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Effects on inequality in life expectancy from a social ecology perspective

BMC Public Health volume 18, Article number: 243 (2018) Cite this article

Abstract

Background

Inequality in life expectancy (ILE) is defined as inequality in the distribution of expected span of life-based on data from survival tables estimated using the Atkinson inequality index. ILE can be influenced by socio-ecological indicators including the Gini coefficient, secondary education, output per worker, and old age pension. This study examined the effects on ILE from a social ecology perspective.

Methods

This analysis is based on ILE data from 108 countries obtained from the United Nations Development Programme. Data on socio-ecological indicators were obtained from the United Nations database. The associations between socio-ecological indicators and ILE were assessed using correlation coefficients and multiple regression models.

Results

Significant correlations were evident between ILE and the following indicators from a socio-ecological perspective: Gini coefficient (GC: r = 0.335, p = 0.001) as an indicator of income inequality, female population with at least some secondary education (FSE: r = − 0.757, p = 0.001), male population with at least some secondary education (MSE: r = − 0.741, p = 0.001), output per worker as a measure of labor productivity (OPW: r = − 0.714, p = 0.001), and number of old age pension recipients (OPR: r = − 0.641, p = 0.001). In multivariate regression, the ILE predictors were higher GC and lower levels of FSE, MSE, OPW, and OPR (R2 = 0.648, p < 0.001).

Conclusions

Socio-ecological factors have an important effect on ILE. Policies that address ILE should consider targeted socio-ecological factors, such as the Gini coefficient of income inequality, that give a personal perspective of economic deprivation, attainment of at least a secondary education by both females and males that gives a social environment perspective, output per worker that indicates labor productivity, and the number of old age pension recipients that indicates social security from a public policy perspective.

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Background

We are interested in identifying country-level socio-ecological factors that affect inequality in life expectancy (ILE) among countries. ILE is defined by inequality in the distribution of expected span of life-based on data from life tables estimated using the Atkinson inequality index [1, 2]. The quality of these estimates relies on the quality of the data in the life Table (UN 2016b). ILE can be influenced by country-level socio-ecological indicators that include the Gini coefficient, secondary education, output per worker, and old age pension recipients. Thus, ILE is a useful indicator that can help develop ways to reduce health inequalities [3].

Although studies of ILE have been investigated in various countries [4,5,6,7], which socio-ecological factors in various countries affect ILE are unclear [6, 7]. Especially, a retrospective analysis of country-level socio-ecological factors that contribute to ILE could help identify the most important determinants of ILE [3, 8]. With this goal, this study considers how ILE correlates with socio-ecological indicators.

The basic hypothesis of this paper is that as the income level of a country increases, life expectancy increases commensurately [3, 8, 9]. Several studies that estimated ILE for various countries between 2001 and 2012 [4, 6, 7, 10] showed the relationship between black and white populations, social class, indigenous and non-indigenous people, and women and men in terms of ILE. The relationship between income disparity and life expectancy has been reported [9, 11,12,13], as has the effect of educational inequalities on life expectancy [14,15,16,17,18]. Areas that have been less studied include the relationship of ILE with income inequality, education, labor productivity [19, 20], and the number of old age pension recipients [21, 22].

The present study examines the possible associations between ILE and socio-ecological inequality using several indicators: (1) national income inequality on the personal level [3, 8, 13, 23, 24]; (2) education of at least a secondary education by males and females, which is an indicator of the social environment [15, 17, 18]; (3) labor productivity [19] (Cervellati and Sunde 2005); and (4) the number of old age pension recipients [21], which indicates public policy. The authors expect that countries with low-ILE populations will feature combinations with higher level indicators of socio-ecological perspective (national income, secondary educational attainment, labor productivity, and old age pension recipients).

The determinants of health inequalities are well known. However, ILE is influenced by biological, psychosocial, and environmental factors [3, 8, 18, 24,25,26,27]. The aforementioned socio-ecological status components have not been studied in relation to ILE. Life expectancy can be predicted by country-level socio-ecological factors including income inequality and education [9, 11,12,13,14,15,16,17,18, 28], labor productivity, and the population of old age pension recipients [19,20,21,22]. Whether these factors are associated with ILE is examined in the present study. In addition, we examine the association between ILE and the GC as country-level personal indicator of income inequality in the whole world, the population of both males and females with at least some secondary education (MSE and FSE, respectively) from a social environmental approach, output per worker (OPW) as a means of indicating labor productivity, and the number of old age pension recipients (OPR) as a measure of public policy perspective. Some studies have investigated the effects of income inequality and schooling on life expectancy. However, the associations between ILE and GC, MSE and FSE, OPW, and OPR have not been examined. This study examined the effects on ILE based on social ecology factors.

Methods

ILE framework from a socio-ecological perspective

The framework proposed by this study depicts the socio-ecological indicators of ILE. The relationships between a number of socio-ecological indicators (GC, MSE, FSE, OPW, and OPR) and ILE were examined (Figure 1). Disparities in ILE are influenced by socio-ecological factors. Healthy ageing is a multifactorial characteristic that is influenced by biological, psychosocial, and environmental factors [3, 18, 25,26,27,28]. Especially, healthy aging refers to being physically mobile for at least 100 years, or optimising opportunities for good health, without disease, with preserved functional capacity and with a degree of socio-ecological wellbeing, as exemplified through an active life as a part of society [18, 25,26,27,28,29,30,31,32,33]. However, ILE may be affected or controlled by the socio-ecological environment of a country as well as hereditary factors [28]; the present study excludes ILE-related hereditary or biological factors. This study focused on socio-ecological factors based on a macroscopic theory [3, 18, 26,27,28].

Fig. 1
figure 1

Conceptual framework of country-level socio-ecological indicators for ILE. ILE: Inequality in life expectancy, (%), 2010–2015 . GC: Gini coefficient, (A value of 0 represents absolute equality, a value of 100 absolute), 2005–2013. FSE: Female population with at least some secondary education, (% ages 25 and older), 2005–2014. MSE: Male population with at least some secondary education, (% ages 25 and older), 2005–2014. OPW: Output per worker, (2011 PPP $), 2005–2012. OPR: Old age pension recipients, (% of statutory pension age population), 2004–2012

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Based on the collective findings, it is reasonable to think that country-level ILE and socio-ecological indicators may differ between countries (see Figure 1). Some studies have addressed the association between ILE and black and white populations, indigenous and non-indigenous people, and women and men [4, 6, 7, 10].

This study proposes a socio-ecological framework for ILE focusing on interpersonal economic factors, social environment, and public policy as targets for promotion of life expectancy [34,35,36]. We assume that appropriate changes in the country-level ILE will produce changes in the GC as a measure of economic deprivation from a personal perspective, FSE and MSE attainment from a social environment perspective, OPW as a measure of labor productivity, and the number of OPR as a measure of the welfare of the older population from a public policy perspective.

The hypothesis of this study is that the associations between output (ILE, including differences in ILE between countries) and progress (country-level GC, MSE, FSE, OPW, and OPR) predict health inequalities (Figure 1). These factors can be explained by differences in ILE. Therefore, one is a conceptual model of ILE and the other is a framework comprising socio-ecological indicators that relate to ILE (see Figure 1). In addition, the outputs of this study are based on populations. Thus, the outputs are averaged across the populations and then compared using standard statistical methods (Kim and Kim 2016a, 2017).

Evaluated ILE

ILE is the inequality in the distribution of expected length of life. It has been used to compare health disparity between countries. Such comparisons inform policy decisions that are contingent on changes in ILE [37]. ILE is defined by inequality in the distribution of expected span of life based on data from life tables estimated using the Atkinson inequality index [1, 2]. Estimates of life expectancy at birth are provided by the United Nations Population Division of the UN Department of Economic and Social Affairs [38, 39]. ILE was calculated from the 2010–2015 life tables from UNDESA in 2015 [38]. This distribution is presented over age intervals (0–1, 1–5, ...., 85+ years), with the mortality rates and average estimated age at death specified for each interval. ILE is estimated from the abridged life Tables (5-year age cohorts) and reflects the current inequality in mortality patterns—some people die before the age of one and others die at 75 or later [40]. The Atkinson inequality index A (1) is calculated as A (1) = 1- (geometric mean length of life / arithmetic mean length of life) [1, 41], but we used data from UN [40], which already applied the Atkinson method, to calculate the ILE (%) from 2010 to 2015.

Models and statistical methods

Models of this study estimate ILE in relation to each variable have been developed, to examine the associations between differentials in ILE and socio-ecological indicators. The models generate a framework of the components of the socio-ecological perspective [3, 24, 33]. Three models were developed from the socio-ecological perspective. Model 1 considers the (personal level + social environment). Model 2 considers (personal level + public policy). Model 3 considers (personal perspective + social environment + public policy). Predictors of ILE—GC, MSE, FSE, OPW, and OPR—were used to create a model combining the three models. These variables reflect the components of the socio-ecological indicators [3, 24]. The associations between ILE and indicators of socio-ecological perspective in these models are assessed using Pearson correlation coefficients and multiple regression models [3, 24, 28, 33] and assessed the influence in the magnitude of covariates on the ILE.

In addition, the figure with pairwise scatter plots of all 6 variables have with the correlation coefficients in the above diagonal boxes (Figure 2). From the scatters would be able to ascertain whether correlation coefficients are the correct tool to summarise the relationships [42].

Fig. 2
figure 2

Pairwise scatter plots of all 6 variables; Matrix Plot of ILE, GC, FSE, MSE, OPW, OPR

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Data collection

Data for ILE analysis were obtained from the ILE study conducted by the UN [2]. The indicators of socio-ecological perspective were obtained from datasets provided by the UN [2, 40, 43]. The study utilized demographic databases from 108 countries.

Six socio-ecological factors were used. ILE from 2010 to 2015, inequality in the distribution of expected length of life based on data from life tables estimated using the Atkinson inequality index, was calculated from the 2010–2015 life tables provided by UNDESA [38, 39]. GC was used; a value of 0 represents utter equality and a value of 100 utter inequality, which presented a measure of the difference in the distribution of income among individuals or households within a country from an entirely equal distribution [2]. FSE from 2005 to 2014 as a percentage of those 25 years of age or older, a percentage of the population aged 25 years or older who have reached a secondary level of education [2]. MSE from 2005 to 2014 which is expressed the same as for FSE. OPW from 2005 to 2012, labor productivity, output per unit of labor input, expressed as GDP per worker, (in 2005 international dollars using purchasing power parity rates, 2011 PPP $), for which data refer to the most recent year available [2]. The sixth factor is OPR from 2004 to 2012, people above the statutory pensionable age receiving an old age pension (contributory, non-contributory or both), expressed as a percentage of the eligible population [43].

Results

Differentials in ILE and indicators

Table 1 presents descriptive statistics for the ILE ranges between countries and the values of socio-ecological indicators. ILE ranged from 2.8% in Iceland to 51.2% in Sierra Leone. The mean ILE was 14.82%, with a spread of 48.4%.

Table 1 Descriptive Statistics of Variable
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GC ranged from 24.8 in Ukraine to 65 in South Africa, with a mean of 38.12 and a spread of 40.2. FSE ranged from 0.9% in Burkina Faso to 100% in Canada, Austria, Finland, and Estonia, with a mean of 58.46% and a spread of 99.1%. Similarly, MSE ranged from 3.2% in Burkina Faso to 100% in Canada, Austria, Finland, and Estonia, with a mean of 64.16% and a spread of 96.8%.

OPW ranged from $1857 in Malawi to $92,694 in Norway, with a mean of $29,829 and a spread of $90,837. Finally, OPR ranged from 0.9% in Sierra Leone to 100% in Lesotho, Kyrgyzstan, Bolivia, the Maldives, Mongolia, Mauritius, the Russian Federation, Lithuania, Slovakia, the Czech Republic, Finland, Austria, France, Iceland, Sweden, Germany, Netherlands, Denmark, Switzerland, and Norway, with a mean of 58.48% and a spread of 99.1%.

Predictive variables for the ILE

Data from an analysis of the socio-ecological indicators for the 108 countries are presented in Tables 2, 3, and 4. ILE was correlated with GC, MSE, FSE, OPW, and OPR (Table 2). Significant positive correlations were found between ILE and GC (r = 0.335, p = 0.001), FSE (r = − 0.757, p = 0.001), MSE (r = − 0.741, p = 0.001), OPW (r = − 0.714, p = 0.001), and OPR (r = − 0.614, p = 0.001).

Table 2 Correlations Coefficient for the ILE
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Table 3 Multiple regression models for predicting ILE
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Table 4 Multivariate regression models for predicting ILE
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To investigate the direct relationships between ILE and the socio-ecological indicators, multiple regression analysis was conducted. The regression analysis on the socio-ecological indicators revealed the strongest predictors among the three regression models (see Tables 3, 4). Finally, the predictors of ILE were used to build a model in which higher ILE was predicted by higher values of GC and lower MSE, FSE, OPW, and OPR (R2 = 0.648, p < 0.001).

Lower values of some country-level socio-ecological indicators were associated with a significant effect on the ILE, whereas higher values of GC were also associated with a significant effect on the ILE. These results indicate the great impact of socio-ecological factors on ILE. Thus, countries with low levels of MSE, FSE, OPW, and OPR and high levels of GC had high ILEs, as seen in Model 3.

Discussion

Shortcomings in socio-ecological perspective are a primary source of inequality. Inequalities in national income, education, labor productivity, and social security have negative ramifications for health promotion development [3, 18, 24, 33]. From a socio-ecological perspective, income inequality (GC), education attainment (MSE and FSE), and labor productivity (OPW) and social security (OPR) have significantly improved over time, but have not led to perfect socio-ecological equity. Socio-ecological inequality remains a major barrier to human development [3, 24, 28, 33]. Inequality from a socio-ecological perspective has increased in many health domains, coinciding with an unequal income distribution [24, 27, 44]. To confirm whether higher ILE is vulnerable to changes in these indicators, this study examined the associations between ILE and socio-ecological indicators.

GC, MSE, FSE, OPW, and OPR contribute to ILE [3, 8, 9, 11,12,13,14,15,16,17,18, 24, 27, 45,46,47]. Decreases in GC and increases in MSE, FSE, OPW, and OPR lead to decreases in ILE, suggesting that improving these factors can improve ILE.

In the 108 countries examined, MSE, FSE, OPW, and OPR values were the lowest in less developed regions and higher in more developed regions. Country-level socio-ecological factors that influence standard of living, can predict ILE and corresponding country-level socio-ecological inequality [45, 48]. Consequently, socio-ecological indicators are likely major contributing factors to ILE and indirectly reflect country-level socio-ecological conditions required for healthy living. Therefore, as individuals’ health status and standard of living apparent based on socio-ecological indicators values decrease and increase, so too does ILE. The national income inequality index, secondary education attainment, labor productivity, and social security are controlled measures that are crucial determinants of ILE.

In addition, higher levels of income inequality and relative poverty occur in more developed countries [23, 24, 27, 49]. However, presently more developed countries displayed higher income and educational status, and lower ILE. In less developed countries, country-level income and educational inequality have likely contributed to poor progress in attaining health equality [18, 24, 27]. During the study period, as income inequality determined by the Gini coefficient decreased and education level (at least secondary education) increased, ILE decreased as well. ILE had a consistent influence, independent of income inequality and secondary education attainment from a socio-ecological perspective.

Access to labor productivity and social security via OPW and OPR can improve the quality of life and authorize individuals to take charge of their own health, which ultimately promotes better health [18, 33]. Raising the age at which senior citizens qualify for public pension benefits is a detriment to the health of low income seniors [50]. Life expectancy appears to be a real labor productivity effect [51]. Better health increases the labor supply and productivity. Historically, advances in health have been major contributors to economic growth [52]. Thus, OPW and OPR can be seen as innovative tools for health promotion when viewed from a public policy prospective, and their values have implications that can help to improve the standard of living.

A limitation of this study is a lack of comparable data between some of the countries. The inequality-adjusted human development index in the UN database [40] captures inequality using the index of human development. However, it is not association-sensitive, meaning that it does not account for overlapping inequalities (whether the same people are at the lower end of each distribution). In addition, our findings may not apply to individuals in a particular population, which is a limitation of a socio-ecological study. However, this problem also applies to other observational studies and randomized controlled trials [24, 27]. This study would not be able to the selection of education variables that other education like primary, higher and literacy status.

Finally, our hypothesis could be tested that the associations between ILE and country-level socio-ecological factors predict the years of longevity and full health of a population. In the proposed models, it is evident that if countries improve values of GC, MSE, FSE, OPW, and OPR, they can obtain lower ILE. Therefore, policies that improve these country-level socio-ecological factors are expected to have latent effects on ILE. In addition, a lesson from the experiences of the 108 countries studied is that governments should attempt to reduce income inequality and increase access to education in secondary education, labor productivity, and access to old age pensions. Meeting these goals could decrease the risk factors for ILE and increase the standard of living. Thus, the findings of this study must be used to implement strategies related to ILE. These strategies should include addressing country-level socio-ecological indicators.

Conclusions

This study identified five country-level socio-ecological indicators as important contributors to inequality in life expectancy. These were higher overall national income inequality level, lower female and male secondary education attainment, lower labor productivity, and fewer old age pension recipients. Country-level socio-ecological indicators seem to have an important effect on inequality in life expectancy. Thus, policies that address country-level inequality in life expectancy should consider target socio-ecological factors, such as the GC of income inequality, as an intrapersonal measure of economic deprivation, female and male attainment of at least a secondary education from a social environment perspective, output per worker as labor productivity, and the number of old age pension recipients from a public policy perspective.

Abbreviations

FSE:

Female population with at least some secondary education

GC:

Gini coefficient

ILE:

Inequality in life expectancy

MSE:

Male population with at least some secondary education

OPR:

Old age pension recipients

OPW:

Output per worker (labour productivity)

References

  1. Atkinson AB. On the measurement of inequality. J Econ Theory. 1970;2(3):244–63.

    Article  Google Scholar 

  2. United Nations (UN). Human development report 2015: Statistical annex, Statistical Table 3: Inequality-adjusted Human Development Index, Inequality in life expectancy and Gini coefficient, 216–219; Table 5: Gender Inequality Index, Secondary education, 224–227; Table 13: Work and employment, Labour productivity, 254–257. http://hdr.undp.org/sites/default/files/hdr_2015_statistical_annex.pdf Accessed 16 August 2016a.

  3. Kim JI, Kim G. Country-level socioeconomic indicators associated with healthy life expectancy: income, urbanization, schooling, and internet users: 2000–2012. Soc Indic Res. 2016a;129(1):391–402.

    Article  Google Scholar 

  4. Geronimus AT, Bound J, Waidmann TA, Colen CG, Steffick D. Inequality in life expectancy, functional status, and active life expectancy across selected black and white populations in the United States. Demography. 2001;38(2):227–51.

    Article  CAS  PubMed  Google Scholar 

  5. Donkin A, Goldblatt P, Lynch K. Inequalities in life expectancy by social class, 1972–1999. Health Stat Q. 2002;15:5–15.

    Google Scholar 

  6. Zhao Y, Dempsey K. Causes of inequality in life expectancy between indigenous and non-indigenous people in the northern territory, 1981-2000: a decomposition analysis. Med J Aust. 2006;184(10):490–4.

    PubMed  Google Scholar 

  7. Hosseinpoor AR, Harper S, Lee JH, Lynch J, Mathers C, Abou-Zahr C. International shortfall inequality in life expectancy in women and in men, 1950-2010. Bull World Health Organ. 2012;90(8):588–94.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kim JI, Kim G. Relationship between the remaining years of healthy life expectancy in older age and national income level, educational attainment, and improved water quality. Int J Aging Hum Dev. 2016b;83(4):402–17.

    Article  PubMed  Google Scholar 

  9. Wilkinson RG. Income distribution and life expectancy. BMJ: British Medical Journal. 1992;304(6820):165–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Brønnum-Hansen H, Baadsgaard M. Increasing social inequality in life expectancy in Denmark. The European Journal of Public Health. 2007;17(6):585–6.

    Article  PubMed  Google Scholar 

  11. De Vogli R, Mistry R, Gnesotto R, Cornia GA. Has the relation between income inequality and life expectancy disappeared? Evidence from Italy and top industrialised countries. J Epidemiol Community Health. 2005;59(2):158–62.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Regidor E, Calle ME, Navarro P, Domı́nguez V. Trends in the association between average income, poverty and income inequality and life expectancy in Spain. Soc Sci Med. 2003;56(5):961–71.

    Article  PubMed  Google Scholar 

  13. Rasella D, Aquino R, Barreto ML. Impact of income inequality on life expectancy in a highly unequal developing country: the case of Brazil. J Epidemiol Community Health. 2013; jech-2012

  14. Messias E. Income inequality, illiteracy rate, and life expectancy in Brazil. Am J Public Health. 2003;93(8):1294–6.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Spoerri A, Zwahlen M, Egger M, Gutzwiller F, Minder C, Bopp M. Educational inequalities in life expectancy in German speaking part of Switzerland 1990-1997: Swiss National Cohort. Swiss Med Wkly. 2006;136(9–10):145–8.

    PubMed  Google Scholar 

  16. Jagger C, Gillies C, Moscone F, Cambois E, Van Oyen H, Nusselder W, Robine JM. Inequalities in healthy life years in the 25 countries of the European Union in 2005: a cross-national meta-regression analysis. Lancet. 2009;372(9656):2124–31.

    Article  Google Scholar 

  17. Van Raalte AA, Kunst AE, Lundberg O, Leinsalu M, Martikainen P, Artnik B, et al. The contribution of educational inequalities to lifespan variation. Popul Health Metrics. 2012;10:3.

    Article  Google Scholar 

  18. Kim JI, Kim G. Labor force participation and secondary education of gender inequality index (GII) associated with healthy life expectancy (HLE) at birth. Int J Equity Health. 2014a;13(1):106.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Cervellati M, Sunde U. Human capital formation, life expectancy, and the process of development. Am Econ Rev. 2005;95(5):1653–72.

    Article  PubMed  Google Scholar 

  20. Castelló-Climent A, Doménech R. Human capital inequality, life expectancy and economic growth. Econ J. 2008;118(528):653–77.

    Article  Google Scholar 

  21. Borck R. On the choice of public pensions when income and life expectancy are correlated. Journal of Public Economic Theory. 2007;9(4):711–25.

    Article  Google Scholar 

  22. Whiteford P, Pension WE. Challenges and pension reforms in OECD countries. Oxf Rev Econ Policy. 2006;22(1):78–94.

    Article  Google Scholar 

  23. Wilkinson RG, Income PKE. Inequality and socioeconomic gradients in mortality. Am J Public Health. 2008;98(4):699–704.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kim JI, Socio-ecological KG. Perspective of older age life expectancy: income, gender inequality, and financial crisis in Europe. Glob Health. 2017b;13(1):58.

    Article  Google Scholar 

  25. Candore G, Balistreri CR, Listì F, et al. Immunogenetics, gender, and longevity. Ann N Y Acad Sci. 2006;1089(1):516–37.

    Article  PubMed  Google Scholar 

  26. Kim JI. Social factors associated with centenarian rate (CR) in 32 OECD countries. BMC International Health and Human Rights. 2013;13:16.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kim JI, Kim G. Factors affecting the survival probability of becoming a centenarian for those aged 70, based on the human mortality database: income, health expenditure, telephone, and sanitation. BMC Geriatr. 2014b;14:113.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Kim JI, Kim G. Social structural influences on healthy aging: community-level socioeconomic conditions and survival probability of becoming a centenarian for those aged 65 to 69 in South Korea. The International Journal of Aging & Human Development. 2015;81(4):241–59.

    Article  Google Scholar 

  29. Rowe JW, Kahn RL. Human aging: usual and successful. Science. 1987;237(4811):143–9.

    Article  CAS  PubMed  Google Scholar 

  30. Baker J, Meisner BA, Logan AJ, Kungl AM, Weir P. Physical activity and successful aging in Canadian older adults. J Aging Phys Act. 2009;17(2):223–35.

    Article  PubMed  Google Scholar 

  31. Hsu HC. Impact of morbidity and life events on successful aging. Asia Pac J Public Health. 2011;23(4):458–69.

    Article  PubMed  Google Scholar 

  32. Bousquet J, Malva J, Nogues M, Mañas LR, Vellas B, Farrell J, MACVIA Research Group. Operational Definition of Active and Healthy Aging (AHA): The European Innovation Partnership (EIP) on AHA Reference Site Questionnaire: Montpellier October 20–21, 2014, Lisbon July 2, 2015. J Am Med Dir Assoc. 2015;16(12):1020–6.

    Article  PubMed  Google Scholar 

  33. Kim JI, Kim G. Country-level socioeconomic indicators associated with survival probability of becoming a centenarian among older European adults: gender inequality, male labor force participation, and proportions of women in parliaments. J Biosoc Sci. 2017;49(2):239–50.

    Article  PubMed  Google Scholar 

  34. McLeroy KR, Bibeau D, Steckle A, Glanz K. An ecological perspective on health promotion programs. Health Educ Behav. 1988;15(4):351–77.

    CAS  Google Scholar 

  35. Robinson T. Applying the socio-ecological model to improving fruit and vegetable intake among low-income African Americans. J Community Health. 2008;33(6):395–406.

    Article  PubMed  Google Scholar 

  36. Ostrom EA. General framework for analyzing sustainability of social-ecological systems. Science. 2009;325(5939):419–22.

    Article  CAS  PubMed  Google Scholar 

  37. Salomon JA, Wang H, Freeman MK, Vos T, Flaxman AD, Lopez AD, Healthy MCJ. Life expectancy for 187 countries, 1990–2010: a systematic analysis for the global burden disease study 2010. Lancet. 2013;380(9859):2144–62.

    Article  Google Scholar 

  38. UNDESA (United Nations Department of Economic and Social Affairs). 2015 Revision. World Population Prospects. Population Division Database. Detailed Indicators. https://esa.un.org/unpd/wpp/ Accessed 14 June. 2016a.

  39. UNDESA (United Nations Department of Economic and Social Affairs). World Population Prospects: The 2012 Revision. New York. http://esa.un.org/unpd/wpp/ Accessed 15 April 2016b.

  40. United Nations (UN). Human development report: Inequality-adjusted Human Development Index. http://hdr.undp.org/en/faq-page/inequality-adjusted-human-development-index-ihdi#t293n97 Accessed 16 August 2016b.

  41. Hertog S. The association between two measures of inequality in human development: income and life expectancy. Technical paper (no. 2013/7). New York: Population Division, United Nations; 2013.

    Google Scholar 

  42. Kontis V, Bennett JE, Mathers CD, Li G, Foreman K, Ezzati M. Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble. Lancet. 2017;389(10076):1323–35.

    Article  PubMed  PubMed Central  Google Scholar 

  43. United Nations (UN). Human development report 2015: Work for human development, Table A1.1 Work with exploitation, risks and insecurities, Security from employment, Old age pension recipients 2004–2012, 47–50, Published for the United Nations Development Programme (UNDP), New York, USA. http://hdr.undp.org/sites/default/files/2015_human_development_report_1.pdf Accessed 16 August 2016c.

  44. Elgar FJ, Pförtner TK, Moor I, De Clercq B, Stevens GW, Currie C. Socioeconomic inequalities in adolescent health 2002–2010: a time-series analysis of 34 countries participating in the health behaviour in school-aged children study. Lancet. 2015;385(9982):2088–95.

    Article  PubMed  Google Scholar 

  45. Kennedy BP, Kawachi I, Prothrow-Stith D. Income distribution and mortality: cross sectional ecological study of the Robin Hood index in the United States. BMJ. 1996;312(7037):1004–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Khan HR, Asaduzzaman M. Literate life expectancy in Bangladesh: a new approach of social indicator. Journal of Data Science. 2007;5:131–42.

    Google Scholar 

  47. Khan HR, Islam AA, Ababneh F. Substantial gender gap reduction in Bangladesh explained by the proximity measure of literacy and life expectancy. J Appl Stat. 2016;43(13):2377–95.

    Article  Google Scholar 

  48. Grzywacz JG, Marks NF. Social inequalities and exercise during adulthood: toward an ecological perspective. J Health Soc Behav. 2001;42(2):202–20.

    Article  CAS  PubMed  Google Scholar 

  49. Granados JA. Hrealth at advanced age: social inequality and other factors potentially impacting longevity in nine high-income countries. Maturitas. 2013;74(2):137–47.

    Article  PubMed  Google Scholar 

  50. Emery JH, Fleisch VC, McIntyre L. Legislated changes to federal pension income in Canada will adversely affect low income seniors' health. Prev Med. 2013;57(6):963–6.

    Article  PubMed  Google Scholar 

  51. Bloom DE, Canning D, Sevilla J. The effect of health on economic growth: a production function approach. World Dev. 2004;32(1):1–13.

    Article  Google Scholar 

  52. Suhrcke M, McKee M, Stuckler D, Arce RS, Tsolova S, Mortensen J. The contribution of health to the economy in the European Union. Public Health. 2006;120(11):994–1001.

    Article  PubMed  Google Scholar 

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Funding

There was no funding source.

Availability of data and materials

The data used for this study comes from the United Nations database. Detailed information on the Human development report (2015) is provided on the United Nations, Statistical Annex via access dataset http://hdr.undp.org/sites/default/files/hdr_2015_statistical_annex.pdf upon request.

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Authors and Affiliations

  1. Division of Social Welfare and Health Administration, Wonkwang University, Iksan, Republic of Korea

    Jong In Kim

  2. Institute for Longevity Sciences, Wonkwang University, Iksan, Republic of Korea

    Jong In Kim

  3. Global Management of Natural Resources, University College London (UCL), London, UK

    Gukbin Kim

Authors
  1. Jong In Kim
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  2. Gukbin Kim
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Contributions

JIK and GK participated in the design of the study. JIK and GK conducted the statistical analysis. KJI prepared the initial draft of the manuscript. JIK and GK contributed to writing the manuscript and critically revised the manuscript. JIK supervised the data analysis and writing of the manuscript. JIK and GK approved the final version of the manuscript.

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Correspondence to Jong In Kim or Gukbin Kim.

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JIK and GK declare that they have no competing interests.

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Kim, J.I., Kim, G. Effects on inequality in life expectancy from a social ecology perspective. BMC Public Health 18, 243 (2018). https://doi.org/10.1186/s12889-018-5134-1

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BMC Public Health

ISSN: 1471-2458

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