Assessing the efficiency of Malaysia health system in COVID-19 prevention and treatment response

Adapting Efficiency Analysis in Health Systems: A Scoping Review of Data Envelopment Analysis Applications During the COVID-19 Pandemic

Objective: To synthesize the current evidence base concerning the application of Data Envelopment Analysis (DEA) in healthcare efficiency during the COVID-19 pandemic using a scoping review of 13 primary studies. Methods: We consulted databases including Web of Science (WoS) and Scopus, as well as manual search entries up to September 2022. Included studies were primary applications of DEA for assessing healthcare efficiency during the COVID-19 pandemic. Key findings derived from thematic analysis of repeating pattern observations were extracted and tabulated for further synthesis, taking into consideration the variations in DMU definitions, the inclusion of undesirable outputs, the influence of external factors, and the infusion of advanced technologies in DEA. Results: The review observed a diverse application of DMUs, ranging from healthcare supply chains to entire national health systems. There was an evident shift towards incorporating undesirable outputs, such as mortality rates, in the DEA models amidst the pandemic. The influence of external and non-discretionary factors became more pronounced in DEA applications, highlighting the interconnected nature of global health challenges. Notably, several studies integrated advanced computational methods, including machine learning, into traditional DEA, paving the way for enhanced analytical capabilities. Conclusions: DEA, as an efficiency analysis tool, has exhibited adaptability and evolution in its application in the context of the COVID-19 healthcare crisis. By recognizing the multifaceted challenges posed by the pandemic, DEA applications have grown more comprehensive, integrating broader societal and health outcomes. This review provides pivotal insights that can inform policy and healthcare strategies, underscoring the importance of dynamic and comprehensive efficiency analysis methodologies during global health emergencies.

An assessment of economy- and transport-oriented health performance

BACKGROUND

Good health can prolong one's lifespan and is a fundamental human right. Thus, human health is being influenced by prejudiced from sociological, environmental, economic, and geographic aspects. The economy and transportation system pose a serious challenge to the assessment of the health performance of economies.

OBJECTIVE

This study aims to assess the health performance of Organization for Economic Cooperation and Development (OECD) economies by using economic and transport-related indicators and examining the role of health expenditure and governance in improving efficiency.

METHODS

This study measures the economy- and transport-oriented health efficiency of 35 OECD economies for the period of 2000-2022. In the first stage, this study employs a slacks-based measure and the data envelopment analysis-window analysis approach to conduct individual (economy and transportation) and joint assessments to measure health efficiency. In the second stage, this study uses the tobit regression method to investigate the effects of influencing factors, namely, government general health and pharmaceutical expenditures, the medical infrastructure, and governance, on health efficiency.

RESULTS

Empirical results reveal that a 1-unit change in the health expenditure during the research period improves economy-oriented health efficiency by 71% and transport-oriented health efficiency by 58%. The econometric analysis demonstrates that all the coefficients of economy- and transport-oriented health efficiency are significant and positive. Notably, a 1-unit change in the medical infrastructure increases economy- and transport-oriented health efficiency by 50.8%, and a 1% increase in pharmaceutical expenditure increases the health, economy, and transport efficiency scores by 16.3%, 33%, and 58.6%, respectively.

CONCLUSIONS

The findings suggest that some of the economies were efficient with regard to their health-oriented outputs, that is, quality of life and mortality and morbidity rates, and most of the economies demonstrated excellent economic performance. The findings of the transport-oriented health efficiency assessment reveal that the economies were unable to perform well in the last year of the research period owing to the nationwide lockdowns. Nonetheless, they demonstrated efficiency in the first half of the research period. The joint assessment of economy- and transport-oriented health efficiency indicates that economic and transport input resources can adversely affect the GDP and life expectancy simultaneously, and the medical infrastructure, pharmaceutical expenditure, and number of medical graduates serve as constructive stimuli for health efficiency improvement.

Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks

Pandemics like COVID-19 have illuminated the significant disparities in the performance of national healthcare systems (NHCSs) during rapidly evolving crises. The challenge of comparing NHCS performance has been a difficult topic in the literature. To address this gap, our study introduces a bi-criteria longitudinal algorithm that merges fuzzy clustering with Data Envelopment Analysis (DEA). This new approach provides a comprehensive and dynamic assessment of NHCS performance and efficiency during the early phase of the pandemic. By categorizing each NHCS as an efficient performer, inefficient performer, efficient underperformer, or inefficient underperformer, our analysis vividly represents performance dynamics, clearly identifying the top and bottom performers within each cluster of countries. Our methodology offers valuable insights for performance evaluation and benchmarking, with significant implications for enhancing pandemic response strategies. The study's findings are discussed from theoretical and practical perspectives, offering guidance for future health system assessments and policy-making.

Utilizing disease transmission and response capacities to optimize covid-19 control in Malaysia

OBJECTIVES

Public Health Social Measures (PHSM) such as movement restriction movement needed to be adjusted accordingly during the COVID-19 pandemic to ensure low disease transmission alongside adequate health system capacities based on the COVID-19 situational matrix proposed by the World Health Organization (WHO). This paper aims to develop a mechanism to determine the COVID-19 situational matrix to adjust movement restriction intensity for the control of COVID-19 in Malaysia.

METHODS

Several epidemiological indicators were selected based on the WHO PHSM interim guidance report and validated individually and in several combinations to estimate the community transmission level (CT) and health system response capacity (RC) variables. Correlation analysis between CT and RC with COVID-19 cases was performed to determine the most appropriate CT and RC variables. Subsequently, the CT and RC variables were combined to form a composite COVID-19 situational matrix (SL). The SL matrix was validated using correlation analysis with COVID-19 case trends. Subsequently, an automated web-based system that generated daily CT, RC, and SL was developed.

RESULTS

CT and RC variables were estimated using case incidence and hospitalization rate; Hospital bed capacity and COVID-19 ICU occupancy respectively. The estimated CT and RC were strongly correlated [ρ = 0.806 (95% CI 0.752, 0.848); and ρ = 0.814 (95% CI 0.778, 0.839), p < 0.001] with the COVID-19 cases. The estimated SL was strongly correlated with COVID-19 cases (ρ = 0.845, p < 0.001) and responded well to the various COVID-19 case trends during the pandemic. SL changes occurred earlier during the increase of cases but slower during the decrease, indicating a conservative response. The automated web-based system developed produced daily real-time CT, RC, and SL for the COVID-19 pandemic.

CONCLUSIONS

The indicators selected and combinations formed were able to generate validated daily CT and RC levels for Malaysia. Subsequently, the CT and RC levels were able to provide accurate and sensitive information for the estimation of SL which provided valuable evidence on the progression of the pandemic and movement restriction adjustment for the control of Malaysia.

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.

Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic

The coronavirus infection COVID-19 killed millions of people around the world in 2019-2022. Hospitals were in the forefront in the battle against the pandemic. This paper proposes a novel approach to assess the effectiveness of hospitals in saving lives. We empirically estimate the production function of COVID-19 deaths among hospital inpatients, applying Heckman's two-stage approach to correct for the bias caused by a large number of zero-valued observations. We subsequently assess performance of hospitals based on regression residuals, incorporating contextual variables to convex quantile regression. Data of 187 hospitals in England over a 35-week period from April to December 2020 is divided in two sub-periods to compare the structural differences between the first and second waves of the pandemic. The results indicate significant performance improvement during the first wave, however, learning by doing was offset by the new mutated virus straits during the second wave. While the elderly patients were at significantly higher risk during the first wave, their expected mortality rate did not significantly differ from that of the general population during the second wave. Our most important empirical finding concerns large and systematic performance differences between individual hospitals: larger units proved more effective in saving lives, and hospitals in London had a lower mortality rate than the national average.

COVID-19 Vaccine Acceptance and Hesitancy among Healthcare Workers in Lusaka, Zambia; Findings and Implications for the Future

The uptake of COVID-19 vaccines is critical to address the severe consequences of the disease. Previous studies have suggested that many healthcare workers (HCWs) are hesitant to receive the COVID-19 vaccine, further enhancing hesitancy rates within countries. COVID-19 vaccine acceptance and hesitancy levels are currently unknown among HCWs in Zambia, which is a concern given the burden of infectious diseases in the country. Consequently, this study assessed COVID-19 vaccine acceptance and hesitancy among HCWs in Lusaka, Zambia. A cross-sectional study was conducted among 240 HCWs between August and September 2022, using a semi-structured questionnaire. Multivariable analysis was used to determine the key factors associated with vaccine hesitancy among HCWs. Of the 240 HCWs who participated, 54.2% were females. A total of 72.1% of the HCWs would accept being vaccinated, while 27.9% were hesitant. Moreover, 93.3% of HCWs had positive attitudes towards COVID-19 vaccines, with medical doctors having the highest mean attitude score (82%). Encouragingly, HCWs with positive attitudes towards COVID-19 vaccines had reduced odds of being hesitant (AOR = 0.02, 95% CI: 0.01-0.11, p < 0.001). Overall, acceptance of the COVID-19 vaccine among HCWs in Lusaka, Zambia, was high, especially by those with positive attitudes. However, the current hesitancy among some HCWs is a concern. Consequently, there is a need to address this and encourage HCWs to fully promote vaccination programs going forward.

Uncertain Malmquist productivity index: An application to evaluate healthcare systems during COVID-19 pandemic

Evaluation of healthcare systems, as a key organization providing different health services, is essential. This issue becomes more crucial when occurring crises such as a pandemic. They need to keep track of their success in the face of the crisis to assess the effects of policy changes and their capability to respond to new challenges. The Malmquist Productivity Index (MPI) is measured to analyze the causes of productivity change between two periods of time. The estimation of the traditional MPI requires reliable and detailed information on the inputs and outputs of decision-making units. However, there are a lot of situations where input and/or output may be imprecise. It is not manageable to reliably measure certain measurement indices, such as quality of treatment or system flexibility. For such cases, experts are invited to model their opinion. Uncertainty theory is a mathematical branch rationally dealing with belief degrees. The primary objective of this study is to apply MPI concept in the nonparametric approach of data envelopment analysis to calculate the efficiency of systems over different periods of time under uncertain conditions. Accordingly, we consider the MPI when inputs and outputs are belief degrees of experts. Furthermore, the sensitivity of the model is analyzed to determine the reliability of the results to the variation of variables. Finally, as an illustrative example, we explore longitudinal efficiency of healthcare systems during COVID-19 pandemic. According to the results of our model, the majority of the countries have improved in the second period which can be the result of efforts to improve pandemic preparedness. The decomposition of MPI into efficiency changes and technical changes indicates that the rise in productivity is entirely related to the progressive change of the production frontier related to policymaking. This application attempts to demonstrate how crucial it is to take uncertainties into account when comparing the performance of different systems over periods of time. The developed model enables us to consider the uncertainty existing in COVID-19 pandemic. The proposed model can handle more accurately the uncertainty during the pandemic. Thus, the result could be more reliable, which can benefit decision-makers in regard to performance improvement.

Efficiency Measurement Using Data Envelopment Analysis (DEA) in Public Healthcare: Research Trends from 2017 to 2022

With the shifting healthcare environment, the importance of public healthcare systems is being emphasized, and the efficiency of public healthcare systems has become a critical research agenda. We reviewed recent research on the efficiency of public healthcare systems using DEA, which is one of the leading methods for efficiency analysis. Through a systematic review, we investigated research trends in terms of research purposes, specific DEA techniques, input/output factors used for models, etc. Based on the review results, future research directions are suggested. The results of this paper provide valuable information and guidelines for future DEA research on public healthcare systems.

Breast Cancer Management in the Era of Covid-19; Key Issues, Contemporary Strategies, and Future Implications

During the COVID-19 pandemic, several priority diseases were not getting sufficient attention. Whilst breast cancer is a fatal disease affecting millions worldwide, identification and management of these patients did not initially attract critical attention to minimize the impact of lockdown, post-lockdown, and other measures. Breast cancer patients' conditions may not remain stable without proper care, worsening their prognosis. Proper care includes the timely instigation of surgery, systemic therapy, and psychological support. This includes low-and middle-income countries where there are already concerns with available personnel and medicines to adequately identify and treat these patients. Consequently, there was a need to summarize the current scenario regarding managing breast cancer care during COVID-19 across all countries, including any guidelines developed. We systematically searched three scientific databases and found 76 eligible articles covering the medical strategies of high-income countries versus LMICs. Typically, diagnostic facilities in hospitals were affected at the beginning of the pandemic following the lockdown and other measures. This resulted in more advanced-stage cancers being detected at initial presentation across countries, negatively impacting patient outcomes. Other than increased telemedicine, instigating neo-adjuvant endocrine therapy more often, reducing non-essential visits, and increasing the application of neo-adjuvant chemotherapy to meet the challenges, encouragingly, there was no other significant difference among patients in high-income versus LMICs. Numerous guidelines regarding patient management evolved during the pandemic to address the challenges posed by lockdowns and other measures, which were subsequently adopted by various high-income countries and LMICs to improve patient care. The psychological impact of COVID-19 and associated lockdown measures, especially during the peak of COVID-19 waves, and the subsequent effect on the patient's mental health must also be considered in this high-priority group. We will continue to monitor the situation to provide direction in future pandemics.

The role of facilities management in fighting COVID-19 outbreak: Evidence from Malaysian public hospitals

The aim of this study is to provide a holistic review of the fight against COVID-19 in developing countries, particularly Malaysia. Specifically, the study aims to determine how facilities management delivery in public hospitals can be improved to ensure readiness in handling COVID-19 cases. We conducted a review of the literature and reliable media updates on COVID-19 and services management. A critical synthesis of COVID-19 information was conducted to scrutinise the technical aspects and highlight how facilities management can be improved to ensure hospital readiness in managing COVID-19 cases. The data and information used in the present study were collected up to the time of writing this paper, which leaves a room for further studies. Nonetheless, this study's recommendations are useful for understanding the present and future pandemics. This study is a first attempt to summarise the data on facilities management in relation to the COVID-19 pandemic in the Malaysian context. The study's findings are suitable for the developing countries in managing healthcare management practices in the fight against COVID-19. This study aims to highlight current issues in order to provide a more objective assessment of facilities management to ensure hospital readiness in handling COVID-19 cases.

COVID-19 and sustainable development goals: A bibliometric analysis and SWOT analysis in Malaysian context

The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments' aims to meet UN Sustainable Development Goals (SDGs); leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies.

A network Data Envelopment Analysis to estimate nations' efficiency in the fight against SARS-CoV-2

The ongoing outbreak of SARS-CoV-2 has been deeply impacting health systems worldwide. In this context, it is pivotal to measure the efficiency of different nations' response to the pandemic, whose insights can be used by governments and health authorities worldwide to improve their national COVID-19 strategies. Hence, we propose a network Data Envelopment Analysis (DEA) to estimate the efficiencies of fifty-five countries in the current crisis, including the thirty-seven Organisation for Economic Co-operation and Development (OECD) member countries, six OECD prospective members, four OECD key partners, and eight other countries. The network DEA model is designed as a general series structure with five single-division stages - population, contagion, triage, hospitalisation, and intensive care unit admission -, and considers an output maximisation orientation, denoting a social perspective, and an input minimisation orientation, denoting a financial perspective. It includes inputs related to health costs, desirable and undesirable intermediate products related to the use of personal protective equipment and infected population, respectively, and desirable and undesirable outputs regarding COVID-19 recoveries and deaths, respectively. To the best of the authors' knowledge, this is the first study proposing a cross-country efficiency measurement using a network DEA within the context of the COVID-19 crisis. The study concludes that Estonia, Iceland, Latvia, Luxembourg, the Netherlands, and New Zealand are the countries exhibiting higher mean system efficiencies. Their national COVID-19 strategies should be studied, adapted, and used by countries exhibiting worse performances. In addition, the observation of countries with large populations presenting worse mean efficiency scores is statistically significant.

A network Data Envelopment Analysis to estimate nations' efficiency in the fight against SARS-CoV-2

The ongoing outbreak of SARS-CoV-2 has been deeply impacting health systems worldwide. In this context, it is pivotal to measure the efficiency of different nations' response to the pandemic, whose insights can be used by governments and health authorities worldwide to improve their national COVID-19 strategies. Hence, we propose a network Data Envelopment Analysis (DEA) to estimate the efficiencies of fifty-five countries in the current crisis, including the thirty-seven Organisation for Economic Co-operation and Development (OECD) member countries, six OECD prospective members, four OECD key partners, and eight other countries. The network DEA model is designed as a general series structure with five single-division stages - population, contagion, triage, hospitalisation, and intensive care unit admission -, and considers an output maximisation orientation, denoting a social perspective, and an input minimisation orientation, denoting a financial perspective. It includes inputs related to health costs, desirable and undesirable intermediate products related to the use of personal protective equipment and infected population, respectively, and desirable and undesirable outputs regarding COVID-19 recoveries and deaths, respectively. To the best of the authors' knowledge, this is the first study proposing a cross-country efficiency measurement using a network DEA within the context of the COVID-19 crisis. The study concludes that Estonia, Iceland, Latvia, Luxembourg, the Netherlands, and New Zealand are the countries exhibiting higher mean system efficiencies. Their national COVID-19 strategies should be studied, adapted, and used by countries exhibiting worse performances. In addition, the observation of countries with large populations presenting worse mean efficiency scores is statistically significant.

Assessing the Efficiency and Productivity of the Hospital Clinics on the Island of Rhodes during the COVID-19 Pandemic

(1) Background: The aim was to measure the efficiency and productivity of 15 specialty clinics during the COVID-19 pandemic period 2020-2021 in the General Hospital of Rhodes. (2) Methods: An input-oriented data envelopment analysis and the Malmquist productivity index are used. Labor and capital were used as inputs, and in-patient discharges and days were used as outputs. (3) Results: Five out of the seven clinics in the pathology sector appeared fully efficient with an optimal productivity, and the rest showed progress in 2021. In 2020 the COVID-19 pathology clinic appeared to be inefficient and less productive, while in 2021, it showed a positive performance change. The surgical sector showed very high efficiency rates or even reached an optimal efficiency in both years. The productivity measurement, in most of the surgical clinics, was satisfactory to very high. In 2020 the COVID-19 surgical clinic appeared to be more efficient and productive than in 2021 when its performance declined. (4) Conclusions: The hospital responded to the pressure during the pandemic, by increasing its efficiency and productivity from 2020 to 2021. This was due to the accomplishment of the appropriate organizational changes in the infrastructure, human resources, and technology. The efficiency and productivity assessments should be incorporated in the hospitals' decision making.

Using network data envelopment analysis to assess the sustainability and resilience of healthcare supply chains in response to the COVID-19 pandemic

The widespread outbreak of a new Coronavirus (COVID-19) strain has reminded the world of the destructive effects of pandemic and epidemic diseases. Pandemic outbreaks such as COVID-19 are considered a type of risk to supply chains (SCs) affecting SC performance. Healthcare SC performance can be assessed using advanced Management Science (MS) and Operations Research (OR) approaches to improve the efficiency of existing healthcare systems when confronted by pandemic outbreaks such as COVID-19 and Influenza. This paper intends to develop a novel network range directional measure (RDM) approach for evaluating the sustainability and resilience of healthcare SCs in response to the COVID-19 pandemic outbreak. First, we propose a non-radial network RDM method in the presence of negative data. Then, the model is extended to deal with the different types of data such as ratio, integer, undesirable, and zero in efficiency measurement of sustainable and resilient healthcare SCs. To mitigate conditions of uncertainty in performance evaluation results, we use chance-constrained programming (CCP) for the developed model. The proposed approach suggests how to improve the efficiency of healthcare SCs. We present a case study, along with managerial implications, demonstrating the applicability and usefulness of the proposed model. The results show how well our proposed model can assess the sustainability and resilience of healthcare supply chains in the presence of dissimilar types of data and how, under different conditions, the efficiency of decision-making units (DMUs) changes.

What will the ambitions of primary healthcare be in the 21st century?

This article attempts to analyze the problem of the modernity of primary healthcare, as well as the reforms to be implemented in a new context characterized by COVID-19. This article offers another vision to follow, to build a modern primary healthcare system. It is a descriptive and analytical study, which addresses the failure of the health system in general, as it proposes the reforms necessary to provide equitable, efficient and modern primary healthcare. The results clearly show that we can no longer act unilaterally; multi-sector efforts at the national level should be encouraged: we must act, in a preventive way, on the causes of the disease; international agreements on the environment, prices of basic foods and medicines, etc. are much needed to improve the health status of middle and low income countries, the case of the majority of African countries. So, different solutions can be conveyed by primary healthcare, in order to improve the whole health system.

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.

Emergency medical resource allocation among hospitals with non-regressive production technology: A DEA-based approach

This paper proposes an approach for medical resource allocation among hospitals under public health emergencies based on data envelopment analysis (DEA). First, the DEA non-regressive production technology is adopted to ensure that the DMU can always refer to the most advanced production technology throughout all production periods. Based on the non-regressive production technology, two efficiency evaluation models are presented to calculate the efficiencies of DMUs before and after resource allocation. Our theoretical analysis shows that all the DMUs can be efficient after medical resource allocation, and thus a novel resource allocation possibility set is developed. Further, two objectives are considered and a bi-objective resource allocation model is developed. One objective is to maximize the output target realizability of the DMUs, while the other is to ensure the allocated resource to each DMU fits with its operation size, preperformance, and operation practice (i.e., proportion of critically ill patients). Additionally, a trade-off model is proposed to solve the bi-objective model to obtain the final resource allocation results. The proposed approach contributes by ensuring that the medical resources are allocated in such a way that they can all be efficiently used as well as considering multiple objectives and practical constraints that make the approach more fitted with the practical application scenarios. Finally, a case study of 30 hospitals in Wuhan during the COVID-19 epidemic is applied to illustrate the proposed approach.

A Review on the 40 Years of Existence of Data Envelopment Analysis Models: Historic Development and Current Trends

DEA, incepted in 80s, has emerged as a popular decision-making technique, for determining the efficiency of similar units. Due to its simplicity and applicability, DEA has gained the attention of scientists and researchers working in diverse areas, which has contributed towards a rich literature both in terms of theoretical development as well as different applications. This paper tries to bring together the near 40 years of existence of DEA in a concise format by discussing the popular DEA models, their advantages and shortcomings, and different applications of DEA. It also provides a brief bibliometric analysis to highlight the development of DEA over the years in terms of publication trends, highly cited papers, journal citation, etc.

Exploring the Performance of International Airports in the Pre- and Post-COVID-19 Era: Evidence from Incheon International Airport

Considering the socio-economic importance of Incheon International Airport, this study explored the changes in its aeronautical and non-aeronautical efficiency between 2001 and 2021. The study was conducted to measure and observe the changes in efficiency during the pre- and post-pandemic era of COVID-19. We employed a two-stage analytical approach to obtain the results using a set of desirable and undesirable variables. For the first stage, we employed a novel network data envelopment analysis–window analysis model to find the efficiency measures; for the second stage, we applied the Tobit regression analysis to observe the impact of some control variables on efficiency levels. The empirical results from the efficiency analysis stage revealed that, although the pandemic negatively affected the efficiency of this airport, the gain from appropriate strategies mitigated the excessive efficiency decline. Moreover, aeronautical activities showed better efficiency than non-aeronautical activities during the study period. In addition, further investigation of the second-stage analysis implied that an outbreak of pandemic diseases such as COVID-19 would dramatically impact international hubs such as Incheon International Airport; however, focusing on the import and export activities, in addition to increasing the connectivity with other airports, would improve the efficiency.

COVID-19 and the efficiency of health systems in Europe

BACKGROUND

This study aims at analyzing the efficiency of the health systems of 31 European countries in treating COVID-19, for the period January 1, 2020 - January 1, 2021, by incorporating some factors from a multidimensional perspective.

METHODS

The methodology used in the research was Data Envelopment Analysis (DEA), through which efficiency scores for health systems have been calculated. The research was performed considering three stages: the first wave (January 1-June 15), the relaxation period (June 15-October 1) and the second wave (October 1-December 31). In evaluating the determinants of the efficiency of health systems, six major fields of influence were taken into account: health care, health status, population, economic, cultural/societal and governmental issues, all covering 15 indicators. After measuring the efficiency, we used the Tobit type regression to establish the influencing elements on it.

RESULTS

The results for the public health systems of European states were determined for each country and period. We evaluated the efficiency of health systems in Europe against COVID-19, starting from health inputs (COVID-19 cases, physicians, nurses, hospital beds, health expenditure) and output (COVID-19 deaths). The obtained outputs show that, especially in the first phase of the pandemic, the inefficiency of the health systems was quite high, mainly in Western countries (Italy, Belgium, Spain, UK). In the relaxation phase and in the second wave, the Western states, severely affected at the beginning of the pandemic, began to take adequate measures and improve the efficiency of their sanitary systems. Instead, Eastern European countries were hit hard by the inefficiency of health systems (Bulgaria, Greece, Hungary, Romania). After Tobit regression, results of the study show that the influencing elements are different for the three stages: concerning the first wave, comobirdities, population age, and population density are important; for relaxation period a great influence have government effectiveness and power distance; with respect to second wave, the relevant factors are education and population density.

CONCLUSIONS

The results obtained could serve as starting points for health policymakers to perform comparative analyzes in terms of good practices in the health system and to develop national plans to better deal with health crises. At the same time, they can be used internationally to achieve a coherent and effective community response to the pandemic.