Socioeconomic deprivation, medical services accessibility, and income-related health inequality among older Chinese adults: evidence from a national longitudinal survey from 2011 to 2018

Abstract

Background

Health inequality poses a challenge to improving the quality of life of older adults as well as the service system. The literature rarely explores the moderating role of medical services accessibility in the association between socioeconomic deprivation and health inequality.

Objective

This study examines the socioeconomic deprivation and medical services accessibility associated with health inequality among older Chinese adults, which will contribute to the medical policy reform.

Methods

Using data from the 2011, 2014, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), we analyse 14,232 older adults. This paper uses a concentration index (CI) to measure the income-related health inequality among the target population and employs a recentered influence function–concentration index–ordinary least squares (RIF-CI-OLS) model to empirically analyse the correlation between socioeconomic deprivation and health inequality among older Chinese adults. Based on the correlation analysis, we discuss the moderating effect of medical services accessibility.

Results

We find that health inequality exists among older Chinese adults and that the relative deprivation in socioeconomic status (SES) is significantly associated with health inequality (⁠$β∈[0.1109,0.1909]$⁠, P < 0.01). The correlation between socioeconomic deprivation and health inequality is moderated by medical services accessibility, which means that an increase in medical services accessibility can weaken the correlation between socioeconomic deprivation and health inequality.

Conclusion

China needs an in-depth reform of its medical services accessibility system to promote the equitable distribution of medical services resources, strengthen medical costs and quality management, and ultimately mitigate the SES reason for health inequality among older Chinese adults.

Introduction

Health inequality is an important indicator of an individual’s quality of life and health outcomes.¹ Over the past decade, China’s government has recognized accessibility and equality as 2 major goals of its medical system reform and active ageing policies.² Promoting health equality among older adults is fundamental to fostering active ageing. However, with an ageing population, the health inequality among older adults poses severe challenges to improving and building a service system for older adults. The health inequality among older adults is an important issue in both developed and developing countries. In 2021, the number of adults over age 60 in China reached 264 million, accounting for nearly 19% of the total population.³ Compared with the United States and Japan, China’s ageing distribution structure demonstrates an “urban and rural inversion.”

The World Health Organization’s (WHO) Global Report on Aging and Health asserted that countries’ policies must improve the health status of both the affluent and ordinary older adults, as well as provide special assistance to those with a lower socioeconomic status (SES) to reduce the health disparities between elderly individuals and the degree of health inequality.⁴ Since the reform of the health system in 1978, the Chinese government has made important improvements and increased medical services accessibility, which is significant to reducing the health inequalities among older Chinese adults. Existing studies on the endogenous relationship between SES and health inequality have covered all age groups⁵; however, few scholars have analysed directly this relationship and the role of medical services accessibility has been even emphasized less. An urgent focus to identify the mechanisms by which medical services accessibility moderates the correlation between individual SES and health is thus needed. The acquired knowledge must be used to develop policies that dissipate the health inequalities among older adults. Thus, we analyse the correlation between socioeconomic deprivation and health inequality for older adults and examine the role of medical services accessibility policy from a health inequality perspective.

Our findings have some implications for designing and improving social policies that facilitate active ageing, comprehensively eliminate health inequality in the target group, and promote equal healthy ageing as follows. This study empirically analyzes the association between socioeconomic deprivation and health inequality among older adults using a combination of methods, such as Quality of Well-Being (QWB), concentration index (CI), the entropy method, relative deprivation (RD) indices, and the recentered influence function–concentration index–ordinary least squares (RIF-CI-OLS) method. We examine the moderating role of medical services accessibility in the association between socioeconomic deprivation and health inequality among older Chinese adults, embed SES indicators and medical services accessibility into health inequality determination models, and reveal the potential health inequality reduction mechanisms in the context of severe ageing and active healthy ageing.

Literature review and hypotheses development

Health inequality

Since the 1970s, numerous studies have explored health inequalities, with early studies focussing on health disparities. After the 1990s, SES-related health inequalities began to receive more attention. Some studies regarded health as a state of normal body functions without disease. However, the WHO defines health in much broader terms, including mental health and social adaptation, as the ability to meet basic needs and engage in most human activities. Sen’s theory of development as freedom considered health to be comprising ability and well-being. This provides an important research basis for examining health inequality mechanisms, as Sen’s health equity theory asserts that health inequality is related to process fairness and broader social justice concerns.⁶ Individuals’ capability, freedom, or well-being reflects the alternative combinations of functions they can achieve. Health is regarded as a basic condition that allows full social capabilities and life outcomes.⁷

Health inequalities have been termed as “health disadvantage,” “health disparity,” and “health gradient”—all manifestations of health inequality.⁸ Relative to health quality, health disadvantages are common and include economic, medical, political, and other disadvantages. Health disadvantages are also more concentrated in economically backward groups.⁹ Health disparities include both reasonable and unreasonable disparities. Reasonable health disparities are related to individual choices and preferences, while unreasonable health disparities are related to exogenous environmental factors, such as the original environment and gender of a family member. Health gradients focus on health differences between different social classes: rich and high-status people are healthier than poor and low-status people, especially in terms of life expectancy.¹⁰

Many health inequality studies have focussed on health disparities and measures of health inequality associated with group differences in older adults. Although health inequality has received widespread attention from many disciplines, there is a lack of uniformity in the definition and types of health inequality within the academic community. For instance, Wagstaff and Watanabe argued that there are pure and socioeconomic health inequalities.¹¹ Braveman believed that health inequality does not include all health differences, but only the systematic ones in the health levels of social groups with different advantages, such as the poor and women, who tend to experience more health risks and disease inequality.¹² The literature has proposed various tools to measure socioeconomic health inequality. Recently, scholars have also developed several measures that decompose health inequality indices to explain health inequalities among rural older adults.^8,13

Socioeconomic deprivation and health inequality

Since the 1990s, the literature has increasingly recognized that health disparities’ main contributing factors were not exclusively natural and that health inequality was related to SES.¹⁴ The social determinants of health have been listed in the conceptual action framework for reducing health inequality, including several SES-related factors: income, medical service utilization and accessibility, social capital, and cumulative inequality.¹⁵ Many studies found that health inequality was affected by medical care conditions. The relationship between health insurance and health inequalities has also been explored. Pan et al. used the RIF-CI-OLS method to analyse health inequality influencing factors in terms of demographic constraints, SES, medical services accessibility, and later living environment.¹⁶ They found that the health inequalities among older adults in rural China are mainly caused by household income disparity, family medical expenditure, and living arrangements.

Many scholars are concerned about findings that suggest that lower education levels contribute to health inequality¹⁷; however, higher levels of education do not necessarily promote health. More highly educated people may experience a high degree of psychological stress, which may affect their mental health.¹⁸ Other scholars have found that urban–rural migration and social mobility have a significant impact on the health inequality of older adults.¹⁹ Baeten et al. studied income-related health inequalities in China, and found that older women, especially in rural areas, faced more severe inequalities, while in developed regions, income inequality had a smaller impact on health inequalities than in other urban areas.²⁰ Income is the most important factor affecting health inequality among this group. The consensus among scholars is that there is a strong positive correlation between health and income.

Medical services accessibility and health inequality

Theoretically, equality in health production factors (medical services supply) may decrease older adults’ health differences or health inequalities.^2,16 Many factors are associated with health inequality among older adults, including physiological, behavioural demographic, and social factors, such as SES and social capital. Differences in the ability to pay for medical services have led to accessibility and utilization inequalities, which may exacerbate health inequality among older adults.

Medical services accessibility refers to reasonable access to qualified medical and health resources, which includes price acceptability, traffic convenience, resource availability, subjective acceptability, and medical services accessibility supply and demand matching.²¹ In general, medical services accessibility can be summarized as supplier and demand accessibility. Supply-side accessibility refers to the ability of medical services accessibility provider to provide sufficient adequate, comprehensive, and fair medical resources. Demand-side accessibility refers to residents’ access to healthcare. The relationship between RD SES and health inequality and the role of medical services accessibility in this relationship can be analysed from both the supply and demand perspectives.

From the supply perspective, medical services are a special product requiring a high level of professionalism, which gives doctors and medical institutions a certain degree of monopoly power. However, medical services also have uncertainty traits. Serious information asymmetry in the medical services market means that patients who go to the hospital for treatment usually do not know what type of medical services they require or to what extent the medical services they receive are beneficial to their health. Consequently, the provision of medical services induces excessive demand through medical service and price discrimination, whereby suppliers can maximize their benefits.

In the era of China’s planned economy, the barefoot doctor system was established to better resolve the problems of medical services accessibility in rural areas and for low-income groups. This was hailed by the WHO and the World Bank as a model for maximizing health benefits with minimal investment. However, after 1978, China underwent a market-oriented reform of its medical system. In the early stage, the market became an important system for allocating medical services resources; since then, the process of medical marketization has deepened. The SARS outbreak in 2003 forced China to start reflecting on the gaps in its medical services system. In 2005, the State Council of China announced that it would increase the government’s responsibility and adhere to the nature of medical services. In 2007, China stated it would establish a basic medical and health system that would cover both urban and rural residents by 2020.

Over the past decade, China’s healthcare system has undergone reforms in 5 areas: social health security, essential medicines, primary medical care, basic public health services, and public hospitals. Although the Chinese government has gradually attached importance to the allocation of medical services resources, they are still scarce in suburban, rural, and less-developed areas. This is because it is difficult to attract the corresponding medical service providers and medical service supply capacity is insufficient, which lead unequal health opportunities and outcomes.

From the demand perspective, the need for medical services generally lacks the price elasticity that medical services require. Even if the price of medical services increases, patients cannot necessarily give up treatment. As a result, patients, rather than price levels, affect healthcare demand. Moreover, medical service demands that are closely related to health investment are not only an individual decision, but also a comprehensive family one. In other words, families are more inclined to invest in a family member’s health when it is associated with stronger economic opportunities, while the investment in elderly healthcare is relatively less valued. In the absence of publicly funded medical care and medical security, older adults’ access to medical services depends on household spending. Since families and individuals differ in their ability to pay for healthcare, older individuals with lower personal ability to pay have lower access to healthcare, resulting in health inequality among different socioeconomic classes and individuals. Since the 1978 reform, the burden of medical and health expenditure in China has shifted from the government to residents; thus, the government budget for health expenditure has been relatively low in the composition of total health expenditure. As the cost of medical services increases, some elderly patients have to forgo medical treatment due to economic pressure, resulting in poor medical services accessibility.

Hypotheses development

Based on this analysis, we propose the following research hypotheses, as per Fig. 1:

   

Research design

Data

This study uses data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and China Health Statistical Yearbook. The CLHLS was organized by the Center for Healthy Aging and Development at Peking University and the National Development Research Center. The survey covers 23 provinces (municipalities or autonomous regions) across the country, targeting older adults aged 65 and over and adults aged 35–64. The CLHLS has the highest quality survey database for adults over the age of 60. The questionnaires are divided into “survivor” older adults and family members of the deceased elderly. The content includes information on basic demographic characteristics, as well as living, health, medical, and pension conditions. The baseline survey was conducted in 1998 with follow-up surveys in 2002, 2005, 2008, 2011, 2014, and 2018. Based on China’s new medical improvement process (as of 2009), we use the 3-year follow-up data from 2011, 2014, and 2018. The CLHLS contains rich data on basic status, socioeconomic background, economic status, health, and quality of life evaluation of older adults, which helps accurately measure their quality of life (through the Quality of Well-Being Scale [QWB]) index and describe them.

To obtain medical and health accessibility data, we merge the CLHLS macro-statistical data with 2012, 2015, and 2019 Chinese Health Statistical Yearbook data. Measures include the number of beds in medical institutions, number of health technicians, and outpatient and inpatient medical expenses, which measure medical care accessibility. After eliminating outliers and missing values, we retained 14,232 valid observations.

Measurements and variables

Independent variable: health inequality

Health inequality refers to avoidable systemic health differences between different socioeconomic groups, which is known internationally as socioeconomic-related health inequality. We used CI to measure health inequality, which captures the socioeconomic dimensions of health inequality and better reflects the socioeconomic characteristics of health inequality across the elderly population compared with other measures.²² To facilitate quantitative analysis, we used the RIF for CI, represented by RIF_CI. This algorithm represents individual health inequality.^23,24

Income and health level are 2 key factors needed to calculate RIF_CI. Income levels were measured using per capita household income, and health level was characterized by QWB. The WHO’s World Report on Aging and Health asserts that health refers to the absence of disease or frailty, and the physical, psychological, and social adaptation abilities required to function.⁴ To compare different older adults’ health statuses, we selected the QWB as a health status indicator. The QWB was compiled by Anderson et al. to describe health-related quality of life and provide information for epidemiological studies.²⁵Equation (1) calculates the individual health score. The QWB value is between 0 and 1, with scores closer to 0 indicating a worse health condition (where a score of 0 is death), and scores closer to 1 indicate better health (where a score of 1 equal complete health). The QWB health score is calculated as follows:

where MOB (Mobility Scale), PAC (Physical Activity Scale), SAC (Social Activity Scale), and CPX (Symptom Problem Complexes) represent the weights for action indicators, physical activity, social activity, and symptoms, respectively. We calculated the QWB score, which compares the QWB scale with the CLHLS questionnaire data. The QWB scale item weights and corresponding number of the CLHLS questionnaire can be found in Supplementary Material.

The health inequality index, CI, takes values within [−1, 1] and indicates health inequality favouring low- or high-income older adult groups when values are within [−1, 0) or (0, 1]. The CI is zero when the concentration curve and fair line are completely coincident, which indicates a completely fair state. When the concentration curve is above the fair line, CI is below 0; this “negative correlation” indicates that the health status of the vulnerable group is worse. The CI is greater than 0 when the concentration curve is below the fair line; this “positive correlation” indicates that the group has better health. The formula is as follows:

where h and y represent the QWB health status and income level (per capita household income), respectively; μ represents the average health level; and R_i is the income level ranking in the full sample. The RIF_CI value is then calculated based on the following CI:

where v^CI is the target statistic CI determined by the joint distribution (F_H, F_Y) of the health level h and income level y, ν^AC is the absolute CI, and IF is the influence function of the CI, which is calculated as follows:

The rifvar(.) function calculates the RIF values for many statistics. The beneficial property of RIF_CI is E(RIF_CI) = CI, that is, the mean of the RIF value of statistic CI is equal to the CI itself, which makes RIF_CI a bridge variable between group and individual variables. The marginal effect of the explanatory variables on RIF_CI is equivalent to the marginal effect on CI itself; thus, the usual least squares assumptions are satisfied and the RIF-CI-OLS model process can be realized.^23,24

Dependent variable: socioeconomic deprivation

We used the RD index to measure socioeconomic deprivation. The value range of RD is between 0 and 1, with scores closer to 0 indicating lower relative SES deprivation, and scores closer to 1 indicating higher relative SES deprivation. The formula for calculating socioeconomic deprivation is as follows:

where SES represents socioeconomic status measured by 3 common criteria: education, income, and occupation. We used the entropy method to calculate SES (see Table 1).

Moderator variable: medical services accessibility

Previous research identified 5 aspects of access to medical service: availability (A1), accessibility (A2), accommodation (A3), affordability (A4), and acceptability (A5).²¹ Availability is the quantity and type suitability of both the supply and demand sides; accessibility is the distance relationship between the supplier and patient; accommodation is the way in which service resources are organized and patients’ ability to adapt to these approaches; affordability is the relationship between service prices and patient income, ability to pay, and health insurance; and acceptability is the attitude–compatible relationship between providers and patients. Table 2 presents these 5-dimensional medical services accessibility measurements. We calculated medical services accessibility using the entropy method following the above framework, and we measured this using macro-statistical and CLHLS data.

Controlled variables

Previous studies identified the factors associated with older adults’ health inequality, such as gender, age, marital status, income, education, occupation, and lifestyle.²⁶ We included these factors as control variables, divided into demographic characteristics, SES, and living conditions of older adults. The demographic variables include gender, age, and marital status; SES variables include years of education, occupational type before age 65, resident type, and region; and the living conditions of older adults include physical activity and household hygiene. These variables’ statistical characteristics are reported in Table 3.

The health inequality index RIF_CI value for the rural subsample (0.10) is larger than the value for the urban subsample (0.09), and the value of RIF_CI for the western subsample (0.10) is higher than the value for the eastern subsample (0.09), suggesting that health inequalities are more severe in rural and western regions. The socioeconomic deprivation index value for the rural subsample (0.76) is larger than the value for the urban subsample (0.29), and the value for the central subsample (0.55) is higher than the value for the eastern subsample (0.50), indicating that the RD of SES is more pronounced in rural and central regions. The value of the medical services accessibility index for the urban subsample (0.59) is greater than the value for the rural subsample (0.36), the value for the eastern subsample (0.50) is greater than the value for the western subsample (0.48), and the value for the central subsample (0.45) indicates significant urban–rural disparity in medical services accessibility.

Statistical analysis

As previously mentioned, we used the RIF-CI to represent individuals’ health inequality. Measurements of inequality are well established in the literature and widely applied in studies on health inequality, including the slope and relative index of inequality, range, dissimilarity index, Gini coefficient, and CI.^27–29 Among the above indices, only the relative inequality index and CI satisfied 3 basic requirements for measuring health inequality: (i) it must reflect the socioeconomic dimension of the health inequality, (ii) it must encompass the experience of the entire population, and (iii) it must be sensitive to changes in the hierarchy of socioeconomic groups. However, the CI is more prominent in terms of immediate visual appeal than the relative inequality index.²² Moreover, Regidor pointed out that compared with other indices, the size and sign of CI depend on the observed gradient between socioeconomic level and health, making it possible to compare health socioeconomic inequalities at different times and across different regions. Therefore, this study uses CI as an indicator of health inequality.³⁰ However, CI has certain limitations: (i) it can be applied only in those cases in which the socioeconomic categories can be ordered in accordance with a strict hierarchical ranking, and (ii) CI is a population variable, meaning it is not possible to directly take CI as the explained variable from a regression estimation at the individual level. Therefore, we need to establish the correlation between the health inequality index (group level) and explanatory variables (individual level) through RIF.^23,24

Moreover, the RIF is useful for decomposition. The RIF value of a specific individual indicates how the statistics would change if the individual were removed from the sample. Assuming a linear relationship between the dependent and independent variables means that the RIF is the dependent variable in an ordinary least square (OLS) regression, whose coefficients equal the marginal effects of the covariates X on the CI. This is referred to as RIF-CI-OLS decomposition, which better reveals causal parameters under empirical conditions.²³

The first step was to construct the RIF-CI-OLS model through RIF regression decomposition. According to the analytical principles of RIF-CI-OLS, we calculated the correlation estimation model of socioeconomic deprivation and the health equality of older adults as follows:

where RIF_CI_ipt is the CI recentered influence function of an older adult i who lived in province p in year t and indicates the health inequalities faced by individual older adults; α is a constant term; $SESRDi$ indicates relative deprivation in socioeconomic status among older adults; β is the core parameter to be estimated; X_ipt is the control variable group; $ω¯ i$ is the year fixed effect; ϱ_p is the province fixed effect; ξ_ip is the year-province fixed effect; and ε_ipt represents the random disturbance term.

The second step added medical services accessibility to the RIF-CI-OLS model to examine the correlation between medical services accessibility and health inequality (Equation (7)).

The third step tested medical services accessibility’s moderating effect on the association between socioeconomic deprivation and health inequality. The medical services accessibility and socioeconomic deprivation interaction term is added to estimate the moderating effect. Equation (8) is as follows:

On the basis of the significant medical services accessibility coefficient in Equation (7), if the interaction term coefficient (μ) on medical services accessibility and $SESRDi$ is significantly positive (negative), then medical services accessibility has strengthened (weakened) the correlation between socioeconomic deprivation and health inequality. This study is reported as per the Standards for Observational Studies Reporting guidelines.

Results

Correlation between socioeconomic deprivation and health outcomes

Table 4 presents the regression results for the correlation between the socioeconomic deprivation and older adults’ health inequality index. The coefficients on the socioeconomic deprivation in the health inequality index RIF_CI are 0.1131 (P < 0.01), 0.1109 (P < 0.01), 0.1109 (P < 0.01), and 0.1909 (P < 0.01) after controlling for the variable group and gradually adding year, area, year-area, and individual fixed effects to the model, respectively. These results indicate that widening socioeconomic deprivation may exacerbate health inequalities among older adults, thus supporting Hypothesis 1.

The results reveal that health inequalities among older adults are related to gender, age, marriage, education, and type of residence. Model 4 shows that gender, education, occupation, and type of residence are significantly positively associated with older adults’ health inequality. This result implies that the more highly educated, those with higher-level jobs (higher SES), and urban residents are more likely to encounter health inequality; however, these groups are on the “good” side of that inequality. Meanwhile, their counterparts, including female, less educated, with lower-level jobs (lower SES), and rural individuals, are on the “bad” side of that inequality.

Robustness test and endogenous analysis

To test the robustness of the model results, we constructed panel data from the urban, rural, eastern, central, and western subsamples, and re-estimated the model (Equation (6)). These results are presented in Table 5. After considering control variables and year, area, year-area, and individual fixed effects, the correlation coefficients on SES_RD in the health inequality index RIF_CI of older adults in each subsample were all significantly positive (P < 0.01), verifying our finding that increasing SES_RD exacerbates older adults’ health inequality.

The correlation between SES_RD and older adults’ health inequality in the urban sample was stronger than in the rural sample. The effect between SES_RD and older adults’ health inequality also increased in the eastern, central, and western subsamples.

There are 2 potential endogeneity problems in our model: (i) there may be a “two-way causal” relationship between SES_RD and older adults’ health inequality, and (ii) regional heterogeneity may lead to biases in the estimated results. To rectify the former, we used the instrumental variable (IV) method to re-estimate the model. The IV selects 1 SES_RD lag period and conducts a weak IV test. In the latter, we used panel fixed effects to re-estimate the model and observed the change in the estimated results relative to the original model.

Table 6 presents the results of the endogenous analysis. These results reveal that the Cragg–Donald Wald F value in the IV estimation model is greater than the critical value of 16.38 at the 10% confidence level of the Stock–Yogo test; thus, the weak identification hypothesis of IVs is rejected. After considering year, area, and year-area fixed effects, the correlation coefficient on the IVs in the health inequality index RIF_CI was 0.1002, significant at 0.01. This further strengthens the empirical findings of the original model. The estimation results of the fixed effect balanced panel data model find that, after considering province heterogeneity, the correlation coefficient on SES_RD in the older adults’ health inequality index was 0.2122, which is significant at 0.01.

The moderating effect of medical services accessibility

The original model revealed that SES_RD had a significant positive correlation with older adults’ health inequality index RIF_CI. As presented in Table 7, SES_RD is closely related to RIF_CI (health inequality), c, and the correlation coefficient on medical services accessibility’s i SES_RD in RIF_CI is −0.0717, which is significant at 0.01. This indicates that AC had a negative moderating effect on the correlation between SES and health inequality, which provides quantitative evidence for Hypothesis 2.

As demonstrated in Fig. 2, the slopes of the 2 straight lines represent the effect of SES_RD on RIF_CI under higher and lower levels of medical services accessibility. The slope is smaller when medical services accessibility is higher, indicating that the correlation between differences in socioeconomic deprivation and health inequalities is attenuated when medical services accessibility is more abundant, which verifies Hypothesis 3.

Discussion

This study used longitudinal multiyear data to explore the moderating role of medical services accessibility in the correlation between socioeconomic deprivation and health inequality for older Chinese adults. The most important contribution of this study is that it uses RIF to determine the relationship between the socioeconomic deprivation and health inequality among older adults. We also explored the moderating effect of medical services accessibility on the correlation between socioeconomic deprivation and health inequality in older adults, which enriches the research on relevant mechanisms and points out the potential mechanisms that reduce health inequality in the context of active ageing, which provides new evidence for policymakers to further reform medical services and ultimately reduces the health inequality among older adults.

Interpretation of the results

This study confirms that socioeconomic deprivation is significantly associated with health inequality in older Chinese adults. One explanation is that older adults with low SES find it difficult to meet their own needs. In countries with high-income inequality, the relationship between SES and older adults’ needs satisfaction is stronger, and income disparities make it difficult for low-income groups or regions to obtain scarce resources.³¹ Another explanation is that older adults with higher SES tend to have higher levels of education, and education is associated with health through health knowledge and behaviour. Access to higher education further deepens health inequalities. For example, older adults with higher education tend to have higher levels of health, and older adults with lower education levels are less likely to have good health status.⁸

This study analysed the relationship between medical services accessibility and health inequality among older Chinese adults by decomposing the factors that influence older adults’ health inequality. The RIF-CI-OLS model results further showed that the correlation between socioeconomic deprivation and health inequality can be mitigated by providing more convenient medical services and improving the availability and adaptability of medical services. First, there is a serious mismatch between supply and demand in the Chinese medical market. Medical resources are concentrated in tertiary public hospitals, resulting in low quality medical services for older adults in rural areas.¹⁶ Improving medical services supply resources will ensure the health of older adults in rural areas. Locating medical resource providers closer to where older adults reside provide them with more direct medical care access, which may help more effectively diagnose and treat chronic diseases, thereby alleviating health inequality.³² Second, older adults living in unified housing, such as in the same nursing home, enjoy the same level of medical services, which can reduce the health inequality caused by socioeconomic deprivation to some extent. Third, medical insurance only covers a portion of medical expenses, leaving patients to make co-payments; however, older adults with low SES have fewer funds for medical services and may be forced to give up medical services under financial pressure. Countries with low out-of-pocket funding for medical expenses facilitate increased medical services access for all, reducing financial constraints for the poor, which, in turn, reduces health inequality.³³ Finally, older adults with lower SES have lower levels of health literacy, less confidence in communicating with physicians, and receive poorer quality medical services. Improving physicians’ attitude toward patients can also reduce health inequality.³⁴

Limitations

There are still some points worth improving in this study. First, it is difficult to precisely measure the health inequality among rural older adults, as it stems from a combination of complex factors. It is difficult to define a comparable survey scale and scientific evaluation criteria for subgroup comparisons, since publicly available microdata may not support the measurement and decomposition of all the health inequality factors for older adults examined in this study. Second, the factors associated with older adults’ health inequality are multidimensional and collectively contribute to older adults’ health inequality. Investigating these factors requires further research on the mechanisms underlying heterogeneity of health inequality in ageing China. Third, as this study is correlational rather than experimental, we did not explore causality in depth. Although we confirmed the correlation by using IV method to alleviate the endogeneity of socioeconomic factors and income-related health inequality, the conclusions are still questionable due to causality. Thus, more quasi-experimental studies are needed to further estimate the causal relationship between SES and income-related health inequality.

Other study limitations include the lack of more granular measures of medical services accessibility, such as actual health facility use and hospital admissions. Moreover, China’s social policy practices on the medical services accessibility are still being improved. Thus, the mechanisms of socioeconomic deprivation and medical services accessibility correlating health inequality among older adults are latent and need to be examined in a multidimensional, multigroup, and cross-period manner using operational policy analysis and empirical models. Finally, although this study used a large CLHLS 2011–2018 sample of older adults, there may still be some sample selection issues, such as selection bias due to unobservable factors. Therefore, more empirical studies are needed to verify the adequacy of this study.

Comparison with the literature

This study is further step toward better understanding the moderating role of medical services accessibility in the correlation between socioeconomic deprivation and health inequality for older adults. The relationship between socioeconomic deprivation and health inequalities has been verified, as our findings are consistent with previous findings that lower SES is associated with poorer health.^7,35

Families often tend to pay for the medical expenses of older adults; thus, we measure family demand elasticity and construct an explanatory variable to explain the changes related to income and health services on health inequality. The link between elasticities reduces the influence of endogenous factors during the empirical process. However, the observed inequalities in access due to SES may result from differences in patient choices; thus, research on related topics requires continuous expansion of analytical thinking.

Many scholars have explored this field of elderly health and medical care. For instance, Brinda et al. used WHO’s health survey data to survey participants in each year, using the number of visits to medical institutions to measure health in the elderly.³⁶ They found that better economic conditions and higher education could improve older adults’ economic status, while poor social status exacerbates their condition. McIntyre and Chow used data from non-emergency coronary revascularization procedures in the UK National Health Service, and found substantive differences in waiting times among public hospitals between patients with different SES, reporting differences up to 35%, or 43 days, between the most and least deprived population quintile groups.³⁷ Our study compensates for health inequality index measures after controlling complex factors, such as age, gender, and economic development level. However, the conclusions may be biased, characterizing the professionalism of medical services accessibility in terms of supply and overcoming uncertainty.

This study found that medical services accessibility can regulate the correlation between socioeconomic deprivation and health inequality of older adults; however, there is a lack of studies on the moderating effect of medical services accessibility. As for the literature, Fan et al. analysed that unequal distribution of medical resources is the main cause of health inequality among older adults in China.³⁸ Cortez et al. specifically discussed the influence of medical services accessibility on treatment outcomes of skin diseases.³⁹ By contrast, this study combined socioeconomic deprivation and medical services accessibility into the health inequality model, confirming another aspect of the importance of improving medical services accessibility to address health inequalities. Since we found that the correlation between socioeconomic deprivation and health inequalities in older adults is related to medical services accessibility, more pragmatic research is needed to address the issue of medical services accessibility. Future studies should thus consider the vertical relationship between specific detailed indicators of medical services accessibility and health inequalities, as well as the effectiveness of policy related to medical services accessibility.

Implications

The study has many implications for policymakers. First, it suggests that the government should speed up the reform of the medical payment system, control the cost and quality of medical services, and provide medical services to the poor. Second, policymakers should strengthen the construction of rural basic medical service facilities and incentives for rural medical service personnel, which will increase medical services resources in rural and less-developed areas, gradually eliminating health inequality. Third, it is important to improve the training system of medical service providers, so that the elderly can have more confidence in communicating with doctors and obtaining health information. Future research should focus on the influencing mechanism between medical services accessibility and health inequality.

Conclusions

This study provides empirical evidence on the complex relationship between socioeconomic deprivation, medical services accessibility, and health inequality among older adults. The results demonstrated the health inequality among older adults in China, with socioeconomic deprivation as an important underlying factor. It is worth noting that medical services accessibility moderates the correlation between socioeconomic deprivation and older adults’ health inequality. The correlation between socioeconomic deprivation and older adults’ health inequality is weakened when medical services accessibility is adequate. Overall, this study confirms that the efforts to reduce health inequality in older adults must consider the role of medical services accessibility, especially for those from rural areas who have a special need for medical services and support. This demand poses challenges to China’s medical services and social security systems; thus, it is important to strengthen interventions to increase medical services accessibility. In the short term, it is critical to improve the supply of basic medical services and increase medical investment in vulnerable older adults. In the long term, the government should target key pathways to reduce health inequities and improve equity and sustainability in the health security system for older adults. During the current pandemic, government policymakers should pay particular attention to vulnerable older adults’ needs and provide more responsive medical and social services to avoid further increasing health inequalities. The CLHLS data are a valuable source in this longitudinal study. Future studies should employ longitudinal and cross-generational data to enhance the holistic understanding of socioeconomic deprivation outcomes, medical services accessibility, and health inequality in older adults.

Funding

This research was funded by the China Social Science Foundation. “Research on the alleviation mechanism of health inequality among the rural elderly and policy optimization from the perspective of active aging.” (Grant No. 21CSH011).

Ethical approval

Not applicable.

Conflict of interest

None declared.

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

References