Background  

In Asia, there are approximately 4.4 billion people spread across highly diverse countries, from economic powerhouses like China and Singapore to poorer economies such as Laos, Cambodia and Myanmar.1 Overall, the continent is often cited as the fastest growing and most dynamic region in the world. Over the past number of years, Asian societies have also made impressive progress in ensuring better healthcare services, especially those targeted towards improving maternal and infant health and increasing life expectancy.2 However, whether economic gains have translated to efficient health systems across the region is still not well studied.

It is important that the healthcare resources in Asia are used efficiently. In Asia, generally government spending on healthcare is low compared with total health expenditure, and it is often not focused on those who need it most.3 For example, in the South Asia region, governments spend 31% of total health expenditure, which is about 1% of gross domestic product (GDP).4 5 In many Asian countries, personal health expenses or out-of-pocket payments are a major cause of poverty.1 6 For instance, from a study of 11 Asian countries, it was found that 78 million people are pushed  into poverty due to out-of-pocket spending for healthcare.7 Ageing populations and non-communicable diseases that are often preventable but expensive to treat (eg, diabetes and cancers linked to tobacco) impose and will continue to impose heavy costs on households and public health budgets. Moreover, a major challenge for Asian countries is the control and prevention of different communicable diseases (eg, HIV/AIDS, tuberculosis and polio) due to the movement of people across borders and the exchange of goods.8

In light of this, it is very important that the health systems of these countries are efficient in using their resources. The WHO has estimated that due to inefficiency about 20%–40% of total healthcare resources are being wasted per year among its member countries. Furthermore, this rate is higher in low-income and middle-income countries.9 In Asia, the variation in efficiency across income groups and contexts can perhaps lead to lessons learnt in addressing it. In order to address inefficiency, Asia’s health systems can look towards different dimensions of performance such as their effectiveness, efficiency, access, equity and quality.10 A great deal of practitioner and academic literature have analysed the relationship between the efficient production of health services and universal health coverage as well as the widespread importance of measuring overall health system performance.9 11

Assessing the efficiency of healthcare systems is a difficult process as analyses often encounter methodological problems, particularly due to the need for appropriate and valid outcome indicators.12 Despite the empirical difficulties in applying efficiency concepts to health systems, efficiency can be measured on both micro and macro levels.13 Measuring health system efficiency at a macro level is particularly important in order to understand health system performance across the globe and take required action to minimise inefficiency.11 14

A number of studies have analysed the healthcare efficiency in the Americas,15 16 Western Europe17 18 and Asia19 20 to shed light on the efficiency of different national healthcare systems. A systematic review on measuring efficiency related to several aspects of healthcare performed by Hollingsworth et al.21 Dimas et al 22 evaluated the productivity of Greek public hospitals and found that productivity changes were dominated by technical change. Additionally, Zere et al 23 measured the technical efficiency and productivity of hospitals in South Africa and examined the impact of hospital characteristics on efficiency and productivity.23

Several studies have reported on different determinants of health system efficiency. For example, a study conducted in China reported that GDP per capita, proportion of primary health worker and population density were the key determinants of the efficiency in Chinese health system.24 Another study in the Canadian context reported that readmission, obesity and smoking and average income of the population are key determinants of health system efficiency.25

In an international study of efficiency in 170 Asian and non-Asian countries, it was observed that Asian countries were comparatively in the middle with respect to health system efficiency scores.26 This indicates that there is room for improvement to optimise health benefits from the available health sector resources. In this region, a number of studies have been conducted at the country level to address health systems efficiency,27 28 but cross-country comparison of the health system efficiency is limited.29

Asian countries are not homogeneous in terms of area, population and economic conditions; however, they have public health functions and a number of their health system outcomes are in common.30 Many of the countries share similar health systems problems, including a high burden of diseases due to the geographical contiguity, disease patterns and social conditions and inadequate resources for healthcare. Understanding health systems efficiency in different Asian countries could promote shared learning and highlight key areas of best practice, as well as areas where improvement is needed. Furthermore, given geographical proximity and many strong relationships experienced with nearby countries, there is likely to be relative ease in the ability to practically understand, learn and apply nuance about healthcare systems from one country to another.

A study on the efficiency of health systems in this region will help to provide lessons through comparison across countries. This paper aims to achieve this goal through measuring the technical efficiency and scale efficiency of the healthcare systems of selected Asian countries.

Methods

This study employed two stages of efficiency analysis using cross-sectional data. In the first stage, data envelopment analysis (DEA) was used to estimate the country efficiency scores. In the second stage, a regression analysis and a bootstrap method were employed to identify the factors associated with the health system efficiency. The software package STATA V.13 was used for all of the analyses.

Results

The descriptive statistics of the selected input, outcome and environmental variables are shown in table 1. The per capita health expenditure ranges from a minimum of US$88.03 (Bangladesh) to a maximum of US$4405.13 (Japan) with a mean, median,  and SD of 1133.71, 663.94 and 1157.72 respectively. The number of physicians per 1000 people ranges from a minimum of 0.1 at Timor-Leste to maximum 4.8 at Georgia. However, the number of inpatient beds per 1000 people is the smallest in Iran (0.1) and the highest in Japan (13.7). The average smoking prevalence of the adult male people among the studied countries is 42.2% and average primary education completion rate is 96.5% of the relevant age group.

Among the countries analysed, HALE at birth was a minimum of 53.2 years in Afghanistan and a maximum of 75.9 years in Singapore. The infant mortality rate ranged from 2.0 deaths per 1000 live births in Japan to 65.7 deaths per 1000 live births in Pakistan. On average, there were 19.9 deaths per 1000 live births in the studied countries. The scatter matrix of the input and output variables shows that inputs, for instance, increase in per-capita healthcare expenditure was associated with improved health outcomes (eg, HALE at birth and reduced infant mortality) (figure 1).

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Figure 1

Association across health systems input and outcome.

The mean CRS and VRS technical efficiency scores were 0.780 and 0.919, respectively (table 2). Whereas, the mean scale efficiency score was 0.847. Considering VRS efficiency, Afghanistan has the lowest score of 0.812. Both VRS and CRS technical efficiency scores were positively correlated with per capita health expenditure, HALE at birth and negatively correlated with infant mortality (online supplementary table 1).

Out of 46 countries studied, only four (8.7%) countries showed the maximum level of efficiency (efficiency score 1.00) in VRS and CRS technical efficiency scale. All of these four countries showed scale efficiency of 1.00 implying that these countries created the best practice frontier based on their input and output combinations. Around 39.1% (18 countries) of the studied countries showed increasing returns to scale, 52.2% countries (24 countries) showed decreasing returns to scale and four efficient countries showed CRS production function of their health systems.

More than half of the countries (30 countries) had VRS efficiency, and five countries had CRS efficiency greater than 90% (online supplementary figure 1).

Result from Tobit regression and bootstrap analysis for associated factors with the inefficiency

Tobit regression and smoothed bootstrap were used to relate VRS efficiency scores to two health service production variables and four environmental variables in two separate models (table 3). Negative associations with the inefficiency scores in the Tobit model represent positive relation of health system efficiency with the explanatory variables. However, positive associations with the explanatory variables in the smoothed bootstrap model represent positive relations of the health system efficiency with the explanatory variables.

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Table 3

Result from Tobit regression and smoothed bootstrap analysis

Physician density, income status of countries and smoking prevalence among males exhibited statistically insignificant associations with the health system efficiency in the both models. The density of bed (more than three to fewer than or equal to five) had a significantly negative association with the inefficiency scores (ie, positive association with the efficiency) compared with less than one bed density category. Countries having more than one and less than or equal to three bed density had no significant association with the inefficiency scores. However, after the bootstrapping, this category became significant and the significance level increased for the rest two categories (ie, more than three and less than or equal to five bed density and more than five bed density). The association of bed density in the both models indicates that sample countries with less than one bed density have lower technical efficiency of their health systems. Furthermore, the primary education completion rate was significantly negatively associated with the inefficiency score in the Tobit model, which indicates that countries with higher percentage of primary education completion rate have higher health system efficiency. Similar association was observed in the bootstrap model. In case of population density, we found in the both models that countries having more than 200 population per square kilometre were more efficient in their health system efficiency compared with the countries with less than or equal to 100 population per square kilometre.

Sensitivity of the efficiency scores

We conducted sensitivity analysis using various combinations of input and output variables. In all of these cases, the average of the efficiency scores varied from 0.812 to 0.936. The most sensitive combination was found while using the HALE at age 60 years as the outcome variable. The efficiency score changed from 0.919 (main model) to 0.812 (considering input as HALE at age 60 years) (figure 2).

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Figure 2

Results from the sensitivity analysis of efficiency scores. CRS, constant returns to scale; DMUs, decision-making units; VRS, variable returns to scale.

In table 4, mean efficiency scores are presented by the income categories of the countries. The highest mean VRS technical efficiency were observed for high-income countries (0.934; 95% CI 0.905 to 0.963), followed by upper middle-income (0.914; 95% CI 0.894 to 0.935), and low and lower middle income countries (0.913; 95% CI 0.891 to 0.935). If all the health systems operated at maximum efficiency at their given input level, the high-income, upper middle-income, low-income and lower middle-income countries could improve their health system outcome (eg, HALE at birth and reduce infant mortality) by 6.6%, 8.6% and 8.7% respectively.

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Table 4

Mean efficiency scores according to income level of Asian countries

Discussion

The main findings of this paper demonstrated that about 91.3% of the studied Asian countries are technically inefficient with respect to using healthcare systems resources (using a proxy of per capita health expenditure). The study findings showed that among the studied Asian countries, the majority of the efficient countries belonged to the high-income group (Cyprus, Japan and Singapore). Only one country belonged to the low- and lower middle-income group (Bangladesh). Among the 46 countries studied, only four countries (Bangladesh, Japan, Singapore and Cyprus) showed CRS efficiency, indicating that they were operating at their most efficient level. Of the 14 high-income countries studied, nine countries (75.0%) had health system production at decreasing returns to scale. This implies that although the highest number of efficient countries belonged to the high-income group, a large number of these countries’ health system production had more resources than the ideal situation. A similar situation was observed for the upper middle-income countries. Of the 13 countries, 10 (76.9%) had decreasing returns to scale. Only 5 (23.8%) out of 21 low-income and lower middle-income countries were producing at decreasing returns to scale. Although these low-income and lower middle-income countries are not efficient, most of their production follows increasing returns to scale.

It was observed that the average of the efficiency scores increased from the low-income and lower middle-income countries to high-income countries. An important policy implication of this study could be that the technically inefficient low-income and lower-middle income countries on average can improve their health systems outcome by 8.7%, middle-income country by 8.6% and high-income country by 6.6% using the existing levels of per-capita health expenditure. An international study found a similar conclusion that health systems performance is most efficient in the developed countries according to simple efficiency scores.64

The overall healthcare efficiency in different countries varied considerably.65 66 Among the low-income and lower middle-income countries studied, one country demonstrated the most efficient health systems (Bangladesh). This country has both technical and scale efficient health systems, like the high-income countries (Japan, Singapore, and Cyprus).67 A possible reason for the high efficiency of this country could be a focus on infant mortality and child health as prioritised in past Millennium Development Goals and in current Sustainable Development Goals agendas, which relates to the outcome variables used in this study.

The DEA result showed that more than 60% of the low-income and lower middle-income countries had health system efficiency greater than 90%. This result implies that these countries produce good health at low cost and therefore make good use of health systems resources.68 This result suggests that it is possible for countries to have a high-efficiency score with poor health outcomes because of their low expenditure on resources and increasing returns to scale production function. In other words, given their moderate consumption of inputs and challenging social environments, these countries can achieve good health outcomes, relative to the other countries. Similar findings were observed for Mexico and Turkey relative to other countries in a study of the OECD countries.39 It should be noted that this study only used per-capita health expenditure and there are other factors that influence health outcomes as well. For example, differences in life expectancy and infant mortality between populations can be due to lifestyles, preferences56 69 70 social class, occupation71 and environmental factors.72 73 On a more macroscopic level, the results could also be impacted by a variety of contextual factors among countries such as different political institutions, economic landscapes, health-seeking behaviour patterns and burden of diseases among other things. However, in this study, we attempted to address by including variables addressing the number of physicians, number of inpatient beds and population density, along with two environmental factors namely primary completion rate of relevant age group and smoking prevalence among the adult male population to take into consideration some of this variation. The results showed that more than three and less than five beds per 1000 population significantly influenced the efficiency score. A low number of beds cannot serve a large proportion of the population and therefore the systems may be inefficient. Similarly, a high number of beds may often be left unused and make the health systems inefficient. The countries having more than 200 people living per square kilometre had a higher level of efficiency in their health systems.

A limitation of DEA methodology is that it works in a deterministic way, meaning that the results entirely depend on the numeric values in the dataset. As the DEA approach compares DMUs, the number and nature of DMUs in the data set can noticeably change the results. For example, if a more efficient country is added to the dataset, it would move the frontier, causing some of the efficiency scores of other countries to fall. This is a key aspect of the methodology used.

Additionally, it is important to note that the use of a different set of variables might have generated different conclusions. In the future, if additional data become available for a larger number of countries in the region, the number of variables analysed could be increased to include an understanding with a greater degree of complexity in health system efficiency.

Another data limitation is the comparability of health expenditures among the Asian countries. While recognising that it is not possible to solve the inherent issues, we made an attempt to minimise it. Since the actual amount of healthcare expenditure across different countries may not be comparable due to the difference in purchasing power parity across countries, we used health expenditures as constant of 2011 in PPP as an input in the DEA model.39 Also, when we included health expenditure at current USD per capita as an input in the DEA model, we found that the efficiency score did not change significantly.

We applied sensitivity analysis in an attempt to overcome these limitations (figure 2). Our results were consistent while using several combinations of inputs and outputs variables, which is reassuring and strengthens the findings from this study.

Conclusions

This study provides an empirical picture of the technical efficiency of the healthcare systems of 46 Asian countries. The study shows that inefficiency exists in the healthcare systems of most of the countries studied; however, the results point to three high-income and one lower middle-income country, which efficiently used healthcare systems resources. The interpretation of the inefficient countries identified through this study is that they can improve health outcomes using the current level of per-capita health expenditure. These countries could use these results to direct their attention to benchmarking their health systems within their regional or another comparative group in order to understand their health system performance in a more detailed way. This study addresses the need to understand efficiency issues, as well as potentially identify good examples of countries that efficiently allocate and use resources to make their healthcare systems more technically efficient. It narrows a gap in the literature as there are few countries studying healthcare efficiency in Asia and looking comparatively in this manner.