Catastrophic health expenditure and its inequality in rural China: evidence from 2013-2018 CHARLS

doi:10.21203/rs.3.rs-2184568/v1

Background: Catastrophic health expenditure (CHE) is an important indicator for measuring health inequality. Previous studies mainly focused on whole populations rather than specific vulnerable groups through the regional survey or cross-sectional data. China’s rural area is often associated with an underdeveloped economy and insufficient health resources. This study aims to update the information on the extent and trends in incidence and inequality of CHE among the households of rural China through CHARLS panel data.

Methods：Data were obtained from three waves of the China Health and Retirement Longitudinal Study (CHARLS): 2013, 2015, and 2018. CHE was defined as the proportion of household health expenditures to the non-food expenditure more than 40% threshold. The inequality of CHE was measured by the concentration index. Decomposition methods were used to decompose the concentration index into its determining components.

Results：The incidence of CHE was 23.41% (95% CI: 0.22, 0.25) in 2013, 21.36% (95% CI: 0.20, 0.23) in 2015 and 28.97% (95% CI: 0.27, 0.31) in 2018 in rural households. The concentration index was negative: -0.1528 (95% CI: -0.1941, -0.1115) in 2013, -0.1010 (95% CI: -0.1442, -0. 0577) in 2015 and -0.0819 (95% CI: -0.1170, -0.0467) in 2018. Economic status, age, and chronic disease were the main contributors to the inequality of CHE.

Conclusions：The incidence of CHE in rural households of China displayed an upward trend. Furthermore, there existed a strong pro-poor inequality of CHE in rural China. Economic status, age, and chronic diseasewere the main contributors to the pro-poor inequality. Health policies to allocate resources and services are needed to satisfy the needs of rural households and provide more accessible and affordable health services. More concerns need to be directed toward households with chronic diseases and households with the elderly. Meanwhile, policymakers need to pay more attention to relieving the incidence of CHE and promoting health equality.

Catastrophic health expenditure

Inequality

Concentration index

Decomposition

Rural China

Health expenditure is considered “catastrophic” when it seriously affects the household’s quality of life, and it is recognized as catastrophic health expenditure (CHE). Generally, CHE may force households to cut their basic healthcare necessities, sell assets, and even be moved into long-term debt [1]. As a major developing country, although China had made considerable achievements in economic development and healthcare reform, CHE had also presented a concerning healthcare challenge for China [2]

Especially, China’s rural area is often associated with an underdeveloped economy and insufficient healthcare resources; therefore, CHE is also highly close to rural households. Evidence from the Fourth National Health Service Survey (NHSS) investigated that the rural households incurred CHE was 14.4% [3], while based on data from the Sixth NHSS of Jiangsu examined that the incidence of CHE in rural households was 32.28% [4]. Meanwhile, a wealth of studies indicated that China’s New Cooperative Medical Scheme (NCMS) was limited effectively to prevent rural households from CHE, which remains at a disconcerting level, despite over 95% of coverage [5–8]. These studies emphasized that CHE negatively affected the quality of life and even trapped rural households in a vicious circle of “illness due to poverty and poverty caused by illness”.

Income-related inequality concerning CHE in rural areas attracts considerable attention from numerous scholars. Abundant studies supported the notion that income-related inequality in rural areas existed a strong pro-poor inequality of CHE by the methods of concentration index, treatment-effect model, or chi-square tests [9–14]. Besides, a growing number of studies reported that economic status, education levels, lifestyle, households with elderly, with disabilities or chronic diseases, and other factors were the critical risk factors for CHE and its inequality [15–17]. Therefore, identifying income-related inequality of CHE is merit in developing appropriate strategies and further achieving health equality for rural China.

These studies highlighted the influence of CHE and proved that more care should be taken of CHE for rural households, providing essential theoretical references for this study. Nevertheless, there were few holistic studies in relation to the extent, trend, and relevant factors of CHE in rural China, and the use of panel data was rare. In addition, the utilization of concentration curve and index approaches in the previous analysis to measure income-related CHE inequality was insufficient. Finally, although some studies examined the income-related inequality of CHE by other methods, they ignored the quantified related contribution of the determinants of CHE. In light of this, this study aims to (1) measure the overall extent and trend of CHE among the rural households of China. (2) to update the information on income-related inequality of CHE in rural households. (3) to further decompose income-related inequality into the contributing factors and quantify the contribution of the determinants of CHE. (4) to provide implementable recommendations for improving government policies and reducing the incidence and income-related inequality of CHE. This study will be of great reference significance to facilitate understanding and improving CHE in rural China.

Ethics

The ethics review of Peking University approved the CHARLS study (approval number IRB00001052–11015). Informed consent was obtained, and the data were anonymized for the analysis.

Data

Data were derived from CHARLS, which covered 450 communities in 150 counties from 28 of the 32 provinces in mainland China. CHARLS, implemented by Peking University, aimed to collect a nationally representative sample of people aged 45 years or older to support aging and health-related research through a structured questionnaire (the data and questionnaire are available at http://charls.pku.edu.cn/). After data cleaning, 2,575 households were finally enrolled in this study. The detailed process was shown in Fig. 1.

- Fig. 1 around here-

Measures

The contents of the CHARLS questionnaire include basic personal information, household structure, health status, and other information. According to the questionnaires, rural households were defined by the item: “Was your address in the village or city/town?”. The dependent variable is whether the household occurs CHE, which is a binary variable. The key independent variables include gender, age, marriage, household size, economic status, education, insurance, smoking status, alcohol consumption, disability, chronic disease, and health utilization.

The incidence of CHE refers to the ratio of households with CHE to all sample households in the survey [18]. CHE is defined as 40% or more of the household capacity to pay (CTP) expenditure allocated towards the annual out-of-pocket (OOP) healthcare payment [19]. The indicator is calculated to determine whether CHE occurred:

$$CHE=\left\{\begin{array}{c}1 if\frac{OOP{ }_{h}}{CTP{ }_{h}}\gg 40\%\\ 0 if\frac{OOP{ }_{h}}{CTP{ }_{h}}<40\%\end{array}\right\}$$

1

Measuring CHE inequality

The concentration curve, concentration index, and its decomposition were applied to analyze the inequality of CHE. The concentration curve and index were used to measure the extent of income-related inequality in the distribution of CHE across households. When the Concentration curve is above the line of equality, it denotes that inequality is concentrated in poor households; conversely, when it is under the line of equality, which denotes the opposite. The concentration index is contributed to facilitating the identification of an effective way to reduce inequality. The concentration index ranges from − 1 to 1, with a value of 0 indicating complete equality across income groups; a positive concentration index denotes that households with high-economic status are easier to suffer CHE than their low-economic counterparts, whereas a negative index denotes the opposite [20]. The CI formula was as follows:

$$\left(\text{C}=\frac{2}{{\mu }}\text{c}\text{o}\text{v}\left({\text{Y}}_{\text{i}},{\text{R}}_{\text{i}}\right)\right)$$

2

Where $cov$ is the covariance, ${Y}_{i}$ is the outcome variable, ${\mu }$ is the mean of ${Y}_{i}$; ${R}_{i}$ stands for the fractional ranks of household income. Ri = i/N, i = 1 denotes the lowest-income households, i = N denotes the highest-income households.

Logistic regression was employed to analyze the odds ratios (OR) for CHE after controlling for confounding factors.

Decomposition methods

Inequality can be further explained by decomposing the concentration index into its determining components. These determinants were selected according to previous research, and constrained by the variables collected in the investigation [21]. Since CHE was a dummy variable, a probit model was employed to decompose and standardize the inequality of CHE. The regression model was:

$$\text{y}={\alpha }^{m}+{\sum }_{j}{\beta }_{j}^{m}{x}_{j}+{\sum }_{k}{\gamma }_{k}^{m}{Z}_{k}+\epsilon$$

3

Where ${\beta }_{j}^{m}$ and ${\gamma }_{k}^{m}$are marginal effects, namely ${dy/dx}_{j}$ and ${dy/dz}_{k}$; ε is the residual term. The decomposition result of the concentration index of the dependent variable $\text{y}$ is as follows:

Where $\text{C}$ is the concentration index of $\text{y}$, ${\mu }$ is the mean of $\text{y}$; ${C}_{j}$ is the concentration index of ${x}_{j}$; ${C}_{\mu }$is the concentration index of the residual term; $\overline{{x}_{j}}$and $\overline{\epsilon }$are the mean of ${x}_{j}$and $\epsilon$, respectively.

Household descriptive statistics

Table 1 presents summary descriptive statistics for independent variables in 2013. 50.32% of the heads of households were male and the average age was 64.46 years, about 88% were living with a spouse, and the household size was 1.69 people. The average household income was 29,948.32 yuan per year roughly. Nearly 28% of the sample had junior high school level or above education. Insurance covered the overwhelming majority of households (96.23%), 61.59% of the household members smoked, and nearly half of the household members drank (48.66%). It was reported that roughly households with disabled members and chronicle members accounted for 7% and 67%, respectively. The annual outpatient and inpatient rates of the sample were 30% and 11%, and the annual outpatient time and inpatient time was 0.45 and 0.08, respectively.

Table 1

Household descriptive statistics in 2013 (N = 2,575)
Variables	Definition	2013 Mean (S.D.)
Sex	0 = Female 1 = Male	0.49(0.50)
Age	0 = < 50years 1 = 51 ~ 60years 2 = 61 ~ 70years 3 = > 71years	1.89(0.80)
Marriage	0 = With spouse 1 = Else (unmarried, divorced, widowed, etc.)	0.12(0.33)
Household size	Continuous variables	1.69(0.46)
Economic status	1 = Poorest 2 = Poor 3 = Average 4 = Richer 5 = Richest	3.00(1.41)
Education	0 = Elementary school and below 1 = Junior high school and above	0.28(0.45)
Insurance	0 = No 1 = Yes	0.96(0.19)
Smoke	0 = No 1 = Yes	0.62(0.49)
Drink	0 = No 1 = Yes	0.49(0.50)
Disability	0 = No 1 = Yes	0.07(0.25)
Chronic disease	0 = No 1 = Yes	0.67(0.47)
Outpatient	0 = No 1 = Yes	0.30(0.46)
Outpatient times	Continuous variables	0.45(1.32)
inpatient	0 = No 1 = Yes	0.11(0.31)
Inpatient times	Continuous variables	0.08(0.37)
Note: Since there was no significant change in the descriptive data of the households in 2013–2018, only presented those of 2013; S.D., The standard deviation is shown in parentheses.

-Table 1 around here-

The incidence of CHE in rural China

Figure 2 illustrates the incidence of CHE from 2013 to 2018. The incidence of CHE was 23.41% (95% CI: 0.22, 0.25) in 2013, 21.36% (95% CI: 0.20, 0.23) in 2015 and 28.97% (95% CI: 0.27, 0.31) in 2018.

- Fig. 2 around here-

Determinants of the CHE

Table 2 presents the results of the logistic regression analysis of the longitudinal data. Compared with 2013, the incidence of CHE decreased by 0.87 times (95% CI:0.75, 1.00) in 2015 and increased by 1.46 times (95% CI:1.27, 1.68) in 2018 approximately. Having elderly members significantly increased the incidence of CHE. Specifically, the results revealed that households with aged 61–70 and older than 70 were estimated to be 3.11 (95% CI: 1.56 to 6.19) and 5.08 (95% CI: 2.54 to 10.16) times more likely to suffer CHE than the households aged younger than 50 years, respectively. Compared with living with a spouse, not living with a spouse decreased the incidence of CHE by approximately 0.69 times (95% CI: 0.52 to 0.91). The economic status of households was another critical driver of CHE; for example, the richer and the richest group was 0.66 (95% CI: 0.52 to 0.84) and 0.55 (95% CI: 0.44 to 0.71) times less likely than the poorest group to suffer CHE, respectively. Those whose households had member(s) with chronic disease was 1.52 (95% CI: 1.29 to 1.79) times more likely to experience CHE than households with no chronicle member(s). Regarding health care, those who used outpatient services and outpatient times were 1.41 (95% CI: 1.20 to 1.66) and 1.10 (95% CI: 1.04 to 1.15) times more likely than those who did not use such services to occur CHE.

Table 2

Determinants of the CHE by logistic regression model (N = 2,575)
Variables	Odds Ratio	95%C.I.
		lower-bound	upper-bound
Year(compare with 2013)
2015	0.87	0.75	1.00
2018	1.46	1.27	1.68
Sex(compare with female)
Male	0.92	0.80	1.05
Age (compare with younger than 50)
51 ~ 60 years	1.94	0.97	3.86
61 ~ 70 years	3.11	1.56	6.19
Older than 70years	5.08	2.54	10.16
Marriage (compared with a spouse)
Else (unmarried, divorced, widowed, etc.)	0.69	0.52	0.91
Household size	1.03	0.84	1.27
Economic status (compared with poorest)
Poor	1.01	0.80	1.26
Average	0.91	0.73	1.14
Rich	0.66	0.52	0.84
Richest	0.55	0.44	0.71
Education (compared with elementary school and below)
Junior high school and below	0.87	0.73	1.05
Insurance (compare with no insurance)
Insurance	1.06	0.72	1.58
Smoke (compared with no smoke)
Smoke	0.93	0.79	1.09
Drink (compared with no drink)
Drink	0.79	0.68	0.92
Disability (compared with no disability)
Disability	1.16	0.88	1.54
Chronic disease (compared with no chronic disease)
Chronic disease	1.52	1.29	1.79
Outpatient (compared with no outpatient)
Outpatient	1.41	1.20	1.66
Outpatient times	1.10	1.04	1.15
Inpatient (compared with no inpatient)
Inpatient	1.18	0.88	1.58
Inpatient times	1.20	0.94	1.52
Note: C.I., contribution interval.

-Table 2 around here-

Concentration curve and index of CHE in rural China

Figure 3 shows that from 2013 to 2018, the concentration curve for rural households lay above the line of equality, indicating that CHE was more concentrated among low-economic households. Table 3 reveals the concentration index from 2013 to 2018. A positive concentration index indicates that rich households were more likely to suffer CHE, whereas a negative index denotes the opposite. In total, the CI for CHE was negative and decreased significantly from − 0.1528 to − 0.0764 for rural households from 2013 to 2018. The results were all negative, indicating that the inequality of CHE was mainly concentrated in poor rural households.

Table 3

Concentration index of CHE in rural China
time	Concentration index (CI)	95% C.I.
time	Concentration index (CI)	lower-bound	upper-bound
2013	-0.1528	-0.1941	-0.1115
2015	-0.1010	-0.1442	-0.0577
2018	-0.0819	-0.1170	-0.0467
Note: CI, contribution index; C.I., contribution interval.

- Fig. 3 around here-

- Table 3 around here-

Decomposition of concentration index

Table 4 presents the contributions of each determinant to CI. A positive (negative) contribution denotes that the variable raised (reduced) the pro-rich (poor) inequality. In particular, we found that economic status, age, and having chronic disease had the largest (73.50%, 63.26%, 57.72%), second largest (25.13%, 31.51%, 33.91%), and third largest (1.69%, 2.86%, 6.12%) contributions, respectively, to the inequality of CHE from 2013 to 2018.

Table 4

Decomposition of concentration index of incidence
Variables	2013		2015		2018
Variables	Contribution to CI	Contribution to CI %	Contribution to CI	Contribution to CI %	Contribution to CI	Contribution to CI %
Male	-0.0006	0.37	-0.0010	1.02	-0.0007	0.88
Age
51 ~ 60 years	0.0179	-11.68	0.0322	-31.90	0.0132	-16.13
61 ~ 70 years	-0.0040	2.62	-0.0049	4.83	-0.0026	3.19
Older than 70years	-0.0522	34.19	-0.0591	58.58	-0.0384	46.85
Junior high school and below	0.0012	-0.77	-0.0020	2.03	-0.0052	6.34
Marriage (else)	0.0044	-2.86	0.0055	-5.41	0.0040	-4.85
Insurance	0.0002	-0.14	-0.0002	0.24	0.0002	-0.30
Household size	-0.0012	0.79	-0.0022	2.18	0.0036	-4.37
Economic status
Poor	-0.0021	1.36	0.0043	-4.29	-0.0035	4.26
Average	0.0001	-0.05	-0.0001	-0.05	0.0001	-0.10
Richer	-0.0235	15.41	-0.0192	19.06	-0.0146	17.81
Richest	-0.0868	56.78	-0.0490	48.54	-0.0293	35.75
Smoke	-0.0010	0.63	0.0003	-0.33	-0.0001	0.13
Drink	0.0000	-0.03	0.0000	-0.32	0.0000	0.05
Disability	-0.0002	0.14	-0.0004	0.35	-0.0009	1.10
Chronic disease	-0.0026	1.69	-0.0029	2.86	-0.0050	6.12
Outpatient	-0.0006	0.38	-0.0004	0.38	-0.0002	0.30
Outpatient times	-0.0015	0.98	-0.0033	3.24	0.0001	-0.09
Inpatient	-0.0005	0.35	-0.0005	0.46	-0.0003	0.40
Inpatient times	0.0001	-0.07	0.0003	-0.30	0.0002	-0.24
Note: CI, contribution index.

-Table 4 around here-

This study estimated the overall incidence and trend of CHE among rural households in China with longitudinal data from the CHARLS. We also explored the related determinants and inequality of CHE in rural China. It had important reference value for narrowing the health gap across different income groups and improving health equality. Our study has three essential findings.

Firstly, we did observe that the incidence of CHE in rural China displayed an upward trend. Although it was consistent with related literature [3, 9, 21–25], it was unexpected because policies were implemented in this period to reduce the incidence of CHE. For instance, in 2013, China’s central government launched the strategy of Targeted Poverty Alleviation (TPA) to prevent people from being reduced to poverty by health expenditure and other reasons. TPA aimed to get 83 million rural poor people out of poverty, which was defined as under the national absolute poverty line of 2,300 yuan per capita in 2010 by the end of 2020 [23, 26]. TPA, therefore, was of great value to innovating the poverty alleviation methods for rural households. It is, therefore, difficult to explain why the incidence of CHE continued to rise during the period. One potential explanation is that age, economic status, chronic disease, disability, and outpatient had a significant effect on CHE in this study. Numerous studies indicated that households with members with elderly, low-economic status, chronic disease, disability and often utilize healthcare face higher financial risks than other households, and there was distributive income-related inequality in terms of CHE incidence [21, 29, 30]. Another explanation is the massive increase in demand for healthcare so that direct and indirect health expenditure also increased significantly [21]. However, in China, the main payment method for hospital charges is fee-for-service. In a profit-seeking environment, ineffective expenditure controls and with inappropriate risk-sharing mechanisms by the hospital, OOP healthcare payment remains relatively high. As a consequence, patients and their households have greater financial risk and a higher probability of incurring CHE [29, 30].

Secondly, there existed a strong pro-poor inequality of CHE in rural China, identifying inequalities is essential for achieving health equality. The study found that the contribution of economic status made the greatest pro-poor contribution to the inequality of suffering CHE, although those displayed a downward trend. In other words, rising incomes in rural households of China increased differentials in suffering CHE. Besides, age and chronic disease are also closely linked with CHE; numerous studies have shown that of all groups, the elderly are at the greatest risk of incurring CHE [21, 31–33]. In addition, inequality continues to impede the achievement of optimal and equitable health gains [34, 36]. The contribution of age and chronic disease all displayed a downward trend; moreover, the concentration index remained negative and decreased significantly from − 0.1528 in 2013 to -0.0819 in 2018. Although we observed that inequality of CHE decreased among rural households, they were all negative during the study period, indicating a pro-poor inequality in CHE. Potential explanations might be: First of all, the rapid aging of the population is a growing concern. According to China's seventh national population census, the population aged 65 and above accounted for 13.50% in the past 10 years, evidence from iiMedia Research reported that in 2019, the health expenditure of the elderly over 60 years in China was 1.6 times that of non-elderly, and the degree of aging was closely related to health expenditure 3, which would have a major financial burden for their households and society. Then, with the human epidemiological spectrum transitioning, chronic disease has become one of the most important threats to health. According to the “Report on Nutrition and Chronic Disease Status of Chinese Residents (2020)”, the incidence of hypertension and diabetes in Chinese residents aged 18 and above were 27.5% and 11.9% [37]. Finally, compared to the younger counterparts, the elderly population has always been susceptible to chronic disease [38]; therefore, it develops an additive effect, especially in rural households. Since the rural elderly with low income and the absence of social security mechanisms lead to a greater possibility of incurring CHE for households [39, 40]. Hence, we must consider not only concern poor households that had been experienced CHE but also focus on households that are at risk of poverty due to economic status, age, chronic disease, and other inequality factors.

Thirdly, the result showed that social health insurance programs had neither a statistical significance for CHE nor relieved the financial burden for rural households. The social health system is known to have a positive impact on protecting households from CHE [41, 42]. However, regardless of whether the rural households participated in health insurance, the incidence of CHE was high. The finding was similar to previous literature [42, 43]. Potential explanations might be: Firstly, in the research sample, only 3.77% of the 2575 households did not cover by any form of the health insurance program; therefore, fewer samples made that variable was not significant for CHE. Furthermore, due to medical prices increasing and the limited economic ability of rural households, numerous households could not afford health expenditures, which was not reflected in the questionnaire; hence, abandoning treatment and even restraining medical needs were main reasons for underestimating the health expenditures and CHE. More importantly, the weak performance of social health insurance in financial protection for rural households demonstrates that the health security system needs to further improve its effectiveness and sustainability to satisfy the basic health needs of rural households, and to reduce the risk of CHE for rural households of China.

Our research had an important reference value for recognizing the extent and trend of the CHE of rural households in China. At present, there were few holistic studies on the extent of CHE and relevant factors that influence it among rural households in China, and the use of panel data was rare. In addition, introducing the concentration index and its decomposition into the framework of this research allows for a more comprehensive quantification of the related factors that influence CHE.

It is worth noting that this study had its limitations. Firstly, the data were self-reported and limited to the pre-specified questions, personal preference and recall bias, which might make it prone to measurement errors. Secondly, although this analysis covered CHE in 2013, 2015 and 2018, it was not continuous, hence, the data might not be comprehensive enough to identify the changes in inequality of CHE. Last but not least, our estimation of CHE considered only incurred health expenditure, and the adverse impact of health expenditure on rural households that did not seek or give up treatment because they could not afford it was not examined, which might be underestimated incidence and inequality of CHE.

This study provides new knowledge about the extent, trend, relevant factors, and inequality of CHE in the rural households of China by CHARLS panel data. According to the results, the incidence of CHE in rural China displayed an upward trend and its inequality mainly focused on the pro-poor households. Economic status, age, and chronic disease were the main contributors to the inequality of CHE. Therefore, the following policy suggestions are put forward: First of all, health policies to allocate accessibly and afford resources and services are needed to satisfy the needs of rural households, especially for poor households. Then, additional strategies are needed to further reduce the socioeconomic differences and narrow the health gap between different income groups, and more concerns need to be directed toward households with chronic diseases and households with the elderly [39]. Last but not least, there is an urgent need for Chinese policymakers to improve the accuracy of health insurance schemes to target the vulnerable rural households of had been experiencing or have been experiencing CHE.

CHE: Catastrophic health expenditure;

CHARLS: China Health and Retirement Longitudinal Study;

NHSS: National Health Service Survey

NCMS: New Cooperative Medical Scheme

CTP: Capacity to pay;

OOP: Out-of-pocket healthcare payments;

CI: Concentration index;

OR: Odds ratio;

TPA: Targeted Poverty Alleviation

Acknowledgements

We would like to acknowledge the China Health and Retirement Longitudinal Study (CHARLS) team for providing the data.

Authors’ contribution

WZ participated in the study design, data analysis and interpretation, and was the primary person responsible for drafting the manuscript. MS contributed to study design, data analysis and reviews. DL conceived of the study design, and participated in data analysis, writing and revision. TZ and WL participated in revision. All authors read and approved the final manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (72164031&72204128), and the Natural Science Foundation of Inner Mongolia (2020BS07002). The funding bodies had no role in the design of the study or collection, analysis, and interpretation of data, and they did not contribute to the writing of the manuscript.

Availability of data and materials

This data was drawn from the Data were derived from the China Health and Retirement Longitudinal Study (CHARLS). They are opened to everyone. Researchers who want to use these data can visit http://charls.pku.edu.cn/.

Ethics approval and consent to participate

The data used in this study were retrieved from the CHARLS. All participants in the survey signed or marked (if illiterate). The ethics review of Peking University approved the CHARLS study (approval number IRB00001052–11015).

Consent for publication

Not applicable

Competing Interests

The authors declare that they have no competing interests

Authors details

¹School of Public Administration, Inner Mongolia University, Hohhot, China

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No competing interests reported.

Catastrophic health expenditure and its inequality in rural China: evidence from 2013-2018 CHARLS

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Abstract

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Introduction

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