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.
Background: Personal socio-economic abilities are crucial as it affects health inequalities. These multidimensional inequalities across the regions have been structured and fixed. This study aimed to analyze health vulnerabilities by regional cluster and identify regional health disparities of self-rated health, using nationally representative cross-sectional data. Methods: This study used personal and regional data. Data from the Community Health Survey 2021 were analyzed. K-means cluster analysis was applied to 250 si-gun-gu using administrative regional data. The clusters were based on three areas: physical environment, health-related behaviors and biological factors, and the psychosocial environment through the conceptual framework for action on the social determinants of health. And binary logistic regression analyses were conducted to examine the differences in self-rated health status by the regional clusters, controlling human biology, environment, lifestyle, and healthcare organization factors. Results: The most vulnerable group was group 3, the moderate vulnerable group was group 1, and the least vulnerable group was group 2. The group 2 was more likely to have high self-rated health status than the moderate vulnerable group (odds ratio [OR], 1.023; p<0.001). And the group 3 showed low self-rated health status than the moderate vulnerable group (OR, 0.775; p<0.001). However, the moderate vulnerable group had significantly higher self-rated health status than the most vulnerable group (group 2: OR, 1.023; p<0.001; group 3: OR, 0.775; p<0.001). Conclusion: These results demonstrate that community members' health status is influenced by regional determinants of health and individual levels. And these contribute to understanding the importance of specific and differentiated interventions like locally tailored support programs considering both individual and regional health determinants.
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.
Objectives : In this study, 3,107 patients were used to evaluate the impact based on raw data of 2014 and the health status and medical expenses income quintile was collected and data was analyzed. Methods : Analysis method was the average comparison, ANOVA, subjected to a multiple logistic regression analysis, the statistical test was the t-test and the scheffe post verification. Results : Gender(p<.000), age(p<.000), marital status(p<.000) educational status (p<.000), easement(p<.000), medication(p<.000), subjective health status(p<.005) were analyzed. First quintile identified that the highest amount was spent in the Chungcheong region, the 2nd quintile showed that the highest output was in the Gyeongsang region. The 3rd and 4th quintiles indicated that the highest expenditure was in the Seoul metropolitan region. The 5th quintile showed that the Chungcheong was the highest once again and the Jeolla region was the lowest in terms of expediture. Conclusions : Future medical research on income will require the government's Big Data collection to create the primary basis for policy making in order to improve the efficiency, effectiveness and equity of medicine spending.
Objectives: We aimed to identify the factors related to depression and quality of life in patients with hypertension by using multilevel regression analysis. Methods: In 2019, 229 043 participants in the Korean Community Health Survey were selected as the study group. Individual factors were identified using data from the 2019 Community Health Survey. Regional factors were identified using data from the National Statistical Office of Korea. Multilevel regression analysis was conducted to find individual and local factors affecting depression and quality of life in patients with hypertension and to determine any associated interactions. Results: As individual factors in patients with hypertension, women, those with lower education-levels, recipients of basic livelihood benefits, and those with poor dietary conditions showed stronger associations with depression and quality of life. As regional factors and individual-level variables in patients with hypertension, lower gross regional personal income, fewer doctors at medical institutions, and lower rates of participation in volunteer activities presented stronger associations with depression and quality of life. In addition, the associations of depression with gross regional personal income, the number of doctors at medical institutions, and dietary conditions were significantly stronger in patients with hypertension than in patients without hypertension. The associations of gender and employment status with quality of life were also significantly greater. Conclusions: Policy interventions are needed to adjust health behaviors, prevent depression, and improve quality of life for patients with hypertension, especially for those with the risk factors identified in this study.
Purpose: This study aimed to identify individual- and regional-level factors associated with perceived good health and multimorbidity among older adults. Methods: Secondary analysis of the 2017 Korea Community Health Survey was conducted on a sample of 67,532 older adults. The individual level data set was combined with regional-level factors from the administrative data released on the Korea National Statistical Office website. Distribution of perceived good health and multimorbidity in 254 public health centers were calculated using sampling weights and presented as percentages. Multilevel logistic regression analyses were used to identify individual- and regional-level factors associated with perceived good health and multimorbidity. Results: Overall, 21.1% of subjects perceived their health to be good, ranging from 9.3% to 39.4% by region. The prevalence of multimorbidity was 15.9%, and varied between 6.6% and 22.6% by region. At the individual level, perceived good health was associated with men, younger age, higher educational levels, higher income levels, and those married and living with a partner and maintaining a healthy lifestyle. At the regional level, higher rates of health center personnel among public officials and higher levels of financial independence were associated with perceived good health. Multimorbidity was associated with marital status and healthy lifestyle, and higher rates of health center personnel among public officials. Conclusion: Regional factors such as health care personnel and local economy could affect population health. Our findings suggest the need to consider individual- and regional-level factors to promote good health among older adults and reduce the health gap by region.
Background: This study purposed to analyze regional factors related to gastric cancer screening rate provided by national cancer screening program in Korea. Methods: The unit of analysis was administrative districts of si gun gu level. Dependent variable was regional gastric cancer screening rate provided by national cancer screening program, and regional variables were selected to represent the regional characteristics such as demographic, health behavior and status, socioeconomic, and health resource. Tobit regression was applied for the analysis. Results: Analysis results showed that gastric cancer screening rate was varied depending on regions from 47.8% to 69.1%. Tobit regression showed that gastric cancer screening rate had negative relationships with smoking rate, financial independence rate, and National Health Insurance premium per capita. And regional gastric cancer screening rate had positive relationships with sex ratio and number of gastric cancer screening center. Conclusion: Regional characteristics should be considered in establishing regional policies for increasing the gastric cancer screening rate.
Background: This study aims to figure out the gaps in health status by estimating amenable mortality rate by region, reflecting the characteristics of Korea, and estimating the years of life lost (YLL) per capita by disease. Methods: People who died from amenable diseases between 2008 and 2018 were extracted from the cause of death statistics provided by Statistics Korea. The age-standardized amenable mortality rates were estimated to compare the health status of 229 regions. YLL per capita was calculated to compute the burden of diseases caused by treatable deaths by region. The YLL per capita by region was calculated to identify the burden of disease caused by amenable deaths. Results: First, while the annual amenable mortality rate in Korea is on a steady decline, but there is still a considerable gap between urban and rural areas when comparing the mortality rates of 229 areas. Second, YLL per capita due to the amenable deaths is approximately 14 person-years during the analysis period (2008-2018). Conclusion: Although the health status of Koreans has continuously improved, there is still a gap in health status region by region in terms of amenable mortality rates. Amenable death accounts for a loss of life equivalent to 14 person-years per year. Since the amenable mortality rate is an indicator that can measure the performance of the health care system, efforts at each local area are required to lower it.
Nan-He Yoon;Sunghun Yun;Dongmin Seo;Yoon Kim;Hongsoo Kim
Health Policy and Management
/
v.33
no.4
/
pp.479-488
/
2023
Background: By applying the suggested criteria for needs-based chronic medical care and long-term care delivery system for the elderly, the current status of delivery system was identified and regional delivery systems were categorized according to quantity and quality of delivery system. Methods: National claims data were used for this study. All claims data of medical and long-term care uses by the elderly and all claims data from long-term care hospitals and nursing homes in 2016 were analyzed to categorize the regional medical and long-term care delivery system. The current status of the delivery system with a high possibility of transition to a needs-based appropriate delivery system was identified. The necessary and actual amount of regional supply was calculated based on their needs, and the structure of delivery systems was evaluated in terms of the needs-based quality of the system. Finally, all regions were categorized into 15 types of medical and care delivery systems for the elderly. Results: Of the total 55 regions, 89.1% of regions had an oversupply of elderly medical and care services compared to the necessary supply based on their needs. However, 69.1% of regions met the criteria for less than two types of needs groups, and 21.8% of regions were identified as regions where the numbers of institutions or regions with a high possibility of transition to an appropriate delivery system were below the average levels for all four needs groups. Conclusion: In order to establish an appropriate community-based integrated elderly care system, it is necessary to analyze the characteristics of the regional delivery system categories and to plan a needs-based delivery system regionally.
Objectives: To examine whether the socioeconomic characteristics of communities (contextual effects) are related to the self-rated health of community residents after controlling individual characteristics (compositional effects). Methods: A linked data set including information on individuals from raw data of 1998 Korean National Health and Nutrition Survey(KNHNS) and information on the regions where the individuals lived from the socioeconomic statistical indices of Si-Gun-Gu (city-county-ward) in 1998 was established. The contextual factors of communities were generated from these socioeconomic indices through factor analysis. The contextual effects of community over and above the individual characteristics on the self-rated health were investigated using multilevel analysis. Results: The contextual factors of the community expressed as the factor scores have influence on the self-rated health of their residents above the compositional factors. When the communities were categorized into 5 groups (highest, high, middle, low, lowest) according to each of their factor scores, for factor 1 reflecting urbanization reversely, the residents of the communities that had the high, middle, and low factor scores showed significantly poor subjective health status than the residents of the lowest (most urbanized) group. For factor 2 reflecting community services and health resources, the subjective health status of the residents gradually became poorer when the group went from the highest to the lowest, and the low and lowest groups showed a significant difference. For factor 3 reflecting the manufacturing industry, as compared with the communities that have the highest factor scores, the other 4 groups showed significantly poorer subjective health status. And for factor 4 reflecting the scale of the regional government, as compared with the middle group, the rest of the 4 groups showed significantly better self-rated health. Conclusions: There existed regional contextual effects on their residents' health in Korean adults. To make policies tackling these contextual effects possible, more elaborate researches to find more specific factors and to explain the mechanisms of how health is influenced by the contextual factors are needed.
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