Abstract
Background and Aim
Health is viewed as a form of human capital and a necessary basis for people to realize capabilities. Moreover, socioeconomic inequality in health outcome widens income inequality and exacerbates social inequality. The aim of this study is to measure socioeconomic inequality in health outcomes among the elderly in China.
Methods
The data used in this study were sourced from China Health and Retirement Longitudinal Study in 2015, including 5643 participants aged 60 and above. Concentration curve and concentration index were applied to measure the extent of socioeconomic inequality in health outcomes among older adults. Furthermore, the decomposition method of concentration index proposed by Wagstaff was employed to quantify each determinant’s contribution to the measured socioeconomic inequality in health outcomes.
Results
The concentration index of Activity of Daily Living Scale and Center of Epidemiological Survey-Depression Scale score were −0.0064 and −0.0158, respectively, indicating pro-rich inequality in physical and mental health among the elderly. The decomposition analysis revealed that household income (41.15%), aged 70–79 (17.37%), being male (8.38%), and living in urban area (5.78%) were key factors to explain the pro-rich inequality in physical health. Furthermore, the results also suggested that household income (68.41%), being male (17.55%), having junior high school education (10.67%), and living in urban area (6.49%) were key factors to explain the pro-rich inequality in mental health.
Conclusion
This study revealed that there are pro-rich inequalities in physical and mental health among the elderly in China, and the degree of pro-rich inequality in mental health is higher than that in physical health. Moreover, the results also suggested that household income is the biggest contributor to socioeconomic inequality in physical and mental health. Furthermore, this study found that educational attainment makes a substantial contribution to socioeconomic inequality in health outcomes, while the contribution of health insurance to health inequality is limited.
Keywords: socioeconomic inequality, health outcomes, elderly, decomposition of concentration index, China
Introduction
With the rapid development of the economy, income inequality in China is constantly growing and has reached very high levels.1 Some studies have revealed that income inequality could have a negative effect on health outcomes,2–5 which will in turn further widen income inequality and create a vicious cycle.3 China, as one of the largest developing countries in the world, has witnessed increased health inequality.6 Health is viewed as a form of human capital and a necessary basis for people to realize capabilities.7,8 Moreover, socioeconomic inequality in health outcomes widens income inequality and exacerbates social inequality. Furthermore, socioeconomic inequality in health outcomes also causes many social problems, such as unemployment and poverty problems,9 which are not conducive to social stability.
In January 2009, Opinions on Deepening Health System Reform was issued by the Chinese government, beginning a new round of health care system reform. The core concept of this round of reform is to provide basic medical and health care system to all of the Chinese population as a public good. An important goal of this round of reform is to reduce health disparities among residents.10 The central government has increased health investment in underdeveloped areas. Furthermore, the central government has invested more than RMB 70 billion (US$ 9.93 billion) to support the development of county-level hospitals and community-level health institutions.11 As an important national strategic plan in the health sector, the Healthy China 2030 Plan was released in October 2016, with an emphasis on providing fair, accessible, systematic, and continuous health services to all of the Chinese population in order to improve the national health.
With the constant decline of birth rate and the steady increase in life expectancy, the aging population has become an important problem in China. Furthermore, improving the health outcome of the elderly has become a focus of health policymakers. China, which has the largest number of older people, has entered a period of population ageing since 2000. By the end of 2017, the number of older people aged 60 and above reached 240.90 million, accounting for 17.3% of the total population.12 Furthermore, it is estimated that the number of older people aged 60 and above will exceed 400 million and account for more than 30% of the total population by 2050.13 As physical function declines with age, older adults are more likely to suffer from health problems than their younger counterparts.14–16 Most older adults have some kinds of chronic diseases, such as hypertension, heart disease, stroke, diabetes, osteoporosis, hearing loss, visual loss, and Alzheimer's. Furthermore, older adults are usually lonely and some of them suffer from severe depression due to the fact that their sons and daughters have left home for study or work. In general, a person’s health care expenditure at the age of 65 and above accounts for approximately 70% of his or her lifetime health care expenditure.17 Indeed, those health problems pose a daunting challenge to the health care system in China. In 1990, Healthy Aging was put forward by the World Health Organization, and its core notion is to improve the health outcomes of the elderly.
The past few years have witnessed a growing number of researches undertaken to explore socioeconomic inequality in health outcomes. Van Doorslaer et al used the data of nine industrialized countries to construct concentration curves of self-assessed health, and his study revealed that inequalities in health favoured the higher income groups.18 Xu et al decomposed the socioeconomic inequalities in depressive symptoms among the elderly in China and discovered that there was a pro-rich inequality in depressive symptoms.19 A study conducted by Xie revealed that there is a strong pro-rich inequality in health outcomes, and the higher-income people tend to have better health outcomes.20 In addition, the research of Gu et al suggested that there is a pro-rich inequality in health outcomes in Jiangsu Province of China, while health service utilization is more concentrated among poor people.21 Moreover, the study of Su et al demonstrated that the pro-rich health inequity was much higher for the rural health insurance scheme than that for the urban ones by using decomposition analysis.22 Furthermore, Peng and Wang used the data obtained from China Health and Nutrition Survey (CHNS) and found that there was a pro-rich inequality in health outcomes among children in China.23 Overall, previous studies on socioeconomic inequality in health outcomes in China are quite limited. On the one hand, previous studies mainly used self-assessed health as health outcome variable, whilst they ignored the decomposition of socioeconomic inequality in physical health. On the other hand, few empirical studies used the determinants of health model to select independent variables in order to investigate the socioeconomic inequality in health outcomes.
This study aimed to fill the gaps by measuring socioeconomic inequality in health outcomes among the elderly in China. The results of this study could shed light on future socioeconomic inequality in health outcome studies in China. This study highlighted some important strengths. To the best of our knowledge, this is the first empirical study to decompose the concentration index of Activity of Daily Living Scale (ADL) score using a nationwide database. Furthermore, this study employed the determinants of health model to guide the selection of independent variables to ensure a reasonable selection of variables.
Methods
Data Source
The data used in this study were sourced from China Health and Retirement Longitudinal Study (CHARLS), which was conducted by National School of Development at Peking University in 2015. CHARLS data is freely available at http://charls.pku.edu.cn/. The CHARLS is a nationally representative survey on adults aged 45 and above in China. Its aim is to collect a set of high-quality microdata to analyze the current status of the aging population and promote interdisciplinary research on the aging problem in China. This study was conducted every 2 years. Its national baseline survey was performed in 2011, approximately 17,000 respondents were sampled from 450 communities or villages in 150 counties or districts. The third wave of the survey was performed between July and August in 2015, which is the latest data of CHARLS at the time of this study. By using multistage probability-proportional-to-size (PPS) sampling method, 21,095 respondents aged 45 and above were sampled from 450 communities or villages in 28 provinces in China. The data questionnaire contains information about older adults’ demographics, health outcomes, health insurance, lifestyle, location and income, etc., which is quite suitable for this study. Considering the fact that this study focused on the socioeconomic inequality in health outcomes among the elderly, we removed respondents under 60. After data cleaning, a total of 5643 participants aged 60 and above were included in this study.
Variables
Outcome Variables
The World Health Organization defines health as a state of complete physical, mental, and social well-being, not merely the absence of disease or infirmity.24 According to this definition, physical and mental health were adopted to measure the health outcome of older adults.
The physical health of respondents was assessed using the Activity of Daily Living Scale (ADL), which is a generic instrument developed by Lawton and Brody in 1969.25 The scale includes the older adults’ status of using the toilet, eating, dressing, controlling urination and defecation, getting into or out of bed, bathing or showering. These questions have four response levels, ranging from I do not have any difficulty, I have difficulty but can still do it, I have difficulty and need help, to I can not do it. These questions were scored from 1 for I do not have any difficulty to 4 for I can not do it. The 8 questions were summed to get an ADL score for each older adult, which ranges from 6 to 24, with a higher ADL score indicating a weaker ability of daily living.
In addition, considering the fact that the Center of Epidemiological Survey-Depression Scale (CES-D) is a useful instrument to screen for depressive symptoms,26 this study employed a Chinese-version CES-D 10-question form to measure the mental health of older adults. A study has demonstrated that this scale has a good internal consistency reliability among middle-aged and older adults in China.27 This scale has ten questions, including eight negatively oriented and two positively oriented questions, such as “I had trouble keeping my mind on what I was doing” and “I was happy”. These questions have four response levels, which range from rarely or none of the time (<1 day), some or a little of the time (1–2 days), occasionally or a moderate amount of the time (3–4 days), to most or all of the time (5–7 days). Furthermore, the negatively oriented questions were scored from 1 for rarely or none of the time (<1 day) to 4 for most or all of the time (5–7 days), while the positively oriented questions were scored from 1 for most or all of the time (5–7 days) to 4 for rarely or none of the time (<1 day). These 10 questions were summed to get a CES-D score for each older adult, ranging from 10 to 40, with a higher CES-D score indicating severer depressive symptoms.
Independent Variables
The determinants of health model, which is put forward by the World Health Organization, revealed the factors that influence the health outcome and impact mechanism. This model has been widely adopted to determine factors that influence the health outcome of residents.28–30 According to this model, we selected the independent variables, and description of independent variables is listed in (Table 1). Independent variables used in this study were classified into five categories. The first type of independent variable describes the biology and genetics of older adults, including two variables: age and gender. The second type of independent variable describes the health behaviors of older adults, including three variables: smoking, drinking, and social interaction, indicating whether the older adult had these behaviors or not. The third type of independent variable describes the socioeconomic characteristics of older adults, including three variables: marital status, educational attainment, and household income. The fourth type of independent variable describes the communities and regions, including two variables: geographic location and residency location. The fifth type of independent variable focuses on health policy, including a variable: health insurance, which indicates whether or not the older adult was insured by health insurance.
Table 1.
Description of Independent Variables
| Variables | Description of Variables |
|---|---|
| Age | |
| 60–69a | |
| 70–79 | 70–79 = 1, else = 0 |
| ≥ 80 | 80 and above = 1, else = 0 |
| Gender | |
| Femalea | |
| Male | Female = 0, male = 1 |
| Marital status | |
| Divorced or elsea | |
| Married | Divorced or else = 0, married = 1 |
| Educational attainment | |
| Primary school and belowa | |
| Junior high school | Junior high school = 1,else = 0 |
| Senior high school | Senior high school = 1, else = 0 |
| Higher education | Higher education = 1, else = 0 |
| Household income, RMB | Continuous variable |
| Health insurance | |
| Without health insurancea | |
| Covered by health insurance | Covered by health insurance = 1, else = 0 |
| Geographic location | |
| Living in eastern regiona | |
| Living in central region | Living in central region = 1, else = 0 |
| Living in western region | Living in western region = 1, else = 0 |
| Residency location | |
| Living in rural areaa | |
| Living in urban area | Living in urban area = 1, else = 0 |
| Smoking | |
| Noa | Never smoke |
| Yes | Smoke = 1, else = 0 |
| Drinking | |
| Noa | Drank no alcohol last year |
| Yes | Drank alcohol last year = 1, else = 0 |
| Social interaction | |
| Noa | Had no social interaction activities last month |
| Yes | Had social interaction activities last month = 1, else = 0 |
Note: aIndicates the reference group.
Statistical Analysis
In this study, concentration curve and concentration index were applied to measure the extent of socioeconomic inequality in health outcomes among older adults. The concentration curve provides a visual impression of socioeconomic inequality in the distribution of health outcomes and depicts how shares of the health outcome variable (y-axis) are accounted for by the cumulative percentage of adults ranked by household income from the poorest to the richest (x-axis).19 In addition, the concentration index is a standard tool to assess the degree of socioeconomic inequality in the field of health care, which is defined as twice the area between the concentration curve and the line of equality (the diagonal).31,32 Moreover, the concentration index ranges between −1 and +1.33–36 When the concentration curve lies above the line of equality, the concentration index ranges from −1 to 0, which indicates that this outcome variable is more concentrated among the lower income group and vice versa.37 Furthermore, when the concentration curve farther lies above the line of equality, the absolute value of concentration index is larger, which suggests a greater degree of socioeconomic inequality in health outcomes.38 If there is no socioeconomic inequality, the concentration index will take the value of zero and the concentration curve will coincide with the line of equality.39,40 In addition, the formula used for calculating the concentration index is:
 |
(1) |
where C denotes the concentration index of health outcome, μ indicates the mean of health outcome, y is the health outcome indicator, and r is the fractional rank of household income.
The decomposition method of concentration index proposed by Wagstaff was employed to quantify each determinant’s contribution to the measured socioeconomic inequality in health outcomes. A study suggested that the decomposition method based on OLS regression model is commonly used when the health outcome is a continuous variable.41 Given the fact that health outcomes used in this study are continuous variables, we employed the decomposition method based on OLS regression model to conduct the decomposition of concentration index.
The linear relationship between health outcome and independent variables could be specified as:
 |
(2) |
where y denotes the health outcome indicator, α represents the intercept term, βk are the coefficients, xki stands for the factors that influence the health outcome of the elderly, and εi is the error term.
The decomposition of concentration index could be written as:
 |
(3) |
where C is the concentration index of health outcome, 
are the coefficients, 
represents the mean of 
, 
stands for the mean of health outcome, 
is the concentration index for 
, and 
denotes the generalized concentration index for ε.
All statistical analyses used in this study were performed with the Stata 15.1 software (Stata Corp LP, College Station, TX, USA).
Ethical Considerations
The study received ethical approval from the Ethics Committee of Shanghai Jiao Tong University.
Results
Characteristics of the Study Population
(Table 2) displays the characteristics of the study population. In this study, more than 60% of the respondents aged between 60 and 69. In addition, 58.11% of the elderly were male, and most of them were married. Moreover, only 2.62% of them had higher education, and the mean f household income was RMB 14,201.93 (US$ 2015.63). Furthermore, approximately 80% of the elderly were covered by health insurance. 42.05% of the older adults lived in the eastern region, and more than 70% of them lived in rural area. The proportion of smoking, drinking last year, and engaging in social interaction last month was 35.18%, 34.47%, and 49.48%, respectively.
Table 2.
Characteristics of the Study Population
| Independent Variable | All (N = 5643) |
|---|---|
| Age | |
| 60–69, n (%) | 3610 (63.97) |
| 70–79, n (%) | 1627 (28.83) |
| ≥ 80, n (%) | 406 (7.19) |
| Gender | |
| Female, n (%) | 2364 (41.89) |
| Male, n (%) | 3279 (58.11) |
| Marital status | |
| Divorced or else, n (%) | 1252 (22.19) |
| Married, n (%) | 4391 (77.81) |
| Educational Attainment | |
| Primary school and below, n (%) | 4641 (82.24) |
| Junior high school, n (%) | 721 (12.78) |
| Senior high school, n (%) | 133 (2.36) |
| Higher education, n (%) | 148 (2.62) |
| Household income, RMB | |
| Mean (SD) | 14,201.93 (112,105.60) |
| Health Insurance | |
| Without health insurance, n (%) | 1113 (19.72) |
| Covered by health insurance, n (%) | 4530 (80.28) |
| Geographic Location | |
| Living in eastern region, n (%) | 2373 (42.05) |
| Living in central region, n (%) | 1370 (24.28) |
| Living in western region, n (%) | 1900 (33.67) |
| Residency Location | |
| Living in rural area, n (%) | 3999 (70.87) |
| Living in urban area, n (%) | 1644 (29.13) |
| Smoking | |
| No, n (%) | 3658 (64.82) |
| Yes, n (%) | 1985 (35.18) |
| Drinking | |
| No, n (%) | 3698 (65.53) |
| Yes, n (%) | 1945 (34.47) |
| Social Interaction | |
| No, n (%) | 2851 (50.52) |
| Yes, n (%) | 2792 (49.48) |
Note: SD represents the standard deviation.
Socioeconomic Inequality in Health Outcomes
The concentration index of ADL score was −0.0064. Given the fact that the health outcome variable employed in this study is an indicator of ill health, the negative concentration index value suggests that low-income people are more likely to have higher ADL scores than high-income people. That is to say, poor people tend to have worse physical health outcomes than rich people. Furthermore, Figure 1 presents the concentration curve of ADL score. The concentration curve mainly lay above the line of equality, which is consistent with the result of concentration index.
Figure 1.
Concentration curve of ADL score.
In addition, the concentration index of CES-D score among the elderly was −0.0158. Similar to the concentration index of ADL score, the negative concentration index value of CES-D score indicates that poor people in China are more likely to suffer from depressive symptoms than rich people. In other words, poor people tend to have worse mental health outcomes than rich people. (Figure 2) reports the concentration curve of CES-D score. The concentration curve lay above the line of equality, which indicates that depressive symptoms were more concentrated among poor people than rich people, and this result is consistent with the value of concentration index.
Figure 2.
Concentration curve of CES-D score.
Decomposition of Inequality in Physical Health
(Table 3) reports the result of decomposition analysis of concentration index in ADL score. As can be seen, the concentration index values of living in urban area, smoking, and drinking were positive, which indicates that these variables are more concentrated among rich people. Considering the fact that health outcome variable used in this study is an indicator of ill health, the negative contribution to concentration index suggests that this independent variable increases the degree of pro-rich inequality in health outcomes. Decomposition analysis results revealed that household income (41.15%), aged 70–79 (17.37%), being male (8.38%), and living in urban area (5.78%) were key factors to explain the pro-rich inequality in physical health. In addition, having a junior high school education aggravated the pro-rich inequality, and the decomposed value for ADL score was 4.41%. On the contrary, the contribution rate of health insurance to concentration index was only 0.46%, which indicates that being covered by health insurance made a relatively smaller contribution to the pro-rich inequality in physical health.
Table 3.
Decomposition Analysis of Concentration Index in ADL Score
| Variable | Elasticity | Ck | Absolute Contribution to C | Percentage Contribution to C |
|---|---|---|---|---|
| Age | ||||
| 60–69 | Ref | |||
| 70–79 | 0.0119 | −0.0938 | −0.0011 | 17.37 |
| ≥ 80 | 0.0065 | −0.0717 | −0.0005 | 7.19 |
| Gender | ||||
| Female | Ref | |||
| Male | −0.0088 | 0.0610 | −0.0005 | 8.38 |
| Marital status | ||||
| Divorced or else | Ref | |||
| Married | 0.0049 | 0.0108 | 0.0001 | −0.81 |
| Educational Attainment | ||||
| Primary school and below | Ref | |||
| Junior high school | −0.0020 | 0.1455 | −0.0003 | 4.41 |
| Senior high school | −0.0006 | 0.1790 | −0.0001 | 1.61 |
| Higher education | −0.0001 | 0.1629 | 0.00001 | 0.23 |
| Household income | −0.0202 | 0.1315 | −0.0027 | 41.15 |
| Health Insurance | ||||
| Without health insurance | Ref | |||
| Covered by health insurance | −0.0132 | 0.0022 | 0.00003 | 0.46 |
| Geographic Location | ||||
| Living in eastern region | Ref | |||
| Living in central region | 0.0036 | 0.0385 | 0.0001 | −2.14 |
| Living in western region | −0.0080 | 0.0011 | 0.000009 | 0.14 |
| Residency Location | ||||
| Living in rural area | Ref | |||
| Living in urban area | −0.0072 | 0.0514 | −0.0004 | 5.78 |
| Smoking | ||||
| No | Ref | |||
| Yes | −0.0027 | 0.0226 | −0.0001 | 0.93 |
| Drinking | ||||
| No | Ref | |||
| Yes | −0.0027 | 0.0530 | −0.0001 | 2.22 |
| Social Interaction | ||||
| No | Ref | |||
| Yes | −0.0152 | −0.0050 | 0.0001 | −1.18 |
Notes: Ck indicates the concentration index of each explanatory variable; C stands for the concentration index of each independent variable; Ref represents the reference group.
Decomposition of Inequality in Mental Health
(Table 4) shows the result of decomposition analysis of concentration index in CES-D score. The decomposition analysis indicated that household income (68.41%), being male (17.55%), having junior high school education (10.67%), and living in urban area (6.49%) were key factors to explain the pro-rich inequality in mental health. In addition, those contributions were offset by positive contributions from aging 70–79 (−5.61%), above 80 years of age (−3.26%), living in central region (−3.78%).
Table 4.
Decomposition Analysis of Concentration Index in CES-D Score
| Variable | Elasticity | Ck | Absolute Contribution to C | Percentage Contribution to C |
|---|---|---|---|---|
| Age | ||||
| 60–69 | Ref | |||
| 70–79 | −0.0094 | −0.0938 | 0.0009 | −5.61 |
| ≥ 80 | −0.0072 | −0.0717 | 0.0005 | −3.26 |
| Gender | ||||
| Female | Ref | |||
| Male | −0.0454 | 0.0610 | −0.0028 | 17.55 |
| Marital Status | ||||
| Divorced or else | Ref | |||
| Married | −0.0383 | 0.0108 | −0.0004 | 2.61 |
| Educational Attainment | ||||
| Primary school and below | Ref | |||
| Junior high school | −0.0116 | 0.1455 | −0.0017 | 10.67 |
| Senior high school | −0.0018 | 0.1790 | −0.0003 | 2.07 |
| Higher education | −0.0019 | 0.1629 | −0.0003 | 1.99 |
| Household income | −0.0821 | 0.1315 | −0.0108 | 68.41 |
| Health Insurance | ||||
| Without health insurance | Ref | |||
| Covered by health insurance | −0.0062 | 0.0022 | 0.00001 | 0.09 |
| Geographic Location | ||||
| Living in eastern region | Ref | |||
| Living in central region | 0.0155 | 0.0385 | 0.0006 | −3.78 |
| Living in western region | 0.0111 | 0.0011 | 0.00001 | −0.08 |
| Residency Location | ||||
| Living in rural area | Ref | |||
| Living in urban area | −0.0199 | 0.0514 | −0.0010 | 6.49 |
| Smoking | ||||
| No | Ref | |||
| Yes | −0.0009 | 0.0226 | −0.00002 | 0.12 |
| Drinking | ||||
| No | Ref | |||
| Yes | −0.0080 | 0.0530 | −0.0004 | 2.68 |
| Social Interaction | ||||
| No | Ref | |||
| Yes | −0.0276 | −0.0050 | 0.0001 | −0.87 |
Notes: Ck indicates the concentration index of each explanatory variable; C stands for the concentration index of each independent variable; Ref represents the reference group.
Discussion
This study investigated socioeconomic inequality in health outcomes among the elderly by using the data from CHARLS which was conducted in 2015. This study found that the concentration index values of both ADL and CES-D scores were negative, which suggests that there are pro-rich inequalities in physical and mental health among the elderly in China. This result is consistent with the finding of Gu et al.42 who found that there existed significant pro-rich inequality in health among the elderly in China. Furthermore, we observed that the absolute values of concentration index in health outcomes in this study were lower than those in nine industrialized countries,18 indicating a lower level of health inequality in China. Moreover, this study also revealed that the absolute value of concentration index in CES-D score is obviously higher than that in ADL score, which indicates that the degree of pro-rich inequality in mental health is higher than that in physical health.
Results from the decomposition analysis suggested that household income was identified as the biggest contributor to the pro-rich inequality in physical health. Moreover, this study also found that household income was identified as the most important factor for the pro-rich inequality in mental health, which is consistent with the finding of Xu et al.19 Furthermore, another important finding in this study is that educational attainment makes a substantial contribution to socioeconomic inequality in health outcomes, indicating that people with a higher level of educational attainment tend to have better health outcome, which is consistent with the finding of Xie20 and Zhou et al.43 The reason can be divided into two aspects. On the one hand, people with higher educational attainment are more likely to have more health knowledge and attach more importance to the prevention of diseases. On the other hand, people with higher educational attainment tend to have higher income and can invest more resources in health, leading to better health outcomes. Moreover, the results of this study also revealed that the contribution of health insurance to health inequality is relatively lower, which is consistent with the findings of Liu et al.44 who discovered that the contribution rate of health insurance to concentration index was only 0.13%. The reason may lie in the fact that the insured rate of health insurance is extremely high and people are mainly insured by it. In addition, we found that residency location was a key factor to explain the pro-rich inequality in physical and mental health, which suggests that urban residents are more likely to have better health outcomes than their rural counterparts. The reason is that urban residents have more access to high-quality health services than rural residents.
This research highlighted several recommendations that might be helpful to narrow socioeconomic inequality in health outcomes among the elderly. Firstly, it is quite necessary for the Chinese government to perfect the income distribution mechanism and intensify efforts in poverty alleviation to narrow the gap in household income, thereby reducing income-related health outcome inequality. Secondly, the Chinese government ought to pay increased attention to the health-related quality of life and access to health services among older adults with lower income. Moreover, the Chinese government also needs to perfect the urban and rural medical assistance program and improve the protection level of medical assistance for older adults living in poverty. Thirdly, the Chinese government should strengthen health education, improve health awareness to promote healthy lifestyles, and strengthen the prevention of diseases, which can eventually lead to the improvement of socioeconomic equality in health outcomes among the elderly. Last but not least, there is also a great need for China to increase investment in basic and higher education and improve residents’ level of educational attainment, which could relieve inequality in educational attainment between low-income and high-income people.
This study suffered from several limitations that warrant mention. Firstly, given the fact that this study only uses a cross-sectional data for decomposition analysis of concentration index, we cannot explore the findings based on causal relationships. Secondly, some important independent variables which may significantly affect health outcomes of the elderly, such as the regional air pollution45 and individual health literacy,46 cannot be included in this study due to the unavailability of data. Thirdly, self-reported information on health outcomes may be subject to recall bias, which may negatively affect the accuracy of estimation in this study. In CHARLS, different recall periods were used to reduce recall bias; thus, this problem is not serious.
Conclusions
In summary, this study revealed that there are pro-rich inequalities in physical and mental health among the elderly in China, and the degree of pro-rich inequality in mental health is higher than that in physical health. Moreover, the results also suggested that household income is the biggest contributor to socioeconomic inequality in physical and mental health. Furthermore, this study found that educational attainment makes a substantial contribution to socioeconomic inequality in health outcomes, while the contribution of health insurance to health inequality is limited.
Acknowledgment
This study was funded by National Social Science Foundation of China (15BJL003), Shanghai Philosophy and Social Science Planning Project (2014BJL002), Research Foundation of Shanghai Municipal Development and Reform Commission of 2018, Scientific Research Foundation of Outstanding Young Teachers of Shanghai Jiao Tong University and SMC of 2016, and Urban Governance Foundation in Humanities and Social Sciences in Shanghai Jiao Tong University (16JCCS16).
Abbreviations
CHARLS, China Health and Retirement Longitudinal Study; ADL, Activity of Daily Living; CES-D, Center of Epidemiological Survey-Depression Scale.
Author Contributions
SL designed the study. JS analyzed the data and drafted the manuscript. RZ analyzed the data. All authors contributed to data analysis, drafting or revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.
Disclosure
The authors report no conflict on interest in this work.
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Data Citations
- Banister J, Bloom DE, Rosenberg L. Population aging and economic growth in china. the program on the global demography of aging working paper no.53; 2010. Available from: http://www.hsph.harvard.edu/program-on-the-global-demography-of-aging/WorkingPapers/2010/PGDA_WP_53.pdf.