Juyeon Lee1, Jinwook Bahk2, Ikhan Kim3, Yeon-Yong Kim4, Sung-Cheol Yun5, Hee-Yeon Kang6, Jeehye Lee3, Jong Heon Park4, Soon-Ae Shin7, Young-Ho Khang8. 1. People's Health Institute, Dongjak-gu, Seoul 07004, Republic of Korea. 2. Department of Public Health, Keimyung University, Daegu 42601, Republic of Korea; Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul 03080, Republic of Korea. 3. Department of Health Policy and Management, Seoul National University College of Medicine, Seoul 03080, Republic of Korea. 4. Big Data Steering Department, National Health Insurance Service, Wonju-si, Gangwon-do 26464, Republic of Korea. 5. Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, Seoul 05505, Republic of Korea. 6. Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul 03080, Republic of Korea. 7. Big Data Steering Department, National Health Insurance Service, Wonju-si, Gangwon-do 26464, Republic of Korea; Gwanak Branch Office, National Health Insurance Service, Seoul 08761, Republic of Korea. 8. Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul 03080, Republic of Korea; Department of Health Policy and Management, Seoul National University College of Medicine, Seoul 03080, Republic of Korea. Electronic address: yhkhang@snu.ac.kr.
Abstract
BACKGROUND: Little is known about within-country variation in morbidity and mortality of cerebrovascular diseases (CVDs). Geographic differences in CVD morbidity and mortality have yet to be properly examined. This study examined geographic variation in morbidity and mortality of CVD, neighborhood factors for CVD morbidity and mortality, and the association between CVD morbidity and mortality across the 245 local districts in Korea during 2011-2015. METHODS: District-level health care utilization and mortality data were obtained to estimate age-standardized CVD morbidity and mortality. The bivariate Pearson correlation was used to examine the linear relationship between district-level CVD morbidity and mortality Z-scores. Simple linear regression and multivariate analyses were conducted to investigate the associations of area characteristics with CVD morbidity, mortality, and discrepancies between morbidity and mortality. RESULTS: Substantial variation was found in CVD morbidity and mortality across the country, with 1074.9 excess CVD inpatients and 73.8 excess CVD deaths per 100,000 between the districts with the lowest and highest CVD morbidity and mortality, respectively. Higher rates of CVD admissions and deaths were clustered in the noncapital regions. A moderate geographic correlation between CVD morbidity and mortality was found (Pearson correlation coefficient = .62 for both genders). Neighborhood level indicators for socioeconomic disadvantages, undersupply of health care resources, and unhealthy behaviors were positively associated with CVD morbidity and mortality and the relative standing of CVD mortality vis-à-vis morbidity. CONCLUSIONS: Policy actions targeting life-course socioeconomic conditions, equitable distribution of health care resources, and behavioral risk factors may help reduce geographic differences in CVD morbidity and mortality in Korea.
BACKGROUND: Little is known about within-country variation in morbidity and mortality of cerebrovascular diseases (CVDs). Geographic differences in CVD morbidity and mortality have yet to be properly examined. This study examined geographic variation in morbidity and mortality of CVD, neighborhood factors for CVD morbidity and mortality, and the association between CVD morbidity and mortality across the 245 local districts in Korea during 2011-2015. METHODS: District-level health care utilization and mortality data were obtained to estimate age-standardized CVD morbidity and mortality. The bivariate Pearson correlation was used to examine the linear relationship between district-level CVD morbidity and mortality Z-scores. Simple linear regression and multivariate analyses were conducted to investigate the associations of area characteristics with CVD morbidity, mortality, and discrepancies between morbidity and mortality. RESULTS: Substantial variation was found in CVD morbidity and mortality across the country, with 1074.9 excess CVD inpatients and 73.8 excess CVD deaths per 100,000 between the districts with the lowest and highest CVD morbidity and mortality, respectively. Higher rates of CVD admissions and deaths were clustered in the noncapital regions. A moderate geographic correlation between CVD morbidity and mortality was found (Pearson correlation coefficient = .62 for both genders). Neighborhood level indicators for socioeconomic disadvantages, undersupply of health care resources, and unhealthy behaviors were positively associated with CVD morbidity and mortality and the relative standing of CVD mortality vis-à-vis morbidity. CONCLUSIONS: Policy actions targeting life-course socioeconomic conditions, equitable distribution of health care resources, and behavioral risk factors may help reduce geographic differences in CVD morbidity and mortality in Korea.
Keywords:
CVD morbidity; CVD mortality; South Korea; behavioral risk factors; geographic variation; health care resource; neighborhood socioeconomic status