**5. Discussion**

The measurement and explanation of income-related inequality in health care attracts much policy interest, especially if reducing health inequality is high on the agenda. In this study, we track changes in the distribution of health care resources across income levels over a 15-year period, during which the government adopted a series of measures that gradually led to a profound reform of the health care system in China. We also explore the influence of various factors (e.g., demographic, socio-economic and health-related characteristics) on inequality.

Our study provides fresh evidence on the uneven access to health care and facilities. The results for the two types of indices we have used to estimate the extent of income-related inequality of health point in the same direction: high-income people tend to obtain more preventive care and use more hospital services, while low-income patients mostly seek care from village clinics/community health centres and folk doctors. The gap of preventive care between the rich and poor might be due to the fact that preventive care is only partially reimbursed by insurance and requires more cost-sharing. Although folk doctor care is not covered by insurance either, it is usually less costly and more accessible compared with hospital services, especially in remote rural areas. This utilization pattern is consistent with evidence from developed countries, with a pro-poor distribution in primary care use and pro-rich distribution in the use of specialised care (Van Doorslaer et al. 2000; Van Doorslaer et al. 2004; Van Doorslaer and Masseria 2004). The pro-poor distribution of the OOP burden is in line with the findings of utilization patterns, indicating that affordability remains a common barrier for the poor to access health care. Further decomposition analysis of the pro-poor inequality in OOP burden suggests that the inequality is largely driven by demographic and socio-economic factors. Higher levels of education, employment and occupation tend to be associated with a larger extent of pro-poor inequality of the OOP burden. In line with the findings of previous research, our results suggest that the recent expansion of social health insurance has a limited impact on the reduction of this inequality (Coté et al. 2013; Cai et al. 2017).

The study findings need to be interpreted in the light of the following limitations. First, we use equivalised household income to measure living standard, but in low- and middle-income countries income is not always a dependable indicator of a household's socioeconomic status, especially when day labour with volatile incomes and subsistence farming and fishing are common (Wagstaff 2009b; Wagstaff et al. 2003). However, in the context of China, income measures were regarded as more reliable than household expenditure since expenditure data might be distorted by the high saving rates of Chinese households (Sun et al. 2010; Yang 2013). Second, the nine provinces included in our analyses vary considerably in terms of demographics and economic development levels, so that comparing households' incomes in fairly rich and prosperous areas in the eastern region

with those in worse-off and more rural areas without accounting for the differences in purchasing power might be problematic. Third, although the data have a longitudinal (panel) structure, this feature is not exploited in the empirical analysis since health care utilization was only reported by people who fell sick during the study period. Therefore, our results indicate the association between income-related inequality in the burden of OOP payments and various demographic and socio-economic characteristics and do not intend to obtain causal inference. Fourth, in the survey OOP payments are reported for the last four weeks only, and therefore there is a high risk of random high expenditure and random zero expenditure. Previous literature also pointed out that CHNS has a much lower OOP level on average compared to other household surveys in China because it might ignore the expenditure of people who were still in hospital at the time of the interview (Wagstaff and Lindelow 2008). However, with a longer reporting period (e.g., one year), OOP might also suffer from recall bias. As far as OOP payments are concerned, data on the previous month is the only source we could obtain. We need to bear in mind that we might underestimate or overestimate the OOP burden given the limitations stated above. Fifth, health care utilization behaviour is usually shaped by both financial (e.g., price elasticity, income levels, insurance coverage) and non-financial factors (e.g., health care need, quality of care, availability of transportation, health care personnel and infrastructure). In this paper, we focus on financial access to care, but evidence is lacking with respect to whether or not the health care reforms led to any change in non-financial access barriers and how this varied across different socioeconomic groups. Finally, primary care facilities and high-level specialised hospitals tend to serve different types of patients so that the observed inequality in facility use might also be related with differential levels of health care needs across income. It would be valuable to obtain more objective and reliable quality measurements for a rigorous assessment of the scale of inequality in health care use. The above issues could be the subject of future research through well-designed surveys and field studies conducted in more recent years.

#### **6. Conclusions**

Inequality in health care is a common challenge worldwide, especially in low- and middle-income countries that are looking for means of ensuring access to basic health care and protecting poor patients from health payment-induced impoverishment. Our findings have high relevance in the debate over the use of publicly sponsored health insurance programmes in tackling income dependence of health care use in China and other developing countries. An important policy lesson drawn from the study is that broad insurance coverage at population level does not necessarily lead to equal access to good quality health care. Our findings show there are still inequalities in the use of preventive care and hospital services across people from different income groups, indicating that the poor are faced with a heavy financial burden due to high insurance co-payments and insufficient coverage. Insufficient coverage of preventive care among the poor could lead to a disease-poverty trap as minor conditions would develop into severe illnesses that require specialist care from high-level hospitals and long-term use of medication (Xu et al. 2007). Early detection through screening or diagnostic tests could be a more cost-effective strategy compared with expensive acute care to tackle the challenges of the recent epidemiological transitions from infectious diseases to non-communicable diseases. Therefore, preventive care should be an integral part of a comprehensive insurance coverage to adjust for the socio-economic gradient in disease burdens (Yang 2013). To reduce the socio-economic gap in the access to health services, it is important to extend benefit packages to preventive care and hospital services. The expansion can be achieved incrementally as government subsidies and insurance premiums increase over the years, so that the means to extend the types of services covered by the insurance are compatible with the means to achieve equity. In addition, a well-functioning primary care system would provide more affordable and good-quality health care for patients from vulnerable socio-economic groups. Compared to investing most of the public resources in specialised hospitals, strengthening the delivery

of basic needs-oriented primary care is a more viable way to benefit the majority of the patients.

In recent years, the Chinese government has attached greater importance to achieving a more balanced allocation of resources to primary care clinics and high-level hospitals by increasing funding for strengthening community health centres in cities and village clinics and township hospitals in rural areas. However, there is still a lack of well-trained personnel in many primary care facilities so that they cannot sufficiently meet the needs of the wide population (Mossialos et al. 2016). A comprehensive insurance coverage for health services combined with a strong primary care delivery system could help reduce disparities in health and health care across incomes. Even though Chinese policymakers have already started to address some of the issues identified above, stronger and more positive policy responses still need to be developed to close the socioeconomic gap in the access to health resources.

**Author Contributions:** Conceptualization, M.Y. and G.E.; methodology, M.Y. and G.E.; formal analysis, M.Y. and G.E.; data curation, M.Y.; writing—original draft preparation, M.Y.; writing review and editing, G.E.; funding acquisition, M.Y. and G.E. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by PhD scholarship from the Lancaster University Management School.

**Informed Consent Statement:** The data collectors of the China Health and Nutrition Survey (CHNS) obtained informed consent from all subjects involved in the study.

**Data Availability Statement:** The data used by this study are publicly available at: http://www.cpc. unc.edu/projects/china. Data were accessed and downloaded in January 2020.

**Acknowledgments:** Financial support by PhD scholarship from Lancaster University Management School is gratefully acknowledged. This research uses data from China Health and Nutrition Survey (CHNS). We thank the National Institute for Nutrition and Health, China Centre for Disease Control and Prevention, Carolina Population Centre (P2C HD050924, T32 HD007168), the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, R24 HD050924, and R01-HD38700) and the NIH Fogarty International Centre (D43 TW009077, D43 TW007709) for financial support for the CHNS data collection and analysis files from 1989 to 2015 and future surveys, and the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009, Chinese National Human Genome Centre at Shanghai since 2009, and Beijing Municipal Centre for Disease Prevention and Control since 2011.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **Appendix A**

In this appendix we provide the formulas of the indices we have calculated and of the dependent variables we have used in the regression-based decompositions. More details can be found in Kessels and Erreygers (2019).

We consider a population of *n* individuals, labelled by the subscript *i* = 1, 2, ... , *n*. Let *h* stand for health, *y* for income, and *r* for income rank (with the poorest person having rank 1, the second poorest rank 2, etc.). Since all health outcome variables of the paper are bounded between 0 and 1, we have used the bounded-variable versions of the indices. The rank-dependent index is equal to:

$$R = \frac{4}{n} \sum\_{i=1}^{n} \left[ \frac{(2r\_i - 1)}{n} - 1 \right] h\_i \tag{A1}$$

while the level-dependent index is equal to:

$$L = \frac{1}{n} \sum\_{i=1}^{n} \left[ \frac{y\_i}{\mu(y)} - 1 \right] h\_i \tag{A2}$$

where μ(*y*) represents mean income.

The decomposition analysis in the paper is applied to OOP burden, which is an illhealth variable. Let *z* be this variable. The dependent variable *d<sup>R</sup>* of the decomposition regression for the rank-dependent index is then defined as:

$$d\_i^R = 4\left[1 - \mu(z) - \frac{(2r\_i - 1)}{n}(1 - z\_i)\right] \tag{A3}$$

where μ(*z*) is the mean of the ill-health variable. Likewise, the dependent variable *d<sup>L</sup>* of the decomposition regression for the level-dependent index is equal to:

$$d\_i^L = 1 - \mu(z) - \frac{y\_i}{\mu(y)}(1 - z\_i) \tag{A4}$$






below 5% level.


*Economies* **2022** , *10*, 321

**Table A2.** Effect of selected

demographic

 and

socioeconomic

 variables in the

decomposition

 of the

level-dependent

 indices for OOP burden



1. For each wave, the first column shows the estimated marginal effects and the second the logworth values. Logworth values in bold indicate significance at or below5%level.

#### **References**


Dong, Keyong. 2009. Medical insurance system evolution in China. *China Economic Review* 20: 591–97. [CrossRef]

Elwell-Sutton, Timothy M., Chao Qiang Jiang, Wei Sen Zhang, Kar Keung Cheng, Tai H. Lam, Gabriel M. Leung, and Catherine Mary Schooling. 2013. Inequality and inequity in access to health care and treatment for chronic conditions in China: The Guangzhou Biobank Cohort Study. *Health Policy and Planning* 28: 467–79. [CrossRef]

Erreygers, Guido. 2009. Correcting the concentration index. *Journal of Health Economics* 28: 504–15. [CrossRef]


Zhang, Xin, Qunhong Wu, Yongxiang Shao, Wenqi Fu, Guoxiang Liu, and Peter C. Coyte. 2015. Socioeconomic Inequities in Health Care Utilization in China. *Asia-Pacific Journal of Public Health* 27: 429–38. [CrossRef]

Zhou, Zhongliang, Jianmin Gao, Ashley Fox, Keqin Rao, Ke Xu, Ling Xu, and Yaoguang Zhang. 2011. Measuring the equity of inpatient utilization in Chinese rural areas. *BMC Health Services Research* 11: 201. [CrossRef]

MDPI St. Alban-Anlage 66 4052 Basel Switzerland Tel. +41 61 683 77 34 Fax +41 61 302 89 18 www.mdpi.com

*Economies* Editorial Office E-mail: economies@mdpi.com www.mdpi.com/journal/economies

MDPI St. Alban-Anlage 66 4052 Basel Switzerland Tel: +41 61 683 77 34

www.mdpi.com ISBN 978-3-0365-6893-5