The purpose of this study is to examine the regional particulate matter health effects from the ecological point of view and to provide the basis for establishing measures for regional adaptation to particulate matter health effects. The health effects of regional particulate matter were divided into deaths due to respiratory diseases, cardiovascular diseases, cerebrovascular diseases and total deaths in municipal levels. The results showed that the risk death due to respiratory diseases in Gyeongbuk, Chungbuk, Gangwon and Jeonbuk areas was higher than the national average. The risk of death due to cerebrovascular diseases in some areas such as Jeonbuk and Gyeongbuk areas was higher than the national average. The total mortality in the Jeonnam, Gyeongnam, Gyeongbuk, and Chungbuk provinces was higher than the national average, indicating that the risk of disease death varies according to the particulate matter-related disease. As a result of examining the factors affecting the death of particulate matter-related diseases by region, unlike previous studies, respiratory disease mortality tended to decrease with increase of PM10 concentration, and disease mortality in cardiovascular disease was not affected by PM10. However, cerebrovascular disease mortality was associated with higher PM10 concentration. In PM2.5, respiratory disease mortality tended to decrease with increasing PM2.5 concentration as in PM10, but in case of cardiovascular disease, cerebrovascular disease death, and total death, disease mortality was increased with increasing PM2.5 concentration, indicating that the particulate matter health effect was greater at PM2.5 than PM10. In addition, it was confirmed that the health effects of particulate matter may be different depending on the source of particulate matter in each region. This study suggests that it is necessary to establish a countermeasure for the adaptation of particulate matter effect to the region considering the health effects of particulate matter, regional particulate matter emission sources and emissions.
Machine learning is a subfield of the growing research on artificial intelligence (AI). Specifically, it focuses on the development of algorithms and technology that enable computers to learn independently using data. Machine learning, widely regarded as instrumental in advancements in image processing, video and voice recognition, and Internet searches, has proven to be quite effective as a tool for anomaly prediction and detection. Anomaly detection refers to the process by which one finds instances or data, out of a given pool, that diverge from expected patterns. In this study, we define the concept of anomaly detection, after which we apply the anomaly detection methodology to the given sets of health and welfare policy data to perform an exploratory analysis. We discuss the issues involved in the application of anomaly detection and summarize the policy implications. The data subjected to our analysis include the fludeoxyglucose positron emission tomography (FDG-PET) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), regarding health, and the results of the Elderly Survey 2017.
Using twelve waves of data from the Korean Welfare Panel Study (2006-2017), we evaluate mechanisms linking employment type to various health outcomes, including depression, self-esteem, and self-rated health with a focus on differences between standard and other types of employment. Guided by prior research, we examine several mechanisms such as economic insecurity and psychosocial stressors, using fixed-effects models that control for unobserved time-invariant individual heterogeneity. Our findings confirm the importance of selection in that much of the association between employment type and health observed in simple cross-sectional OLS models loses significance in fixed-effects models. We also find supporting evidence for the mediating role of economic insecurity and psychosocial stressors. For instance, the lower levels of satisfaction in job and life conditions help explain lower self-esteem of male nonstandard workers relative to standard workers. It is also interesting that the hypothesized mediators often suppress the relationship between employment status and health in which a significant relationship is revealed only when the specific mediator is taken into account. Findings of this study will shed valuable insights on the pathways in which specific employment types affect men’s and women’s health outcomes.
The National Health Insurance has been expanding its coverage with a view to helping households ease their economic burden of illness. Concerns remain, however, that social health safety nets as they stand do not sufficiently prevent impoverishment due to illness.
The 23rd Global Social Security Forum: The Challenges and Role of Poverty Policy: Brazil and the UK's Experience in Focus11 December 2019 - 1:00 p.m. to 5:30 p.m.Brahams Hall(19th Floor), Hotel President, SeoulHosted by Sejong Welfare Foundation and the Korea Institute for Health and Social Affairs Program 13:00-13:20 Registration 13:20-14:00 Presentation: Poverty and Anti-Poverty Policy in the UK, David Gordon, Professor, University of Bristol 14:00-14:30 Presentation: The Impact of Universal Credit on the Debts of the Poor in the UK, Rod Hick, Professor, Cardiff University 14:30-15:00 Discussion Moderator: Moon Jin-Young, Professor, Sogang University Discussants: Kim, Ki-tae, Associate Research Fellow, KIHASA; Jung Yun-Tae, Senior Research Fellow, Sejong Welfare Foundation; Choi Young Jun, Professor, Yonsei University 15:00-15:30 Coffee Break 15:30-16:00 Presentation: Brizilian Poverty Policy, Ana Paula Matias, Directory Technical Advisor, National Secretariat for the Promotion of Human Development 16:00-16:30 Presentation: Social Protection and Family Benefit in Brazil, Joana Mostafa, Research Specialist, Institute for Applied Economic Research of the Federal Government of Brazil 16:30-17:00 Presentation: History of Social Security in Brazil, Martene Santos, Professor, University of Brasilia 17:00-17:30 Discussion Moderator: Lee Chang Gon, President, Hankyoreh Economy & Society Research Institute Discussants: Kwak Yoonkyung, Associate Research Fellow, KIHASA; Won Il, Research Fellow, Sejong Weflare Foundation; Lee Jeong-Hee, Research Fellow, Korea Labor Institute
The 5th Inclusive Welfare Forum: Income Distribution Trends and the Challenges Facing the Inclusive StateNoori Ballroom II(6th Floor), Four Seasons Hotel, SeoulHoted by the Korea Institute for Health and Social Affairs Program 10:00-10:30 Registration 10:30-12:00 Morning Session Moderator: Nam Chan Seop, Vice Chair, Korea Association of Social Welfare Policy Presentation: Income Distribution Trends in Japan and Their Implications for Korea, Lee Kang Kuk, Professor, Ritsumeikan University Presentation: Income Distribution Trends and the Directions of Redistribution Policy, Choo Byung Ki, Professor, Seoul National University Discussants: Lee Seung Yoon, Professor, Ewha Womans University; Lee Won Jin, Associate Research Fellow, KIHASA; Choi Yoo Seok, Professor, Hallim University 12:00-13:30 Lunch 13:30-14:00 Opening Remarks: Cho Heung-seek, President, KIHASA Congratulatory Remarks: Park Neung-hoo, Minister of Health and Welfare; Kim Yeon Myung, Chief Social Policy Advisor, Office of the President 14:00-14:50 Keynote Speech Conditions for Sustainable Inclusive Policy: the Nordic Model, Choi Yeon Hyuk, Professor, Linnaeus University, Sweden 14:50-15:10 Coffee Break 15:10-16:40 Afternoon Session Moderator: Seok Jae Eun, Chair, Korea Association of Social Welfare Policy Presentation: Inequality in the UK: the Limitations of the Selective Welfare State, Jeong Hee Jeong, Kent University, UK Presentation: Diagnosing Innovative Inclusive State: the Future Direction of Social Policy , Choi Young Joon, Yonsei University Discussants: Choi Han Soo, Professor, Kyungbook National University; Kim Ki Tae, Associate Research Fellow, KIHASA, Kim Jin Wook, Professor, Seogang University 16:40-16:50Coffee Break 16:50-18:00Wrap-up Discussion