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Objective: The Brazilian remote rurality has been classified more reliably only recently, according to demographic density, proportion of urban population, and accessibility to urban centers. It comprises 5.8% of the municipalities, in nearly half of the states, with a population of 3,524,597 (1.85%). Remote rural localities (RRL) have reduced political/economic power, facing greater distances and barriers. Most health strategies are developed with the urban space in mind. We aim to understand how RRL are positioned concerning efficiency/effectiveness in health, compared to other urban-rural typologies of Brazilian localities, focusing on Primary Health Care (PHC), and its organizational models. Methods: We evaluated the efficiency and effectiveness of the organizational models using the health production model, from 2010-2019, gradually deepening the immersion into the RRL reality. We analyzed the human and financial resources dimensions, emphasizing teams, the results of PHC actions, and health levels. We used the fixed effects model and data envelopment analysis, cross-sectioned by intersectional inequities. We compared the Brazilian states with and without RRL, Brazilian municipalities according to rural-urban typologies, and RRL clusters. Results: Brazilian RRL states show superior resource/health efficiency through services utilization according to health needs. The remote rural typology demonstrated greater efficiency and effectiveness in health than the other typologies in the RRL states. The organizational models with the Family Health Strategy (FHS) teams and the Community Health Worker (CHW) visits played a key role, together with local per capita health expenditures and intergovernmental transfers. Thus, financial resources and health professionals are essential to achieve efficient/effective results in health services. Among the RRL, the Amazon region clusters stand out, denoting the importance of riverine and fluvial health teams, the proportion of diagnostic/treatment units in addition to the proportion of illiteracy and adolescent mothers along with the inequity of reaching high levels of schooling between gender/ethnicity. Conclusion: Hopefully, these elements might contribute to gains in efficiency and effectiveness, prioritizing the allocation of financial/human resources, mobile FHS teams, availability of local diagnosis/treatment, and basic sanitation. Finally, one should aim for equity of gender/ethnicity in income and education and, above all, of place, perceived in its entirety.

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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.

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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.

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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.

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The objective of this article is to propose a new composite index (CI) that helps to determine the most effective location of servers in an Emergency Care System (ECS), using Benefit of the Doubt (BoD)/Data Envelopment Analysis (DEA) and the Hypercube queuing model. The CI proposed was developed in four stages: (1) definition of a number of possible ECS configurations through the application of mathematical partitions and permutations; (2) application of the hypercube queuing model to determine performance parameters for each ECS configuration; (3) application of DEA/BoD to build the CI and generate performance rankings, and (4) evaluation of the rankings obtained to define the best configuration for the ECS analyzed. Data from two real cases from Brazil were used to assess the CI proposal. The results obtained confirm that: (a) the hypercube model could, relatively quickly, determine the configuration parameters generated; (b) the application of an appropriate DEA/BoD model enabled the different configurations to be ranked with good discrimination; (c) a pattern in the relationship between ambulance concentration and configuration effectiveness could be identified; and (d) the CI proposed would benefit ECS managers who are making resource location decisions.

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The measurement of performance within the water industry holds significant importance for policymakers, as it can help guide decision-making for future development and management initiatives. In this study, we apply data envelopment analysis (DEA) cross-efficiency techniques to evaluate the productivity change of the Chilean water industry during the years 2010-2018. Water leakage and unplanned interruptions are included in the analysis as quality of service variables. Moreover, we use cluster analysis and regression techniques to better understand what drives productivity change of water companies. The results indicate that the Chilean water industry is characterized by considerable high levels of inefficiency and low levels of productivity change. This is due to the existence of technical regress whereas gains in efficiency were small. Concessionary water companies were found to be more productive than full private and public water companies. Best and worst performers need to make efforts to reduce production costs and improve service quality. Other factors such as customer density and ownership type statistically affect productivity.

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This article proposes an approach for the classification of industrial engineering programs offered by different higher education institutions (HEIs) in Colombia, using data envelopment analysis (DEA) and validating the results with cluster analysis. To perform this classification, data from 5318 industrial engineering students from 93 higher education institutions are used as a basis for classification based on the Saber11 and SaberPro state tests. The state tests are used to measure graduates' academic performance in the data envelopment analysis. With the efficiency results it was possible to classify higher education institutions (HEIs) into three large groups. Subsequently, this classification was validated through cluster analysis. The results show a correct classification of 77%.

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Protected areas play an important role in biodiversity conservation and tourism. Significant efforts have been made to increase the amount of protected lands. A problem of increasing the amount of public protected areas is that governments and other institutions face difficulties in providing the necessary resources for effective management. Accordingly, managers must be as efficient as possible but the lack of comparative methods makes the evaluation of efficiency difficult. Using Data Envelopment Analysis, a non-stochastic and non-parametric approach, information from 29 protected areas in 5 countries was analyzed to compare management efficiency amongst them. The first result found is the level of management efficiency that each park has in comparison with the others parks. The other important result is a prediction of the changes in the outputs if there is a hypothetical budget change. These results allow the generation of information for decision making.

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Drinking water treatment systems (DWTSs) are energy intensive facilities, and are an example of the water-energy nexus. Benchmarking energy efficiency is a valuable tool for improving the economic and environmental performance of such facilities. Data envelopment analysis (DEA) is typically used to assess efficiency, allocating flexible weights (FSW) to variables that maximise energy efficiency scores for each DWTS (DEA-FSW). It means that different conditions are applied to each DWTS. Moreover, the DEA-FSW approach has finite discriminatory power which limits cross-unit comparison of energy efficiency hindering the benchmarking of DWTSs. To overcome these limitations, our study explored the effect of estimating the energy efficiency scores of DWTSs by allocating common sets of weights (CSW) within DEA (DEA-CSW). This approach was applied empirically on a sample of 146 DWTSs. Evaluated DWTSs had poor energetic performance based on both DEA-FSW and DEA-CSW estimates (low energy efficiency scores: 0.329 and 0.163, respectively). Even in the optimistic scenario, the average energy efficiency score was low (0.220), with potential electricity savings of 78 % by DWTPs when energy efficient. Unlike DEA-FSW, DEA-CSW allowed energy efficient DWTSs to be distinguished from the 146 facilities. Significant differences in the weights allocated to electricity and pollutants removed from raw water were reported for both approaches, and contributed to diverging energy efficiency scores. In conclusion, this study demonstrated the relevance of using suitable methods to generate comparable results for water companies, allowing the energy performance of DWTSs to be objectively evaluated for benchmarking purposes.

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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.

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In this study, the policy analysis matrix (PAM) and data envelopment analysis (DEA) approaches were used to assess lemon producers' productivity in Mersin, Turkey, as well as the international competitiveness of lemon cultivation within the scope of its production plan. According to the findings, most enterprises are inefficient, and the profitability of lemons improved from 2863.5 USD ha-1 to 6606.0 USD ha-1 with special prices within the framework of profit-maximising production plans. Regarding social prices, an increment from 3500.8 USD ha-1 to 8071.5 USD ha-1 was proposed to create a more sustainable production plan. To retain competitiveness in the Turkish lemon trade, it has been established that enterprises should transition to a more efficient production structure. For this reason, it has been concluded that inefficiencies in using inputs should be eliminated, and the dissemination of technology and advanced applications will make producers more competitive.

Neste estudo, as abordagens da matriz de análise de políticas (PAM) e da análise envoltória de dados (DEA) foram usadas para avaliar a produtividade dos produtores de limão em Mersin, Turquia, bem como a competitividade internacional do cultivo de limão no âmbito de seu plano de produção. De acordo com os resultados, a maioria das empresas é ineficiente e a lucratividade dos limões melhorou de 2863,5 USD ha-1 para 6606,0 USD ha-1 com preços especiais no âmbito dos planos de produção para maximizar o lucro. Com relação aos preços sociais, foi proposto um incremento de 3.500,8 USD ha-1 para 8.071,5 USD ha-1 para criar um plano de produção mais sustentável. Para manter a competitividade no comércio de limão turco, foi estabelecido que as empresas devem fazer a transição para uma estrutura de produção mais eficiente. Por isso, concluiu-se que as ineficiências no uso de insumos devem ser eliminadas, e a disseminação de tecnologia e aplicações avançadas tornará os produtores mais competitivos.

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The COVID-19 pandemic required managerial and structural changes inside hospitals to address new admission demands, frequently reducing their care capacity for other diseases. In this regard, this study aims to support the recovery of hospital productivity in the post-pandemic context. The major challenge will be to make use of all the resources the institution has obtained (equipment, beds, temporarily hired human resources) and to increase production to meet the existing repressed demand. To support evidence-based decision-making at a major university hospital in Rio de Janeiro, hospital managers and operations research analysts designed an approach based on multiple methodologies. Besides multimethodology, one important novelty of this study is the application of a productivity frontier function to future scenario planning through the quantitative DEA methodology. Concept maps were used to structure the problem and emphasize stakeholders' perspectives. In sequence, data envelopment analysis (DEA) was applied, as it combines benchmarking best practices and assigns weights to inputs and outputs. To guarantee that the efficiency measurement considers all inputs and outputs before any inclusion of expert judgment, the scope was redirected to full dimensional efficient facet, if any, or to maximum efficient faces. The results indicate that production scenarios proposed by stakeholders based on the Ministry of Health parameters overestimate the viable production framework and that the scenario that maintains temporary human resource contracts is more compatible with quality in health provision, teaching, and research. These findings will serve as a basis for decision-making by the governmental agency that provided temporary contracts. The present methodology can be applied in different settings and scales.

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Parametric and non-parametric frontier applications are typical for measuring the efficiency and productivity of many healthcare units. Due to the current COVID-19 pandemic, hospital efficiency is the center of academic discussions and the most desired target for many public authorities under limited resources. Investigating the state of the art of such applications and methodologies in the healthcare sector, besides uncovering strategical managerial prospects, can expand the scientific knowledge on the fundamental differences among efficiency models, variables and applications, drag research attention to the most attractive and recurrent concepts, and broaden a discussion on the specific theoretical and empirical gaps still to be addressed in future research agendas. This work offers a systematic bibliometric review to explore this complex panorama. Hospital efficiency applications from 1996 to 2022 were investigated from the Web of Science base. We selected 65 from the 203 most prominent works based on the Core Publication methodology. We provide core and general classifications according to the clinical outcome, bibliographic coupling of concepts and keywords highlighting the most relevant perspectives and literature gaps, and a comprehensive discussion of the most attractive literature and insights for building a research agenda in the field.

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This research analyzed the efficiency situation of corn farms operating in the Adana province of Turkey. In this context, required farm management data were collected from 111 corn farmers by using face to face survey method during the 2019-2020 cultivation season. To determine the technical efficiency (TE) levels of corn farms, Data Envelopment Analysis (DEA) was applied. Furthermore, factors that cause the inefficiency in corn farms were detected by using the Tobit regression model. According to research results, the average TE levels of corn farms in the research area under the variable return to scale conditions are reported as 0.887 (111 farms). These results suggested that if farms reduced their input use by 11.3% on average, they can achieve the same output level and be able to reach full technical efficiency. The most ineffective source in terms of farms performance is machine expenditures with 68.2% of excessive use followed by labor use. In this regard, mechanization modernization, education and training of the labor force and more sensitive fertilizers and pesticide use can increase the efficiency of corn farms. Results of the Tobit regression model indicated that factors such as experience, education, number of tractors and size of the irrigated area positively influenced the TE, whereas family size in corn farming has a negative effect.

Esta pesquisa tem como objetivo analisar a situação de eficiência das fazendas de milho operando na província de Adana, na Turquia. Neste contexto, os dados necessários de gestão da fazenda foram coletados de 111 produtores de milho usando o método de pesquisa frente a frente durante a temporada de cultivo de 2019-2020. Para determinar os níveis de eficiência técnica (TE) das fazendas de milho, foi aplicada a Análise Envoltória de Dados (DEA). Além disso, os fatores que causam a ineficiência nas fazendas de milho foram detectados por meio do modelo de regressão Tobit. De acordo com os resultados da pesquisa, os níveis médios de TE das fazendas de milho na área de pesquisa sob as condições de retorno variável à escala são encontrados em 0,887 (111 fazendas). Esses resultados sugerem que, se as fazendas reduzirem o uso de insumos em 11,3% em média, podem atingir o mesmo nível de produção e alcançar eficiência técnica plena. A fonte mais ineficaz em termos de desempenho das fazendas são os gastos com máquinas, com 68,2% do uso excedente continuado com o uso de mão de obra. Nesse sentido, a mecanização, a modernização, a educação e o treinamento da força de trabalho e o uso de fertilizantes e pesticidas mais sensíveis podem ser sugeridos para aumentar a eficiência das fazendas de milho. Os resultados do modelo de regressão Tobit indicam que fatores como experiência, escolaridade, número de tratores e tamanho da área irrigada influenciaram positivamente no TE, enquanto o tamanho da família na cultura do milho tem efeito negativo.

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Since the 1980's, some Mexican municipalities have subcontracted waste collection services to private companies, with an eye on increasing the efficiency of this service. However, the impact of private management on the Mexican waste sector performance has not been evaluated. In this study, the efficiency of waste collection in Mexico was analyzed to test the hypothesis that private companies are more efficient at waste collection than municipal governments. A two stage double bootstrap Data Envelopment Analysis (DEA) was applied to a sample of 1,626 municipalities. In the first stage, unbiased efficiency scores were calculated, and in the second stage, these scores were regressed against a set of environmental covariates which were thought to affect efficiency, including a dummy variable to identify the municipalities where waste is collected by a private company. Results suggest that private waste collection companies are more efficient than municipal governments. Other environmental variables such as population density, average household income, and tourism were also found to affect waste collection efficiency. The analysis also indicates that curbside collection is associated with a higher efficiency, while separate collection of waste is negatively correlated to efficiency.

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Aiming to assist the environmental sustainability of the Brazilian Amazonian agriculture, this article developed an eco-efficiency index, indicating the possible limits to maximize economic and environmental objectives, taking into account the best practices in the municipalities of the region. Shadow prices of degraded areas and forest preservation were also estimated using data envelopment analysis with directional distance functions. The results indicate that, on average, the analyzed municipalities are able to expand the production and the forest areas by 38% and reduce degraded areas and their inputs in the same proportion. The shadow prices allowed the estimation of the annual opportunity cost of the degraded areas and the preservation of the forest on the farms. The first, US$ 3,131,571, represented 0.04% of the annual output value, indicating that the internalization of that cost should be a low burden for the eco-efficient producer. The second, the total cost of preserving 80% of the area of property, represented US$ 120,890,662 or 1.7% of the annual income of the biome producers studied. Therefore, the main conclusion of this work is that the internalization of negative and positive externalities of agricultural production in the Amazonian biome does not make agricultural production economically unfeasible in the region. In addition, the reimbursement of damages avoided by carbon sequestration, through the Clean Development Mechanism (CDM) established by the Kyoto Conference, should further increase the economic and environmental sustainability of agriculture in the area.

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Many developing countries have highly unequal health systems across their regions. The pandemic of COVID-19 brought an additional challenge, as hospital structures equipped with doctors, intensive care units and respirators are not available to a sufficient extent in all regions. Using Data Envelopment Analysis, we create a COVID Index to verify whether the hospital structures in 543 Brazilian microregions are adequate to deal with COVID-19 and to verify whether public policies were implemented in the right direction. The results indicate that hospital structures in the poorest microregions were the most vulnerable, although the peak of COVID-19 occurred in the richest microregions (Sao Paulo). The Southeast states could relocate hospital resources or even patients between their regions. The relocation was not possible in many states in the Northeast, as the health system poorly assisted the interior of these states. These findings reveal that the heterogeneity of microregions' hospital structures follows the patterns of socioeconomic inequalities. We conclude that it is easier for the wealthier regions to reallocate hospital resources internally than for the poorest regions. By using the COVID Index, policymakers and hospital managers have straightforward information to decide which regions must receive new investments and reallocate underutilized resources.

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This paper assesses the technical performance of Brazilian banks while accounting for risk, which is considered as an undesirable outcome of banking. To this end, frontier techniques based on Data Envelopment Analysis and directional distance functions are applied to a sample of 124 banks and data for the six-year period 2014-19. Our main finding is that the Brazilian banking industry could notably increase its production of conventional outputs without additional input usage and while maintaining the same levels of risk. Besides, investment banks are found to be more efficient than commercial banks mainly because of their superior managerial performance.

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BACKGROUND: Health equity, although addressed in several publications dealing with health efficiency analysis, is not easily translated into the operationalization of variables, mainly due to technical difficulties. Some studies provide evidence that it does not influence health outcomes; others demonstrate that its effect is an indirect one, with the hegemony of material living conditions over its social connotation. The aim of this article is to evaluate the role of health equity in determining health outcomes, in an international comparative analysis of the effectiveness and efficiency of health systems. METHOD: Fixed Effects Model Panel and Data Envelopment Analysis, a dynamic and network model, in addition to comparative analysis between methods and health impacts. The effect variables considered in the study were life expectancy at birth and infant mortality, in 2010 and 2015, according to the sociocultural regions of the selected countries. Inequity was assessed both economically and socially. The following dimensions were considered: physical and financial resources, health production (access, coverage and prevention) and intersectoral variables: demographic, socioeconomic, governance and health risks. RESULTS: Both methods demonstrated that countries with higher inequity levels (regarding income, education and health dimensions), associated or not with poverty, are the least efficient, not reaching the potential for effective health outcomes. The outcome life expectancy at birth exhibited, in the final model, the following variables: social inequity and per capita health expenditure. The outcome infant mortality comprehended the level of education variable, in association with the following healthcare variabels: care seeking due to diarrhea in children under five, births attended by skilled health professionals and the reduction in the incidence of HIV. CONCLUSION: The dissociation between the distribution of health outcomes and the overall level of health of the population characterizes a devastating political choice for society, as it is associated with high levels of segregation, disrespect and violence from within. Countries should prioritize health equity, adding value to its resources, since health inequties affect society altogether, generating mistrust and reduced social cohesion.

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The European Union (EU) has launched two regional investment programs of European Funds (FE) in the last decade. One covers the period of 2007 to 2013, and the second from 2014 to 2020. Among the goals contained in FE regulations is that of achieving sustainable growth through the conversion of fossil energy production systems to renewable energy. This research has had a goal to determine whether the countries of the Eurozone maintain homogeneous levels of efficiency in the use of these resources to improve the levels of environmental quality related to the use of this type of energy. The adopted research method for efficiency analyses was Data Envelopment Analysis (DEA). Findings revealed that the efficiency in the use of renewable energies is very uneven among the analyzed countries and that these differences are maintained throughout the analyzed period. These results suggest that the criteria for the distribution of the funds should be modified. The current distribution is mainly based on the per capita income of the countries and/or regions. In this way, compliance with the European Green Pact approved in September 2020 would be guaranteed.

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