
==== Front
BMC Health Serv Res
BMC Health Serv Res
BMC Health Services Research
1472-6963
BioMed Central London

39285453
11510
10.1186/s12913-024-11510-1
Research
Has socioeconomic inequality in perceived access to health services narrowed among older adults in China?
https://orcid.org/0000-0002-8515-2577
Cai Jiaoli 12
Li Yue 1
Li Ruoxi lucid_lee@163.com

3
Coyte Peter C. 4
1 https://ror.org/01yj56c84 grid.181531.f 0000 0004 1789 9622 School of Economics and Management, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing, 100044 China
2 https://ror.org/01yj56c84 grid.181531.f 0000 0004 1789 9622 Research Center for Central and Eastern Europe, Beijing Jiaotong University, Beijing, 100044 China
3 https://ror.org/011xvna82 grid.411604.6 0000 0001 0130 6528 School of Economics and Management, Fuzhou University, No.2 Wulongjiang North Avenue, Daxue New District, Fuzhou, Fujian 350108 China
4 https://ror.org/03dbr7087 grid.17063.33 0000 0001 2157 2938 Institute of Health Policy, Management and Evaluation, University of Toronto, Health Sciences Building,155 College Street, Suite 425, Toronto, ON M5T 3M6 Canada
16 9 2024
16 9 2024
2024
24 107720 7 2023
29 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Objective

To analyze the degree, evolution and causes of socioeconomic inequality in perceived access to health services among the older adults in China.

Methods

The data used in this study were drawn from the 4 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2008, 2011, 2014, 2018. Erreygers index (EI) was used to measure socioeconomic inequality in perceived access to health services in each survey wave. A panel logit regression model was used to examine the impact of socioeconomic status on perceived access to health services. The recentered influence function (RIF) regression decomposition method was used to explore the causes of socioeconomic inequality in perceived access to health services. Inverse probability weighting (IPW) was employed to adjust estimates for missing responses and loss to follow-up.

Results

“Pro-rich” socioeconomic inequality in perceived access to health services in China was found with inequality falling through time. The older adults with higher incomes, who had adequate financial support, and those who were wealthier compared with other residents reported lower socioeconomic inequality in perceived access to health services. Having basic health insurance and access to care resources when ill can help alleviate such inequalities.

Conclusions

Socioeconomic inequality in perceived access to health services was shown to be responsive to policies that enhance health insurance coverage and support the provision of (paid and unpaid) caregiving for the older adults.

Keywords

Inequality in perceived access to health services
Socioeconomic status
Older adults
Recentered influence function
China
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 72374021; 71904011 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Health services are considered to be one of the causes of health inequalities [1, 2]. Inequalities in access to and use of health services are a major public health concern globally [3]. Regardless of an individual’s socioeconomic status, health services should be available as needed [4]. Still, socioeconomic inequalities in access to and the use of health services were found in many countries, such as Western countries even those with universal health insurance [5–7] and low- to middle-income countries [3, 8–10]. Socioeconomic inequality manifests itself as pro-rich inequality or pro-poor inequality. Pro-rich and pro-poor inequality in the use of health services have been reported for different types of health care services. Pro-rich inequality means that the rich use more health services. Pro-poor inequality means that the poor use more health services. For example, there were significant pro-rich inequalities in specialist and dental care, while pro-poor inequalities in emergency care, inpatient care and general practitioner care in Turkey [3]. Socioeconomic inequalities in access to and utilization of health care services are a global research and political priority [11].

In China, population aging has become an important policy issue for labour markets as well as for the health and social care systems of China. In 2022, the proportion of the population over 60 years of age in China was 19.8%, accounting for 280.04 million people. Among this age group, there were 209.78 million people aged 65 years and older that accounted for 14.9% of the national population [12]. As the population ages, population health challenges are gradually shifting from traditional infectious diseases to chronic diseases, disabilities and other non-communicable diseases related to population aging [13]. The demand for health services among the older adults is huge. However, like other countries, China also suffers from inequality in the utilization of health services. For example, Fu et al. found a pro-rich inequality in the use of health services among older people in China [14]. Gong et al.’study, which focused on aged 45 years and older, found that those with higher socioeconomic status were more likely to have a physical examination and use inpatient services than other individuals [15]. While the literature has focused on the factors that account for inequalities in the actual use of health services, it has largely ignored the determinants of inequalities in perceived access to health services should people need it. By making the distinction between inequality in actual and perceived access to health services insights may be gleaned in order to inform policies designed to alleviate each type of inequality. The central government of China launched the healthcare system reform plan in April 2009, with the goal of providing basic health services for all Chinese citizens by 2020 [16]. Studying the inequalities in perceived access to health services should people need it can also reflect people’s attitudes towards the outcome of China’s healthcare system reform.

In the previous literature of analyzing socioeconomic inequality in access to and the use of health services, Wagstaff decomposition method [17] and cross-sectional data are often used. However, there are potential concerns with Wagstaff decomposition method, such as it explaining the degree of variation in health services only rather than the covariance between health services and socioeconomic rank [18]. Cross-sectional data limit opportunities to explore temporal trends in inequality. Therefore, the current study used a more general decomposition method called the recentered influence function (RIF) regression decomposition method to explore the determinants of socioeconomic inequality in perceived access to health services [19, 20]. We also used longitudinal data spanning 10 years to analyze changes in socioeconomic inequality in perceived access to health services by the older adults in China. The 10-year span can reflect whether China has achieved success on the path of healthcare reform.

The main purpose of this study was to assess socioeconomic inequality in perceived access to health services among the older adults in China and to explore the causes of such inequality if it were identified. Two contributions are made to the literature. First, this study uses longitudinal data that are representative of China to assess temporal trends in socioeconomic inequality in the perceived access to health care services if those services are needed. Second, this study uses the most recent and more general decomposition methods to identify the determinants of socioeconomic inequality in perceived access to health services among the older adults when they need such services.

In order to achieve the research purpose, longitudinal data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) for 2008–2018 were used. Erreygers index (EI) was used to measure socioeconomic inequality in perceived access to health services in each survey wave. A panel logit regression model was used to examine the impact of socioeconomic status on perceived access to health services. The recentered influence function (RIF) regression decomposition method was used to explore the causes of socioeconomic inequality in perceived access to health services.

This study is organized as follows: The next section introduces the methods, including the conceptual framework, data, variables, measures and statistical analysis. The results, both descriptive and empirical, are then presented. Then we introduce a discussion section to interpret the findings, policy implications and study limitations. Last, we make conclusion.

Methods

Conceptual framework

In this study, we used a predictor framework as proposed by Andersen and Newman’s Behavioural Model of Health Service Utilization [21, 22]. Here, the use of health services is dependent on three sets of factors: predisposing factors; enabling factors; and needs-based factors. Predisposing factors refer to the predisposition to use health care services, such as gender, age, etc. Enabling factors refer to the factors that promote access to health care services, such as income, health insurance, etc. Needs-based factors refer to the primary drivers of health service use, such as the occurrence and severity of underlying diseases and conditions. The predisposing factors considered in this study include gender, age, marital status, place of residence, and educational attainment. The enabling factors include income, whether there was sufficient financial support, self-rated economic status, living arrangements, basic health insurance coverage, whether there was a retirement pension, whether there was a public old-age insurance, and primary caregiver in case of illness. The needs-based factor was measured using self-rated health indicator.

Data

The data used in this study were drawn from the 4 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2008, 2011, 2014, 2018. There were 8 waves of the CLHLS in total. The baseline survey and the follow-up surveys with replacement for deceased elders were conducted in 1998, 2000, 2002, 2005, 2008, 2011, 2014, 2018. The survey sample offers representative coverage for 23 of China’s 31 provinces, including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan, Guangxi, Sichuan, Chongqing, Shaanxi. Multi-stage unequal proportion target random sampling method was adopted in the survey. About 50% of counties/county-level cities/districts were randomly selected from 23 provinces/municipalities/autonomous regions [23]. The population in the survey areas accounts for about 85% of the total population of China [24]. In the 1998 baseline survey and the 2000 wave, the CLHLS aimed to interview all centenarians who volunteered to participate in the sampled counties and/or cities. For each centenarian with a predesignated random code, one nearby octogenarian (aged 80–89) and one nearby nonagenarian (aged 90–99) of a predesignated age and sex were interviewed on a random basis. Since the 2002 wave, three nearby elders aged 65–79 of predefined age and sex were interviewed in conjunction with every two centenarians. The term nearby generally indicates the same village or the same street if applicable, or the same town, county, or city [25]. Those interviewees who were still surviving in the follow-up waves were re-interviewed. Those older adults who were interviewed but subsequently died before the next wave were replaced by new interviewees of the same sex and age (or within the same 5-year age group) [25]. The response rate was about 90% for each survey wave and there was a relatively low sample attrition [23, 26]. A systematic and relatively detailed assessment of data quality of the CLHLS shows that the data quality is high [25, 27]. The survey covers a wide range of personal characteristics for each survey respondent. While there were 8 waves of the CLHLS, only data from 4 waves (2008, 2011, 2014 and 2018) were used in this study. The first three surveys lack information on two important study variables, namely “income” and “health insurance”. To analyze the situation spanning 10 years, our study was restricted to four survey waves: 2008 to 2018. The sample size of 49,785 was obtained by combining the samples from four waves. Some variables have missing values. If all missing values were deleted, there would be 30,524 observations left. A large number of missing values may bias the estimates. Therefore, we used multiple imputation to impute values for the main missing variable, and the number of observations after multiple imputation is 43,381.There were 20,306 and 23,075 respondent observations in urban and rural areas respectively. The number of older adults aged 80 years and above among the respondent observations was 30,147 (or 69% of the analysis sample).

Variables

Outcome measure: perceived access to health services

The dependent variable for this study was perceived access to health services should respondents need service. We focused on the perceptions of access health services, rather than actual use. The CLHLS asks about perceived access to health services by asking “Can you get adequate health care services when you are sick?” The answer has two options: Yes or No. The outcome variable used in this study was dichotomous. If respondents were affirmative in their response to the question, the outcome variable was coded as unity, otherwise it was coded as zero.

Explanatory variables

The core explanatory variable addressed in this study was socioeconomic status. Commonly used indicators to measure socioeconomic status include income, education, self-evaluation of social class, etc. [28]. In order to measure socioeconomic status comprehensively, we selected as many indicators as possible from the survey. Based on the CLHLS data, four indicators were used to measure socioeconomic status of the older adults: income; education; the adequacy of financial support for living; and self-rated economic status.

Income was measured by per capita annual income of households and it was used in the analysis after log-transformation to deal with the skewness of the data. The unit of income was Chinese Yuan (CNY). The education of the older adults was measured by the number of years of schooling. Whether financial supports were adequate for living was converted to a dichotomous variable based on responses to the question “Do all of your financial supports cover your daily costs?” Self-rated economic status was drawn from the question “How do you rate your economic status compared with local residents?” The answer contains five options: very rich, rich, neutral, poor and very poor. The first two responses were classified as “self-rated rich”, and the last two responses were classified as “self-rated poor”, these categorizations resulted in the formation of a categorical variable with three categories: self-rated rich; neutral; and self-rated poor. The value of the Variance inflation factor (VIF) among the four variables measuring socioeconomic status of older adults was 1.68, indicating that there was no multicollinearity among these variables.

The selection of control variables was based on Andersen and Newman’s Behavioural Model of Health Service Utilization [21, 22]. Control variables were drawn from possible predisposing, enabling and needs-based factors. The predisposing factors in this study include gender, age, marital status, place of residence, and the education. Enabling factors include income, whether there was sufficient financial support, the self-rated economic status, living arrangements, basic health insurance coverage, whether there was a retirement pension, whether there was a public old-age insurance, and primary caregiver in case of illness. The needs-based factor was measured using self-rated health indicator. Moreover, to better reflect regional differences and temporal changes in perceived access to health services, region and yearly dummy variables were used. Based on the long-term evolution of its economic development level and geographical location, the 31 provinces in mainland China can be divided into three major economic regions (zones), namely the Eastern region, the Middle region and the Western region. The Eastern region includes 12 provinces, the Middle region includes 9 provinces, and the Western region includes 10 provinces. CLHLS covers only 23 of the 31 provinces. Therefore, the Eastern region of this study included 11 provinces: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan. The Middle region included 8 provinces: Anhui, Jiangxi, Henan, Hubei, Hunan, Shanxi, Jilin and Heilongjiang. The Western region included 4 provinces: Guangxi, Chongqing, Sichuan and Shananxi. The Eastern region has the highest level of social and economic development, followed by the Middle and then the Western region.

Measurement and decomposition of socioeconomic inequality in perceived access to health services

The concentration index (CI) introduced by Wagstaff et al., measures the extent to which inequalities in some health construct is systematically related to socioeconomic status, and it is often used to measure socioeconomic inequality in perceived access to health services [29]. The formula used for the concentration index is:

1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:CI=\frac{2}{\mu\:}cov({h}_{i},{R}_{i})$$\end{document}

where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{h}_{i}$$\end{document} is perceived access to health services. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\mu\:$$\end{document} is the mean of the perceived access to health services. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{R}_{i}$$\end{document} represents the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:i$$\end{document}th individual’ relative rank in the distribution of socioeconomic status. The concentration index is between − 1 and 1. If CI > 0, then it means that there is a pro-rich inequality. If CI < 0, then it means that there is a pro-poor inequality. If CI = 0, then it means that there is no inequality. The larger absolute value of the index, the higher the degree of inequality. Because the perceived access to health services variable in our study is dichotomous and bounded, Erreygers index (EI) [30], a corrected version of the concentration index (CI), was used in our study. The EI is calculated as:

2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:EI\left(h\right)=\frac{4\mu\:}{{h}^{max}-{h}^{min}}CI$$\end{document}

where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{h}^{max}$$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{h}^{min}$$\end{document} represent the maximum and minimum value to the perceived access to health services. In this study, the maximum and minimum values are 1 and 0 respectively. The interpretation of EI is similar to CI.

The recentered influence function (RIF) regression decomposition method [20] was used to decompose socioeconomic inequality in perceived access to health services. In short, there were two steps for this method: first, calculation of the RIF of the inequality index; and second, regressing the RIF on a set of covariates yielding the marginal effects of the covariates on the inequality index.

This technique assumes a linear relationship between the RIF vector and the covariates, so ordinary least squares (OLS) regressions can be used, with the estimated coefficients being the marginal effects of the covariates on the inequality index [20]. We employed the RIF-OLS regression of Erreygers index (EI) to estimate the marginal effects of covariates on socioeconomic inequality in perceived access to health services.

The RIF-OLS equation can thus be expressed as function of covariate, X, and an error term.

3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\text{R}\text{I}\text{F}}_{\text{i}}^{\text{E}\text{I}}={{\alpha\:}}_{0}+{{\alpha\:}}_{1}{\text{X}}_{\text{i}}+{{\epsilon\:}}_{\text{i}}$$\end{document}

Where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\text{R}\text{I}\text{F}}_{\text{i}}^{\text{E}\text{I}}$$\end{document} is the RIF of the Erreygers index (EI). \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\text{X}}_{\text{i}}$$\end{document} is a set of covariates, including predisposing factors, enabling factors and needs-based factors. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{{\alpha\:}}_{1}$$\end{document} is the coefficient, representing the marginal effect of the covariate. A positive coefficient indicates that the factor increases inequality, and a negative coefficient indicates that the factor reduces inequality. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{{\epsilon\:}}_{\text{i}}$$\end{document} is the error term with E(\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{{\epsilon\:}}_{\text{i}}$$\end{document}|\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\text{X}}_{\text{i}}$$\end{document}) = 0.

For details, readers can refer to Heckley et al. (2016) [20].

Statistical analysis

We first analyzed the distribution of perceived access to health services stratified by socioeconomic status. Second, we calculated the EI over the study period (2008–2018) to analyze temporal trends in socioeconomic inequality in perceived access to health services. Due to the existence of urban-rural dual system, we divided the samples into urban and rural areas, and calculated the EI respectively. We also stratified the samples by age. Third, we analyzed the impact of socioeconomic status on perceived access to health services by using a panel logit regression model because the data are longitudinal and the dependent variable is a binary discrete variable. The results of the Hausman test were not significant, which supported our choice of a random-effects model. Finally, the RIF regression decomposition method was used to decompose socioeconomic inequality in perceived access to health services. To control for the influence of loss to follow-up due to mortality and other forms of attrition, we estimated regression models using inverse probability weighting (IPW) [31, 32]. The weights were estimated by fitting a logit regression model with variables that contributed to follow-up. These variables included perceived access to health services, predisposing factors, enabling factors, and needs-based factors. Based on this model, we calculated probabilities for each participant and applied the inverse of these probabilities as weights in the subsequent analysis. Higher weights were assigned to individuals with key demographic, socioeconomic, and health factors that contribute to a higher probability of dropping out of the study. To mitigate potential bias due to missing data, we conducted ten rounds of multiple imputation with chained equations (MICE) to address missingness in four key missing variables: education, income, self-rated health, and primary caregiver in case of illness. The following analysis was based on the multiply imputed data. All analyses were performed using Stata version 16.

Results

Characteristic of the sample

Table 1 reports descriptive statistics for the study variables. While 94.59% of the older adults reported that they would be able to access health services if they should need it, a sizeable minority (5.41%) accounting for 2,346 (of 43,381) older adults respondents indicated that they would not be able to access services even when they needed such care. The average age of the older adults respondents was 86 years, 37% of the sample had a spouse, while 63% were single, divorced, or widowed. About 80% of respondents reported having adequate financial supports for living. The average number of years of education was 2.5 years, with 81% of respondents covered through basic health insurance. About 20% of respondents had a retirement pension, and 17% had old-age insurance. About half of the respondents reported good self-rated health.

Table 1 Descriptive statistics

Variables	Category	Mean	Standard Deviation	Minimum	Maximum	
Dependent variable						
 Perceived access to health services	Yes=1;No=0	0.95	0.23	0	1	
Independent variables						
Predisposing factors						
 Gender	Male=1;Female=0	0.44	0.50	0	1	
 Age	Continuous variable	86.38	11.29	65	117	
 Married and having a spouse	Yes=1;No=0	0.37	0.48	0	1	
 Place of residence	Urban=1;Rural=0	0.47	0.50	0	1	
 Education	Continuous variable	2.51	3.69	0	23	
Enabling factors						
 Income(Chinese Yuan, CNY)	Continuous variable	27081.65	26550.50	0	99,990	
 Adequate financial support	Yes=1;No=0	0.81	0.39	0	1	
 Self-rated economic status						
  Rich	Yes=1;No=0	0.16	0.37	0	1	
  Neutral	Yes=1;No=0	0.69	0.46	0	1	
  Poor	Yes=1;No=0	0.15	0.36	0	1	
 Living arrangement						
  Live with family	Yes=1;No=0	0.81	0.39	0	1	
  Living in Institutions	Yes=1;No=0	0.02	0.15	0	1	
  Live alone	Yes=1;No=0	0.17	0.37	0	1	
 Social security						
  Basic health insurance	Yes=1;No=0	0.81	0.39	0	1	
  Retirement pension	Yes=1;No=0	0.19	0.39	0	1	
  Public old-age insurance	Yes=1;No=0	0.17	0.38	0	1	
 Primary caregiver in case of illness						
  Spouse	Yes=1;No=0	0.25	0.43	0	1	
  Children	Yes=1;No=0	0.68	0.46	0	1	
 Others (including relatives, friends and society)	Yes=1;No=0	0.05	0.22	0	1	
  No caregivers	Yes=1;No=0	0.02	0.14	0	1	
Need-based factors						
 Self-rated health						
  Poor	Yes=1;No=0	0.16	0.37	0	1	
  Neutral	Yes=1;No=0	0.37	0.48	0	1	
  Good	Yes=1;No=0	0.47	0.50	0	1	
Region						
  Eastern China	Yes=1;No=0	0.47	0.50	0	1	
  Middle China	Yes=1;No=0	0.28	0.45	0	1	
  Western China	Yes=1;No=0	0.25	0.43	0	1	
Year						
 2008	Yes=1;No=0	0.37	0.48	0	1	
 2011	Yes=1;No=0	0.20	0.40	0	1	
 2014	Yes=1;No=0	0.14	0.35	0	1	
 2018	Yes=1;No=0	0.29	0.45	0	1	
Observations	43,381	

Distribution of perceived access to health services

Table 2 shows the distribution of perceived access to health services among different socioeconomic groups. Pearson’s chi-squared test was used to judge whether there were significant differences between groups. Among the lowest-income groups, the older adults reported the larger percentage of perceived inability to access adequate health services should they need it. As income increased, perceived access to health services increased as expected. There were significant differences in perceived access to health services among the four groups (p < 0.001). The results demonstrate that a decline in socioeconomic status was associated with an increase in the percentage of those who would not be able to access adequate health services.

Table 2 The relationship between socioeconomic status and perceived access to health services

	Perceived access to health services
No. & (%) of respondents	P value	Observations	
No	Yes	
Income level			< 0.001		
 First quantile	1,199(11.05)	9,649(88.95)		10,848	
 Second quantile	640(4.55)	13,424 (95.45)		14,064	
 Third quantile	183(2.27)	7,890(97.73)		8,073	
 Fourth quantile	324(3.12)	10,072(96.88)		10,396	
Self-rated economic status			< 0.001		
 Poor	1,391(21.70)	5,019(78.30)		6,410	
 Neutral	885(2.95)	29,128(97.05)		30,013	
 Rich	70(1.01)	6,888(98.99)		6,958	
Adequate financial support			< 0.001		
 No	1,477(17.81)	6,816(82.19)		8,293	
 Yes	869(2.48)	34,219(97.52)		35,088	
Whether received any education			< 0.001		
 No	1,662(6.86)	22,565(93.14)		24,227	
 Yes	684(3.57)	18,470(96.43)		19,154	
Note Percentages are in brackets; P-value results are obtained by Pearson chi-squared test

The older adults who reported self-rated poor economic status were more likely to report difficulties in access to health services. There were significant differences in perceived access to health services by economic status with the rich reporting fewer difficulties than the poor (p < 0.001).

The perceived ability to access health services was significantly (p < 0.001) greater among the older adults with adequate financial supports compared to those with inadequate supports. A similar phenomenon was found with educational attainment with perceived access to health services significantly greater among those with more years of education (p < 0.001).

The results demonstrated that there were significant differences in perceived access to health services when stratified by different measures of socioeconomic status.

Socioeconomic inequality in perceived access to health services from 2008 to 2018

Erreygers index (EI) was used to measure socioeconomic inequality in perceived access to health services for each survey wave. By comparing the EI value in each survey wave, we may assess the temporal trends in the degree of the socioeconomic inequality in perceived access to health services over the study period (2008–2018). Because the determinants of and temporal trends in the EI may vary regionally, estimates were calculated separately for rural and urban areas. Additionally, separate indices were formed for those aged 80 years and older and those under 80 years.

Table 3 reports the socioeconomic inequality in perceived access to health services over the study period. EI was always greater than zero over the study period, indicating pro-rich inequality in perceived access to health services among the older adults. This suggests that the older adults in higher socioeconomic groups were more likely to have sufficient access to health services should they need it than those in lower socioeconomic groups. Except in 2014, the inequality was always greater in rural areas and in those aged 80 years and older than their urban and younger counterparts, respectively.

Table 3 Socioeconomic inequality in perceived access to health services over time

Samples	2008	2011	2014	2018	overall	
The whole	0.1570	0.1122	0.0463	0.0464	0.1138	
Urban	0.0858	0.0831	0.0421	0.0413	0.0669	
Rural	0.1641	0.1254	0.0369	0.0475	0.1272	
Aged 80 and above	0.1591	0.1213	0.0446	0.0534	0.1190	
Under 80	0.1500	0.0836	0.0548	0.0233	0.0934	
Note The above results are EI values, which are all significant at the 1% level

Figure 1 shows the temporal trend in the index of inequality from 2008 to 2018 for various groups. Table 4 shows the percentage of annual changes in inequality over time. Although inequality increased slightly between 2014 and 2018, the overall trend was that inequality decreased between 2008 and 2018. In all survey years, the inequality in perceived access to health services showed a continuous downward trend in urban areas and among older adults under 80 years old.

Fig. 1 The trend of inequality from 2008–2018

Table 4 Annual changes in inequality over time

Samples	2008–2011(%)	2011–2014(%)	2014–2018(%)	
The whole	-28.50	-58.76	0.21	
Urban	-3.10	-49.32	-1.92	
Rural	-23.58	-70.60	28.86	
Aged 80 and above	-23.76	-63.22	19.70	
Under 80	-44.29	-34.48	-57.48	
Note The percentage of annual changes in inequality over time

The impact of socioeconomic status on perceived access to health services

Table 5 shows the results of using logit regression to analyze the impact of socioeconomic status on perceived access to health services for various samples. From the combined sample, see column (1), an increase in education and income was associated with an increase in perceived access to health services. In line with expectations, those with adequate financial supports for living and those with higher self-rated economic status were more likely to report greater perceived access to health services than their counterparts.

Table 5 Impact of socioeconomic status on perceived access to health services

Variables	Category	(1)
Whole sample	(2)
Urban sample	(3)
Rural sample	(4)
Aged 80 and above	(5)
Under 80	
Predisposing factors							
Gender	Male=1;Female=0	-0.042	-0.137	0.019	-0.005	-0.083	
		(0.062)	(0.112)	(0.076)	(0.072)	(0.123)	
Age	Continuous variable	-0.011***	-0.011**	-0.011***			
		(0.003)	(0.005)	(0.004)			
Married and having a spouse	Yes=1;No=0	0.438***	0.418**	0.459***	0.470***	0.302*	
		(0.089)	(0.166)	(0.109)	(0.105)	(0.169)	
Place of residence	Urban=1;Rural=0	0.574***			0.592***	0.455***	
		(0.060)			(0.067)	(0.125)	
Education	Continuous variable	0.036***	0.053***	0.026*	0.050***	-0.008	
		(0.012)	(0.018)	(0.015)	(0.015)	(0.018)	
Enabling factors							
Income	Continuous variable	0.048***	0.048*	0.045**	0.044***	0.075**	
		(0.015)	(0.025)	(0.019)	(0.016)	(0.034)	
Adequate financial support	Yes=1;No=0	1.149***	1.063***	1.196***	1.137***	1.277***	
		(0.067)	(0.121)	(0.083)	(0.075)	(0.133)	
Self-rated economic status	Reference: Poor						
Rich		1.921***	1.640***	2.115***	1.959***	1.721***	
		(0.148)	(0.225)	(0.205)	(0.164)	(0.303)	
Neutral		1.329***	1.279***	1.361***	1.346***	1.247***	
		(0.065)	(0.121)	(0.081)	(0.074)	(0.130)	
Living arrangement	Reference: Live alone						
Live with family		0.179***	0.352***	0.118	0.100	0.493***	
		(0.069)	(0.122)	(0.084)	(0.075)	(0.171)	
Living in Institutions		0.843***	1.175***	0.552*	0.816***	0.919	
		(0.233)	(0.325)	(0.323)	(0.252)	(0.652)	
Social security							
Basic health insurance	Yes=1;No=0	0.486***	0.189*	0.619***	0.500***	0.512***	
		(0.060)	(0.108)	(0.074)	(0.066)	(0.136)	
Retirement pension	Yes=1;No=0	0.397***	0.542***	0.218	0.332**	0.759***	
		(0.123)	(0.152)	(0.212)	(0.144)	(0.221)	
Public old-age insurance	Yes=1;No=0	0.222***	0.163	0.273**	0.206**	0.303**	
		(0.081)	(0.124)	(0.108)	(0.095)	(0.146)	
Primary caregiver in case of illness	Reference: No caregivers						
Spouse		0.486***	0.189*	0.619***	0.500***	0.512***	
		(0.060)	(0.108)	(0.074)	(0.066)	(0.136)	
Children		0.397***	0.542***	0.218	0.332**	0.759***	
		(0.123)	(0.152)	(0.212)	(0.144)	(0.221)	
Others (including relatives, friends and society)		0.222***	0.163	0.273**	0.206**	0.303**	
		(0.081)	(0.124)	(0.108)	(0.095)	(0.146)	
Need-based factors							
Self-rated health	Reference: Poor						
Neutral		0.143**	0.342***	0.062	0.153**	0.072	
		(0.064)	(0.113)	(0.080)	(0.072)	(0.134)	
Good		0.483***	0.552***	0.451***	0.475***	0.540***	
		(0.070)	(0.121)	(0.087)	(0.078)	(0.148)	
Region	Reference: Western China						
Eastern China		0.271***	0.210*	0.265***	0.289***	0.171	
		(0.068)	(0.112)	(0.087)	(0.076)	(0.145)	
Middle China		-0.167**	-0.051	-0.232***	-0.114	-0.495***	
		(0.066)	(0.117)	(0.081)	(0.074)	(0.143)	
Year	Reference: 2008						
2011		0.041	-0.158	0.105	-0.054	0.594***	
		(0.067)	(0.118)	(0.085)	(0.075)	(0.137)	
2014		0.401***	0.197	0.469***	0.304***	0.978***	
		(0.086)	(0.155)	(0.106)	(0.099)	(0.173)	
2018		0.428***	0.048	0.643***	0.295***	1.226***	
		(0.079)	(0.127)	(0.107)	(0.088)	(0.163)	
Constant		1.020***	0.303	1.076***	2.104***	1.791***	
		(0.295)	(0.513)	(0.369)	(0.196)	(0.363)	
Observations		43,381	20,306	23,075	30,147	13,234	
Number of id		28,729	15,427	16,858	21,799	8,413	
Note The data in parentheses are standard errors; *, **, and *** are significant at the 10%, 5%, and 1% levels, respectively; Income is in logarithmic form

When the sample was stratified by urban and rural residence, see columns (2) and (3), all socioeconomic indicators continued to be significantly associated with perceived access to health services. When the sample was stratified by age group, see columns (4) and (5), all socioeconomic indicators except education continued to be positively correlated with perceived access to health services. Educational attainment was not statistically significant among older adults under 80 years old.

The other variables in Table 5 were control variables. Being married, having basic health insurance, having a spousal caregiver and self-rated good health were all important in enhancing perceived access to health services.

Decomposing socioeconomic inequality in perceived access to health services

The results of decomposing socioeconomic inequality in perceived access to health services using the RIF regression decomposition method are shown in Table 6. Each socioeconomic indicator has a different impact on inequality. In the total sample and the sample of older adults under 80 years old, educational attainment exacerbated inequality. In all groups, inequality was lower for respondents reporting adequate financial supports and those who deemed their self-rated economic status as being rich. An increase in income was associated with an decline in the inequality in perceived access to health services.

Table 6 RIF regression decomposition of socioeconomic inequality

Variables	Category	(1)
Whole sample	(2)
Urban sample	(3)
Rural sample	(4)
Aged 80 and above	(5)
Under 80	
Predisposing factors		rifEI	rifEI	rifEI	rifEI	rifEI	
Gender	Male=1;Female=0	-0.003	0.017	-0.007	-0.002	0.000	
		(0.011)	(0.013)	(0.016)	(0.014)	(0.015)	
Age	Continuous variable	-0.000	0.000	-0.001*			
		(0.000)	(0.001)	(0.001)			
Married and having a spouse	Yes=1;No=0	-0.060***	-0.019	-0.091***	-0.057***	-0.051***	
		(0.013)	(0.015)	(0.020)	(0.016)	(0.017)	
Place of residence	Urban=1;Rural=0	-0.018*			-0.015	-0.028**	
		(0.009)			(0.011)	(0.013)	
Education	Continuous variable	0.003**	0.001	0.003	0.003	0.003*	
		(0.001)	(0.001)	(0.002)	(0.002)	(0.002)	
Enabling factors							
Income	Continuous variable	-0.022***	-0.017**	-0.021***	-0.022***	-0.019**	
		(0.005)	(0.007)	(0.007)	(0.006)	(0.009)	
Adequate financial support	Yes=1;No=0	-0.122***	-0.098***	-0.109***	-0.125***	-0.121***	
		(0.016)	(0.020)	(0.021)	(0.019)	(0.023)	
Self-rated economic status	Reference: Poor						
Rich		-0.198***	-0.196***	-0.139***	-0.194***	-0.192***	
		(0.020)	(0.027)	(0.026)	(0.023)	(0.031)	
Neutral		-0.273***	-0.230***	-0.260***	-0.277***	-0.240***	
		(0.020)	(0.027)	(0.025)	(0.023)	(0.031)	
Living arrangement	Reference: Live alone						
Live with family		0.041**	-0.013	0.067***	0.051***	-0.009	
		(0.018)	(0.023)	(0.024)	(0.020)	(0.028)	
Living in Institutions		-0.103***	-0.089**	-0.222**	-0.100**	-0.092	
		(0.038)	(0.039)	(0.094)	(0.042)	(0.088)	
Social security							
Basic health insurance	Yes=1;No=0	-0.042***	-0.009	-0.048**	-0.037**	-0.063***	
		(0.013)	(0.014)	(0.020)	(0.015)	(0.024)	
Retirement pension	Yes=1;No=0	0.062***	0.021**	0.024	0.066***	0.029**	
		(0.010)	(0.009)	(0.027)	(0.013)	(0.014)	
Public old-age insurance	Yes=1;No=0	-0.015*	-0.008	-0.025*	-0.014	-0.015	
		(0.009)	(0.011)	(0.013)	(0.011)	(0.012)	
Primary caregiver in case of illness	Reference: No caregivers						
Spouse		-0.341***	-0.266***	-0.373***	-0.366***	-0.334***	
		(0.067)	(0.090)	(0.090)	(0.093)	(0.083)	
Children		-0.373***	-0.291***	-0.409***	-0.386***	-0.382***	
		(0.067)	(0.092)	(0.090)	(0.092)	(0.085)	
Others (including relatives, friends and society)		-0.284***	-0.230**	-0.252**	-0.304***	-0.249**	
		(0.075)	(0.098)	(0.109)	(0.099)	(0.115)	
Need-based factors							
Self-rated health	Reference: Poor						
Neutral		-0.032*	-0.033*	-0.037	-0.038*	-0.004	
		(0.017)	(0.019)	(0.024)	(0.020)	(0.025)	
Good		-0.048***	-0.033*	-0.054**	-0.060***	-0.003	
		(0.016)	(0.019)	(0.022)	(0.019)	(0.023)	
Region	Reference: Western China						
Eastern China		-0.023**	-0.032**	-0.004	-0.025*	-0.011	
		(0.012)	(0.013)	(0.018)	(0.014)	(0.017)	
Middle China		0.058***	0.001	0.086***	0.056***	0.067***	
		(0.014)	(0.016)	(0.020)	(0.017)	(0.021)	
Year	Reference: 2008						
2011		-0.022*	-0.007	-0.007	-0.012	-0.062***	
		(0.013)	(0.016)	(0.018)	(0.015)	(0.020)	
2014		-0.052***	-0.025	-0.060***	-0.051***	-0.069***	
		(0.012)	(0.016)	(0.018)	(0.015)	(0.020)	
2018		-0.045***	-0.019	-0.040**	-0.039***	-0.080***	
		(0.011)	(0.013)	(0.016)	(0.013)	(0.015)	
Constant		1.097***	0.837***	1.162***	1.090***	1.056***	
		(0.095)	(0.129)	(0.131)	(0.111)	(0.149)	
Observations		43,381	20,306	23,075	30,147	13,234	
R-squared		0.065	0.052	0.059	0.062	0.082	
Note The same as Table 5

Table 6 also shows the results of other control variables. Looking at the overall sample (see column (1)), living with family and having a retirement pension increased inequality in perceived access to health services. Those who had a spouse, lived in an urban area, living in institutions, had basic health insurance coverage, had public old-age insurance, had caregivers and rated their health as good and neutral reported a lower socioeconomic inequality in perceived access to health services.

When the sample was stratified by urban and rural residence, see columns (2) and (3), the factors affecting inequality were slightly different. Age was negatively associated with the inequality in rural areas. Living in institutions, having caregivers and self-rated their health as good reduced the inequality in both urban and rural areas. Having a spouse, having basic health insurance, and having public old-age insurance reduced the inequality in rural areas.

When the sample was stratified by age group, see columns (4) and (5), those having a spouse, having basic health insurance and having caregivers reported lower socioeconomic inequality in perceived access to health services for both age groups. Having a retirement pension increased socioeconomic inequality for both age groups. Self-rated good and neutral health reduced inequality among those aged 80 years and older (columns (4)).

Discussion

This study used the CLHLS’s longitudinal tracking data from 2008 to 2018 to assess the relationships between: various socioeconomic status indicators and perceived access to health services; and the influence of socioeconomic status after controlling for confounders on both perceived access to health services and on socioeconomic inequality in perceived access to health services. Socioeconomic status was measured by income, educational attainment, the adequacy of financial supports for living, and self-rated economic status compared with local residents. A Pearson’s chi-square test was used to assess whether there were significant differences in the distribution of perceived access to health services by socioeconomic status. A panel logit model was used to explore the direct effects of socioeconomic status on perceived access to health services. The Erreygers index (EI) was used to measure the degree of socioeconomic inequality in perceived access to health services. Finally, RIF regression decomposition methods were used to explore the causes of socioeconomic inequality in perceived access to health services.

We found that there was a significant positive association between the socioeconomic status and perceived access to health services. All socioeconomic indicators were all significantly positively related to perceived access to health services. Among those older adults under 80 years old, educational attainment was not associated with perceived access to health services. Except for educational attainment, the three other socioeconomic indicators (income, adequate financial supports, and self-rated economic status) were all significantly related to a lower inequality in perceived access to health services. The findings suggest that those reporting higher socioeconomic status probably have strong economic resources and personal networks that assist in accessing health services should they need it. Our findings are consistent with previous studies. For example, a recent study in Botswana found that the poor were less likely to have received health care the last time they needed health care than the non-poor [33]. Another American study found that low income was a barrier to accessing health care among the older adults [34]. Cylus and Papanicolas reported that low income was associated with perceived access barriers across 29 Europe countries, but perceptions of difficulties accessing health care were not concentrated uniquely among low-income groups [35]. Our finding was consistent with Gao et al.’s and Luo et al.’s studies [36, 37] which found that the older adults with higher incomes were more likely to use inpatient services than those in lower income groups. But both of these studies focused on the inequality in actual use of health services. Luo et al.’s study found that the older adults with higher education were more likely to use hospitalization services than those with fewer years of education [37], which is also consistent with our findings. We found that educational attainment exacerbated the inequality in perceived access to health services among the entire sample of older adults and those under 80 years old. People with higher education levels tend to have higher expectations and standards for health care, so they may be more sensitive to differences in the quality and accessibility of health care. Our findings suggest that increasing the income of the older adults and ensuring the older adults have adequate financial supports and supporting gains in self-rated economic status will help reduce inequality in perceived access to health services.

From 2008 to 2018, there was pro-rich inequality in perceived access to health services, and the inequality showed an overall downward trend. We believe that this decline may be heavily due to the China’s health care system reform plan in 2009. The 2009 reform identified five priority areas: accelerating the construction of the basic medical security system; establishing an essential drug system; improving the capacity of primary healthcare network; promoting equitable access to public health services; and advancing pilot reforms of public hospitals [38]. The goal of the reform is to achieve universal coverage of essential health services for all Chinese citizens by 2020. China’s health care reform has made great progress. One study reported that the share of the population covered by 3 social health insurance schemes increased from 15% in 2000 to 85% in 2008, to more than 97% in 2015 [16]. Another study reporeted that from 2009 to 2019, China has made substantial progress in improving equal access to care and enhancing financial protection, especially for people of a lower socioeconomic status [39]. Our findings on pro-rich inequality were consistent with the studies by Fu et al. and Kim et al. [14, 40]. Fu et al. analyzed socioeconomic inequality in the possibility and frequency of outpatient and inpatient service use, and found that the use of all services favored the better-off [14]. Kim et al. also found pro-rich inequalities in the use of both outpatient and inpatient care among the older adults [40]. But these two previous studies focused on inequality in actual utilization of health services, whereas we explored inequality in perceived access to health services. Our finding was partially inconsistent with the Chinese study by Yang [41]. While Yang found pro-poor and increasing inequality in folk doctor care, Yang also reported (in line with our results) pro-rich and declining inequality in preventive care. Yang’s research focused on adults in the general population, while our study focused exclusively on the older adults. It is worth noting that we focused on whether the respondents can get adequate health care services when they are sick. We did not distinguish between the type and amount of health services provided, as this information was lacking in the questionnaire. However, it cannot be ignored that people’s perception of accessing health services or the actual use of health services may vary depending on the type and amount of health services provided.

This study has also yielded some other interesting findings. We found that individuals with (unpaid) caregivers as well as those living in institutions were more likely to report both perceived access to health services and lower socioeconomic inequality compared to their counterparts. Our findings echo an earlier study [42]. It is possible that the direct assistance in health and social care offered to those with caregivers and those living in institutions places them in an opportune situation to further access timely care when it is needed. We also found that having basic health insurance also increased the possibility of perceived access to health services and reduced socioeconomic inequality. This finding is consistent with a recent study that found that the implementation of the Urban Residents Basic Medical Insurance (URBMI) program in China increased the use of health services by urban residents [43]. Another Chinese study shows that enrolment in the New Rural Cooperative Medical Scheme (NCMS) significantly increased the probability of seeking treatment at a public village clinic relative to self-treatment [44]. China has gradually established several health insurance schemes [43]. The Urban Employees’ Basic Medical Insurance (UEBMI) (established in 1999) targeted the urban Employees.In order to strengthen health services for rural residents, the government launched the New Rural Cooperative Medical System (NCMS) in 2003. The Urban Resident Basic Medical Insurance (URBMI) pilot program began in 2007 and fully scaled-up across the country in 2009. URBMI is for the elderly, the unemployed, and children (including students) who are not covered by other health insurance.Our findings support the success of China’s health insurance program. A study conducted in the Philippines also found that the expansion in the National Health Insurance Program for older adults promoted equitable access to health services [45]. Further support for this finding was reported with respect to improvements in National Health Insurance coverage in Ghana that was associated with an increase in the use of health services among the rural older adults but pro-rich inequality exists in healthcare utilization between the rich and the poor [46]. Health insurance tends to lower the out-of-pocket costs of health care to users, which raises utilization particularly among the poor who tend to be more responsive to the decline in out-of-pocket costs.

Some potential limitations should be noted in the study. First, the dependent variable used was perceived access to health services as derived from responses to the question “Can you get adequate health care service when you are sick?”. Responses to this question were based on subjective judgments by respondents. This does not necessarily reflect actual use of health services when they are sick. Second, there are many possible indicators of socioeconomic status. The four indicators selected in this study were based on the data available from the CLHLS. Indicators such as housing wealth were not available from the CLHLS, so the indicators used in this study reflect a subset of all possible indicators that may have been employed. However, the four indicators that were used included subjective and objective indicators and offer a somewhat comprehensive set of measures. Third, due to data unavailability, our study may overlook other possible factors influencing perceived access to health services, such as distance to hospitals. However, based on Andersen and Newman Behavioural Model of Health Service Utilization, we selected as many control factors as possible. Last, the study focused on the older adults in China, caution should be exercised in generalizing the findings to other populations. Similarly, caution should be exercised about applying the results to countries with different health systems than China.

Conclusions

Our study found a positive and significant association between socioeconomic status and perceived access to health services. We focus on perceived, rather than actual, inequalities in access to health services. We found socioeconomic inequality in perceived access to health services in China that was pro-rich and followed a declining temporal trend for all sub-groups of the population studied herein. Although educational attainment improves perceived access to health services, it does not itself reduce socioeconomic inequality in perceived access to health services. Instead, educational attainment increased the inequality in perceived access to health services. Improvements in income and the adequacy of financial supports and gains in self-rated economic status will help reduce socioeconomic inequality in perceived access to health services for the older adults. Enhanced health insurance coverage for the older adults would further reduce socioeconomic inequality. Enhancements in the provision of necessary support services, including both formal and informal caregiving, especially from spouses, other family members, relatives and friends, as well as formal care from health care institutions increase the perceived access to health services when individuals need those services. As the older adults living alone face the greatest risk of being unable to access health services if they should need it, there is an important need to prioritize and target policies to such populations.

Acknowledgements

Not applicable.

Author contributions

J.C. and R.L. contributed to the design of the study. J.C. and Y.L. participated in statistical analysis and wrote the first draft. P.C.C. critically revised the paper for important intellectual content. All authors reviewed the manuscript.

Funding

This work was funded by National Natural Science Foundation of China (grant number 72374021,71904011).

Data availability

The datasets used and analyzed during the current study are available from Peking University Open Research Data Platform: https://opendata.pku.edu.cn/dataverse/CHADS.

Declarations

Ethics approval and informed consent

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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