Health systems efficiency in China and ASEAN, 2015–2020: a DEA-Tobit and SFA analysis application

Abstract

Objective

To evaluate the health systems efficiency in China and Association of Southeast Asian Nations (ASEAN) countries from 2015 to 2020.

Design

Health efficiency analysis using data envelopment analysis (DEA) and stochastic frontier approach analysis.

Setting

Health systems in China and ASEAN countries.

Methods

DEA-Malmquist model and SFA model were used to analyse the health system efficiency among China and ASEAN countries, and the Tobit regression model was employed to analyse the factors affecting the efficiency of health system among these countries.

Results

In 2020, the average technical efficiency, pure technical efficiency and scale efficiency of China and 10 ASEAN countries’ health systems were 0.700, 1 and 0.701, respectively. The average total factor productivity (TFP) index of the health systems in 11 countries from 2015 to 2020 was 0.962, with a decrease of 1.4%, among which the average technical efficiency index was 1.016, and the average technical progress efficiency index was 0.947. In the past 6 years, the TFP index of the health system in Malaysia was higher than 1, while the TFP index of other countries was lower than 1. The cost efficiency among China and ASEAN countries was relatively high and stable. The per capita gross domestic product (current US$) and the urban population have significant effects on the efficiency of health systems.

Conclusions

Health systems inefficiency is existing in China and the majority ASEAN countries. However, the lower/middle-income countries outperformed high-income countries. Technical efficiency is the key to improve the TFP of health systems. It is suggested that China and ASEAN countries should enhance scale efficiency, accelerate technological progress and strengthen regional health cooperation according to their respective situations.

JK, RP and JF are joint first authors.

Strengths and limitations of this study

• Health systems efficiency in China and Association of Southeast Asian Nations countries was specifically analysed by using data envelopment analysis and stochastic frontier approach models.

• More independent variables of Tobit regression models were included.

• Confined to the data unavailability, we only chose the health outcome as the output indicators and obtained the missing data from national statistical yearbooks or interpolated.

Introduction

Health system efficiency has long been the focus of health policymakers and researchers for its importance to health equity and sustainability. The efficiency of the health system can be defined as the degree to which the health system achieves the outcomes with the given input of health resources.¹ An efficient health system makes full use of the resources while an inefficient system is wasteful of resources. In 2000, the WHO conducted an overall efficiency measurement in all member countries and reached the conclusion that 20%–40% of total health resources were wasted every year due to inefficiency worldwide.² Owing to the scarcity of health resources, increasing health systems efficiency is essential to ensure the availability of health services and enhance the population’s health. The improvement of health system efficiency is also critical to the progress of universal health coverage (UHC).³ Furthermore, last couple of years have witnessed remarkable changes in the health sector due to the fast development of medical technology, rapid ageing of populations, and growing burdens of non-communicable and communicable diseases.⁴ It was also found that health spending rose both in per capita terms and in share of gross domestic product (GDP) worldwide even under the COVID-19 pandemic in 2020.⁵ However, the economic stagnation caused by the pandemic combined with the continuing costs calls for reducing unnecessary healthcare and better use of resources,⁶ which highlights the importance of improving health system efficiency. What’s more, previous studies also found that even with the increasing health spending, many countries were still underspending on health.⁷ Therefore, the efficiency of healthcare systems is an intriguing topic which deserves further research.

ASEAN, officially the Association of Southeast Asian Nations consists of 10 countries in Southeast Asia—Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand and Vietnam. It is a region of wide diversity in politics, economy and social culture. In light of this, health systems among the 10 ASEAN member states are of great differences.⁸ Over the years, Countries in the region have been making major strides to strengthen health systems and have achieved substantial health gains in improving life expectancy and reducing the maternal, neonatal and child health mortality.^8–10 Besides all the progress, ASEAN countries are still facing great challenges in the healthcare sector including health disparities among and within countries, the shortage and inadequate distribution of health human resources, the rise of chronic non-communicable diseases as well as the new and re-emerging infectious diseases.^10–13 China has implemented a profound healthcare reform since 2009. Since the healthcare reform, Chinese government has put momentous financial investment on the healthcare sector and made impressive progress in enhancing people’s health.¹⁴ From 2009 to 2019, the total health expenditure in China increased year by year with an average annual growth rate of 14.17%.¹⁵ However, the rapid increasing health spending may pose threat to the long-term financial sustainability of social health insurance programmes.¹⁶ Besides, considering the persistent limited access of health and impoverishment by illness as well as the huge population density, a study on the health systems efficiency is essential to the fulfilment of China’s health reform.

Connected by the land border and sea, China and ASEAN countries are close neighbours. Both sides share deep historical links, sociocultural connections and political economic dynamics since ancient times. China and ASEAN established cooperation in the health sector after the 2003 SARS pandemic.¹⁷ Recent years have seen vigorous cooperation between China and ASEAN countries in fields like public health, traditional medicine, the health industry, health human resources and hospital management.¹⁸ The COVID-19 pandemic stresses the magnitude of regional and global health collaboration, and the Chinese government proposed to work with ASEAN countries to jointly build a ‘health shield’ for the region.¹⁹ As a region which contains almost 30% of the world’s population and one of the fastest-growing and dynamic regions in the world, it is important for China and ASEAN countries to use the health resources efficiently.

Many studies have analysed the efficiency in the health sectors from the macro healthcare system level and the micro healthcare institution level. For health systems efficiency, in 2001, based on the findings of the World Health Report 2000 Evans et al further estimated the relation between the health outputs (population health) and inputs, and revealed that besides increasing health resources input, most countries could achieve important gains by using the actual resources more efficiently.²⁰ Following, many scholars explored the efficiency of the health systems in Asian countries,²¹ Organisation for Economic Cooperation and Development countries,^{22 23} European Union state,²⁴ Eastern Mediterranean Region,²⁵ Middle East and North Africa²⁶ and ASEAN countries,²⁷ etc, and the findings suggested that differences of the health systems efficiency existing among regions and countries and many of the countries still need to make efforts to improve the health systems efficiency. Besides the cross-national researches, there are also a number of studies examining the health systems efficiency from a single country’s perspective. A study targeted the healthcare system efficiency in Lebanon showed that health system efficiency has improved after the health system reform.²⁸ Robert Kolesar et al assessed the technical efficiency of Cambodia’s public health services in the COVID-19 era and made suggestions to increase the efficiency.²⁹ The overall efficiency of the health system in Malaysia during COVID-19 proved to be high from its response and one of the essential success factors is to allocate the health resources according to the population density.³⁰ Studies about the health system efficiency in China and some provinces in China also exhibited the need to increase the usage efficiency of health resources.^31–33 The previous studies laid the foundation for health systems efficiency analysis. Meanwhile, they also suggested that there are still a short of researches specific to China and ASEAN countries.

Health conditions in China and ASEAN countries have been greatly improved through the Millennium Development Goals (MDGs) from 2000 to 2015.^{34 35} While China and ASEAN countries still face similar health problems from the double burden of non-communicable and communicable diseases and inadequate of health resources especially under the impact of the COVID-19 pandemic. Accordingly, the objectives of this study are to examine the technical efficiency of the health systems among China and ASEAN countries from 2015 to 2020 and to understand the variables that influence the health systems technical efficiency, so as to help both sides better understand each other’s health conditions, find out the deficiencies in the usage of health resources, improve the health capacities and to explore the key areas of future health cooperation.

Methods

Data envelopment analysis

Data envelopment analysis (DEA) has been a dominant performance measurement tool in measuring health efficiency since its invention in the late 1970s.^{36 37} It is a non-parametric linear programming method to measure the technical efficiency of different decision-making units (DMUs) by constructing and determining an efficient frontier.³⁸ The value of technical efficiency is considered to vary between 0 and 1, where 1 means the best efficiency value of the DMUs while 0 refers to the worst, and the efficiency is positively correlated with the value.^{37 39} There are mainly two kinds of DEA models, namely radial and non-radial, and the radial models contain CCR (Charnes-Cooper-Rhodes) and BCC (Banker-Charnes-Cooper) models.⁴⁰ According to the return to scale assumption, the CCR models assume that the production has constant returns to scale while the BCC models assume variable returns to scale.³⁷ Besides, based on the correlation between the input and output, DEA models are categorised into input-oriented and output-oriented. According to the previous studies,^40–42 the out-oriented BCC model is chosen to assess China and ASEAN countries health system efficiency.

Malmquist index model

Malmquist index measures the changes in productivity of DMUs during a certain period by analysing the total factor productivity (TFP) which consists of the technical change and technological change. Although DEA models can assess several inputs and outputs simultaneously,⁴⁰ it can only compare the efficiency of different DMUs in the same period and cannot explain the changing trend of the efficiency in different periods. Malmquist index model can cover this shortage of DEA by evaluating the efficiency of DMUs dynamically during a certain period.⁴³

Stochastic frontier approach

Stochastic frontier approach (SFA) is a parametric method which is often used to measure the technical efficiency and cost efficiency based on production frontier theory.^44–47 The main merit of SFA models is that it considers the random effects to the variation in efficiency.⁴⁸ Combined with DEA, SFA is employed to investigate the cost efficiency of health systems among China and ASEAN countries, so as to analyse the characteristics of the cost of healthcare. The value of cost efficiency is considered to vary from 0 to 1, and the larger the value, the higher the efficiency.

Tobit regression model

Tobit regression model is frequently used to explore the influencing factors of the technique efficiency by modelling the censored variables. One restriction of the Tobit regression model is that only the dependent variables or ‘censored’ data can be observed.⁴⁹ The relative technique efficiency and cost efficiency measured by DEA model and SFA method range from 0 to 1 which depends on the mustard of dependent or ‘censored’. Therefore, in this study, the technique and cost efficiency of health system was used as dependent variable and estimated by Tobit regression analysis.

Data sources

China and 10 ASEAN countries were considered as the DMUs in this analysis. The period of the analysis is from 2015 to 2020. Data of the health system in the eleven countries were obtained from WHO Global Health Observatory, Global Health Expenditure Database, World Bank Database. The missing data were first supplemented by searching the national statistical yearbooks and then interpolated.

Input and output variables for DEA and Malmquist models

Health inputs consist of the human and material resources serving the health service delivery. Guided by previous studies^{25 36 42} and the availability of data, we took the proxies for financial and physical factor which affect the output of health systems as input variables. These input variables are medical doctors (per 1000 population), current health expenditure (CHE) per capita and hospital beds (per 1000 population). For the health output variables, two commonly reported healthcare outcomes indicators reported in the previous studies were chosen for DEA and Malmquist models, namely life expectancy at birth and neonatal mortality rate (per 1000 live births). Considering the positivity of DEA and Malmquist models, the neonatal mortality rate was replaced by the neonatal survival rate (the calculation is: neonatal survival rate=1−neonatal mortality rate/1000). Different from DEA model which can deal with multiple output variables, SFA model is normally used for single output variable. Hence, for SFA model, only life expectancy at birth was taken as the output variable.

Variables for Tobit regression model

Health systems efficiency can be explained by social, economic and political factors.⁴² Hence, population density, GDP per capita (current US$), urban population, population ages 65 and above (% of total population), literacy rate, adult total (% of people ages 15 and above) and CHE as percentage of GDP were expected as the independent variables. Thanks to the huge gap between the variables’ values, we used natural logarithm of four variables, namely population density, GDP per capita (current US$), urban population and literacy rate. Descriptive statistics are presented in table 1.

Table 1

Descriptive statistics of variables

Variables	Min	Max
Input variables
Medical doctors (per 10 000 population)	1.53	26.10
Current health expenditure per capita	52.15	3537
Hospital beds (per 10 000 population)	8.00	50.50
Output variables
Life expectancy at birth (year)	65.81	83.74
Neonatal mortality rate (per 1000 live births)	0.81	25.04
Independent variables
Population density (people per sq. km of land area)	29.41	7965.88
Gross domestic product (GDP) per capita (current US$)	1161.86	66 859.34
Urban population (% of total population)	22.19	100.00
Population ages 65 and above (% of total population)	4.09	13.85
Literacy rate, adult total (% of people ages 15 and above)	71.04	98.18
Current health expenditure as percentage of GDP (%)	2.19	7.51

Patient and public involvement

No patient involved.

Results

Results of the variables

As can be seen in the descriptive statistics of the input, output and independent variables in table 1, there are great diversities in the health sectors among China and the ASEAN member states. The number of medical doctors per 10 000 population ranges from a minimum of 1.53 in Cambodia to maximum 26.1 in Singapore. The per capita CHE is from a minimum of US$52.15 in Laos to a maximum of US$3537 in Singapore with a mean and SD of 468.4 and 754.18, respectively. The hospital beds per 10 000 population is also differentiated from the scope of a minimum of 8 in Cambodia to the maximum of 50.5 in China. Meanwhile, the average life expectancy at birth is 73.57 with a range from a minimum of 65.81 in Myanmar to a maximum of 83.74. The minimum of neonatal mortality rate per 1000 live births is 0.81 while the maximum is 25.04 with a mean of 10.81.

Among the influencing indicators selected in this study, the average population density is 851.69 with the minimum of 29.41 in Laos and the maximum of 7965.88 in Singapore. The lowest GDP per capita is US$1161.86 in Malaysia and the highest is US$66 859.34 in Singapore with the average and SD of US$11 966.07 and US$17 498.97. The urban population ranges from the minimum of 22.19 in Cambodia to the maximum of 100 in Singapore. The percentage of population ages 65 and above is from 4.9% in Brunei to 13.85 in Thailand. The average literacy rate of the total population is 92.50. The average CHE as percentage of GDP (%) is 4.16.

Results of the DEA model

The average technical efficiency score of health systems in China and 10 ASEAN countries was 0.700, over half of the countries had the technical efficiency above the average. The mean pure technical efficiency was 1.000, and the average scale efficiency was 0.701, suggesting that the technical efficiency was mainly determined by scale efficiency. Different from the conclusions of other studies that high-income countries outperformed the lower-middle countries in health systems efficiency, the best performers in China and ASEAN countries are Cambodia, Laos, Myanmar and the Philippines. These four countries reached the optimal level of technical efficiency of 1.000, indicating that the health systems of these four countries were in the best state of efficiency and scale. From the perspective of pure technical efficiency, the pure technical efficiency scores of all countries were 1.000 except Brunei, whose pure technical efficiency reaches 0.998 which was close to 1, revealing that the health technology and management level of all countries were relatively high, and the output maximisation was achieved under the condition of existing health input. In Brunei, Indonesia, Malaysia, Singapore, Thailand, Vietnam and China, the technical efficiency scores were in the state of inefficiency due to insufficient scale efficiency, and returns to scale were all in a decreasing mode, suggesting that health resource input in those countries were not in the best state, and the proportions of health system output were smaller than the proportion of health input increase (see table 2).

Table 2

Technical and scale efficiency scores of the health systems in China and Association of Southeast Asian Nations countries, 2020

Country name	CRS technical efficiency	VRS technical efficiency	Scale efficiency	Returns to scale
Brunei	0.315	0.998	0.316	decrease
Cambodia	1	1	1	–
Indonesia	0.976	1	0.976	decrease
Laos	1	1	1	–
Malaysia	0.759	1	0.759	decrease
Myanmar	1	1	1	–
Philippines	1	1	1	–
Singapore	0.427	1	0.427	decrease
Thailand	0.521	1	0.521	decrease
Viet Nam	0.458	1	0.458	decrease
China	0.250	1	0.250	decrease
Mean	0.700	1	0.701	–

CRS, constant returns to scale; VRS, variable returns to scale.

Results of the Malmquist index model

As shown in table 3, according to the result of the Malmquist index model, the TFP index of the health system in China and ASEAN countries from 2015 to 2020 showed a downward trend, with an average value of 0.962. It shows that the efficiency of the overall health system in China and ASEAN countries as a whole decreased by 1.4% during the 6 years. From the decomposition point of view, the average value of the technical efficiency index was 1.016, which decreased by 0.1 from 2015 to 2020, and the average value of the technical progress efficiency index was 0.947, which increased by 0.045 in 6 years, indicating that the service efficiency of health systems in China and ASEAN countries did not change significantly from 2015 to 2020. The change of TFP was still mainly influenced by technological regression.

Table 3

The Malmquist index and its decomposition of the health systems in China and Association of Southeast Asian Nations (ASEAN) countries, 2015–2020

The Malmquist index and its decomposition of the health systems in China and ASEAN countries as a whole, 2015–2020
Year	Effch	Techch	Pech	Sech	Tfpch
2015–2016	1.083	0.902	1.001	1.082	0.976
2016–2017	1.041	0.914	1	1.041	0.952
2017–2018	0.981	0.98	1	0.981	0.961
2018–2019	1.006	0.955	1	1.006	0.961
2019–2020	0.975	0.986	1	0.975	0.961
Mean	1.016	0.947	1	1.016	0.962

The Malmquist index and its decomposition of the health systems across China and ASEAN countries, 2015–2020
Country name	Effch	Techch	Pech	Sech	Tfpch
Brunei	0.999	0.941	1	0.999	0.939
Cambodia	1	0.918	1	1	0.918
Indonesia	1.030	0.929	1	1.030	0.956
Laos	1	0.976	1	1	0.976
Malaysia	1.050	0.954	1	1.050	1.002
Myanmar	1.004	0.973	1.001	1.003	0.977
Philippines	1.040	0.950	1.001	1.039	0.988
Singapore	1.012	0.954	1	1.012	0.966
Thailand	1.060	0.916	1	1.060	0.972
Viet Nam	0.975	0.970	1	0.975	0.946
China	1.014	0.933	1	1.013	0.945
Mean	1.016	0.947	1	1.016	0.962

To analyse the Malmquist index from the single country perspective, the results showed that only the TFP index of the health system in Malaysia was greater than 1, while other countries were less than 1. But there was little difference in the Malmquist indexes among countries. All countries, including Malaysia, had experienced the technological regression. Except Malaysia and Vietnam, all the other countries witnessed the reduction of TFP mainly caused by technological regression, while Vietnam had both technological inefficiency and technological regression (see table 3).

Results of the SFA model

As shown in figure 1, the cost efficiency scores in China and ASEAN countries were relatively high and stable from 2015 to 2020. Singapore ranked first with the mean cost efficiency of 0.999 during the 6 years, followed by Thailand and China with an average cost efficiency of 0.949 and 0.935, respectively. Cambodia, Laos and Myanmar were the least three countries with the lowest cost efficiency. While the gap among countries were not obvious and there were little changes within countries. Meanwhile, compared with technical efficiency, cost efficiency is generally higher and stable than technical efficiency.

Figure 1. Cost efficiency scores of the health systems in China and Association of Southeast Asian Nations countries, 2015-2020.

Results of the Tobit regression model

Tobit regression model was employed to analyse the technical and cost efficiency scores of health systems in China and ASEAN countries with six independent variables. Table 4 shows that the per capita GDP (current US$) and the urban population had statistical significance on the technical efficiency of health system in China 10 ASEAN countries (p<0.05), where the per capita GDP (current US$) was positively correlated with technical efficiency, and the urban population was negatively associated with technical efficiency. Population density was the main influencing indicators to the cost efficiency with a positive association.

Table 4

Regression results of the Tobit regression model

Variables	Coefficient (95% CI)	P value
Main DEA model regression results
Population density (people per sq. km of land area)	0.0449317 (−0.1083365 to 0.018473)	0.165
Gross domestic product (GDP) per capita (current US$)	0.0672207 (0.0085992 to 0.1258422)	0.025**
Urban population (% of total population)	0.4827829 (−0.8157207 to −0.1498452)	0.004***
Population ages 65 and above (% of total population)	0.0113915 (−0.0151828 to 0.0379659)	0.401
Literacy rate, adult total (% of people ages 15 and above)	0.4643171 (−0.4930024 to 1.421637)	0.342
Current health expenditure (CHE) as percentage of GDP (%)	0.0093055 (−0.0523179 to 0.0709289)	0.767
Main SFA model regression results
Population density (people per sq. km of land area)	0.0188001 (0.0046622 to 0.0329381)	0.009***
GDP per capita (current US$)	0.0021588 (−0.0138504 to 0.0095328)	0.717
Urban population (% of total population)	0340433 (−0.0089688 to 0.0770553)	0.121
Population ages 65 and above (% of total population)	0.0003696 (−0.0020561 to 0.0013168)	0.667
Literacy rate, adult total (% of people ages 15 and above)	0.006653 (−0.0238764 to 0.0238764)	0.669
CHE as percentage of GDP (%)	0.0010322 (−0.0018335 to 0.0038979)	0.480

*p<0.1, **p<0.05, ***p<0.01.⁴³

DEA, data envelopment analysis; SFA, stochastic frontier approach.

Discussion

Performance of health systems based on efficiency

The main findings of this study suggested that China and over half of the ASEAN countries are still confronting technical inefficiency in using the health systems resources. Meanwhile, although the overall trend of cost efficiency in China and ASEAN countries were higher and more stable than technical efficiency, there were still space for improving. The inefficiency in technique and cost may be attributed to the unfinished agenda of achieving the primary healthcare (PHC) system. The strengthening of PHC is essential to improve the efficiency of health system.⁵⁰ Both China and ASEAN countries have made great efforts in achieving PHC and universal health coverage(UHC), however, many countries are still fall short of the expectation or facing new challenges including the financial burden and modernisation of life styles which might in turn exhibited as health inefficiency.^{16 51–55} Among all the studying countries, only Cambodia, Laos, Myanmar and the Philippines reached technical efficiency, indicating that the health input of these four countries have achieved effective output. One possible reason is that these countries have placed neonatal mortality reduction high on the agenda of health systems in achieving the MDGs and the Sustainable Development Goals (SDGs), thereby positively influencing their health outcomes indicators selected in this study.²¹ Among China and the six other ASEAN countries whose technical efficiency scores were less than 1, the scale efficiency is the key to improve the health system efficiency. According to the results of Malmquist index analysis of all countries from 2015 to 2020, combined with the results of decreasing returns to scale, it can be known that although the scale of the health system in China and these ASEAN countries continues to expand, the usage efficiency of health resources is not high, resulting in the growth rate of output lower than that of health input. It suggests that there might be some disadvantages such as unreasonable distribution of health resources, insufficient usage, redundant health input and insufficient health output. The stable trend of the cost efficiency together with the fact that health spending was increasing year by year among all countries except Brunei indicated that the allocation of health input should be further optimised. Therefore, it is important for China and ASEAN countries to better health inputs usage and allocate the health resources, especially the public health funds appropriately and effectively so as to ensure the accountability and transparency of public health expenditure. Besides, all countries need to improve health technology, and take full account of health quality,⁵⁶ population development, economic development and demand for medical services and other factors according to their actual conditions.²⁹

Technological progress is key to improve TFP

It was also found that technological progress is the decisive factor limiting the improvement of TFP in the health system of China and ASEAN countries. In recent years, in the process of advancing the SDGs and UHC, both China and ASEAN countries pay increasing attention to the development and investment in the field of health. However, limited by technological progress, the health resources were not fully used, resulting in low efficiency of health system. Therefore, while increasing health investment and expanding the scale and facilities of health and medical service institutions, China and ASEAN countries should improve the technology of health usage. On the one hand, the promotion and application of new technologies and new methods within the region should be promoted through the exchange of experience within the region, so as to improve the overall health technology; on the other hand, the training of health manpower should be accelerated to improve the ability of health-related workers to use health resources.

Economic development and health resources allocation matters

Per capita GDP (current US$) and the urban population (% of the total population) are the leading influencing factors affecting the efficiency of the health system in China and ASEAN countries. The per capita GDP has a positive impact on both the technical and cost efficiency, indicating that the higher the per capita GDP, the higher the efficiency of the health system. This might be explained by the economic theory that the higher per capita GDP people have, the more willing for them to invest in health, and thus the healthier they are. Furthermore, per capita GDP can help to reduce the infant mortality rate.⁵⁶ The urban population (% of the total population) has a negative impact on the technical efficiency of the health system in China and ASEAN countries, one possible reason might be that there were increasing urban population combined with floating population who were seeking healthcare in cities seized for the certain health resources, and then caused health inefficiency.⁵⁷ This also indicates the uneven allocation of the health resource. Therefore, to speed up the economic development and improve peoples’ health service consumption capability is vital to China and ASEAN countries. Besides, the associations of the urban population (% of the total population) with the health systems efficiency disclosed the importance of the allocation of health resources. China and ASEAN countries need to optimise the distribution of health input according to the density of urban population.

Conclusion

This study has enriched the cross-country studies on the efficiency of health systems and provided reference for the health cooperation between China and ASEAN. It is suggested that China and many of the ASEAN countries confronted technical and cost inefficiency in health systems while the lower/middle-income countries had better performance in the region. Therefore, to allocate the health resources scientifically and to improve the technical management of using the limited resources is important. The per capita GDP (current US$) and the urban population are the key indicators influencing the health systems efficiency. Considering the uneven health systems efficiencies and robust health cooperation among China and ASEAN countries, it is a future trend to strengthen regional health cooperation, share health technology experience and enhance the overall resilience and capacity of the regional health system. Under the current mechanism of health cooperation between China and ASEAN, cooperation can build on lower/middle-income countries and focus on exchanges and communication in the fields of health system reform and construction, health personnel training, vaccine research and development, and universal health coverage, so as to promote the in-depth development of health cooperation between China and ASEAN countries.^{58 59}

Limitations

This study depicts a general picture of the healthcare system efficiency performance of China and ASEAN countries by combining the parametric and non-parametric methods. However, there are some limitations lied in the choice of indicators due to the availability of data. Future studies can take the healthcare delivery indicators such as the inpatient and outpatient amount and the health behaviours, economic and environment factors as independent variables. What is more, the study showed that lower/middle-income countries had higher health efficiency in resource allocation and usage based on the technical and cost efficiency ranking, there should be deeper and further analysis for the future studies.

Data availability statement

Data used in this study were obtained from WHO Global Health Observatory (https://www.who.int/data/gho/data/indicators), WHO Global Health Expenditure Database (https://apps.who.int/nha/database) and World Bank Database (https://data.worldbank.org/) as well as the national statistical yearbooks.

Ethics statements

Patient consent for publication

Not applicable.

Ethics approval

Not applicable.

Footnote

Contributors XQ and QF contributed equally to this paper and are joint last authors, QF is also the guarantor. JK, RP, JF and QF conceived and designed the study. JK, RP and JF collected and analysed the data, and drafted the manuscript. JW, ZL, FH, FY, XS, YC contributed to literature research and reviewing the manuscript. XQ and QF contributed to the interpretation of the results and critically revised the draft. All authors provided advice at different stages. All authors approved the final version of the manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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