Application of DEA-Based Malmquist Productivity Index on Health Care System Efficiency of ASEAN Countries

Three decades in healthcare service efficiency evaluation: a bootstrapping Data Envelopment Analysis (DEA) of Ministry of Health Malaysia

BACKGROUND

One of the most important ways to boost the health system's performance and lower the rising cost of healthcare is to increase its efficiency. The objective of this study is to evaluate the efficiency of the MOH in providing public health services and to gauge the progress of health plans in Malaysia.

METHODS

Three output variables (number of admissions, number of outpatient attendances, and number of maternal and child health attendances) and six input variables (budget allocation, number of doctors, dentists, pharmacists, nurses, and community nurses) were used in a Data Envelopment Analysis (DEA) Window Analysis. Eight input-output models' bias-corrected efficiency scores were obtained using bootstrapping.

SETTING

Ministry level in public health service.

PARTICIPANT

28 Decision making units (DMUs) from 1995 to 2022.

RESULTS

Robust performance over the study period was shown by the mean bias-corrected efficiency score of 0.974 (95% CI: 0.907-0.989) under the Variable Returns to Scale (VRS) model. Lower Constant Returns to Scale (CRS) model scores, on the other hand, draw attention to scale-level inefficiencies. During the COVID-19 pandemic, efficiency decreased due to higher input demands and limited outputs.

CONCLUSIONS

Although MOH has attained a high level of technological efficiency, expanding operations and resolving inequalities in rural areas remain difficult. Targeted tactics including telemedicine adoption, resource redistribution, and a move towards preventive treatment are advised in order to improve fairness and resilience.

Health system efficiency and equity in ASEAN: an empirical investigation

BACKGROUND

Equity and efficiency are two fundamental principles for the sound development of health systems, as advocated by the World Health Organization (WHO). Despite the notable progress made by the Association of Southeast Asian Nations (ASEAN) in advancing their health systems, gaps persist in achieving global health goals. This paper examines the efficiency of health system stages and the fairness of health resource distribution in ASEAN countries, analyzes the underlying causes of the existing gaps, and suggests potential solutions to bridge them.

METHODS

Data spanning 2011 to 2019, sourced from the WHO Global Health Observatory and the World Bank Database, form the foundation of this study. This study employs an enhanced two-stage data envelopment analysis (DEA) to assess the efficiency of health system stages in ASEAN countries. Equity in health resource distribution is evaluated using health resource agglomeration degree and concentration curves across demographic, geographic, and economic aspects. Furthermore, the Entropy-Weighted TOPSIS method is utilized to integrate equity across these dimensions, measuring the overall fairness in health resource allocation across different countries. Finally, rankings of health system fairness and efficiency are compared to assess the overall development level of health systems.

RESULTS

The overall efficiency of the ASEAN health systems from 2011 to 2019 averaged 0.231, with an upward trend in the first stage efficiency at 0.559 and a downward trend in the second stage at 0.502. The health resource agglomeration degree indicated that Singapore, Brunei, and Malaysia had HRAD and HRPD values significantly greater than 1, and Cambodia, Myanmar, and Laos predominantly had indices significantly less than 1. The concentration curve for hospital beds was the closest to the line of absolute equity. During the study period, the health resource concentration curve increasingly approached absolute equity, shifting from above to below the concentration curve. Singapore, Brunei, and Malaysia consistently remained in the first quadrant of the quadrant plot, and Myanmar and Cambodia were consistently in the third quadrant.

CONCLUSION

ASEAN countries face two key challenges in their healthcare systems: first, while many nations such as Indonesia, Thailand, and Vietnam have improved resource allocation efficiency, this hasn't yet translated into better health services. To address this, establishing national health sector steering committees, focusing on workforce training and retention, and implementing centralized monitoring systems are crucial. Second, there is a growing disparity in healthcare development across ASEAN. Promoting balanced resource distribution and leveraging ASEAN's economic integration for regional collaboration will help bridge these gaps and foster more equitable healthcare systems.

Efficiency evaluation, regional technological heterogeneity and determinant of total factor productivity change in China's healthcare system

Enhancing efficiency and productivity in countries' healthcare systems is a global challenge. The Chinese government invested huge resources to improve the efficiency and productivity of the healthcare system across the country. To assess the success of the mission above, this research utilized DEA-SBM Meta frontier analysis alongside the Malmquist Productivity Index. These methodologies were employed to gauge Efficiency, production technology heterogeneity, and productivity of healthcare systems change across 31 mainland Chinese provinces and four distinct geographical regions throughout the study period spanning from 1997 to 2022. Results revealed that the mean efficiency score of China's healthcare system is 0.7672. It indicates a growth potential of 23.28 percent in the operational efficiency of healthcare systems. The eastern region's efficiency level (0.86917) is higher among all four regions. Zhejiang, Shandong, and Guangdong are the top three healthcare-efficiency performers. The technology gap ratio indicates that eastern regions witnessed a high TGR (0.9909), showing the country's attainment of superior healthcare technologies. Beijing, Guangdong, Shanghai, Tianjin, and Zhejiang witnessed higher TGR values among all 31 mainland Chinese provinces. The total factor productivity index of the healthcare system witnessed a slight growth of 0.33%, with an average MI score of 1.0033. Efficiency change (EC) was found to be the main determinant of TFPC as technology change TC is less than EC. Moreover, the MI score of the Western region (1.033) is higher than the corresponding Eastern, northeastern, and central regions. Guizhou, Anhui, and Yunnan were found to be the top three performers in TFPC growth. Finally, the Kruskal-Wallis test confirmed the statistically significant difference among 4 Chinese regions for the healthcare system's efficiency, TFPC, and TGR.

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

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.

Stochastic frontier approach to efficiency analysis of health facilities in providing services for non-communicable diseases in eight LMICs

BACKGROUND

The rising burden of non-communicable diseases presents an increasing public health challenge to many low- and middle-income countries. This problem may be compounded in health systems with lower levels of technical efficiency (TE).

METHODS

This study used recent Service Provision Assessments data to estimate the level of TEs of health facilities in eight countries. Initially, the general and disease-specific service readiness indexes are estimated. Finally, the production function is estimated using the exposures and the outcomes of the model.

RESULTS

Evidence shows that the general and disease-specific service readiness indexes are significantly associated with an increase in the number of outpatient visits. Outpatient visits may increase by 14% with an increase in health worker density. Similarly, outpatient visits may increase by 0.3% with a unit increase in the general and diabetes service readiness indexes. Furthermore, outpatient visits may increase by 0.4% and 0.8% with an increase in services readiness for cardiovascular and respiratory diseases. respectively. Overall, the level of TE score suggests the need for improvement.

CONCLUSIONS

Facility-level service readiness for chronic diseases is quite low. Therefore, improving health outcomes related to chronic diseases requires urgent investment in high-quality health systems in these countries.

Analysing the Efficiency of Health Systems: A Systematic Review of the Literature

BACKGROUND

Efficiency refers the use of resources in ways that optimise desired outcomes. Health system efficiency is a priority concern for policy makers globally as countries aim to achieve universal health coverage, and face the additional challenge of an aging population. Efficiency analysis in the health sector has typically focused on the efficiency of healthcare facilities (hospitals, primary healthcare facilities), with few studies focusing on system level (national or sub-national) efficiency. We carried out a thematic review of literature that assessed the efficiency of health systems at the national and sub-national level.

METHODS

We conducted a systematic search of PubMed and Google scholar between 2000 and 2021 and a manual search of relevant papers selected from their reference lists. A total of 131 papers were included. We analysed and synthesised evidence from the selected papers using a thematic approach (selecting, sorting, coding and charting collected data according to identified key issues and themes).

FINDINGS

There were more publications from high- and upper middle-income countries (53%) than from low-income and lower middle-income countries. There were also more publications focusing on national level (60%) compared to sub-national health systems' efficiency. Only 6% of studies used either qualitative methods or mixed methods while 94% used quantitative approaches. Data envelopment analysis, a non-parametric method, was the most common methodological approach used, followed by stochastic frontier analysis, a parametric method. A range of regression methods were used to identify the determinants of health system efficiency. While studies used a range of inputs, these generally considered the building blocks of health systems, health risk factors, and social determinants of health. Outputs used in efficiency analysis could be classified as either intermediate health service outputs (e.g., number of health facility visits), single health outcomes (e.g., infant mortality rate) or composite indices of either intermediate outputs of health outcomes (e.g., Health Adjusted Life Expectancy). Factors that were found to affect health system efficiency include demographic and socio-economic characteristics of the population, macro-economic characteristics of the national and sub-national regions, population health and wellbeing, the governance and political characteristics of these regions, and health system characteristics.

CONCLUSION

This review highlights the limited evidence on health system efficiency, especially in low- and middle-income countries. It also reveals the dearth of efficiency studies that use mixed methods approaches by incorporating qualitative inquiry. The review offers insights on the drivers of the efficiency of national and sub-national health systems, and highlights potential targets for reforms to improve health system efficiency.

Measuring Productivity and Relative Efficiency of Public Tertiary Traditional Chinese Medicine Hospitals in Hubei, China

Improving the productivity and relative efficiency of traditional Chinese medicine (TCM) hospitals is pivotal for hospital managers and policymakers to optimize the utilization of TCM resources in China. This study aimed to measure the productivity and relative efficiency of public tertiary TCM hospitals in Hubei Province. The input and output indicators data were extracted from the Health Commission of Hubei Province (HCHP) from 2019 to 2021. The Bootstrap-Malmquist-DEA model was employed to measure the productivity and relative efficiency of the hospitals. The statistical significance was set at P < .05. The numbers of total diagnostic patients and discharged patients declined by 23.44% and 28.34% from 2019 to 2020, and then increased by 25.76% and 20.44% respectively from 2020 to 2021. The average bias-corrected technical efficiency (TE) scores of the TCM hospitals from 2019 to 2021 were 0.8391, 0.8048, and 0.8559, indicating good efficiency. The average total factor productivity (TFP) in 2020 and 2021 decreased compared to that in 2019, with scores of 0.7479 and 0.8996, respectively. Between 2019 and 2020, the TFP changes among 19 out of 21 (90.48%) TCM hospitals and the technological changes (TC) among 20 out of 21 (95.24%) were less than 1.0000 (P < .05). The TFP changes of 17 out of 21 (80.95%) TCM hospitals and the TC of 20 out of 21 (95.24%) were less than 1.0000 (P < .05) between 2019 and 2021. COVID-19 might have constrained the provision of healthcare services by the public tertiary TCM hospitals in Hubei Province. Priority should be given to the utilization of healthcare resources, performance evaluation, information system strengthening, and internal hospital management to boost technical efficiency. TCM hospitals need to focus further on technology innovation to improve their technological progress.

Uncertain DEA-Malmquist productivity index model and its application

Uncertain data envelopment analysis (DEA) model make an estimate of the efficiency of decision making unit (DMU) under data uncertainty. The current research on uncertain DEA model is only based on sectional data to calculate DMU’s static efficiency for the DMU’s set in the same period. From this article, we attempt to combine Malmquist productivity index and uncertain DEA model (the uncertain DEA-Malmquist productivity index model) to calculate the dynamic change of DMU’s efficiency over time. Additionally, the impact of technical factors and scale factors on DMU’s efficiency can be further explored and the Malmquist productivity index will be decomposed into pure technical efficiency change, scale efficiency change and technical change. Finally, the arcticle uses the model to analysis the provincial environmental efficiency from 2014 to 2016 in China.

Efficiency and Productivity Differences in Healthcare Systems: The Case of the European Union

This study aims to identify significant differences between the countries of the European Union, follow the course of achievement of the convergence objectives, assess developments against specific common characteristics of the countries, and propose possible measures that could improve the state of health in the EU as a whole by implementing standard cohesion policies. To compare efficiency and productivity among the states of the European Union, we used data envelopment analysis (DEA) and the Malmquist productivity index (MPI). On the basis of our findings, even countries that joined the EU later achieve high technical efficiency values. However, it should be noted that it is in these countries that technical efficiency values tend to decline. The values of the Malmquist productivity index broadly indicate stagnation in western countries and productivity decline in central and eastern European countries. This decline is mainly due to a negative shift in the technological frontier in these countries.

Efficiency measurement and spatial spillover effect of provincial health systems in China: Based on the two-stage network DEA model

The effectiveness of a health care system is an important factor for improving people's health and quality of life. The purpose of this research is to analyze the efficiency and spatial spillover effects of provincial health systems in China using panel data from 2009 to 2020. We employ the two-stage network DEA model to evaluate their efficiencies and use a spatial econometric model for empirical estimation. The results suggest that the overall efficiency, resource allocation efficiency, and service operation efficiency of health systems in different regions of China generally have fluctuating upward trends, with large differences in efficiency among the various regions. Further analysis reveals that the efficiency of China's health system has a significant spatial spillover effect. The level of economic development, fiscal decentralization and old-age dependency ratio are important factors affecting the health system efficiency. Our findings help to identify the efficiency and internal operating mechanisms of China's health system at different stages, and are expected to contribute to policymakers' efforts to build a high-quality health service system.

The impact of human resources for health on the health outcomes of Chinese people

Human resources for health (HRH) is a cornerstone in the medical system. This paper combined data envelopment analysis (DEA) with Tobit regression analysis to evaluate the efficiency of health care services in China over the years between 2007 and 2019. Efficiency was first estimated by using DEA with the choice of inputs and outputs being specific to health care services and residents' health status. Malmquist index model was selected for estimating the changes in total factor productivity of provinces and exploring whether their performance had improved over the years. Tobit regression model was then employed in which the efficiency score obtained from the DEA computations used as the dependent variable, and HRH was chosen as the independent variables. The results showed that all kinds of health personnel had a significantly positive impact on the efficiency, and more importantly, pharmacists played a critical role in affecting both the provincial and national efficiency. Therefore, the health sector should pay more attention to optimizing allocation of HRH and focusing on professional training of clinical pharmacists.

The dynamics of public and private health expenditure on health outcome in Southeast Asia

Though the level of public and private health expenditure per capita in Southeast Asia is comparatively below the level of health expenditure in Organisation for Economic Co-operation and Development (OECD) countries, the former has higher rates of under-five and non-communicable disease mortality rates than the latter. Similarly, life expectancy at birth is considerably higher in OECD compared to Southeast Asia, despite the global progress in recent decades. This study examines the dynamics of public and private health expenditure on health outcomes in Southeast Asia, vis-a-vis two of the Sustainable Development Goals targets. The techniques of fixed effect, random effect and feasible generalised least squares methods are used to obtain robust estimates. Furthermore, the analysis dives deep into the analysis of country-specific impacts of public and private health expenditure on health outcomes using the technique of seemingly unrelated regression. Estimates show that, across Southeast Asia, public health expenditure alone contributes to improving life expectancy at birth, lower level of under-five and non-communicable disease mortality rates. Unlike public health expenditure, private health expenditure contributes to better health outcomes only in Brunei and Singapore but not across the countries of Southeast Asia. The implications of the findings and possible future research areas are highlighted further.

Sustainable Financing Efficiency and Environmental Value in China’s Energy Conservation and Environmental Protection Industry under the Double Carbon Target

Difficulty in financing and low financing efficiency is one of the bottlenecks that restrict the high-quality development of China’s energy-saving and environmental protection industry and economy. The key to improving financing efficiency is to understand its influencing factors. This paper uses data envelopment analysis (DEA) and the Malmquist index to measure the overall financing efficiency and the efficiency of different financing methods of 205 Chinese energy-saving and environmental protection industries from 2015 to 2020 from static and dynamic perspectives, respectively, as well as the Tobit model to estimate the impact of factors such as the digital transformation and green technological innovation of enterprises on financing efficiency. The study shows the following: (1) Static analysis shows that: the financing efficiency of the comprehensive technical efficiency of China’s energy conservation and environmental protection industry is less than one, 5.8% to 23.41% of enterprises have very effective comprehensive technical financing efficiency, and fewer than 9% enterprises have very effective scale efficiency levels. Enterprises may have more room for improving their financing efficiency in the future. The four types of financing are, namely, internal financing, equity financing, fiscal financing, and debt financing, in descending order of efficiency. (2) Dynamic analysis shows that the financing efficiencies of debt financing and fiscal financing are both on an upward trend, while internal and equity financing efficiencies are on a downward trend. Additionally, the technological progress change index and scale efficiency are two key factors affecting the financing efficiency of different financing methods. (3) In terms of financing methods, the comprehensive technical efficiency and scale efficiency of endogenous financing and equity financing are high, while the comprehensive technical efficiency and scale efficiency of debt financing and fiscal financing are low and flat. (4) Digital transformation, green technology innovation, the asset–liability ratio, profitability, and operational capability have a significant positive impact on the financing efficiency of energy-saving and environmental protection enterprises. This paper studies the financing efficiency of China’s energy conservation and environmental protection industry under different financing methods and the mechanism through which key factors affect the financing efficiency of enterprises. It aims to provide a theoretical basis for managing financing methods scientifically and rationally and improving the financing efficiency of the energy conservation and environmental protection industry, as well as to provide practical reference for the implementation of digital transformation, green technology innovation and diversified financing in China and other developing economies.