Browse > Article
http://dx.doi.org/10.14400/JDC.2015.13.8.419

Convergence analysis for geographic variations and risk factors in the prevalence of hyperlipidemia using measures of Korean Community Health Survey  

Kim, Yoo-Mi (Dept. of Health Policy and Management, Sangji University)
Kang, Sung-Hong (Dept. of Health Policy and Management, Inje University)
Publication Information
Journal of Digital Convergence / v.13, no.8, 2015 , pp. 419-429 More about this Journal
Abstract
We investigate how the regional prevalence of hyperlipidemia is affected by health-related and socioeconomic factors with a special emphasis on geographic variations. We focus on the likelihood of hyperlipidemia as function of various region-specific attributes. We analysis a data set at the level of 249 small administrative districts collected from 2012 Korean Community Health Survey by Korea Centers for Disease Control and Prevention. To estimate, we use several methods including correlation analysis, multiple regression and decision tree model. We find that the average prevalence of hyperlipidemia in 249 small districts is 9.6% and its coefficient of variation is 28.3%. Prevalence of hyperlipidemia in continental and capital regions is higher than in southeast coastal regions. Further findings using decision tree model suggest that variations of hyperlipidemia prevalence between regions is more likely to be associated with rate of employee, level of stress, prevalence of hypertension, angina pectoris, and osteoarthritis in their regions.
Keywords
Prevalence of arthritis; Korean Community Health Survey; Social epidemiology; Decision tree; Convergence analysis;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Korean Society of Lipidology and Atherosclerosis, Treatment guidelines for dyslipidemia, Seoul: Korean Society of Lipidology and Atherosclerosis, 2009. (Korean)
2 L. F. Berkman, I. Kawachi, M. M. Glymour, eds., Social Epidemiology, Second edition, New York, NY: Oxford University Press, 2014.
3 Ministry of Health and Welfare, Korea Centers for Disease Control, Korean Community Health Statistics at a glance 2008-2012, Osong: Korea Centers for Disease Control, 2013. (Korean)
4 Y. Liu, G. Salvendy, Visualization support to better comprehend and improve decision tree classification modelling process: A survey and appraisal, Theoretical Issues in Ergonomics Science, Vol. 8, No. 1, pp. 63-92, 2007.   DOI   ScienceOn
5 Expert Panel on Detection Evaluation and Treatment of High Blood Cholesterol in Adults, Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III), JAMA, Vol. 285, pp. 2486-97, 2001.   DOI   ScienceOn
6 R. Alan, Hyperlipidemia, NYU Langone Medical Center, 2014 EBSCO Publishing. http://www.med.nyu.edu/content?ChunkIID=11767, December 15, 2014.
7 Medscape, Hypertriglyceridemia, http://www.medscape.com/, December 14, 2014.
8 M. Marmot, M. J. Shipley, Do socioeconomic differences in mortality persist after retirement? 25 year follow up of civil servants from the first Whitehall study, BMJ, Vol. 313, pp. 1177-80, 1996.   DOI
9 N. Yoshimura, S. Muraki, H. Oka, S. Tanaka, H. Kawaguchi, K. Nakamura, et al., Mutual associations among musculoskeletal diseases and metabolic syndrome components: A 3-year follow-up of the ROAD study, Mod Rheumatol, Vol. 20, pp. 1-11, 2014. [Epub ahead of print]
10 I. Navarro-Millan, S. Yang, S. L. DuVall, L. Chen, J. Baddley, G. W. Cannon, et al., Association of hyperlipidaemia, inflammation and serological status and coronary heart disease among patients with rheumatoid arthritis: data from the National Veterans Health Administration, Ann Rheum Dis, 2015. [Epub ahead of print]
11 Y. K. Sung, S. K. Cho, C. B. Choi, S. Y. Park, J. Shim, J. K. Ahn, et al., Korean Observational Study Network for Arthritis (KORONA): establishment of a prospective multicenter cohort for rheumatoid arthritis in South Korea. Semin Arthritis Rheum, Vol. 41, No. 6, pp. 745-51, 2012.   DOI   ScienceOn
12 M. Marmot, Multilevel Approaches to Understanding Social Determinants. In Social Epidemiology, Edited by Berkman and Kawachi: Oxford University Press, 2000.
13 B. Whalley, D. R. Thompson, R. S. Taylor, Psychological interventions for coronary heart disease: cochrane systematic review and meta-analysis. Int J Behav Med, Vol. 21, No. 1, pp. 109-21, 2014.   DOI
14 Ministry of Labor, Community education mannual of cerebral and cardiovascualr disease prevention, Seoul: Minstry of Labor, 2009. (Korean)
15 G. Kwon, D. Lim, E. Park, J. Jung, K. Kang, Y. Kim, H. Kim, S. Cho, Assessment of applicability of standardized rates for health state comparision among areas: 2008 community health survey, J of Perventive Medicine and Public Health, Vol. 43, No. 2, pp. 174-184, 2010. (Korean)
16 World Health Organization, Cardiovascular diseases(CVDs) fact sheet $N^{\circ}317$, http://www.who.int/mediacentre/factsheets/fs317/en/, February 1, 2015.
17 World Health Organization, Global status report on noncommunicable diseases 2010. Geneva: World Health Organization, 2011.
18 C. D. Mathers, D. Loncar, Projections of global mortality and burden of disease from 2002 to 2030, PLoS Med, Vol. 3, No. 11, e442, 2006.   DOI
19 Statistics Korea, Anual report of the causes of death statistics, Daejeon: Ministry of Statistics, 2014. (Korean)
20 R. H. Nelson, Hyperlipidemia as a risk factor for cardiovascular disease, Prim Care, Vol. 40, No. 1, pp. 195-211, 2013.   DOI
21 Ministry of Health and Welfare, Korea Centers for Disease Control, Korea health statistics 2012: Korea National Health and Nutrition Examination Survey (KNHANES V-3), Seoul: Ministry of Health and Welfare, 2013a. (Korean)
22 R. Ferrari, I. Ford, N. Greenlaw, J. C. Tardif, M. Tendera, H. Abergel, K. Fox, D. Hu, S. Shalnova, P. G. Steg, Geographical variations in the prevalence and management of cardiovascular risk factors in outpatients with CAD: data from the contemporary CLARIFY registry, Eur J Prev Cardiol., 2014. [Epub ahead of print]
23 OECD, Geographic variations in health care: What do we know and what can be done to improve health system performance? OECD Health Policy Studies, OECD Publishing, 2014.