Abstract:PURPOSE: The purpose of this study was to identify the relationship between depression, self-esteem and quality of life in a community population. METHODS: The subjects were given questionnaires which included general characteristics, measures of depression, self-esteem, and quality of life. Data were analyzed by the SPSS/WIN 12.0 program. RESULTS: The mean score of depression was 81.47 (range 40~150). Gender, age, education, marital status, subjective economic status, subjective health status and disease were… Show more
“…While chronic diseases have received much attention, and have been consistently managed nationally, mental disorders have often been overlooked as being unimportant and have not been actively managed despite the fact them being an issue that not only affects individuals, but the society as a whole [ 10 ]. Incidence of depression, one of the most common mental disorders, has shown large regional variations among different populations [ 11 , 12 ], necessitating research on its regional prevalence.…”
OBJECTIVESAchieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions.METHODSUsing health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran’s I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models.RESULTSIn the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms.CONCLUSIONSThe indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions.
“…While chronic diseases have received much attention, and have been consistently managed nationally, mental disorders have often been overlooked as being unimportant and have not been actively managed despite the fact them being an issue that not only affects individuals, but the society as a whole [ 10 ]. Incidence of depression, one of the most common mental disorders, has shown large regional variations among different populations [ 11 , 12 ], necessitating research on its regional prevalence.…”
OBJECTIVESAchieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions.METHODSUsing health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran’s I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models.RESULTSIn the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms.CONCLUSIONSThe indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions.
Purpose:The purpose of the study was to develop a predictive model of Health-related Quality of Life (HRQoL) for Korean Goose daddies -they live alone in Korea to support their families who moved overseas for children's education. Methods: Data were collected from 151 goose daddies from May to June of 2011 by using the structured self-reported questionnaires. The collected data were analyzed using SAS program (version 9.2) and SAS CALIS procedure. Results: Frequency of exercise, monthly income, depression, perceived physical health, and perceived mental health had direct effects on HRQoL and Depression was the variable accounting for major total effect on HRQoL. It could be explained that predictor variables accounted for 76% of the health-related quality of life. Conclusion: In order to improve Goose daddies' HRQoL, predictive factors, such as age, exercise, nutritional status, monthly income, depression, perceived physical health, and perceived mental health, should be considered. Furthermore, should the need of the exercise and diet program, early detection of depression and the treatment for it be emphasized. Also, there is a need to establish institutional structures to support goose daddies in adversity.
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