Factors influencing the technical efficiency of diabetes care at primary care level in Mexico

Diabetes prevalence is rising globally, especially in low- and middle-income countries like Mexico, posing challenges for healthcare systems that require efficient primary care to manage the disease. However, healthcare efficiency is influenced by factors beyond decision-makers, including socioeconomic and political conditions. This study aims to evaluate the technical efficiency of primary healthcare for diabetes patients in Mexico over a 12-year period and explore the impact of contextual variables on efficiency. A longitudinal analysis was conducted using administrative and socio-demographic data from 242 health jurisdictions between 2009 and 2020. Data envelopment analysis with bootstrapping and output orientation was used to measure the technical efficiency; health resources in infrastructure and human resources were used as inputs. As outcome, the number of patients receiving treatment for diabetes and the number of patients with controlled diabetes were considered. Machine learning algorithms were employed to analyse multiple factors affecting the provision of diabetes health services and assess heterogeneity and trends in efficiency across different health jurisdictions. The average technical efficiency in primary healthcare for diabetes patients was 0.44 (CI: 0.41-0.46) in 2009, reaching a peak of 0.71 (CI: 0.69-0.72) in 2016, and moderately declining to 0.60 (CI: 0.57-0.62) in 2020; these differences were statistically significant. The random forest analysis identified the marginalization index, primary healthcare coverage, proportion of indigenous population and demand for health services as the most influential variables in predicting efficiency levels. This research underscores the crucial need for the formulation of targeted public policies aimed at extending the scope of primary healthcare services, with a particular focus on addressing the unique challenges faced by marginalized and indigenous populations. According to our results, it is necessary that medical care management adjust to the specific demands and needs of these populations to guarantee equitable care in Mexico.

Advancing Efficiency Sustainability in Poultry Farms through Data Envelopment Analysis in a Brazilian Production System

The production efficiency factor is widely used to measure the zootechnical performance of a batch of broilers. The unit cost of production brings new elements to improve efficiency evaluation and financial sustainability for this activity in agriculture. This research aims to evaluate the production efficiency level of the crop to maximize the return on investment. This study uses Data Envelopment Analysis (DEA) with the computational processing of the SIAD software (Integrated Decision Support System). The variables selected were poultry housing, age at slaughter, feed consumed, mortality, and unit cost. The chosen output variable was the total available weight. The analysis spans 31 decision-making units (DMUs) composed of integrated producers, unveiling a frontier of efficiency delineated by the most exemplary DMUs. Notably, only two DMUs, specifically DMU 4 and DMU 23, approached the threshold of maximum relative efficiency. This research illuminates the critical role of unit cost in enhancing the assessment of production efficiency and financial sustainability within the agriculture environment. By setting benchmarks for efficient management and operational protocols, our findings serve as a cornerstone for improving practices among less efficient DMUs, contributing significantly to the literature on agricultural efficiency and offering actionable insights for the poultry farming sector.

Sustainable approach towards alternatives for the use of iron ore tailings in the construction sector using Data Envelopment Analysis methodology

Iron ore tailings (IOTs) need to be properly managed to mitigate the environmental, social, and economic impacts of mining activities. To cope with this issue, we use data envelopment analysis (DEA) to evaluate alternatives for using IOT in the construction sector. The classical and weight restriction output-oriented DEA models were used in this analysis. The results show that the ranking of alternatives depends on the aspect being evaluated. Concrete block is the most environmentally friendly alternative when analysing both models. For both social and economic aspects, ceramics produced better results in the classical model, whereas Portland cement showed better outcomes in the weight restriction model. In this sense, the results suggest great potential for the use of IOT in the construction sector, enabling the reduction of risks and social and environmental impacts of tailings dams.

Evaluating health systems' efficiency towards universal health coverage: A data envelopment analysis

To estimate the technical efficiency of health systems toward achieving universal health coverage (UHC) in 191 countries. We applied an output-oriented data envelopment analysis approach to estimate the technical efficiency of the health systems, including the UHC index (a summary measure that captures both service coverage and financial protection) as the output variable and per capita health expenditure, doctors, nurses, and hospital bed density as input variables. We used a Tobit simple-censored regression with bootstrap analysis to observe the socioeconomic and environmental factors associated with efficiency estimates. The global UHC index improved from the 2019 estimates, ranged from 48.4 (Somalia) to 94.8 (Canada), with a mean of 76.9 (std. dev.: ±12.0). Approximately 78.5% (150 of 191) of the studied countries were inefficient (ϕ < 1.0) with respect to using health system resources toward achieving UHC. By improving health system efficiency, low-income, lower-middle-income, upper-middle-income, and high-income countries can improve their UHC indices by 4.6%, 5.5%, 6.8%, and 4.1%, respectively, by using their current resource levels. The percentage of health expenditure spent on primary health care (PHC), governance quality, and the passage of UHC legislation significantly influenced efficiency estimates. Our findings suggests health systems inefficiency toward achieving UHC persists across countries, regardless of their income classifications and WHO regions, as well as indicating that using current level of resources, most countries could boost their progress toward UHC by improving their health system efficiency by increasing investments in PHC, improving health system governance, and where applicable, enacting/implementing UHC legislation.

Efficiency trends of essential public health services and possible influencing factors since the new round health reform in China: a case study from Hainan Province

Objective

This article aimed to evaluate the efficiency trends and influencing factors of essential public health services in Hainan Province after the healthcare reform launched in 2009 in China.

Methods

The efficiency of essential public health services (EPHS) at primary health institutions was assessed using data envelopment analysis (DEA), and the efficiency change was analyzed by employing the Malmquist productivity index (MPI). We used Tobit regression to identify the influence of environmental factors on the efficiency of public health services. The bootstrap method was adopted to reduce the impact of random errors on the result.

Results

The bootstrapping bias-corrected efficiency revealed that the average values of technical efficiency, pure technical efficiency, and scale efficiency were 0.7582, 0.8439, and 0.8997, respectively, which meant that the EPHS in Hainan Province were not at the most effective state. The average bias-corrected MPI was 1.0407 between 2010 and 2011 and 1.7404 between 2011 and 2012. MPIs were less than 1.0000 during other periods investigated, ranging from 0.8948 to 0.9714, indicating that the efficiency of EPHS has been decreasing since 2013. The Tobit regression showed that the regression coefficients of per capita GDP, population density, the proportion of older people aged over 65, and the proportion of ethnic minority population were 0.0286, -0.0003, -0.0316, and - 0.0041 respectively, which were statistically significant (p < 0.05).

Conclusion

There was a short-term improvement in the efficiency of EPHS in Hainan after the launch of the new round of health reform. However, this trend has not been sustained after 2013. In particular, equalized financial investment in essential public health could not fulfill the needs of poor counties. This has resulted in the inability to improve scale efficiency in some counties, which in turn has affected the improvement of overall EPHS efficiency. Therefore, to promote EPHS efficiency sustainably, it is suggested that under this model of provincial control of counties, the equity of resource allocation should be effectively improved while further advancing the technology of service delivery.

Efficiency and productivity analysis of maternal and infant healthcare services in Sub-Saharan Africa

The paper examines the efficiency and productivity of Sub-Saharan African (SSA) countries towards maternal and infant healthcare services between 2015 and 2019. Data envelopment analysis is utilised to evaluate efficiency, and Malmquist-Luenberger's (ML) productivity estimation is employed for productivity analysis. The results indicate inefficiency in SSA maternal and infant healthcare services. Average efficiency is pegged at 85%, and 60% of the countries evaluated had below-average efficiency. Effects of socioeconomic dynamics of countries were analysed. Preliminary estimations on the impact of Gross domestic product (GDP), education, urban population, and total population on efficiency are not significant. Although GDP and education sometimes show that they influence efficiency positively. Sensitivity analysis indicates efficiency to be more responsive to health expenditure, as well as to nurses and midwives. ML Productivity decomposition into technical efficiency change and technological change indicates improvement in technical efficiency as the principal driver of efficiency and productivity. Policy recommendations are made in line with the findings, requirements, and constraints of SSA countries.

Road Safety Efficiency on Interurban Roads in Spain

The slowdown in economic development caused by traffic accidents in Spain, together with the disparity in the risk of death or injury due to traffic accidents in its provinces, makes it necessary to evaluate their road safety performance. Therefore, the objectives of the present research are, on the one hand, to find out the level of road safety efficiency of Spanish provinces in the period 2014-2018. On the other hand, it is also aimed to determine to what extent the annual efficiency change is originated by pure changes in efficiency or by technological changes. To achieve both objectives, the nonparametric technique of Data Envelopment Analysis and the Malmquist Index have been used based on the consideration of the Safety Performance Indicators (SPI) as the inputs of the process. The research findings show changes in the level of road safety efficiency in each of the years of the study period. Thus, it is possible to identify a geographical delimitation of the causes that generate changes in efficiency in recent years. A contraction in efficiency and technological progress is identified in part of the Spanish provinces in the north, northeast, and south of Spain.

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review

The healthcare industry is one application for data envelopment analysis (DEA) that can have significant benefits for standardizing health service delivery. This narrative review focuses on the application of DEA in emergency departments (EDs) and the management of emergency conditions such as acute ischemic stroke and acute myocardial infarction (AMI). This includes benchmarking the proportion of patients that receive treatment for these emergency conditions. The most frequent primary areas of study motivating work in DEA, EDs and management of emergency conditions including acute management of stroke are sorted into five distinct clusters in this study: (1) using basic DEA models for efficiency analysis in EDs, i.e., applying variable return to scale (VRS), or constant return to scale (CRS) to ED operations; (2) combining advanced and basic DEA approaches in EDs, i.e., applying super-efficiency with basic DEA or advanced DEA approaches such as additive model (ADD) and slack-based measurement (SBM) to clarify the dynamic aspects of ED efficiency throughout the duration of a first-aid program for AMI or heart attack; (3) applying DEA time series models in EDs like the early use of thrombolysis and percutaneous coronary intervention (PCI) in AMI treatment, and endovascular thrombectomy (EVT) in acute ischemic stroke treatment, i.e., using window analysis and Malmquist productivity index (MPI) to benchmark the performance of EDs over time; (4) integrating other approaches with DEA in EDs, i.e., combining simulations, machine learning (ML), multi-criteria decision analysis (MCDM) by DEA to reduce patient waiting times, and futile transfers; and (5) applying various DEA models for the management of acute ischemic stroke, i.e., using DEA to increase the number of eligible acute ischemic stroke patients receiving EVT and other medical ischemic stroke treatment in the form of thrombolysis (alteplase and now Tenecteplase). We thoroughly assess the methodological basis of the papers, offering detailed explanations regarding the applied models, selected inputs and outputs, and all relevant methodologies. In conclusion, we explore several ways to enhance DEA's status, transforming it from a mere technical application into a strong methodology that can be utilized by healthcare managers and decision-makers.

The Effect of Exit Time and Entropy on Asset Performance Evaluation

The objective of this study is to evaluate assets' performance by considering the exit time within the risk measurement framework alongside Shannon entropy and, alternatively, excluding these factors, which can be used to create a portfolio aligned with short- or long-term objectives. This portfolio effectively balances the potential risks and returns, guiding investors to make decisions that are in line with their financial goals. To assess the performance, we used data envelopment analysis (DEA), whereby we utilized the risk measure as an input and the mean return as an output. The stop point probability-CVaR (SPP-CVaR) was the risk measurement used when considering the exit time. We calculated the SPP-CVaR by converting the risk-neutral density to the real-world density, calibrating the parameters, running simulations for price paths, setting the stop-profit points, determining the exit times, and calculating the SPP-CVaR for each stop-profit point. To account for negative data and to incorporate the exit time, we have proposed a model that integrates the mean return and SPP-CVaR, utilizing DEA. The resulting inefficiency scores of this model were compared with those of the mean-CVaR model, which calculates the risk across the entire time horizon and does not take the exit time and Shannon entropy into account. To accomplish this, an analysis was conducted on a portfolio that included a variety of stocks, cryptocurrencies, commodities, and precious metals. The empirical application demonstrated the enhancement of asset selection for both short-term and long-term investments through the combined use of Shannon entropy and the exit time.

Integration of Multivariate Statistical Control Chart and Machine Learning to Identify the Abnormal Process Parameters for Polylactide with Glass Fiber Composites in Injection Molding; Part I: The Processing Parameter Optimization for Multiple Qualities of Polylactide/Glass Fiber Composites in Injection Molding

This paper discusses the mixing of polylactide (PLA) and glass fiber which use injection molding to produce a functional composite material with glass fiber properties. The injection molding process explores the influence of glass fiber ratio, melt temperature, injection speed, packing pressure, packing time and cooling time on the mechanical properties of composite. Using the orthogonal table planning experiment of the Taguchi method, the optimal parameter level combination of a single quality process is obtained through main effect analysis (MEA) and Analysis of variance (ANOVA). Then, the optimal parameter level combination of multiple qualities is obtained through principal component analysis (PCA) and data envelopment analysis (DEA), respectively. It is observed that if all the quality characteristics of tensile strength, hardness, impact strength and bending strength are considered at the same time, the optimal process conditions are glass fiber addition 20 wt %, melt temperature 185 °C, injection speed 80 mm/s, holding pressure 60 MPa, holding time 1 s and cooling time 15 s, and the corresponding mechanical properties are tensile strength 95.04 MPa, hardness 86.52 Shore D, impact strength 4.4408 J/cm², bending strength 119.89 MPa. This study effectively enhances multiple qualities of PLA/GF composite.

Research on equity of medical resource allocation in Yangtze River Economic Belt under healthy China strategy

Objective

This study aimed to assess the fairness of medical resource allocation in the Yangtze River Economic Belt, based on the Healthy China strategy. It aimed to identify the issues with resource allocation fairness and provide optimization suggestions.

Methods

To assess the allocation fairness from a geographical population perspective, the study used the Health Resource Concentration and Entropy Weight TOPSIS methods. Additionally, the study analyzed the allocation fairness from an economic level angle, using the Concentration Curve and Concentration Index.

Results

The study found that the downstream area had higher resource allocation fairness than the midstream and upstream areas. The middle reaches had more resources than the upper and lower reaches, based on population concentration. The Entropy Weight TOPSIS method found that Shanghai, Zhejiang, Chongqing, and Jiangsu had the highest comprehensive score index of agglomeration. Furthermore, from 2013 to 2019, the fairness of medical resource distribution gradually improved for different economic levels. Government health expenditure and medical beds were distributed more equitably, while general practitioners had the highest level of unfairness. However, except for medical and health institutions, traditional Chinese medicine institutions, and primary health institutions, other medical resources were mostly distributed to areas with better economic conditions.

Conclusion

The study found that the fairness of medical resource allocation in the Yangtze River Economic Belt varied greatly based on geographical population distribution, with inadequate spatial accessibility and service accessibility. Although the fairness of distribution based on economic levels improved over time, medical resources were still concentrated in better economic areas. The study recommends improving regional coordinated development to enhance the fairness of medical resource allocation in the Yangtze River Economic Belt.

Resource Allocation Efficiency of Urban Medical and Health Financial Expenditure Under the Background of Employees' Health

Introduction

The study proposes a method based on difference-in-differences (DID) to improve the resource allocation efficiency of medical and health financial expenditure to better guarantee the health level of enterprise employees. The DEA method is utilized to measure the comprehensive technology, pure technology, and scale as the resource allocation efficiency values of urban medical and health financial expenditure.

Methods

The proposed method includes the use of DEA to measure the resource allocation efficiency values of urban medical and health financial expenditure. The benchmark regression model and DID model are used to analyze the impact effect, robustness, and parallel trend of the policy.

Results

The study shows that the proposed method effectively evaluates and analyzes the impact of medical comprehensive reform on the resource allocation efficiency of urban medical and health financial expenditure. The comprehensive medical reform can improve the comprehensive efficiency and scale efficiency of urban medical and health financial expenditure, leading to improved resource allocation efficiency of urban employees' medical and health financial expenditure. The results also indicate a significant positive effect on the time trend, which can have a long-term impact and effectiveness.

Discussion

The proposed method can provide useful insights into the resource allocation efficiency of medical and health financial expenditure, which can help improve the health level of enterprise employees. The study suggests that comprehensive medical reform can be an effective way to improve resource allocation efficiency and guarantee the health of employees in urban areas. Further research can be conducted to evaluate the impact of medical reform on other aspects of health care, such as quality and accessibility.

From the Perspectives of Pollution Governance and Public Health: A Research of China's Fiscal Expenditure on Energy Conservation and Environmental Protection

Improving the scale and effectiveness of China's energy conservation and environmental protection fiscal expenditure is crucial to enhancing the capacity of ecological and environmental governance of China, considering the dual perspectives of pollution governance and public health. This article first explains the mechanism by which national energy conservation and environmental protection fiscal expenditure can improve pollution control and promote public health. Secondly, this article scrutinizes the current status and limitations of China's fiscal expenditure, highlighting the contribution of fiscal expenditure in the construction of ecological civilization from the standpoints of environmental governance and public health. Additionally, this study empirically uses DEA to measure the efficiency of the government's fiscal expenditure. Conclusions found that: First, environmental protection fiscal expenditure is mainly focused on technological transformation and pollution control, while relatively little is spent on public health protection. Second, the efficiency of environmental protection fiscal funds is relatively low. These suggestions aim to optimize the positive impact of energy conservation and environmental protection fiscal expenditure for improving pollution governance and promoting public health.

An integrated framework to evaluate and improve the performance of emergency departments during the COVID-19 pandemic: A mathematical programing approach

The emergency department (ED) is one of the most critical and high-risk sections of the health system. Providing quality services at a fast pace is vital in this ward since it directly affects people's lives. The COVID-19 pandemic has turned into a serious challenge for physicians and emergency departments (EDs). The growing number of patients who refer to EDs creates congestion, which will reduce the quality of services. Consequently, managing and operating EDs will be more urgent during this pandemic. Considering this problem, we first used data envelopment analysis (DEA) to evaluate the performance of EDs in the central provinces of Iran. Then, sensitivity analysis was used to determine the main factors affecting the efficiency of this ward. Accordingly, the high number of admitted patients, the congestion of the ward, and the long time required to report the COVID-19 test results were found to be the most influential factors. Finally, drawing on the results of sensitivity analysis, we advance a number of measures to improve these three and other related indicators. Furthermore, appropriate strategies were presented for improving health, COVID-19 management, key performance indicators, and safety indicators in accordance with the results of strengths-weaknesses-opportunity-threat (SWOT) analysis.

Evaluating Research Efficiency in Indonesian Higher Education Institution

The Indonesian government has evaluated the research performance of universities, whose measurement process is projected into resources, management, outputs, and revenues to determine the provision of incentives, grants, and program funding to universities. However, efficiency calculations have shown that the outputs and competition-based incentives that drive scientific productivity are more complex. The most competitive systems must also be the most productive when considering resources. This study aimed to analyze the research efficiency in the Indonesian higher education system. The efficiency was analyzed by maximizing the 13 product outputs from the research budget and university staff. The result was then compared with the existing performance measurement analysis. Data Envelopment Analysis (DEA) was used to evaluate the efficiency based on the data of 47 universities in the Mandiri cluster and 144 in the Utama cluster for the 2014-2018 period. These findings showed that about 68% of universities have an efficiency value of 1 for the Mandiri group, almost 40% in the Utama group, and 41% for the two groups combined. Additionally, this study compared the efficiency analysis and the impact of the performance evaluation. The comparison showed that adding efficiency or productivity factors in the performance evaluation assessment produced a more accurate result.

Does the Efficiency of a Firm’s Intellectual Capital and Working Capital Management Affect Its Performance?

This study explores the efficiency of intellectual capital (ICE) and working capital management (WCME) in the GCC industrial sector and its potential impact on firm performance. The data were gathered from Standard & Poor’s database from 2015 to 2019. This study uses data envelopment analysis (DEA), regression analysis, and robustness tests to accomplish its aims. The results indicate that most firms do not employ their intellectual and working capital investments well and need improvement actions to achieve the best practices. The regression model results reveal that ICE and WCME significantly and positively influence firms’ performance. The results of this study support the resource-based, trade-off, and pecking order theories. The study findings have important implications for many stakeholders; for example, they would be helpful for firm decision-makers in managing their investments in intellectual and working capital to achieve the best practices and improve a firm's performance. In addition, the findings would be helpful for financiers, because high-performance firms are likely to have more reasonable valuations that facilitate debt financing. Moreover, the findings have noteworthy implications for trading procedures as investors aspire to attractive economic returns for their investments in corporations that pasture ICE and WCME issues. Additionally, these findings have important implications for employee job satisfaction and retention by improving IC management.

Technical Efficiency Evaluation of Primary Health Care Institutions in Shenzhen, China, and Its Policy Implications under the COVID-19 Pandemic

(1) Background: Primary health care institutions (PHCI) play an important role in reducing health inequities and achieving universal health coverage. However, despite the increasing inputs of healthcare resources in China, the proportion of patient visits in PHCI keeps declining. In 2020, the advent of the COVID-19 pandemic further exerted a severe stress on the operation of PHCI due to administrative orders. This study aims to evaluate the efficiency change in PHCI and provide policy recommendations for the transformation of PHCI in the post-pandemic era. (2) Methods: Data envelope analysis (DEA) and the Malmquist index model were applied to estimate the technical efficiency of PHCI in Shenzhen, China, from 2016 to 2020. The Tobit regression model was then used to analyze the influencing factors of efficiency of PHCI. (3) Results: The results of our analysis reflect considerable low levels of technical efficiency, pure technical efficiency, and scale efficiency of PHCI in Shenzhen, China, in 2017 and 2020. Compared to years before the epidemic, the productivity of PHCI decreased by 24.6% in 2020, which reached the nadir, during the COVID-19 pandemic along with the considerable reduction of technological efficiency, despite the significant inputs of health personnel and volume of health services. The growth of technical efficiency of PHCI is significantly affected by the revenue from operation, percentage of doctors and nurses in health technicians, ratio of doctors and nurses, service population, proportion of children in the service population, and numbers of PHCI within one kilometer. (4) Conclusion: The technical efficiency significantly declines along with the COVID-19 outbreak in Shenzhen, China, with the deterioration of underlying technical efficiency change and technological efficiency change, regardless of the immense inputs of health resources. Transformation of PHCI such as adopting tele-health technologies to maximize primary care delivery is needed to optimize utilization of health resource inputs. This study brings insights to improve the performances of PHCI in China in response to the current epidemiologic transition and future epidemic outbreaks more effectively, and to promote the national strategy of Healthy China 2030.

Non-human and human service efficiency of long-term care facilities in China

Introduction

Care services provided by long-term care facilities (LTCFs) are currently plagued by care resource shortages and insufficient utilization. The analysis on the temporal and spatial distribution of human resources and non-human resources in LTCFs, could provide a basis to optimize resource allocation and efficient use of limited resources.

Methods

This study used data envelopment analysis to comprehensively evaluate the efficiency of human and non-human resources in different time spans and regions. The spatial Markov chain and spatial correlation were also applied to explore the heterogeneity of and correlation between the service efficiency of LTCFs in different regions and then analyzes the influencing factors of efficiency using Tobit regression model.

Results

The quantitative changes in the service efficiency of LTCFs in various provinces showed a "W" shape in two periods, ranging from 0.8 to 1.6. The overall efficiency of LTCFs in different regions had a lower probability to achieve short-term cross-stage development. Non-human resource efficiency presented a "cluster" distribution mode, demonstrating a great probability to achieve cross-stage development, which might be due to the regional disparities of economic development and land resource. Tobit regression analysis results also showed that the comprehensive efficiency of LTCFs decreases by 0.210 for every square increase in construction space variation. However, human resource efficiency had a significant spatial polarization, making it difficult to develop area linkages. The reason for this might be the nursing staff have relatively stable regional characteristics, weakening the inter-provincial spatial connection. We also found that female workers, aged between 35 and 45 can positively affect the efficiency of LTCFs. Those staff stay focused and improve their skills, which might improve the efficiency of LTCFs. So improving technology and service quality changes by increasing female workers, aged between 35 and 45, and avoiding excessive construction space changes can enhance the growth of service quality and personnel stability of LTCFs.

Conclusion

There is an urgent trade-off among staff quality improvement, resource reduction, construction excessive and substantial regional variation in efficiency. Therefore, strengthening policy support to encourage inter-regional initiatives, particularly highlighting the development of human resources interaction and common development is urgent.

New Methods for Evaluating Energy Infrastructure Development Risks

Many energy technologies that can provide reliable, low-carbon electricity generation are confined to nations that have access to robust technical and economic capabilities, either on their own or through geopolitical alliances. Equally important, these nations maintain a degree of institutional capacity that could lower the risks associated with deploying emergent energy technologies such as advanced nuclear or carbon capture and storage. The complexity, expense, and scrutiny that come with building these facilities make them infeasible choices for most nations. This paradigm is slowly changing, as the pressing need for low-carbon electricity generation and ongoing efforts to develop modular nuclear and carbon capture technologies have opened the door for potentially wider markets, including in nations without substantial institutional capacity. Here, using advanced nuclear technologies as our testbed, we develop new methods to evaluate national readiness for deploying complex energy infrastructure. Specifically, we use Data Envelopment Analysis-a method that eliminates the need for expert judgment-to benchmark performance across nations. We find that approximately 80% of new nuclear deployment occurs in nations that are in the top two quartiles of institutional and economic performance. However, 85% of potential low-carbon electricity demand growth is in nations that are in the bottom two quartiles of performance. We offer iconic paradigms for deploying nuclear power in each of these clusters of nations if the goal is to mitigate risk. Our research helps redouble efforts by industry, regulators, and international development agencies to focus on areas where readiness is low and risk correspondingly higher.

The Relationship between Chronic Non-Communicable Diseases of Fish Farm Household Members and Production Efficiency: The Case of Ghana

Prior studies explored the production and technical efficiency of fish farms and farmers from the perspectives of factors such as credit access and cooperative membership. We focused on the chronic non-communicable diseases (NCDs) of household members and their quantitative impacts on fish farm production efficiency, based on data of earthen pond fish farms from two regions (Bono East and Ashanti) in Ghana. A data envelopment analysis (DEA) and the IV Tobit technique were employed for the study's analysis. From the study's observations, we draw the following conclusions. We found that the NCDs of household members indeed reduce farm production efficiency, and the heterogeneous impact of the NCDs of female members on farm production efficiency was more prominent than that of male members. Insights from this study suggest that the national government should provide farmers with the necessary medical care through the provision of subsidized health insurance, which can facilitate access to healthcare services. Moreover, NGOs and governments should encourage health literacy, i.e., organizing programs aimed at educating farmers on NCDs and their impact on agriculture.

Comparing Hospital Efficiency: An Illustrative Study of Knee and Hip Replacement Surgeries in Spain

WHO's Health Systems Performance Assessment framework suggests monitoring a set of dimensions. This study aims to jointly assess productivity and quality using a treatment-based approach, specifically analyzing knee and hip replacement, two prevalent surgical procedures performed with consolidated technology and run in most acute-care hospitals. Focusing on the analysis of these procedures sets out a novel approach providing clues for hospital management improvements, covering an existing gap in the literature. The Malmquist index under the metafrontier context was used to estimate the productivity in both procedures and its decomposition in terms of efficiency, technical and quality change. A multilevel logistic regression was specified to obtain the in-hospital mortality as a quality factor. All Spanish public acute-care hospitals were classified according to their average severity attended, dividing them into three groups. Our study revealed a decrease in productivity mainly due to a decrease in the technological change. Quality change remained constant during the period with highest variations observed between one period to the next according to the hospital classification. The improvement in the technological gap between different levels was due to an improvement in quality. These results provide new insights of operational efficiency after incorporating the quality dimension, specifically highlighting a decreasing operational performance, confirming that the technological heterogeneity is a critical question when measuring hospital performance.

Analyzing the efficiency of Chinese primary healthcare institutions using the Malmquist-DEA approach: Evidence from urban and rural areas

Background

China has been increasing the investment in Primary Health Care Institutions (PHCIs) since the launch of the New Health Care System Reform in 2009. It is a crucial concern whether the PHCIs can meet residents' need both in urban and rural with the limited government finance, especially encountering the challenge of the COVID-19. This study aimed to reveal the trend of the primary health service efficiency in the past decade, compare the urban-rural differences, and explore relevant factors.

Methods

DEA and Malmquist models were applied to calculate the health service efficiency of PHCIs among 28 provinces in China, with the input variables including the number of institutions, number of beds, number of health technicians, and the outputs variables including the number of outpatients and emergency visits, number of discharged patients. And the Tobit model was used to analyze the factors on the efficiency in urban and rural. A sensitivity analysis for model validations was also carried out.

Results

The average technical efficiency (TE) of urban PHCIs fluctuated from 63.3% to 67.1%, which was lower than that in rural (75.8-82.2%) from 2009 to 2019. In terms of dynamic efficiency, the urban PHCIs performed better than the rural, and the trends in the total factor productivity change were associated with favorable technology advancement. The population density and dependency ratio were the key factors on TE in both of the urban and rural PHCIs, and these two factors were positively correlated to TE. In terms of TE, it was negatively correlated with the proportion of total health expenditure as a percentage of GDP in urban PHCIs, while in rural it was positively correlated with the urbanization rate and negatively correlated with GDP per capita. Besides, the tests of Mann-Whitney U, and Kruskal-Wallis H indicated the internal validity and robustness of the chosen DEA and Malmquist models.

Conclusions

It needs to reduce the health resource wastes and increase service provision in urban PHCIs. Meanwhile, it is necessary to strengthen medical technology and gaining greater efficiency in rural PHCIs by technology renovation.

Factors influencing eco-efficiency of municipal solid waste management in Chile: A double-bootstrap approach

Improving eco-efficiency in the provision of municipal solid waste plays an important role for a sustainable economy. Eco-efficiency of municipal solid waste service providers (MSWSPs) has been generally assessed using the conventional data envelopment analysis (DEA) method. However, this approach is sensitive to data noise and has no statistical properties. To overcome these limitations, in this paper, we adopt the double-bootstrap DEA model to derive robust eco-efficiency scores. This nonparametric method allows conducting statistical inference to explore environmental factors affecting the eco-efficiency of MSWSPs. The empirical approach focused on a sample of 298 MSWSPs in Chile, a middle-income country whose policies for promoting waste recycling are incipient. The results indicated that based on the bias-corrected eco-efficiency scores, the potential saving in costs and unsorted waste could be up to 37.8% on average to generate the same level of output (recycled waste). The findings showed that dealing with data noise and uncertainly is of great importance when conducting benchmarking analysis. The region where the municipality is located, tourism, population density and waste per capita are environmental variables that significantly influenced eco-efficiency of Chilean MSWSPs. Several policy implications are discussed based on the findings of this study.

Building projects with time–cost–quality–environment trade-off optimization using adaptive selection slime mold algorithm

Every project manager deals with various challenges, and almost all tasks have backup plans to ensure efficient success. Therefore, it is essential to manage resources, notably in terms of time, cost, quality, and environmental impact, and this needs to be thoroughly shown. As a result, the adaptive selection slime mold algorithm (ASSMA) is proposed for repetitive projects due to multiple concurrent instances. It is made by merging the tournament selection (TS) method and the slime mold algorithm (SMA) model. The new model’s capabilities are demonstrated using a case study of a rural water pipeline project, and the outcomes of the ASSMA are contrasted with those of the data envelopment analysis (DEA) approach utilized by the previous researcher. Consequently, the ASSMA technique is an effective optimization matching method that can help project managers select the best strategy for a given activity. This study is anticipated to expand significantly and outperform other models by utilizing quality performance metrics.

COVID-19 Quarantine Measures Efficiency Evaluation by Best Tube Interval Data Envelopment Analysis

All countries have responded with a wide range of measures to stop the propagation of coronavirus. We apply best tube interval data envelopment analysis, in order to evaluate efficiency of quarantine measures using imprecise data. Using the Oxford COVID-19 Government Response Tracker’s (OxCGRT) data and given method, we construct time series of efficiency assessment of government responses to COVID-19. In addition, we separate all examined countries into several groups with similar patterns of quarantine measures efficiency. As a result, we highlight China and Vietnam as a benchmark for all other countries, because efficiency of these countries is high for almost whole period of research.

A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability

Safeguarding natural resources and energy is essential to ensure food security for future generations. Given the increase of published papers in the agricultural field applying Data Envelopment Analysis (DEA), this review seeks to address the special requirements of this methodology when implemented in the agricultural sector as well as to classify papers under sustainability aspects (economic, environmental, social). More specifically, 120 papers from Scopus and Web of Science databases were included in this review by using PRISMA methodology, and they were tested in the following groups (i) General information, (ii) DEA implementation, (iii) DEA extensions, (iv) Data type, (v) Data collection and processing, and (vi) Sustainability dimensions. Results indicate that there is a great need for weights use when performing DEA in the agricultural sector, to acquire results with greater explanatory power. Moreover, systematic data collection of multiple factors could lead to the implementation of complex methodologies, providing feasible solutions to the involved stakeholders. Lastly, the social aspect is the least represented dimension out of the three aspects of sustainability, indicating the need for the integration of social factors in such analyses, especially when DEA is used to create a policy framework in a specific area.

Evaluating the Super-Efficiency of Working Capital Management Using Data Envelopment Analysis: Does COVID-19 Matter?

This study evaluates the working capital management (WCM) super-efficiency in UAE firms and determines how the COVID-19 crisis may affect WCM super-efficiency from a statistical perspective. Data envelopment analysis (DEA), difference tests, and Tobit regression techniques are used. Integration of DEA with Tobit regression is required to recognize the potential statistical consequence of COVID-19 on the super-efficiency of WCM. The results show that WCM super-efficiency is moderate and requires decision-makers' corrective measures to attain best practices. The study results show no significant difference in WCM super-efficiency, and COVID-19 does not significantly influence WCM super-efficiency. The results would rouse decision-makers' interests, who would look into the firm's efficiency and weak spots. They would then take major steps to improve efficiency and achieve the best practices. Stakeholders such as managers, financiers, and investors may need these results to monitor how firms manage their assets and liabilities. We contribute to the literature by investigating and evaluating firms' WCM super-efficiency pre and post the COVID-19 pandemic employing a unique technique based on DEA super-efficiency and examining whether COVID-19 has affected WCM super-efficiency to extend the scope of knowledge and stressing the importance of sound WCM, especially in periods of economic distress.

Applying frontier approach to measure the financial efficiency of hospitals

Objectives

The growth in global healthcare capacity has led to increased healthcare costs and a deterioration in the finances of universal health insurance. Hospitals must consider how to improve financial efficiency and service quality in order to survive and operate sustainably.

Methods

This study applies data envelopment analysis (DEA) and stochastic frontier analysis (SFA) to measure the financial efficiency of hospitals and to identify the factors and business strategies to improve profitability.

Results

The findings and recommendations show that (1) the DEA and SFA methods are similar and have reference values; (2) financial efficiency should be improved by reducing medical costs; (3) the quality of medical staff should be improved and manpower reduced; and (4) information, computerisation, and human intelligence in healthcare and management should be enhanced.

Conclusions

In terms of practical applications, this study recommends the promotion of smart healthcare to improve the efficiency and quality of healthcare services, as well as the introduction of artificial intelligence and big data analysis to optimise the use of healthcare manpower. Electronic medical records can be used to reduce the wastage of resources and labour costs, a medication management system can be established, and changes to the procurement system can be made to reduce inventory and improve the efficiency of medical equipment use. It is hoped that this study will provide reference materials and applications for healthcare organisations to improve their operational efficiency and strategies.

Measuring the efficiency of public hospitals: A multistage data envelopment analysis in Fujian Province, China

Objective

The present study aimed to evaluate the operational efficiency of public hospitals in Fujian Province and the factors responsible for the inefficiency of these hospitals and provide relevant suggestions for health policymakers in allocating service resources.

Method

In the first stage of the research, the variables affecting the efficiency of hospitals were extracted by qualitative and quantitative methods, including literature optimization, gray related analysis and gray clustering evaluation. In the second stage, the data envelopment analysis (DEA) method was used to evaluate the operational efficiency of 49 hospitals of different levels and types selected by sampling in 2020. Finally, a Tobit regression model with introduced institutional factors and background factors was established to study the main influencing factors of hospital inefficiency.

Results

In the first stage, 10 input variables and 10 output variables necessary from the mangers' point of view were identified to test efficiency. In the second stage, the average comprehensive TE, PTE, and SE of 49 sample hospitals was 0.802, 0.888, and 0.902, respectively. 22.45% of these hospitals met the effective criteria, i.e., the overall effective rate was 22.45%. The low SE value of the hospital was the main reason hindering the improvement of the comprehensive efficiency value. The overall effective rate of secondary public hospitals (30.77%) was higher than that of tertiary public hospitals (19.44%), and the overall effective rate of public specialized hospitals (30%) was higher than that of general public hospitals (18.92%). Based on the third stage results, the bed occupancy rate (BOR) and the proportion of beds (POB) were major factors affecting the operation efficiency of grade III hospitals (p < 0.01). However, the operating efficiency of grade II hospitals was significantly affected by POB and regional per capita GDP(GDPPC) (p < 0.05). Moreover, the impact of BOR and GDPPC was positive, and POB was negatively correlated with hospital operation efficiency.

Conclusions

The study results indicated that the overall operation efficiency of public hospitals in Fujian Province is low. This study revealed that intervention should be strengthened from a policy and management perspective to improve the operation efficiency of public hospitals.

A Pre-Generation of Emergency Reference Plan Model of Public Health Emergencies with Case-Based Reasoning

Background and Purpose

In the early 21st century, the coronavirus alone has ravaged the world three times. Public health emergencies have caused a tremendous negative impact on public health, daily life, and global economic development, for having the characteristics of complexity and great harm. To tackle these problems, a pre-generation of emergency reference plan model of public health emergencies is proposed to better deal with the outbreak and spread of public health events.

Methods

The method is divided into three stages. First, the modified SEIR model is used to predict the attribute values of the target case. Then, the similar case sets are extracted and filtered by calculating the similarity through the cross-efficiency evaluation method with the parallel system. Finally, the multi-stage emergency effect evaluation model is conducted so that the emergency plan with the best response effect at this stage can be made for reference.

Results

We collected 25 typical events of COVID-19 that occurred in 11 cities in China as historical case bases and target cases, respectively. The result of the experiment verified the feasibility and effectiveness of the proposed method.

Conclusion

This paper presents a new perspective on making a public health emergency plan, which could improve the decision-making accuracy and efficiency, maximize the emergency effect and save precious time for emergency response. This model can provide rapid decision supports for decision-making for public services such as government departments, centers for disease control, medical emergency centers and transport authorities, etc.

Estimating the hospitality efficiency in Mexico using Data Envelopment Analysis

Tourism has been an important source of income and employment for Mexico, and the economic numbers generated by the tourism have been increasing during last 2 decades. However, the question is how much more tourism capacity can Mexico offer? Therefore, it is necessary to search areas with lower hospitality performance to secure tourism growth and create adequate decision-making strategies. In this article, we constructed Data Envelopment Analysis model to estimate the hospitality efficiency of 32 Mexican states during a period from 1992 to 2018. Such a long period creates a unique insight of Mexican tourism. The results revealed high efficiency regarding national tourism, whereas the efficiency is low with respect to international tourism. This may not be unconnected with the fact that international tourism is centered in coastal states, whereas national tourism is mainly located in the in-land states. Such differences create opportunities to target better marketing strategies from the Mexican government.

Efficiency Analysis of Syrian Refugees' Healthcare Services in Turkey and Other 3RP States

Access to healthcare for refugees is often contextually and conceptually diverse. This study set out to evaluate the efficiency of the healthcare services provided for Syrian refugees in Turkey and other refugee response and resilience plan (3RP) states. Data envelopment analysis is utilized for efficiency analysis using primary healthcare system indicators. Efficiency is broken down into pure technical efficiency and scale efficiency to identify causes of inefficiency. Normative analysis is used to employ a teleological approach to better understand current refugee healthcare policies. The findings show a decline in the overall efficiency for Turkey and other 3RP states. However, Turkey's operational practices could be a model for other 3RP states due to its relatively high pure technical efficiency. Scale inefficiency negatively impacts the overall efficiency of Turkey's service, while other 3RP states exhibit a rather consistent performance. The study concludes that Turkey's healthcare system for refugees is inefficient, resulting in inadequate access for Syrian refugees. As such, operational and scale efficiency must be synergized for Turkey to fulfill its obligation to provide adequate healthcare for Syrian refugees. Additionally, COVID-19 was found to have exacerbated the challenges Syrian refugees face accessing healthcare. Policy recommendations have been made in line with the findings of the study.

Cultivated Land Input Behavior of Different Types of Rural Households and Its Impact on Cultivated Land-Use Efficiency: A Case Study of the Yimeng Mountain Area, China

Analyzing cultivated land input behavior (CLIB) at the scale of rural households links with cultivated land-use efficiency (CLUE), this study examined the Yimeng Mountain area in northern China, supported by field survey data from 737 rural households. This research systematically analyzed the characteristics of CLIB of different types of rural households, measured the CLUE of different types of rural households by using a data envelopment analysis (DEA) model, and explored the influence of CLIB on CLUE based on the Tobit regression model. The results show (1) significant differences in the characteristics of the CLIB of different types of rural households in the Yimeng Mountain area. Among them, the highest land, labor, and capital inputs were I part-time rural households (I PTRH), followed by full-time rural households (FTRH). In contrast, II part-time rural households (II PTRH) and non-agricultural rural households (NARH) had higher levels of non-agricultural employment; however, their input levels gradually declined. (2) The CLUE of the sample rural households was generally low and had considerable potential for improvement. Regarding the types of rural households, as the degree of part-time employment increased, the CLUE showed an inverted U-shaped trend of first increased and then decreased, namely, I PTRH > FTRH > II PTRH > NARH. This finding indicates that appropriate part-time employment could help to promote investment in agricultural production and improve the CLUE. (3) The CLIB of rural households had significant effects on CLUE; the literacy of the agricultural labor force, yield-increasing input per unit area, per capita household income, share of agricultural income, operation scale of cultivated land, effective irrigation rate of cultivated land, and soil and water conservation rate of cultivated land had positive effects on improving CLUE. Even so, there was still significant heterogeneity in the degree of influence of different rural household types. The study concluded with some policy recommendations from the perspective of different rural household types to provide references for optimizing farming inputs and improving CLUE.

Using Shannon Entropy to Improve the Identification of MP-SBM Models with Undesirable Output

In the context of the COVID-19 global epidemic, it is particularly important to use limited medical resources to improve the systemic control of infectious diseases. There is a situation where a shortage of medical resources and an uneven distribution of resources in China exist. Therefore, it is important to have an accurate understanding of the current status of the healthcare system in China and to improve the efficiency of their infectious disease control methods. In this study, the MP-SBM-Shannon entropy model (modified panel slacks-based measure Shannon entropy model) was proposed and applied to measure the disposal efficiency of the medical institutions responding to public health emergencies (disposal efficiency) in China from 2012 to 2018. First, a P-SBM (panel slacks-based measure) model, with undesirable outputs based on panel data, is given in this paper. This model measures the efficiency of all DMUs based on the same technical frontier and can be used for the dynamic efficiency analysis of panel data. Then, the MP-SBM model is applied to solve the specific efficiency paradox of the P-SBM model caused by the objective data structure. Finally, based on the MP-SBM model, undesirable outputs are considered in the original efficiency matrix alignment combination for the deficiencies of the existing Shannon entropy-DEA model. The comparative analysis shows that the MP-SBM-Shannon model not only solves the problem of the efficiency paradox of the P-SBM model but also improves the MP-SBM model identification ability and provides a complete ranking with certain advantages. The results of the study show that the disposal efficiency of the medical institutions responding to public health emergencies in China shows an upward trend, but the average combined efficiency is less than 0.47. Therefore, there is still much room for improvement in the efficiency of infectious disease prevention and control in China. It is found that the staffing problem within the Center for Disease Control and the health supervision office are two stumbling blocks.

Analysis of the Operational Efficiency of Basic Medical Insurance for Urban and Rural Residents: Based on a Three-Stage DEA Model

Following the integration of the urban residents' medical insurance into the new rural cooperative medical insurance in 2016, China has now formed a basic medical insurance system with the urban workers' basic medical insurance system and the rural residents' basic medical insurance system as the main entities. With the development of basic medical insurance, the protection for residents is becoming more and more comprehensive, and its fund expenditure also increases, so it is necessary to research the efficiency of the medical insurance fund expenditure. This paper conducts a three-stage DEA analysis of the efficiency of basic health insurance for urban and rural residents in 31 provinces, based on a Chinese panel data from 2017 to 2020. It is found that China's health insurance operation is still in the development stage, with four regions in the efficiency frontier and Guizhou province having the lowest efficiency value nationwide. The GDP and fiscal investment on social security effectively reduce the input redundancy in the basic health insurance operation, which contributes to the efficiency of the health insurance operation. This study further proposes suggestions and countermeasures to improve the operational efficiency of China's basic health insurance, based on the empirical results: (1) develop the economy and broaden the financing sources; (2) improve the level of health care services and improve the efficiency driven by quality; and (3) improve the level of health insurance supervision through multiple measures.

Evaluation and Prediction of Low-Carbon Economic Efficiency in China, Japan and South Korea: Based on DEA and Machine Learning

Addressing global climate change has become a broad consensus in the international community. Low-carbon economic development, as an effective means to address global climate change issues, has been widely explored and practiced by countries around the world. As major carbon emitting countries, there has been much focus on China, Japan and South Korea, and it is of practical significance to study their low-carbon economic development. To further measure their trend of low-carbon economic development, this paper firstly constructs a low-carbon economic efficiency evaluation index system and uses the Slack Based Measure (SBM) model. This is a kind of data envelopment analysis (DEA) method, with undesirable output based on global covariance to measure the low-carbon economic efficiency of 94 provincial-level administrative divisions (PLADs) in China, Japan, and South Korea from 2013 to 2019. Subsequently, this paper uses 10 mainstream machine learning models and combining them with Grid Search with Cross Validation (GridSearchCV) methods, selects the machine learning model with the best prediction effect. The model predicts the low-carbon economic efficiency of PLADs in China, Japan, and South Korea from 2020 to 2024 based on the parameter configuration for the best prediction effect. Finally, according to the research results, this paper proposes targeted advice for regionalized cooperation on low-carbon economic development in China, Japan, and South Korea to jointly address global climate change issues.

Scale Difference from the Impact of Disease Control on Pig Production Efficiency

Simple Summary

This paper uses the data of fixed observation points in the country’s rural areas, the Data Envelopment Analysis model is used to calculate the production efficiency of pig farmers, which shows the efficiency level of pig breeding in the country. Then, the Tobit model is used to analyze that epidemic prevention and control has a positive effect on production efficiency. Finally, the threshold regression model was used to analyze the results: the effect of disease control on production efficiency was different under different breeding scales. When the breeding scale was less than 6.0002, the disease control had a negative impact on the production efficiency. When the breeding scale was greater than 12.9994, the disease control had a positive impact on the production efficiency. The research results of this paper not only help farmers to correctly understand the relationship between epidemic prevention and production efficiency, guide farmers to expand their breeding scale, and actively participate in epidemic prevention and control, but also provide a reference for the government to formulate policies according to different scales to guide farmers to carry out epidemic prevention and control.

Abstract

Epidemic disease prevention plays a critical role in ensuring the healthy development of livestock farming, and the subjective willingness of breeders can be affected by the cost of epidemic disease prevention. To correct the misconception that farmers regard the cost of disease control as an ineffective cost, and to promote the healthy development of the pig breeding industry, our study employed the data envelopment analysis super-efficiency model and panel threshold regression model to evaluate the combination of the cost of epidemic disease prevention and swine productivity using data collected from 1998–2018 across 30 provinces in China. The following results were obtained. (1) The cost of epidemic disease prevention generated a non-linear on swine productivity when the swine farming scale was limited; (2) When the number of animals at the beginning of the year was less than 6.0002, swine productivity was impacted negatively; (3) When the number of animals at the beginning of the year ranged between 6.0002 and 12.9994, the impact was insignificant; (4) A strong correlation was observed between the expenses of epidemic disease prevention and animal productivity when the number of animals at the beginning of the year exceeded 12.9994. These results indicate that publicity should be enhanced to elucidate the combination of epidemic disease prevention and swine productivity among breeders. In addition, the government should introduce relevant policies to encourage the development of large-scale pig farming, such as subsidies for the construction of large-scale farms and insurance.

Holistic Sustainability Assessment of Riparian Buffer Designs: Evaluation of Alternative Buffer Policy Scenarios Integrating Stream Water Quality and Costs

Riparian buffer zones (RBZs) have been shown to be effective best management practices (BMPs) in controlling non-point source pollutants in waterbodies. However, the holistic sustainability assessment of individual RBZ designs is lacking. We present a methodology for evaluating the holistic sustainability of RBZ policy scenarios by integrating environmental and economic indicators simulated in three watersheds in the southeastern USA. We developed three unique sets of 40, 32, and 48 RBZ policy scenarios as decision management objectives (DMOs), respectively, in Back Creek, Sycamore Creek, and Greens Mill Run watersheds (Virginia and North Carolina) by combining the RBZ-widths with vegetation types (grass, urban, naturalized, wildlife, three-zone forest, and two-zone forest). We adapted the RBZ-hydrologic and water quality system assessment data of instream water quality parameters (dissolved oxygen, total phosphorus, total nitrogen, total suspended solids-sediment and biochemical oxygen demand) as environmental indicators, recently published by U.S. EPA. We calculated 20-year net present value costs as economic indicators using the RBZ's establishment, maintenance, and opportunity costs data published by the Natural Resources Conservation Service. The mean normalized net present value costs varied by DMOs ranging from 4% (grass RBZ-1.9 m) to 500% (wildlife RBZ-91.4 m) across all watersheds, due primarily to the width and the opportunity costs. The mean normalized environmental indicators varied by watersheds, with the largest change in total nitrogen due to urban RBZs in Back Creek (60-95%), Sycamore Creek (37-91%), and Greens Mill (52-93%). The holistic sustainability assessments revealed the least to most sustainable DMOs for each watershed, from least sustainable wildlife RBZ (score of 0.54), three-zone forest RBZ (0.32), and three-zone forest RBZ (0.62), respectively, for Back Creek, Sycamore Creek, and Greens Mill, to most sustainable urban RBZ (1.00) for all watersheds.

Determining the Efficiency of the Sponge City Construction Pilots in China Based on the DEA-Malmquist Model

Sponge city construction (SCC) has improved the quality of the urban water ecological environment, and the policy implementation effect of SCC pilots is particularly remarkable. Based on the data envelopment analysis (DEA) model, this study employed the related index factors such as economy, ecology, infrastructure, and the population of the pilot city as the input, and the macro factors of SCC as the output, to scientifically evaluate the relative efficiency between the SCC pilots in China. Eleven representative SCC pilots were selected for analysis from the perspectives of static and dynamic approaches, and comparisons based on the horizontal analysis of the efficiency of SCC pilots were conducted and some targeted policy suggestions are put forward, which provide a reliable theoretical model and data support for the efficiency evaluation of SCC. This paper can be used as a reference for construction by providing a DEA model for efficiency evaluation methods and thus helps public sector decision makers choose the appropriate construction scale for SCC pilots.

Efficiency evaluation of surgical nursing wards based on data envelopment analysis

AIMS

This study aimed to evaluate the efficiency and influencing factors of surgical ward nursing.

BACKGROUND

High-quality development of public hospitals and implementation of nursing fine management require improvements to promote nursing efficiency.

METHOD

This study examined input data, such as number of beds, nurses, and nursing hours, and output data on discharged person-times, surgical output, and case mix index of released patients, in 40 surgical wards in China between September 2020 and August 2021. Nursing efficiency was assessed and analyzed using data envelopment analysis, and changes in efficiency were dynamically evaluated using Malmquist index analysis.

RESULTS

The average total technical efficiency, pure technical efficiency, and scale efficiency were 78%, 94%, and 82%, respectively. In addition, 81% and 67% of surgical wards had input redundancy and output insufficiency, respectively. The monthly average total factor productivity was 118%, and the overall efficiency increased month after month.

CONCLUSION

Overall, nursing service efficiency was low, which was limited by scale efficiency, pure technical efficiency, or both.

IMPLICATIONS FOR NURSING MANAGEMENT

Data envelopment analysis could help nursing managers optimize nursing human resource allocation and nursing performance allocation.

Privatization, Corporatization, and Public-Private Partnership in the Kingdom of Saudi Arabia

There are different governmental reasons and technologies for seeking public-private partnerships throughout the developed countries. Significant motives have been to improve efficiency and risk distribution in comparison to traditional financing techniques and to lessen budget and borrowing limits. In this study, the movement toward privatization enhancing the efficiency of MoH’s hospitals has been assessed using Data Envelopment Analysis (DEA) from 1979 to 2020. Moreover, the efficiencies of the individual hospital were estimated through the DEA model, which includes: scale efficiency (SE), pure technical efficiency (PTE), and overall technical efficiency (OTE). In addition, factor affecting such efficiencies was analyzed through Tobit regression. The VRS results suggest that the numbers of hospitals benefiting from the complete corporatized phase are greater than those benefiting from the last phase (and vice versa in the case of DRS). The variance between inefficient hospitals in the less corporatized phase (2000-2020) was more prominent than in the fully corporatized phase (1988-1999). In conclusion, fully corporatized hospitals (on average) achieve relatively better overall efficiency. It is recommended that additional corporatization might be stimulated by a standardized set of performance measures, which cover both the quality criteria and economic efficiency measurements from a healthcare perspective.

A global trade supply chain vulnerability in COVID-19 pandemic: An assessment metric of risk and resilience-based efficiency of CoDEA method

The COVID-19 pandemic has an adverse impact on the global trade supply chain. Countries where the economy is driven by global trade, either as exporters or importers and are faced with the problem of declining imports and exports. This is due to the interruption of the main players of the global supply chain (i.e., production, logistics and transportation sector) as well as the slow-down in consumption of overseas customers. This paper presents the development of an efficiency related metric from the Coherent Data Envelopment Analysis (CoDEA) method for assessing the vulnerability (or conversely, the robustness) levels of the supply chain system of six ASEAN countries. The results reveal that Thailand is most vulnerable to international supply chain issues indicated by its lowest efficiency score. This is due to Thailand's severe disruption of logistics and transportation systems compared with its neighboring countries. In contrast, Vietnam is the most robust because of its efficiency in the exports sector. Our research reveals that trading partners with a lower risk and the ability to rapidly recover their import volume reflect their less vulnerable supply chains. This research provides the associated strategies to establish a resilient global supply chain in spite of the COVID-19 pandemic.

An Innovation Perspective to Explore the Ecology and Social Welfare Efficiencies of Countries

This study aims to measure the ability of 29 countries in producing competitive products and services that fulfill individual needs and improve the level of welfare with less utilization of natural resources. We build a two-stage network production process model to investigate the ecology efficiency and social welfare efficiency of the countries and then further discriminate the efficient countries in post-analysis. The two-stage network directional distance function is applied to assess the efficiencies of countries, and the network-based ranking approach is used to further discriminate the efficient countries following the panel data between the years 2013 and 2016. Results show that Poland and Spain are strongly referenced by other countries in the ecology stage, whereas Bulgaria, the United States, and Sweden are leaders in the social welfare stage. A remarkable observation is an absence of countries’ efficiency in both ecology and social welfare efficiencies. Most of the 29 countries have lower efficiency in the social welfare stage than in the ecology stage. This study suggests the strengths and highlights the weaknesses of the countries to help the governments efficiently improve and operate their countries.

Does Efficiency of Oral and Dental Health Centers Change by the Development Level of Regions?

OBJECTIVES

This study aimed to analyze the efficiency levels of oral and dental health centers (ODHCs) affiliated to the Ministry of Health from the perspective of the development levels in the regions.

METHODS

Within the framework of this study, the efficiencies of 127 ODHCs are evaluated with the data envelopment analysis based on Charnes, Cooper, Rhodes (CCR); Banker, Charnes, Cooper (BCC); and scale efficiency methods.

RESULTS

It is found that CCR, BCC, and scale efficiencies of the 42 ODHCs located in the first region, the highest development region, are efficient by 31%, 50%, and 38% whereas 13%, 26%, and 26% of other 85 ODHCs located in other regions are efficient, respectively. Additionally, the average efficiency scores of 42 ODHCs in the first region by CCR (0.82), BCC (0.89), and score efficiency (0.93) are much higher than the average efficiency scores of 85 ODHCs in other regions by CCR (0.73), BCC (0.80), and scale efficiency (0.92).

CONCLUSIONS

According to the findings, it is considered that the individuals from the first region use the oral and dental healthcare more than the individuals do in other regions because the access to oral and dental healthcare is higher in the first region and the individuals living in this region have better predisposing and enabling factors. The study results are thought to present evidence-based information to health policy makers regarding the regional planning of oral and dental healthcare.

The productive efficiency of community health service stations in China: Taking Shandong province as a case

The present study evaluated the efficiencies of community health service (CHS) stations based on a survey of 1246 CHS stations, covering nine subcategories in 16 cities in Shandong province. Data envelopment analysis (DEA) was applied to investigate the overall efficiency, the technical and the scale efficiency of community health care resources. The results are, the overall efficiency was 9.47%, and the overall efficiency was generally higher in the central-west region than in the east. There were 23.27% of CHS stations showing technically efficient. The technical efficiency was higher in the east (31.11%) relative to the central-west (19.72%), and 72.71% of CHS stations had a technical efficiency higher than the regional average efficiency. The scale efficiency was 9.31% for CHS stations in Shandong province, being the decisive factor for overall efficiency, and 68.96% of CHS stations showed a scale efficiency above the regional average. Stations held by enterprises and universities, and extended by tertiary hospitals had lower efficiencies than other types of CHS stations. In conclusion, the CHS stations had low efficiencies in general, and scale inefficiencies were the main cause. Related suggestions to improve the efficiency are provided accordingly.

Evaluating R&D efficiency of China’s listed lithium battery enterprises

Promoting the growth of the lithium battery sector has been a critical aspect of China’s energy policy in terms of achieving carbon neutrality. However, despite significant support on research and development (R&D) investments that have resulted in increasing size, the sector seems to be falling behind in technological areas. To guide future policies and understand proper ways of promoting R&D efficiency, we looked into the lithium battery industry of China. Specifically, data envelopment analysis (DEA) was used as the primary approach based on evidence from 22 listed lithium battery enterprises. The performance of the five leading players was compared with that of the industry as a whole. Results revealed little indication of a meaningful improvement in R&D efficiency throughout our sample from 2010 to 2019. However, during this period, a significant increase in R&D expenditure was witnessed. This finding was supported, as the results showed that the average technical efficiency of the 22 enterprises was 0.442, whereas the average pure technical efficiency was at 0.503, thus suggesting that they were suffering from decreasing returns to scale (DRS). In contrast, the performance of the five leading players seemed superior because their average efficiency scores were higher than the industry’s average. Moreover, they were experiencing increasing scale efficiency (IRS). We draw on these findings to suggest to policymakers that supporting technologically intensive sectors should be more than simply increasing investment scale; rather, it should also encompass assisting businesses in developing efficient managerial processes for R&D.

An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic

The spread of coronavirus disease around the world has had an immense impact on most economic sectors. Yet amid the turmoil and chaos from the worldwide pandemic, one industry is thriving noticeably. The coronavirus disease is a once in a lifetime business opportunity for pharmaceutical companies. This study presents an artificial intelligence method composed of optimization and machine learning. Data envelopment analysis (DEA) is used to measure productivities and efficiencies of pharmaceutical companies during the COVID-19 pandemic using the additive model in window analysis, the BCC (Banker-Charnes-Cooper) model, and the CCR (Charnes-Cooper-Rhodes) model. The three models are assessed using DataStream financial information with research and development (R&D) investment. The results indicated the additive model's superiority in window analysis, followed by the BCC and CCR models. In the end, some of well-known data mining algorithms, based on the suggested data, have been evaluated in various tools to find the most efficient tool and algorithm.

The Efficiency Evaluation of Municipal-Level Traditional Chinese Medicine Hospitals Using Data Envelopment Analysis After the Implementation of Hierarchical Medical Treatment Policy in Gansu Province, China

Background: Efficiency evaluation is an integral part of new medical reform and is necessary to solve the problem of limited and unbalanced medical resources. This study evaluated the efficiency of municipal-level Traditional Chinese Medicine hospitals by Data Envelopment Analysis application after a hierarchical medical treatment policy was implemented. We propose solutions to the problems existing in hospital operations and promote the utilization efficiency of medical resources in those hospitals. Methods: The sample included all municipal-level TCM hospitals in Gansu province from 2017 to 2019. The DEA-BCC model was employed to evaluate the relative efficiency of hospital operations, and the Manny-Whitney test was used to compare the input and output variables of technical efficiency efficient and inefficient hospitals. Results: From 2017 to 2019, the growth in the number of staff in secondary hospitals (25.88%) was lower than that in tertiary hospitals (31.98%). However, the increase in the number of beds (16.52%) in secondary hospitals was higher than that in tertiary hospitals (−0.30%). 5 (38.46%) achieved DEA efficient in secondary hospitals and 2 (40.00%) in tertiary hospitals. The means of technical efficiency, pure technical efficiency, and scale efficiency in secondary hospitals were 0.812, 0.887, and 0.908, respectively. The means in tertiary hospitals were 0.868, 0.926, and 0.935, respectively. The hospital areas were statistically different between the TE efficient and inefficient hospitals (P<0.05) in secondary hospitals. However, the number of outpatients between the two groups was statistically different (P<0.05) in tertiary hospitals. Conclusion: In this study, the medical and health services of municipal TCM hospitals in Gansu Province have made great progress. Due to the backward economy of Gansu Province, the classification of diagnosis and treatment of diseases was still based on Western medicine, resulting in the slow medical development of some municipal TCM hospitals. TCM hospitals should improve management efficiency, optimize hospital operation scale, improve the utilization efficiency of medical resources and promote efficient hospital development.

Efficiency of malaria service delivery in selected district-level hospitals in Ghana

Malaria remains an important public health concern. Sub-Saharan African countries carry over 95% of the global burden. Unfortunately, there are also major resource constraints that have limited efforts to reduce the burden. Our study sought to estimate efficiency in the use of malaria resources and to identify potential determinants. We used primary data collected from district-level health facilities in three administrative regions in Ghana from 2014 to 2016. The Data Envelopment Analysis technique was used to estimate efficiency. The Malmquist productivity index was estimated and disaggregated to reflect the sources of productivity change. The findings show an average technical efficiency score of 0.61 with private facilities being more efficient. Productivity changes were driven by changes in technology/innovation advancements. Facility revenue mix and ownership type were important determinants of efficiency. The findings highlight the need to improve resource use in the delivery of specific services such as malaria.

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method's strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.