Ikhan Kim; Hee-Yeon Kang; Young-Ho Khang

doi:10.3346/jkms.2020.35.e365

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Kim, Kang, and Khang: Life Expectancy in Areas around Subway Stations in the Seoul Metropolitan Area in Korea, 2008–2017

Original Article

Preventive & Social Medicine

Journal of Korean Medical Science 2020; 35(44): e365.

Published online: 7 October 2020

DOI: https://doi.org/10.3346/jkms.2020.35.e365

Life Expectancy in Areas around Subway Stations in the Seoul Metropolitan Area in Korea, 2008–2017

Ikhan Kim^1,^2,^*

, Hee-Yeon Kang²

, Young-Ho Khang^2,³

¹Department of Health Policy and Management, Jeju National University School of Medicine, Jeju, Korea.

²Department of Health Policy and Management, Seoul National University College of Medicine, Seoul, Korea.

³Institue of Health Policy and Management, Seoul National University Medical Research Center, Seoul, Korea.

Address for Correspondence: Young-Ho Khang, MD, PhD. Department of Health Policy and Management, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea. yhkhang@snu.ac.kr

Current address:

^*Present address: Department of Medical Humanities and Social Medicine, Kosin University College of Medicine, Busan, Korea

Received 21 June 2020 Accepted 27 August 2020

The Korean Academy of Medical Sciences

(open-access, https://creativecommons.org/licenses/by-nc/4.0/):

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

This study aimed to calculate life expectancy in the areas around 614 subway stations on 23 subway lines in the Seoul metropolitan area of Korea from 2008 to 2017.

Methods

We used the National Health Information Database provided by the National Health Insurance Service, which covers the whole population of Korea. The analysis was conducted on the level of the smallest administrative units within a 200-m radius of each subway station. Life expectancy was calculated by constructing an abridged life table using the number of population and deaths in each area and 5-year age groups (0, 1–4, …, 85+) during the whole study period.

Results

The median life expectancy in the areas around 614 subway stations was 82.9 years (interquartile range, 2.2 years; minimum, 77.6 years; maximum, 87.4 years). The life expectancy of areas around subway stations located in Seoul was higher than those in Incheon and Gyeonggi-do, but variation within the region was observed. Significant differences were observed between some adjacent subway stations. In Incheon and Gyeonggi-do, substantially higher life expectancy was found around subway stations in newly developed urban areas, and lower life expectancy was found in central Incheon and suburbs in Gyeonggi-do.

Conclusion

When using areas around subway stations as the unit of analysis, variation in life expectancy in the Seoul metropolitan area was observed. This approach may reduce the stigma associated with presenting health inequalities at the level of the smallest administrative units and foster public awareness of health inequalities.

Graphical Abstract

Keywords: Health Status, Health Status Disparities, Life Expectancy, Life Tables, Seoul, Republic of Korea

INTRODUCTION

Research on small area–level health indicators is becoming increasingly widespread. As part of the Global Burden of Disease Study, researchers are working to identify hotspots with high burdens of disease in low- and middle-income countries, including those in Africa.1 2 3 4 In the United States, life expectancy and mortality trends are reported on the level of census tracts, which are small areas that roughly correspond to neighborhoods.5 6 In the United Kingdom, the distribution of mortality indicators is also reported by electoral wards.7 8 In the Fair Society, Healthy Lives report issued by the government of England, life expectancy, and disease-free life expectancy were reported by small areas.9 Other high-income countries such as Australia, Canada, and the Netherlands also calculate health indicators by small area units.10 11 12 In Korea, variation in mortality indicators by the smallest administrative units (Dong, Eup, and Myeon) has also been reported.13 14 15

The magnitude of variation in health indicators by small area units tends to be larger than that of variation by larger area units, such as districts. Therefore, the distribution of health outcomes across small area units is highly illustrative of patterns of health inequalities within a district or region. Moreover, small area–level distributions provide useful information for determining priority areas for public health policies and programs by enabling policy-makers to identify vulnerable areas with poor health outcomes within a district.

However, when reporting health inequalities, the possibility of stigmatizing certain local areas increases as the unit of geographical analysis becomes smaller. Conscious of this possibility, researchers have utilized subway stations as geographical reference points. For instance, Cheshire reported life expectancy for various stations of the London Tube.16 In the United States, researchers have also reported life expectancy for cities organized according to transportation landmarks (https://www.rwjf.org/en/library/infographics/minneapolis-map.html). Discrepancies in life expectancy by subway station (or other transportation landmarks) provide valuable information for raising awareness about health inequalities. Michael Marmot,17 in his book Health Gap, described differences in health outcomes by exploring differences in life expectancy across stations of the London Tube in the United Kingdom and the Washington Metro in the United States. Due to rapid urbanization, most of the population in major high-income countries resides in urban settings, and a large proportion of these residents utilize subway systems. Therefore, information on differences in life expectancy by subway stations may be a way of communicating findings to the public in a more familiar and accessible manner than is possible using other geographical classifications, such as census tracts and zip codes.

This study aimed to present life expectancy in the areas around 614 subway stations in the greater Seoul metropolitan area using mortality metrics from Dong, Eup, and Myeon.

METHODS

Data and unit of analysis

This study used data from 2008 to 2017 in the National Health Information Database (NHID). The NHID is operated and managed by the National Health Insurance Service, the single, publicly funded health insurance system for all Korean citizens, and contains information on all citizens who are enrolled in national health insurance.18 Using the NHID eligibility database, mortality in the past 12 months on December 31 of each year from 2008 to 2017 was extracted by the smallest administrative unit, sex, and age group in 5-year intervals (0, 1–4, 5–9, …, 80–84, 85+).

The unit of analysis in this study was the subway station. Of the 24 subway lines in the Seoul metropolitan area as of January 2020, 23 subway lines and 614 stations were included in the study, with the sole exclusion of a line for magnetic levitation trains. After converting the addresses of subway stations into coordinates, the smallest administrative units within a 200-m radius of each station was assigned as the area of that station, as illustrated in Fig. 1.16 If the station was a transfer station with multiple addresses, the smallest administrative units within a 200-m radius of each location were merged together as the area of the station. For example, lines 2, 4, and 5 intersect at the Dongdaemun History & Culture Park station, and the station address for each line is different. In this case, the administrative units within a 200-m radius of each station address were jointly classified as the area of the Dongdaemun History & Culture Park station.

Fig. 1

The process of selecting small administrative units (dong, eup, and myeon) within a 200-m radius (blue circles) of subway stations (yellow dots).

Notes. Dong corresponds to metropolitan, Eup to urban, and Myeon to rural.

The total population and number of deaths by sex, age group, and subway station were calculated using the information on small administrative units from 2008 to 2017. In the case of Seoul, most of the information was used as 348 out of the total 420 small-areas belonged within a 200-m radius of the subway station. Data from 673 small-areas (62.2%) out of the total 1,082 small-areas were used in the case of further including Gyeonggi-do and Incheon.

Statistical analysis

Life expectancy was calculated by constructing an abridged life table using the number of population and deaths by subway station and 5-year age group.19 The adjustment coefficient was one-half of the range of the age group for all except the oldest (85 and older) group, in which the adjustment coefficient was the inverse of the mortality rate. The mortality rate was calculated using the total population and number of deaths by age group, and the probability of death and survival was calculated using adjustment coefficients and mortality rates.

{}_{n}{m_{x}} = \frac{{}_{n}{D_{x}}}{{{}_{n}N}_{x}}

{}_{n}{q_{x} = \frac{n \cdot {}_{n}{m_{x}}}{1 + (n - {}_{n}{a_{x}}) \cdot {}_{n}{m_{x}}}}

_np_x = 1 − _nq_x

Where _nm_x is the mortality rate from age x to x+n, _nN_x refers to the total population in the age group, _nD_x denotes the number of deaths in the age group, _nq_x indicates the probability of death in the age group, _na_x is the adjustment coefficient for the age group, and _np_x is the probability of survival in the age group.

The number of people left alive at age x (100,000 at age 0), the number of deaths in the time interval, the person-years lived in the time interval, and life expectancy at age 0 were calculated using the probability of survival in each age category. The following formulas were used to derive life expectancy:

l₀ =100,000, l_x+n = l_x·_np_x

_nd_x = l_x − l_x+n

_nL_x = n·l_x+n + _na_x·_nd_x

T_{x} = \sum_{a = x}^{\infty} {{}_{n}L}_{a}, e_{x}^{} = \frac{T_{x}}{l_{x}}

Where, l_x is the number of people left alive at age x, _nd_x denotes the number of deaths in the time interval, _nL_x indicates the person-years lived from age x to x+n, T_x is the total number of years lived from age x, and e_x is the life expectancy at age x.

When the number of deaths in a certain age group for the area around a subway station was 0, the number was not replaced.20 21 There was no subway station where the number of deaths was 0 in the age group of 85 and above. The 95% confidence intervals (CIs) of life expectancy were calculated through a Monte Carlo simulation using the probability of death in each age group.22 23 It was assumed that death incidence followed a binomial distribution.20 In total, 10,000 simulated probabilities were generated for each age group, and the average of the simulated probabilities was presented as the life expectancy for each subway station, using the 2.5th percentile as the lower limit and the 97.5th percentile as the upper limit of the 95% CI.13 In this study, we used the statistical software R 3.6.3 (https://www.r-project.org) and QGIS 3.12.1 (https://www.qgis.org) for the analysis.

Ethics statement

This study was approved by the Institutional Review Board (IRB) of Seoul National University Hospital (IRB No. E-1810-008-975) and the National Health Insurance Service of Korea (NHIS-2020-4-097). Informed consent was waived for this study because secondary data were used.

RESULTS

Based on the 2008–2017 NHID data, the median total population in the areas around the 614 subway stations in the Seoul metropolitan area was 547,093, with an interquartile range of 461,227, a minimum of 25,119, and a maximum of 4,565,877. The median number of deaths was 2,030, with an interquartile range of 1,765, a minimum of 236, and a maximum of 19,670.

Fig. 2 presents the distribution of life expectancy in the areas around the 614 subway stations. The median life expectancy was 82.9 years, which was found in the area around the Sinimun station (line 1) and 14 other stations, and was 1.0 year higher than the average life expectancy of 81.9 years in the entire sample over the same period. The interquartile range was 2.2 years (25th percentile: 81.8 years; 75th percentile: 84.0 years). The lowest life expectancy (77.6 years) was found around the Dongducheon station and Soyosan station on line 1, and the highest life expectancy (87.4 years) was found around the Sunae station on Bundang line. The difference between the highest and lowest life expectancies was 9.8 years.

Fig. 2

Histogram of life expectancy in the areas around subway stations in the Seoul metropolitan area: findings from the National Health Information Database, 2008–2017.

Fig. 3 illustrates life expectancy quintiles by subway station on a map. Fig. 3A shows Seoul, and high life expectancies can be observed in areas around the subway stations in the southeastern areas, Yeouido, Mok-dong, and the Han riverside area. Meanwhile, lower life expectancies were found extending from the Seoul station to the Cheongnyangni station and the Gasan Digital Complex station to the Geumcheon-gu Office station along line 1, as well as in some areas north and west of the Han River. A pattern of adjacent subway stations with large discrepancies in life expectancy was found. For example, the City Hall station, which is a transfer station between lines 1 and 2, had a life expectancy of 84.5 years, which was 3.5 years higher than the life expectancy of 81.0 years found for the Jongno 3-ga station, the adjacent transfer station between lines 1 and 3. The difference in life expectancy between the Singil station (82.2 years) and the Yeouido station (86.8 years) was especially large, at 4.6 years.

Fig. 3

Life expectancy in the areas around subway stations in the Seoul metropolitan area: findings from the National Health Information Database, 2008–2017. (A) Life expectancy around subway stations in Seoul. (B) Life expectancy around subway stations in the Seoul metropolitan area.

Fig. 3B shows the entire metropolitan area. Compared to Seoul, the outskirts of Incheon and Gyeonggi-do had relatively low life expectancies. Within Incheon, Yeonsu-gu, through which the Incheon line passes, had a higher life expectancy than other regions in Incheon. In Gyeonggi-do, Bundang-gu in Seongnam and Suji-gu and Giheung-gu in Yongin, through which the Bundang line, Shinbundang line, and EverLine pass, generally had higher life expectancies than other regions. Gwacheon and Dongan-gu in Anyang, and Gunpo (through which line 4 passes) and Ilsanseo-gu and Ilsandong-gu (through which line 3 and the Gyeungui line pass) demonstrated relatively high life expectancies. The life expectancy in the areas around 614 stations along 23 lines is presented in Supplementary Table 1.

A webpage was developed to visualize the life expectancy information by subway station (http://lives.subwaystation.kr). On this webpage, the life expectancy information is shown by subway line, and then the user can click on individual subway stations to see more granular data.

DISCUSSION

This study calculated life expectancy in the areas around 614 subway stations in the Seoul metropolitan area using data from the NHID from 2008 to 2017. The median life expectancy was 82.9 years, the maximum life expectancy was 87.4 years, and the minimum life expectancy was 77.6 years, meaning that the difference between the maximum and minimum life expectancies was 9.8 years. In Seoul, higher life expectancies were found around subway stations south of the Han River, in Yeouido and Mok-dong, and along the Han riverside area, while the life expectancy was lower in stations in midtown Seoul and north and west of the Han River. In some instances, adjacent stations showed large differences in life expectancy. Overall, Seoul had a higher life expectancy than Incheon and Gyeonggi-do. Within Incheon and Gyeonggi-do, variations in life expectancy by subway stations were found.

It is important to understand variation in health outcomes by geographic location in order to identify the socioecological determinants of health and to establish evidence-based policies. Extant research on regional discrepancies of health outcomes has primarily focused on differences between urban and rural areas or between cities in different regions or countries. However, relatively few studies have examined small area-level differences in a single region,24 25 although some studies based in Korea have attempted to calculate health outcomes by the smallest administrative units.14 15 26 Unlike those previous studies, this study selected subway stations as the unit of analysis in order to reduce the possibility of stigmatization that arises when administrative units are used as the unit of analysis.27 Moreover, in contrast to existing studies that used standardized mortality ratio as mortality metric, this study used life expectancy as an outcome. It is anticipated to be helpful for members of the general public and policymakers, as well as researchers, to understand differences in health outcomes in a straightforward manner.22 28 Moreover, the webpage developed as part of this study provides life expectancy information by the subway line and can be utilized by the general public, thereby helping to raise awareness about health inequalities.

Various factors determine the health outcomes of specific geographical areas.29 Previous studies have presented a wide range of contextual and compositional factors at the individual, regional, and national levels.25 30 31 In this study, compared to Incheon and Gyeonggi-do, the life expectancy near the subway stations located in Seoul was relatively higher. Even within Seoul, there was a large gap in life expectancy between areas. High life expectancy was observed in areas with high ground rent such as southeastern part of the Seoul (Gangnam area), Yeouido, and Mok-dong. In the case of Incheon and Gyeonggi-do, the life expectancies of subway stations located in areas newly developed such as Ilsan, Yongin, Bundang, and Song-do were higher than that of other subway stations. These results imply that socioeconomic factors can largely explain the spatial patterning of mortality levels in the Seoul metropolitan area. In deciding where to live, there is an evident tendency to spatial pattern according to socioeconomic position, and ground rent plays a mediating role.32 The subway station used in this study as a unit of analysis also works as a factor to increase the area's ground rent.32 In the Gangnam area and newly developed area which imitates it, people with high socioeconomic position reside due to the excellence of educational conditions, the increase of real estate value, the convenience of transportation, and the development of consumption and culture facilities.33 34 35 This region is not only excellent in the built environment but also showed protective health behaviors and social norms.36 37 Considering the health gap according to the socioeconomic position that exists in Korea,38 39 we suppose that the affinity of the population with high socioeconomic position and the urban development could jointly explain the spatial patterning of areal mortality level. Additional studies seeking to identify factors that explain the discrepancies in health outcomes across small administrative units in Korea are warranted.

This study has some limitations that should be noted. First, a relatively long period (10 years) was set as the study period. When calculating life expectancy in areas with a small population, several methods are available to obtain stable results, one of which is to combine results from multiple years. Although this method has the benefit of producing stable calculations, it is not sensitive for detecting longitudinal changes. Second, when obtaining the total population and number of deaths, no distinction was made between institutionalized and non-institutionalized individuals, even though institutionalization can influence the estimation of health outcomes (including the mortality rate) in small geographic units.40 Therefore if there are social welfare facilities or nursing homes in a certain area, worse health outcomes may be estimated relative to the actual level of health in the community. Third, the magnitude of the variation in life expectancy has decreased compared to that estimated when the administrative unit used as a unit of analysis directly because this study used the average life expectancy of the area around 200 m around the subway station. The median life expectancy using each subway station as an analysis unit in the Seoul metropolitan area was 82.9 years old, and the range was 9.8 years old (range, 77.6–87.4 years old). During the same period, the median life expectancy of each small area in the Seoul metropolitan area was 82.5 years old, and the range was 13.5 years old (range, 74.9–88.4 years old).

This study presents life expectancy in the areas around 614 subway stations in the Seoul metropolitan area using data from the NHID from 2008 to 2017. Variations in life expectancy across the subway stations were observed, and large differences in life expectancy between adjacent small areas were found. These findings are significant in that health outcomes by small administrative units in Korea were estimated using life expectancy, making it possible for policymakers and the general public—not only researchers—to understand the results in a straightforward manner. Moreover, by selecting subway stations as the unit of analysis, the study attempted to minimize the possibility of stigmatization on residents in specific administrative areas. The results of this study provide a more precise estimation of small area–level health outcomes that can serve as evidence for policy-making or public awareness initiatives. Future studies should investigate health outcomes by the smallest administrative units in Korea and identify determinants of health inequalities across small area units.

ACKNOWLEDGMENTS

We thank Drs. Yeon-Yong Kim and Jong Heon Park for their assistance and support in this research. We also thank Prof. Jinwook Bahk for her valuable lecture about calculating life expectancy.

Notes

Funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant No. HI18C0446).

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

Conceptualization: Khang YH.
Data curation: Kang HY.
Formal analysis: Kim I.
Methodology: Kim I, Khang YH.
Writing - original draft: Kim I, Khang YH.
Writing - review & editing: Khang YH.

References

1. Dwyer-Lindgren L, Cork MA, Sligar A, Steuben KM, Wilson KF, Provost NR, et al. Mapping HIV prevalence in sub-Saharan Africa between 2000 and 2017. Nature. 2019; 570(7760):189–193. PMID: 31092927.

2. Reiner RC, Welgan CA, Casey DC, Troeger CE, Baumann MM, Nguyen QP, et al. Identifying residual hotspots and mapping lower respiratory infection morbidity and mortality in African children from 2000 to 2017. Nat Microbiol. 2019; 4(12):2310–2318. PMID: 31570869.

3. Burstein R, Henry NJ, Collison ML, Marczak LB, Sligar A, Watson S, et al. Mapping 123 million neonatal, infant and child deaths between 2000 and 2017. Nature. 2019; 574(7778):353–358. PMID: 31619795.

4. Local Burden of Disease Child Growth Failure Collaborators. Mapping child growth failure across low- and middle-income countries. Nature. 2020; 577(7789):231–234. PMID: 31915393.

5. Dwyer-Lindgren L, Stubbs RW, Bertozzi-Villa A, Morozoff C, Callender C, Finegold SB, et al. Variation in life expectancy and mortality by cause among neighbourhoods in King County, WA, USA, 1990–2014: a census tract-level analysis for the Global Burden of Disease Study 2015. Lancet Public Health. 2017; 2(9):e400–10. PMID: 29253411.

6. Talbot TO, Done DH, Babcock GD. Calculating census tract-based life expectancy in New York state: a generalizable approach. Popul Health Metr. 2018; 16(1):1. PMID: 29373976.

7. Asaria P, Fortunato L, Fecht D, Tzoulaki I, Abellan JJ, Hambly P, et al. Trends and inequalities in cardiovascular disease mortality across 7932 English electoral wards, 1982–2006: Bayesian spatial analysis. Int J Epidemiol. 2012; 41(6):1737–1749. PMID: 23129720.

8. Congdon P. Modelling changes in small area disability free life expectancy: trends in London wards between 2001 and 2011. Stat Med. 2014; 33(29):5138–5150. PMID: 25196376.

9. Strategic Review of Health Inequalities in England post-2010. Fair society, healthier lives: the Marmot review. Updated 2010. Accessed March 24, 2020. http://www.instituteofhealthequity.org/resources-reports/fair-society-healthy-lives-the-marmot-review.

10. Stephens AS, Purdie S, Yang B, Moore H. Life expectancy estimation in small administrative areas with non-uniform population sizes: application to Australian New South Wales local government areas. BMJ Open. 2013; 3(12):e003710.

11. Jonker MF, Congdon PD, van Lenthe FJ, Donkers B, Burdorf A, Mackenbach JP. Small-area health comparisons using health-adjusted life expectancies: a Bayesian random-effects approach. Health Place. 2013; 23:70–78. PMID: 23778148.

12. Ngamini Ngui A, Apparicio P, Moltchanova E, Vasiliadis HM. Spatial analysis of suicide mortality in Québec: spatial clustering and area factor correlates. Psychiatry Res. 2014; 220(1-2):20–30. PMID: 25095757.

13. Kim I, Lim HK, Kang HY, Khang YH. Comparison of three small-area mortality metrics according to urbanity in Korea: the standardized mortality ratio, comparative mortality figure, and life expectancy. Popul Health Metrics. 2020; 18:3.

14. Kim JH, Yoon TH. Comparisons of health inequalities in small areas with using the standardized mortality ratios in Korea. J Prev Med Public Health. 2008; 41(5):300–306. PMID: 18827497.

15. Yoon TH, Noh M, Han J, Jung-Choi K, Khang YH. Deprivation and suicide mortality across 424 neighborhoods in Seoul, South Korea: a Bayesian spatial analysis. Int J Public Health. 2015; 60(8):969–976. PMID: 26022192.

16. Cheshire J. Featured graphic. Lives on the line: mapping life expectancy along the London Tube network. Environ Plann A. 2012; 44(7):1525–1528.

17. Marmot M. The Health Gap: The Challenge of an Unequal World. Oxford, UK: Bloomsbury Press;2015. p. 27–28.

18. Seong SC, Kim YY, Khang YH, Park HJ, Kang HJ, Lee H, et al. Data resource profile: the National Health Information Database of the National Health Insurance Service in South Korea. Int J Epidemiol. 2017; 46(3):799–800. PMID: 27794523.

19. Preston S, Heuveline P, Guillot M. Demography: Measuring and Modeling Population Processes. Malden, MA: Wiley-Blackwell;2000.

20. Eayres D, Williams ES. Evaluation of methodologies for small area life expectancy estimation. J Epidemiol Community Health. 2004; 58(3):243–249. PMID: 14966240.

21. Office for National Statistics. Life Expectancy at Birth: Methodological Options for Small Populations. Norwich, UK: Office for National Statistics;2003.

22. Silcocks PB, Jenner DA, Reza R. Life expectancy as a summary of mortality in a population: statistical considerations and suitability for use by health authorities. J Epidemiol Community Health. 2001; 55(1):38–43. PMID: 11112949.

23. Andreev EM, Shkolnikov VM. Spreadsheet for Calculation of Confidence Limits for Any Life Table or Health-Life Table Quantity. Rostock, Germany: Max Planck Institute for Demographic Research;2010.

24. UN-HABITAT/WHO. Hidden Cities: Unmasking and Overcoming Health Inequities in Urban Settings. Geneva, Switzerland: World Health Organization;2010.

25. Galea S, Freudenberg N, Vlahov D. Cities and population health. Soc Sci Med. 2005; 60(5):1017–1033. PMID: 15589671.

26. Shin H, Lee S, Chu JM. Development of composite deprivation index for Korea: the correlation with standardized mortality ratio. J Prev Med Public Health. 2009; 42(6):392–402. PMID: 20009486.

27. Pearce J. The ‘blemish of place’: stigma, geography and health inequalities. A commentary on Tabuchi, Fukuhara & Iso. Soc Sci Med. 2012; 75(11):1921–1924. PMID: 22917749.

28. Veugelers PJ, Kim AL, Guernsey JR. Inequalities in health. Analytic approaches based on life expectancy and suitable for small area comparisons. J Epidemiol Community Health. 2000; 54(5):375–380. PMID: 10814659.

29. Macintyre S, Ellaway A, Cummins S. Place effects on health: how can we conceptualise, operationalise and measure them? Soc Sci Med. 2002; 55(1):125–139. PMID: 12137182.

30. Northridge ME, Sclar ED, Biswas P. Sorting out the connections between the built environment and health: a conceptual framework for navigating pathways and planning healthy cities. J Urban Health. 2003; 80(4):556–568. PMID: 14709705.

31. Friel S, Akerman M, Hancock T, Kumaresan J, Marmot M, Melin T, et al. Addressing the social and environmental determinants of urban health equity: evidence for action and a research agenda. J Urban Health. 2011; 88(5):860–874. PMID: 21877255.

32. Smith N. The New Urban Frontier: Gentrification and the Revanchist City. Paju, Korea: Dongnyok;2019.

33. Choi EY. The differentiation of reproductions of educational capitals and the formation of the gated city. J Korean Geogr Soc. 2004; 39(3):374–390.

34. Choi E. The formation of rigid cycle of the rich in Gangnam - according to the change of condo prices (1989–2004). J Korean Urban Geogr Soc. 2006; 9(1):33–45.

35. Park BG, Jang JB. Gangnam-ization and Korean urban ideology. J Korean Assoc Reg Geogr. 2016; 22(2):287–306.

36. Kim CS, Yun SC, Kim HR, Khang YH. A multilevel study on the relationship between the residential distribution of high class (power elites) and smoking in Seoul. J Prev Med Public Health. 2006; 39(1):30–38. PMID: 16613069.

37. Kim I, Bahk J, Yoon TH, Yun SC, Khang YH. Income differences in smoking prevalences in 245 districts of South Korea: patterns by area deprivation and urbanity, 2008–2014. J Prev Med Public Health. 2017; 50(2):100–126. PMID: 28372354.

38. Jung-Choi K, Khang YH, Cho HJ. Socioeconomic differentials in cause-specific mortality among 1.4 million South Korean public servants and their dependents. J Epidemiol Community Health. 2011; 65(7):632–638. PMID: 20584732.

39. Khang YH, Lynch JW, Kaplan GA. Health inequalities in Korea: age- and sex-specific educational differences in the 10 leading causes of death. Int J Epidemiol. 2004; 33(2):299–308. PMID: 15082630.

40. Williams ES, Dinsdale H, Eayres D, Tahzib F. Impact of nursing home deaths on life expectancy calculations in small areas. J Epidemiol Community Health. 2004; 58(11):958–962. PMID: 15483314.

SUPPLEMENTARY MATERIAL

Supplementary Table 1

LE in the areas around 614 stations along 23 lines in the Seoul metropolitan area: findings from the National Health Information Database, 2008–2017

jkms-35-e365-s001.xls

TOOLS

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