Journal of Preventive Medicine and Public Health

Brief Report

Life Expectancy and Inequalities Therein by Income From 2016 to 2018 Across the 253 Electoral Constituencies of the National Assembly of the Korea: Jinwook Bahk, Hee-Yeon Kang, Young-Ho Khang; J Prev Med Public Health. 2020;53(2):143-148. Published online March 5, 2020; DOI: https://doi.org/10.3961/jpmph.20.050

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Abstract Summary PDF Supplementary Material

Objectives
We calculated life expectancy and inequalities therein by income for the period of 2016-2018 across the 253 electoral constituencies of the 20th National Assembly election in Korea.
Methods
We obtained population and death data between 2016 and 2018 from the National Health Information Database and constructed abridged life tables using standard life table procedures according to gender and income quintiles for the electoral constituencies of the 20th National Assembly election held in 2016.
Results
Life expectancy across the 253 constituencies ranged from 80.51 years to 87.05 years, corresponding to a gap of 6.54 years. The life expectancy difference by income across the 253 constituencies ranged from 2.94 years to 10.67 years. In each province, the difference in life expectancy by income across electoral constituencies was generally greater than the inter-constituency differences. Constituencies in capital and metropolitan areas showed a higher life expectancy and a lower life expectancy difference by income than constituencies in rural areas.
Conclusions
Pro-rich inequalities in life expectancy by income existed in every electoral constituency in Korea. Both intra-constituency and inter-constituency socioeconomic inequalities in health should be highlighted in future policy-making in the National Assembly.

Summary

Korean summary
이 연구는 2016년 치뤄진 제20대 국회의원 선거구 253개에서 2016-2018년의 기대수명을 산출하고, 소득 상위 20%와 소득 하위 20% 간 기대수명 격차를 제시하였다. 연구 결과, 253개 모든 선거구에서 소득 상위 20%가 소득 하위 20%보다 기대수명이 높게 나타났으며, 선거구 내의 소득에 따른 기대수명 격차가 선거구들 간의 기대수명 차이보다 더 큰 경향을 보였다. 기대수명의 선거구 간 격차를 줄이고, 선거구 내의 소득 계층 간 기대수명 격차를 해소하기 위한 입법적 조치를 마련하는 데에 이 연구의 결과가 근거자료로 유용하게 사용되기를 기대한다.

Citations

Citations to this article as recorded by

Spatio-temporal Analysis of District-level Life Expectancy from 2004 to 2017 in Korea
Hwa-Kyung Lim, Hee-Yeon Kang, Ikhan Kim, Young-Ho Khang
Journal of Korean Medical Science.2021;[Epub] CrossRef
Cancer-free Life Expectancy in Small Administrative Areas in South Korea and Its Associations with Regional Health Insurance Premiums
Eunjeong Noh, Hee-Yeon Kang, Jinwook Bahk, Ikhan Kim, Young-Ho Khang
Journal of Korean Medical Science.2021;[Epub] CrossRef
Income-Related Mortality Inequalities and Its Social Factors among Middle-Aged and Older Adults at the District Level in Aging Seoul: An Ecological Study Using Administrative Big Data
Minhye Kim, Suzin You, Jong-sung You, Seung-Yun Kim, Jong Heon Park
International Journal of Environmental Research and Public Health.2021; 19(1): 383. CrossRef

Original Article

Income Differences in Smoking Prevalences in 245 Districts of South Korea: Patterns by Area Deprivation and Urbanity, 2008-2014: Ikhan Kim, Jinwook Bahk, Tae-Ho Yoon, Sung-Cheol Yun, Young-Ho Khang; J Prev Med Public Health. 2017;50(2):100-126. Published online February 9, 2017; DOI: https://doi.org/10.3961/jpmph.16.069

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Abstract PDF Supplementary Material

Objectives
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.
Methods
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.
Results
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.
Conclusions
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.

Summary

Citations

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Life Expectancy in Areas around Subway Stations in the Seoul Metropolitan Area in Korea, 2008–2017
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SSRN Electronic Journal.2020;[Epub] CrossRef
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English Abstracts

Changes in Mortality Inequality in Relation to the South Korean Economic Crisis: Use of Area-based Socioeconomic Position.: Young Ho Khang, Sung Cheol Yun, In A Hwang, Moo Song Lee, Sang Il Lee, Min Woo Jo, Min Jung Lee; J Prev Med Public Health. 2005;38(3):359-365.

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Abstract PDF: OBJECTIVE
An abrupt economic decline may widen the socioeconomic differences in health between the advantaged and disadvantaged in a society. The aim of this study was to examine whether the South Korean economic crisis of 1997-98 affected the socioeconomic inequality from all-causes and from cause-specific mortality between 1995 and 2001. METHODS: Population denominators were obtained from the registration population data, with the number of death (numerators) calculated from raw death certificate data. The indicator used to assess the geographic socioeconomic position was the per capita regional tax revenue. Administrative districts (Si-Gun-Gu) were ranked according to this socioeconomic measure, and divided into equal population size quintiles on the basis of this ranking. The sex- and 5-year age-specific numbers of the population and deaths were used to compute the sex- and age-adjusted mortality rates (via direct standardization method), standardized mortality ratios (via indirect standardization methods) and relative indices of inequality (RII) (via Poisson regression). RESULTS: Geographic inequalities from all-causes of mortality, as measured by RII, did not increase as a result of the economic crisis (from 1998-2001). This was true for both sexes and all age groups. However, the cause-specific analyses showed that socioeconomic inequalities in mortalities from external causes were affected by South Korean economic crisis. For males, the RIIs for mortalities from transport accidents and intentional self-harm increased between 1995 and 2001. For females, the RII for mortality from intentional self-harm increased during the same period. CONCLUSIONS: The South Korean economic crisis widened the geographic inequality in mortalities from major external causes. This increased inequality requires social discourse and counter policies with respect to the rising health inequalities in the South Korean society.; Summary

Census Population vs. Registration Population: Which Population Denominator Should be used to Calculate Geographical Mortality.: Young Ho Khang, In A Hwang, Sung Cheol Yun, Moo Song Lee, Sang Il Lee, Min Woo Jo, Min Jung Lee; J Prev Med Public Health. 2005;38(2):147-153.

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Abstract PDF: OBJECTIVES
Studies on the geographical differences in mortality tend to use a census population, rather than a registration population, as the denominator of mortality rates in South Korea. However, an administratively determined registration population would be the logical denominator, as the geographical areas for death certificates (numerator) have been determined by the administratively registered residence of the deceased, rather than the actual residence at the time of death. The purpose of this study was to examine the differences in the total number of a district population, and the associated district-specific mortality indicators, when two different measures as a population denominator (census and registration) were used. METHODS: Population denominators were obtained from census and registration population data, and the numbers of deaths (numerators) were calculated from raw death certificate data. Sex- and 5-year age-specific numbers for the populations and deaths were used to compute sex- and age-standardized mortality rates (by direct standardization methods) and standardized mortality ratios (by indirect standardization methods). Bland-Altman tests were used to compare district populations and district-specific mortality indicators according to the two different population denominators. RESULTS: In 1995, 9 of 232 (3.9%) districts were not included in the 95% confidence interval (CI) of the population differences. A total of 8 (3.4%) among 234 districts had large differences between their census and registration populations in 2000, which exceeded the 95% CI of the population differences. Most districts (13 of 17) exceeding the 95% CI were rural. The results of the sexand age-standardized mortality rates showed 15 (6.5%) and 16 (6.8%) districts in 1995 and 2000, respectively, were not included in the 95% CI of the differences in their rates. In addition, the differences in the standardized mortality ratios using the two different population denominators were significantly greater among 14 districts in 1995 and 11 districts in 2002 than the 95% CI. Geographical variations in the mortality indicators, using a registration population, were greater than when using a census population. CONCLUSION: The use of census population denominators may provide biased geographical mortality indicators. The geographical mortality rates when using registration population denominators are logical, but do not necessarily represent the exact mortality rate of a certain district. The removal of districts with large differences between their census and registration populations or associated mortality indicators should be considered to monitor geographical mortality rates in South Korea.; Summary

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