Radiotherapy had not been used in 2/3 of patients with stage III disease. Conclusions: There are socio-economic disparities in cause specific survival. Under-use of radiotherapy may have contributed to poor outcome. Improving education, access and rates of radiotherapy use may improve outcome.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5601-5613
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• 2011

The purpose of this paper is to better understand the socio-economic differentials of Internet access and use. Data from '2008 Korean Internet usage behavior' were used to conduct logistic regression analysis. The findings show that there are significant socio-economic(age, gender, educational attainment, employment situation, and Internet access type) disparity in Internet access(Internet access at home, broadband Internet access). Also socio-economic variables enter in as statistically significant in most specifications for Internet use intensity (Intensive internet use, frequent internet use, less frequent internet use). The gender, household income and education attainment explanations for large differential in Internet use intensity are generally very similar. It is shown that age, education attainment and household income level cause a large digital divide on Internet service use(information use, communication and community use, e-commerce use, Internet banking use).

• Journal of Preventive Medicine and Public Health
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• v.40 no.5
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• pp.345-350
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• 2007

Objectives: To determine the disparity in the rate people undergo health examinations according to socioeconomic position (SEP) and the changes in this disparity with time. Methods: Seoul citizens' health profile data from 1997 to 2005 were analyzed. The study subjects were 40 years old and over, and the total number of subjects was 6,601 in 1997, 8,994 in 2001, and 8,819 in 2005. Those aged 60 years and over were eliminated from the analysis of subjects' occupation. We used education, family income and occupation as indicators of SEP. The age-standardized health examination attendance rate for each year was calculated according to the education, family income and occupation. The odds ratios (ORs) from multiple logistic regressions were adjusted for age. Results: The disparity in the rate of attendance according to the SEP decreased from 1997 to 2005 but still existed. Even though the disparities among the subgroups according to education, family income and occupation were not that high, the disparity between the group with the highest SEP and the other groups was considerable. Conclusions: Our findings suggest that unequal access to health examination services according to socioeconomic position still exists. This disparity has decreased recently but the disparity according to level of education was the greatest.