Bahk J, et al. J Korean Med Sci. 2017 Nov;32(11):1764-1770. https://doi.org/10.3346/jkms.2017.32.11.1764

Jung K, et al. J Korean Med Sci. 2019 Feb;34(8):e65. https://doi.org/10.3346/jkms.2019.34.e65

Background: The aim of this study was to measure differences in quality-adjusted life expectancy (QALE) by income in Korea at the national and district levels. Methods: Mortality rates and EuroQol-5D (EQ-5D) scores were obtained from the National Health Information Database of the National Health Insurance Service and the Korea Community Health Survey, respectively. QALE and differences in QALE among income quintiles were calculated using combined 2008–2014 data for 245 districts in Korea. Correlation analyses were conducted to investigate the associations of neighborhood characteristics with QALE and income gaps therein. Results: QALE showed a graded pattern of inequality according to income, and increased over time for all levels of income and in both sexes, except for low-income quintiles among women, resulting in a widened inequality in QALE among women. In all 245 districts, pro-rich inequalities in QALE were found in both men and women. Districts with higher QALE and smaller income gaps in QALE were concentrated in metropolitan areas, while districts with lower QALE and larger income gaps in QALE were found in rural areas. QALE and differences in QALE by income showed relatively close correlations with socioeconomic characteristics, but relatively weak correlations with health behaviors, except for smoking and indicators related to medical resources. Conclusions: This study provides evidence of income-based inequalities in health measured by QALE in all subnational areas in Korea. Furthermore, QALE and differences in QALE by income were closely associated with neighborhood-level socioeconomic characteristics.

The aim of this study was to measure income differences in smoking prevalence at the district level and to investigate correlations among area deprivation, smoking prevalence, and income differences in smoking prevalence, stratified by urbanity. Data were pooled from the Community Health Survey data of South Korea between 2008 and 2014. The age-standardized prevalence of smoking and its interquintile income differences were calculated. We conducted correlation analyses to investigate the association of the deprivation index with smoking prevalence and interquintile differences in smoking prevalence. Across 245 districts, the median prevalence of smoking in men was 45.9% (95% confidence interval [CI], 43.4 to 48.5%), with an interquartile range (IQR) of 4.6% points. In women, the median prevalence was 3.0% (95% CI, 2.4 to 3.6%) and IQR was 1.6% points. The median interquintile difference in smoking prevalence was 7.4% points (95% CI, 1.6 to 13.2% points) in men and 2.7% points (95% CI, 0.5 to 4.9% points) in women. The correlation coefficients for the association between the deprivation index and smoking prevalence was 0.58, 0.15, -0.22 in metropolitan, urban, and rural areas, respectively, among men, and 0.54, -0.33, -0.43 among women. No meaningful correlation was found between area deprivation and interquintile difference in smoking prevalence. The correlation between smoking prevalence and interquintile difference in smoking prevalence was more evident in women than in men. This study provides evidence of geographical variations in smoking prevalence and interquintile difference in smoking prevalence. Neither smoking prevalence nor the deprivation index was closely correlated with interquintile income difference in smoking prevalence. Measuring inequalities in smoking prevalence is crucial to developing policies aimed at reducing inequalities in smoking.

Objectives The difference between income quintiles in health is relatively well accepted by the general public as a measure of health inequality. However, the slope index of inequality (SII) in health reflects the patterns of all social groups, including the middle 60%, and it could therefore be considered more academically desirable. If these two measures are closely correlated, the widespread use of the difference between income quintiles in health would be better supported. This study was conducted to compare differences between income quintiles in life expectancy (LE) and healthy life expectancy (HLE) with the SII. Design Cross-sectional comparison using correlational analysis of district level income differences in LE and HLE with associated SII. Setting All 252 subnational districts of Korea. Participants A total of 342 439 895 subjects (171 287 729 men, 171 152 166 women) and 1 753 476 deaths (970 928 men, 782 548 women) between 2008 and 2014 were analysed. Primary and secondary outcome measures Difference in LE and HLE by income quintile and associated SII. Results The Pearson correlation coefficients between differences between income quintiles and the SII were generally high: 0.97 (95% CI 0.96 to 0.98) for LE in men and women combined and 0.96 (95% CI 0.94 to 0.97) for HLE in men and women combined. In most districts, the SII was greater than the difference between income quintiles. Conclusion Differences between income quintiles were closely correlated with the SII. The widespread use of differences between income quintiles in health as a measure of health inequality may be preferable for communicating results of health inequality measurements to the public.

Background: This study aimed to compare three small-area level mortality metrics according to urbanity in Korea: the standardized mortality ratio (SMR), comparative mortality figure (CMF), and life expectancy (LE) by urbanity. Methods: We utilized the National Health Information Database to obtain annual small-area level age-specific numbers of population and deaths in Korea between 2013 and 2017. First, differences in the SMR by urbanity were examined, assuming the same age-specific mortality rates in all small areas. Second, we explored the differences in ranking obtained using the three metrics (SMR, CMF, and LE). Third, the ratio of CMF to SMR by population was analyzed according to urbanity. Results: We found that the age-specific population distributions in urbanized areas were similar, but rural areas had a relatively old population structure. The age-specific mortality ratio also differed by urbanity. Assuming the same rate of age-specific mortality across all small areas, we found that comparable median values in all areas. However, areas with a high SMR showed a strong predominance of metropolitan areas. The ranking by SMR differed markedly from the rankings by CMF and LE, especially in areas of high mortality, while the latter two metrics did not differ notably. The ratio of CMF to SMR showed larger variations in small areas in rural areas, particularly in those with small populations, than in metropolitan and urban areas. Conclusions: In a comparison of multiple SMRs, bias could exist if the study areas have large differences in population structure. The use of CMF or LE should be considered for comparisons if it is possible to acquire age-specific mortality data for each small area.