Investigating potential supply of ecosystem services in cultural landscapes through efficiency analysis

One of the paramount challenges in natural resource management revolves around the delicate equilibrium between the demand for and the supply of diverse Ecosystem Services (ESs) within a cultural landscape. Recognizing the centrality of cultural landscapes to human well-being, the sustainability of these landscapes hinges upon the health and stability of ecosystems that can effectively provide the required ESs. Over the long term, the sustainable supply of ESs is constrained by the potential supply of ESs. Understanding the potential supply of ESs is crucial for averting compromises to the ecosystems within a landscape. This article introduces a novel perspective on evaluating the ESs of a landscape by means of efficiency analysis. Instead of presenting the potential supply of ESs in absolute terms, we offer a comparative analysis of ESs' relative supply to associated management costs. In principle, the efficiency of Landscape Units (LUs) is defined as the ratio of the potential supply of multiple ESs to the costs associated with land use and land cover management. The resultant efficiency maps serve as hot and cold spot maps, revealing efficient ecosystem compositions that yield multiple ESs. This composition reflects management efforts, incorporating various management costs. Forests emerge as pivotal ecosystems in landscapes, delivering the most ESs at the lowest costs. These efficiency maps offer valuable insights for regional planners, enabling them to enhance the supply of ES in inefficient LUs by studying the ecosystem structure and associated costs of the most efficient LUs.

Level of technical efficiency and associated factors among health centers in East Gojjam Zone, Northwest Ethiopia: an application of the data envelopment analysis

BACKGROUND

Besides the scarcity of resources, inefficient utilization of available health service resources has been the bottleneck to deliver quality health services in Ethiopia. However, Information regarding the efficiency of health service providers is limited in the country. Health service managers and policy makers must be well informed about the efficiency of health service providers and ways of using limited resources efficiently to make evidence-based decisions. This study aimed to assess the level of technical efficiency and associated factors among health centers in East Gojjam Zone, Northwest Ethiopia.

METHODS

A facility-based cross-sectional study was conducted among 27 randomly selected health centers in East Gojjam zone, Northwest Ethiopia, from October 30, 2022, to April 30, 2023. Using an interviewer-administered questionnaire and document review checklist, health centers' data was collected and entered to Epi-Data version 4.6. The data was exported to Microsoft office excel and Stata version 14 for analysis. A two-stage output-oriented data envelopment analysis with a variable return to scale assumption was employed to determine the level of technical efficiencies. Finally, the tobit regression model was applied to identify the associated factors at 5% level of significance.

RESULTS

In this study, 59.3% of the health centers were technically efficient. The mean technical efficiency score of the health centers was 0.899 ± 0.156. Inefficient health centers could provide more 22, 433 outpatient visits, 1,351 family planning visits, 155 referral services, 206 skilled deliveries and 385 fully vaccinations of children if they were technically efficient as their peer health centers for the same year. From the tobit regression, the catchment population and number of administrative staffs were statistically significant determinants of the technical efficiency of health centers.

CONCLUSIONS

The mean technical efficiency of the health centers in East Gojjam zone, Northwest Ethiopia was high. However, nearly half of the health centers were technically inefficient, which indicates the exitance of a space for further improvements in the productivity of these health centers. Employing excess number administrative staffs (above the optimal level) should be discouraged and selecting appropriate sites where the health centers to be constructed (to have large catchment population coverage) could improve the productivity of health centers.

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.

Predicting COVID-19 and Influenza Vaccination Confidence and Uptake in the United States

This study investigates and compares the predictors of COVID-19 and influenza vaccination confidence and uptake in the U.S. Vaccine hesitancy is defined as the reluctance or refusal (i.e., less than 100% behavioral intention) to vaccinate despite the availability of effective and safe vaccines. Vaccine hesitancy is a major obstacle in the fight against infectious diseases such as COVID-19 and influenza. Predictors of vaccination intention are identified using the reasoned action approach and the integrated behavioral model. Data from two national samples (N = 1131 for COVID-19 and N = 1126 for influenza) were collected from U.S. Qualtrics panels. Tobit regression models were estimated to predict percentage increases in vaccination intention (i.e., confidence) and the probability of vaccination uptake (i.e., intention reaching 100%). The results provided evidence for the reasoned approach and the IBM model and showed that the predictors followed different patterns for COVID-19 and influenza. The implications for intervention strategies and message designs were discussed.

Evaluation and influencing factors of the tourism industry efficiency under carbon emission constraints in China

A significant industrial transformation in China's tourism sector is currently taking place in response to carbon peak and carbon neutrality targets. This paper applies the data envelopment analysis (DEA) model to calculate the efficiency of the tourism industry under carbon emission constraints and further investigates its influencing factors through the Tobit regression. The results are as follows: (1) The tourism efficiency under carbon emission constraints of China from 2000 to 2019 showed a trend of first rising and then declining, and there were obvious regional differences; (2) from 2000 to 2019, the total factor productivity of tourism in China increased significantly, while the contributions of technical progress, pure technical efficiency, and scale efficiency decreased sequentially; (3) the factors of industrial structure, transportation convenience, economic development level, degree of opening to the outside world, and the level of scientific and technological development have varying degrees of influence on tourism efficiency. Based on the analysis results, this paper puts forward several policy suggestions on tourism efficiency and low-carbon development. The findings of this paper have some bearing on developing nations' efforts to boost tourism efficiency and realize high-quality industry growth within the framework of sustainable development.

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.

Approximating the influence of external factors on the technical efficiency score of hospital care: evidence from the federal states of Germany

BACKGROUND

A good health care system and, especially, the provision of efficient hospital care are the goals of national and regional health policies. However, the scope of general hospital care in the 16 federal states in Germany varies considerably from region to region. The objectives of this paper are to evaluate the technical efficiencies of all general hospitals of the 16 federal states for the period from 2015 to 2020, to find out the relation between the exogenous factors and score of efficiency, and also the influence of the COVID-19 pandemic on the results of the technical efficiency of hospital care in the German states.

METHODS

A two-step approach was used. First, an input-oriented Data Envelopment Analysis model with constant returns to scale and variable returns to scale was applied for the 6-year period from 2015 to 2020. The calculation of technical efficiency according to the input-oriented DEA model contains the three components-total technical efficiency (TTE), pure technical efficiency (PTE) and scale efficiency (SE). In the second stage, the influence of exogenous variables on the previously determined technical efficiency was evaluated by applying the tobit regression analysis.

RESULTS

Although the level of average technical efficiency of about 90% is high, total technical efficiency deteriorated steadily from 2015 to 2020. Its lowest point at around 78%, was in the year 2020. The deterioration of the average technical efficiency is notably influenced by the lower results in the years 2019 and 2020. The decomposition of technical efficiency also revealed that the deterioration of overall average efficiency was influenced by both pure technical efficiency (PTE) and scale efficiency (SE). Based on the tobit regression analysis performed, it was possible to conclude that the change in the efficiency score can be explained by the influence of exogenous factors only from 6.4% for overall efficiency and from 7.1% for scale efficiency.

CONCLUSIONS

The results of the analysis of the overall technical efficiency reveal that the aggregated data of all general hospitals of all 16 federal states show a steadily worsening total technical efficiency every year since 2015. Although, especially, the deterioration of the year 2020 with the occurrence of COVID-19 pandemic, contributes to a deteriorated efficiency average, the deterioration of the efficiency values, based on the analysis performed, is also observable between the years 2016 and 2019. Considering the output generated, for inefficient units and the relevant policy authorities in the hospital sector, it can be recommended that the number of beds and in particular the number of physicians, should be reduced as inputs. Based on this study, it is also recommended that decisions to increase the efficiency of general hospitals should be made with consideration of exogenous factors such as the change in the number of general hospitals or the population density in the respective state, as these had explanatory value in connection with the increase in efficiency values. Due to the wide variation in the size of the federal states, the recommendation is more appropriate for federal states with low population density.

The Determinants of Adherence to Public Health and Social Measures Against COVID-19 Among the General Population in South Korea: National Survey Study

BACKGROUND

The COVID-19 pandemic has created devastating health, social, economic, and political effects that will have long-lasting impacts. Public health efforts to reduce the spread of COVID-19 are the priority of national policies for responding to the pandemic globally. Public health and social measures (PHSMs) have been shown to be effective when used alone or in combination with other measures, reducing the risk of spreading COVID-19. However, there is insufficient evidence on the status of compliance with PHSMs in the general population for the prevention of COVID-19 in public areas, including Korea.

OBJECTIVE

The aim of this study was to assess levels of compliance with the recommended PHSMs against SARS-CoV-2 infection and their predictors among the general population by using national data.

METHODS

This study was a secondary data analysis of the National Survey of Infectious Disease Preventive Behaviors in Community, which was conducted by the Korea Centers for Disease Control and Prevention Agency (KDCA) between October 12 and October 30, 2020. The primary study was cross-sectional, using stratified sampling via an adjusted proportional allocation method to select representative samples and ensure the stability of samples. The data were collected through phone interviews conducted by trained enumerators using a structured questionnaire. PHSM adherence was measured using a 10-item comprehensive infectious disease prevention behavior (CIDPB) scale, and each sociocognitive factor, including perceived susceptibility to SARS-CoV-2 infection, perceived severity of SARS-CoV-2 infection, perceived confidence in performing preventive behaviors related to COVID-19, information comprehension ability, and trust in information from the KDCA, was measured. A total of 4003 participants were included in the final analysis. Tobit regression and a decision tree analysis were performed to identify the predictors of preventive measures and the target groups for intervention.

RESULTS

We discovered that women scored 1.34 points higher on the CIDPB scale than men (P<.001). Compared to the group aged 19 to 29 years, those aged 50 to 59 years and those older than 60 years scored 1.89 and 2.48 points higher on the CIDPB scale (P<.001), respectively. The perceived severity of infection, confidence in preventive behaviors, information comprehension ability, and trust in information from the KDCA were significant positive determinants of CIDPBs (P<.001). The perceived susceptibility to infection showed a significant negative relationship with CIDPBs (P<.001).

CONCLUSIONS

Female sex, older age, lower income, and sociocognitive factors were found to be significant determinants of adhering to PHSMs. The findings suggest the need for tailored interventions for target groups; specifically, the age group that was the most active at work indicated the highest potential to spread infection. Adequate public health education and health communication for promoting adherence to PHSMs should be emphasized, and behavior change strategies for those with low perceived confidence in performing PHSMs should be prioritized.

Metabolomic profiles in night shift workers: A cross-sectional study on hospital female nurses

Background and aim

Shift work, especially including night shifts, has been found associated with several diseases, including obesity, diabetes, cancers, and cardiovascular, mental, gastrointestinal and sleep disorders. Metabolomics (an omics-based methodology) may shed light on early biological alterations underlying these associations. We thus aimed to evaluate the effect of night shift work (NSW) on serum metabolites in a sample of hospital female nurses.

Methods

We recruited 46 nurses currently working in NSW in Milan (Italy), matched to 51 colleagues not employed in night shifts. Participants filled in a questionnaire on demographics, lifestyle habits, personal and family health history and work, and donated a blood sample. The metabolome was evaluated through a validated targeted approach measuring 188 metabolites. Only metabolites with at least 50% observations above the detection limit were considered, after standardization and log-transformation. Associations between each metabolite and NSW were assessed applying Tobit regression models and Random Forest, a machine-learning algorithm.

Results

When comparing current vs. never night shifters, we observed lower levels of 21 glycerophospholipids and 6 sphingolipids, and higher levels of serotonin (+171.0%, 95%CI: 49.1-392.7), aspartic acid (+155.8%, 95%CI: 40.8-364.7), and taurine (+182.1%, 95%CI: 67.6-374.9). The latter was higher in former vs. never night shifters too (+208.8%, 95%CI: 69.2-463.3). Tobit regression comparing ever (i.e., current + former) and never night shifters returned similar results. Years worked in night shifts did not seem to affect metabolite levels. The Random-Forest algorithm confirmed taurine and aspartic acid among the most important variables in discriminating current vs. never night shifters.

Conclusions

This study, although based on a small sample size, shows altered levels of some metabolites in night shift workers. If confirmed, our results may shed light on early biological alterations that might be related to adverse health effects of NSW.

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.

Assessment of the total-factor energy efficiency and environmental performance of Persian Gulf countries: a two-stage analytical approach

In recent decades, achieving sustainable economic growth and development through energy efficiency has been a key challenge for Persian Gulf countries. This study presents a two-stage analysis of the energy efficiency and environmental performance of Persian Gulf countries in 2000-2014 using data envelopment analysis and Tobit regression. The hypothesis of this study is that energy efficiency is low in the Persian Gulf countries and these countries have the potential to reduce greenhouse gas emissions. At first, using data envelopment analysis, total-factor energy efficiency and environmental efficiency of the Persian Gulf countries were measured. Then, using Tobit regression, the effects of GDP per capita, oil price, industrialization degree, population size, paper citation rate, foreign direct investment, and the degree of commercial openness on energy efficiency were investigated. The results of the first stage measurements show that Saudi Arabia and the United Arab Emirates had the highest and second highest total-factor energy efficiency, respectively, while Oman and Iran had the lowest, and second lowest, respectively. In terms of environmental performance, the UAE and Qatar proved to have the best and second best performance, respectively, while Iran and Iraq showed the weakest and second weakest performance, respectively. The results of Tobit regression revealed that GDP per capita, oil prices, industrialization degree, and population size had a direct relationship with energy efficiency while the paper citation rate (as an index of science, technology, and innovation) and foreign direct investment had an inverse relationship with energy efficiency. This study shows that the Persian Gulf countries could potentially reduce their energy consumption by up to 18%. Finally, a number of environmentally friendly economic policies and several environmental projects are proposed and it is emphasized that more innovative green technologies should be used to increase energy efficiency and optimize the energy structure to combat climate change.

Modeling of correlated cognitive function and functional disability outcomes with bounded and missing data in a longitudinal aging study

Longitudinal studies of correlated cognitive and disability outcomes among older adults are characterized by missing data due to death or loss to follow-up from deteriorating health conditions. The Mini-Mental State Examination (MMSE) score for assessing cognitive function ranges from a minimum of 0 (floor) to a maximum of 30 (ceiling). To study the risk factors of cognitive function and functional disability, we propose a shared parameter model to handle missingness, correlation between outcomes, and the floor and ceiling effects of the MMSE measurements. The shared random effects in the proposed model handle missingness (either missing at random or missing not at random) and correlation between these outcomes, while the Tobit distribution handles the floor and ceiling effects of the MMSE measurements. We used data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and a simulation study. By ignoring the MMSE floor and ceiling effects in the analyses of the CLHLS, the association of systolic blood pressure with cognitive function was not significant and the association of age with cognitive function was lower by 16.6% (from -6.237 to -5.201). By ignoring the MMSE floor and ceiling effects in the simulation study, the relative bias in the estimated association of female gender with cognitive function was 43 times higher (from -0.01 to -0.44). The estimated associations obtained with data missing at random were smaller than those with data missing not at random, demonstrating how the missing data mechanism affects the analytic results. Our work underscores the importance of proper model specification in longitudinal analysis of correlated outcomes subject to missingness and bounded values.

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

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

Flood-induced vulnerability of a below sea level farming system in southern India: an assessment through coping strategy intensity

This paper investigates the livelihood vulnerability experienced by agricultural households in Kuttanad, a below sea level farming system in southern India, in the aftermath of a major flood in August 2018. For this purpose, we constructed a flood coping strategy index (FCSI), to measure coping strategy intensity, using the data on the severity and frequency of various coping strategies adopted by households. Furthermore, we estimated a Tobit regression model to identify the factors influencing the intensity of coping strategy choices. The FCSI revealed that only two per cent of agricultural households experienced a 'severe' level of vulnerability because of the quick and effective policy response of the Kerala state government. In addition, Tobit regression analysis indicated that female-headed and labour households are more vulnerable than their respective counterparts. While income exerts a negative influence on the degree of livelihood vulnerability, agricultural landholding has a positive effect, as it increases cultivation loss during a flood.

Evaluating the Operational Efficiency and Quality of Tertiary Hospitals in Taiwan: The Application of the EBITDA Indicator to the DEA Method and TOBIT Regression

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis.

Impact of worries associated with COVID-19 on diabetes-related psychological symptoms in older adults with Type 2 diabetes

This study examined the associations between worries associated with COVID-19, diabetes-specific distress, and depressive symptoms in older adults with type 2 diabetes (T2D), who are particularly vulnerable to COVID-19 and its psychological impacts. A cross-sectional online survey was conducted with 84 older adults with T2D from June to December 2020. Participants had little to moderate worries associated with COVID-19, with the greatest worries about the economy recession, followed by a family member catching COVID-19, lifestyle disruptions, and overwhelmed local hospitals. Bivariate correlation and tobit regression revealed that increases in worries associated with COVID-19 were associated with increased diabetes distress and depressive symptoms. Specifically, worries associated with COVID-19 increased diabetes-specific emotional burden and physician-related and regimen-related distress. Increased diabetes distress and depressive symptoms worsened by COVID-19 may ultimately lead to poor glucose control. Additional assessment by mental health experts should be considered for older adults with T2D during and after infectious disease pandemic.

Censored data considerations and analytical approaches for salivary bioscience data

Left censoring in salivary bioscience data occurs when salivary analyte determinations fall below the lower limit of an assay's measurement range. Conventional statistical approaches for addressing censored values (i.e., recoding as missing, substituting or extrapolating values) may introduce systematic bias. While specialized censored data statistical approaches (i.e., Maximum Likelihood Estimation, Regression on Ordered Statistics, Kaplan-Meier, and general Tobit regression) are available, these methods are rarely implemented in biobehavioral studies that examine salivary biomeasures, and their application to salivary data analysis may be hindered by their sensitivity to skewed data distributions, outliers, and sample size. This study compares descriptive statistics, correlation coefficients, and regression parameter estimates generated via conventional and specialized censored data approaches using salivary C-reactive protein data. We assess differences in statistical estimates across approach and across two levels of censoring (9% and 15%) and examine the sensitivity of our results to sample size. Overall, findings were similar across conventional and censored data approaches, but the implementation of specialized censored data approaches was more efficient (i.e., required little manipulations to the raw analyte data) and appropriate. Based on our review of the findings, we outline preliminary recommendations to enable investigators to more efficiently and effectively reduce statistical bias when working with left-censored salivary biomeasure data.

Donors' Interest in Water and Sanitation Subsectors

International efforts have taken place to alleviate poverty by adopting several obligations within the international society; one of these obligations is the provision of safe access to water and sanitation. The MDGs helped people around the world to gain improved water sources and better sanitation. Although the sectoral aid increased from 20% between 1990 and 1992 (only 4.9% distributed for water supply and sanitation (W&S)) to 35% between 2002 and 2004 (only 3.9% allocated for W&S), facts showed that the allocated aid was biased to social aims rather than infrastructural targets. In this study, I am focusing on the donors' commitment for W&S, whether their ODA for these two sub-sectors is aligned with the intentions of the SDGs. I find that donors allocated W&S aid by focusing on governments in general with higher governance indicators, and that poorer countries received a higher allocation of aid.

Technical efficiency and its affecting factors in Tehran's public hospitals: DEA approach and Tobit regression

Background: Measuring hospital efficiency is one of the tools for determining how to use resources. Considering the necessity of measuring the efficiency in hospitals, the current study was conducted to evaluate the efficiency and its determining factors in the Hospitals affiliated to medical universities in Tehran. Methods: This was a descriptive-analytical and longitudinal study. In the first stage of the research, the variables affecting the efficiency of hospitals were extracted using the Delphi method. In the second stage, th. Efficiency of 29 public hospitals in Tehran from 2012 to 2016 was calculated using data envelopment analysis techniques. We performed a sensitivity analysis of the efficiency scores by running the DEA model several times using different combinations of input variables. At last, applying the Tobit regression, factors explaining the inefficiencies of hospitals were determined. Data analysis was done by STATA 12 and SPSS 16 software. Significance level of all the tests was set at .05. 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 mean of hospital efficiency was ascending from 2012 to 2015, and then it descending after 2015. According to the results of sensitivity analysis, despite the variability of technical efficiency during the study period (p<0.0001), the difference between the mean performance scores among different scenarios was not significant (p=0.066). Based on the third stage results, the average length of stay (Beta=-1.60E-12, p=0.030) and educational status (Beta=-2.89E+00, p=0.001) had a significant negative effect on hospitals' efficiency. Conclusion: The study results indicated that the efficiency changes during the years investigated were significant among Tehran public hospitals. The optimal use of inputs to produce hospital services should be on the agenda of health managers and policymakers.

A correlated random parameters tobit model to analyze the safety effects and temporal instability of factors affecting crash rates

Numerous studies have previously used a variety of count-data models to investigate factors that affect the number of crashes over a certain period of time on roadway segments. Unlike past studies which deal with crash frequency, this study views the crash rates directly as a continuous variable left-censored at zero and explores the application of an alternate approach based on tobit regression. To thoroughly investigate the factors affecting freeway crash rates and the potentially temporal instability in the effects of crash factors involving traffic volume, freeway geometries and pavement conditions, a classic uncorrelated random parameters tobit (URPT) model and a correlated random parameters tobit (CRPT) model were estimated, along with a conventional fixed parameters tobit (FPT) model. The analysis revealed a large number of safety factors, including several appealing and interesting factors rarely studied in the past, such as the safety effects of climbing lanes and distance along composite descending grade. The results also showed that the CRPT model was not only able to reflect the heterogeneous effects of various factors, but also able to estimate the underlying interactions among unobserved characteristics, and therefore provide better statistical fit and offer more insights into factors contributing to freeway crashes than its model counterparts. Additionally, the results showed significant temporal instability in CRPT models across the studied time periods indicating that crash factors (including unobserved characteristics and the underlying interactions among them) and their effects on crash rates varied over time, and more attentions should be paid when interpreting crash data-analysis findings and making safety policies. The modeling technique in this study demonstrates the potential of CRPT model as an effective approach to gain new insights into safety factors, particularly when the heterogeneous effects of factors on safety are interactive. Additionally, findings from this study are also expected to assist in developing more effective countermeasures by better understanding the safety effects of factors associated with freeway design characteristics and pavement conditions.

Has the Efficiency of China's Healthcare System Improved after Healthcare Reform? A Network Data Envelopment Analysis and Tobit Regression Approach

Background: A healthcare system refers to a typical network production system. Network data envelopment analysis (DEA) show an advantage than traditional DEA in measure the efficiency of healthcare systems. This paper utilized network data envelopment analysis to evaluate the overall and two substage efficiencies of China’s healthcare system in each of its province after the implementation of the healthcare reform. Tobit regression was performed to analyze the factors that affect the overall efficiency of healthcare systems in the provinces of China. Methods: Network DEA were obtained on MaxDEA 7.0 software, and the results of Tobit regression analysis were obtained on StataSE 15 software. The data for this study were acquired from the China health statistics yearbook (2009–2018) and official websites of databases of Chinese national bureau. Results: Tobit regression reveals that regions and government health expenditure effect the efficiency of the healthcare system in a positive way: the number of high education enrollment per 100,000 inhabitants, the number of public hospital, and social health expenditure effect the efficiency of healthcare system were negative. Conclusion: Some provincial overall efficiency has fluctuating increased, while other provincial has fluctuating decreased, and the average overall efficiency scores were fluctuations increase.

[Spatial and temporal differences and influencing factors of ecological capital efficiency in Northeast China]

Ecological capital is an important fulcrum for the construction of ecological civilization. Measuring the spatial and temporal changes and influencing factors of ecological capital efficiency can help understand the current status of ecological capital efficiency and improve the level of ecological civilization to achieve green development. We used the super-efficient DEA model and Malmquist index to measure the spatial and temporal changes of ecological capital efficiency in Northeast China and analyzed the influencing factors. The results showed that the ecological capital efficiency in Northeast China was generally good, showing a trend of rising first and then decreasing. The ecological capital efficiency of Heilongjiang was high, showing U-shaped development, while that in Jilin and Liaoning provinces was relatively low. Results from the Malmquist index analysis showed that technological progress was the main driving force for the improvement of ecological capital efficiency. The results of Tobit regression analysis showed that the environment scale effect and the demographic effect variables had a significant positive impact on the efficiency of ecological capital. The low efficiency of science and technology investment led to negative correlation between environmental technology effect variables and ecological capital efficiency. To improve the ecological capital efficiency in Northeast China, on the one hand, we should accelerate the adjustment of industrial structure and develop ecological economy, on the other hand, we should increase the input efficiency of science and technology funds and rely on technological progress to realize green development of economy in Northeast China.

Vibrio parahaemolyticus in the Chesapeake Bay: Operational In Situ Prediction and Forecast Models Can Benefit from Inclusion of Lagged Water Quality Measurements

Vibrio parahaemolyticus is a leading cause of seafood-borne gastroenteritis. Given its natural presence in brackish waters, there is a need to develop operational forecast models that can sufficiently predict the bacterium's spatial and temporal variation. This work attempted to develop V. parahaemolyticus prediction models using frequently measured time-indexed and -lagged water quality measures. Models were built using a large data set (n = 1,043) of surface water samples from 2007 to 2010 previously analyzed for V. parahaemolyticus in the Chesapeake Bay. Water quality variables were classified as time indexed, 1-month lag, and 2-month lag. Tobit regression models were used to account for V. parahaemolyticus measures below the limit of quantification and to simultaneously estimate the presence and abundance of the bacterium. Models were evaluated using cross-validation and metrics that quantify prediction bias and uncertainty. Presence classification models containing only one type of water quality parameter (e.g., temperature) performed poorly, while models with additional water quality parameters (i.e., salinity, clarity, and dissolved oxygen) performed well. Lagged variable models performed similarly to time-indexed models, and lagged variables occasionally contained a predictive power that was independent of or superior to that of time-indexed variables. Abundance estimation models were less effective, primarily due to a restricted number of samples with abundances above the limit of quantification. These findings indicate that an operational in situ prediction model is attainable but will require a variety of water quality measurements and that lagged measurements will be particularly useful for forecasting. Future work will expand variable selection for prediction models and extend the spatial-temporal extent of predictions by using geostatistical interpolation techniques.IMPORTANCEVibrio parahaemolyticus is one of the leading causes of seafood-borne illness in the United States and across the globe. Exposure often occurs from the consumption of raw shellfish. Despite public health concerns, there have been only sporadic efforts to develop environmental prediction and forecast models for the bacterium preharvest. This analysis used commonly sampled water quality measurements of temperature, salinity, dissolved oxygen, and clarity to develop models for V. parahaemolyticus in surface water. Predictors also included measurements taken months before water was tested for the bacterium. Results revealed that the use of multiple water quality measurements is necessary for satisfactory prediction performance, challenging current efforts to manage the risk of infection based upon water temperature alone. The results also highlight the potential advantage of including historical water quality measurements. This analysis shows promise and lays the groundwork for future operational prediction and forecast models.

Benchmarking Toronto wastewater treatment plants using DEA window and Tobit regression analysis with a dynamic efficiency perspective

The environmental-economic focus of wastewater treatment and management attracts growing attentions in recent years. The static efficiencies and their dynamic changes are helpful to systematically assess the environmental performance of the water agencies and wastewater treatment plants (WWTPs). Additionally, identifying key factors of efficiencies is critical to improve the operation of WWTPs. In this study, the window method of data envelopment analysis (DEA) was applied to estimate the annual efficiency for four Canadian WWTPs and to explore the variations of annual efficiency under different window lengths. Meanwhile, the Tobit regression analysis was developed to determine the driving forces for WWTPs' efficiency. The empirical results showed that: (i) the selected DEA window length remarkably affected both the average efficiency and the variations; however, it had no impact on the ranking of plants' efficiency; (ii) lower efficiencies were observed in plants with larger capacities due to higher infrastructure and operation investments involved; (iii) both the influent total phosphorus concentrations and influent flow rates had significant effects on the WWTPs' performance. Moreover, the staff and utility expenditures should be reduced to generate greater potential cost savings and efficiency improvement given the treatment technologies employed.

Measuring the efficiency of Palestinian public hospitals during 2010-2015: an application of a two-stage DEA method

Background

While health needs and expenditure in the Occupied Palestinian Territories (OPT) are growing, the international donations are declining and the economic situation is worsening. The purpose of this paper is twofold, to evaluate the productive efficiency of public hospitals in West Bank and to study contextual factors contributing to efficiency differences.

Methods

This study examined technical efficiency among 11 public hospitals in West Bank from 2010 through 2015 targeting a total of 66 observations. Nationally representative data were extracted from the official annual health reports. We applied input-oriented Data Envelopment Analysis (DEA) models to estimate efficiency scores. To elaborate further on performance, we used Tobit regression to identify contextual factors whose impact on inefficient performance is statistically significant.

Results

Despite the increase in efficiency mean scores by 4% from 2010 to 2015, findings show potential savings of 14.5% of resource consumption without reducing the volume of the provided services. The significant Tobit model showed four predictors explaining the inefficient performance of a hospital (p <  0.01) are: bed occupancy rate (BOR); the outpatient-inpatient ratio (OPIPR); hospital’s size (SIZE); and the availability of primary healthcare centers within the hospital’s catchment area (PRC). There is a strong effect of OPIPR on efficiency differences between hospitals: A one unit increase in OPIPR will lead a decrease of 19.7% in the predicted inefficiency level holding all other factors constant.

Conclusion

To date, no previous studies have examined the efficiency of public hospitals in the OPT. Our work identified their efficiency levels for potential improvements and the determinants of efficient performance. Based on the measurement of efficiency, the generated information may guide hospitals’ managers, policymakers, and international donors improving the performance of the main national healthcare provider. The scope of this study is limited to public hospitals in West Bank. For a better understanding of the Palestinian market, further research on private hospitals and hospitals in Gaza Strip will be useful.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-3228-1) contains supplementary material, which is available to authorized users.

Application of Fractal theory for crash rate prediction: Insights from random parameters and latent class tobit models

The repercussions from congestion and accidents on major highways can have significant negative impacts on the economy and environment. It is a primary objective of transport authorities to minimize the likelihood of these phenomena taking place, to improve safety and overall network performance. In this study, we use the Hurst Exponent metric from Fractal Theory, as a congestion indicator for crash-rate modeling. We analyze one month of traffic speed data at several monitor sites along the M4 motorway in Sydney, Australia and assess congestion patterns with the Hurst Exponent of speed (H_speed). Random Parameters and Latent Class Tobit models were estimated, to examine the effect of congestion on historical crash rates, while accounting for unobserved heterogeneity. Using a latent class modeling approach, the motorway sections were probabilistically classified into two segments, based on the presence of entry and exit ramps. This will allow transportation agencies to implement appropriate safety/traffic countermeasures when addressing accident hotspots or inadequately managed sections of motorway.

Spatially Modelling the Association Between Access to Recreational Facilities and Exercise: The 'Multi-Ethnic Study of Atherosclerosis'

Numerous studies have investigated the relationship between the built environment and physical activity. However these studies assume that these relationships are invariant over space. In this study, we introduce a novel method to analyze the association between access to recreational facilities and exercise allowing for spatial heterogeneity. In addition, this association is studied before and after controlling for crime, a variable that could explain spatial heterogeneity of associations. We use data from the Chicago site of the Multi-Ethnic Study of Atherosclerosis of 781 adults aged 46 years and over. A spatially varying coefficient Tobit regression model is implemented in the Bayesian setting to allow for the association of interest to vary over space. The relationship is shown to vary over Chicago, being positive in the south but negative or null in the north. Controlling for crime weakens the association in the south with little change observed in northern Chicago. The results of this study indicate that spatial heterogeneity in associations of environmental factors with health may vary over space and deserve further exploration.

Efficiency of health care in state correctional institutions

Little is known about the efficiency of health care in correction settings. This article reports an efficiency analysis of health care in state correctional institutions (SCIs) in a single, mid-Atlantic state from 2003 to 2006. A two-stage data envelopment analysis was used to estimate the technical efficiency of prison health care and determine inmate and institutional characteristics that were associated with efficiency. Our output variable was the number of infirmary inpatient days for each year of study. The input variable for the first stage was the sum of personnel medical staff costs and other medical operating costs. SCIs with more white prisoners, older prisoners, and higher proportions of inmates with parole violations were significantly less efficient in their provision of health care than other SCIs. There were no SCI characteristics that were predictive of efficiency. These results suggest that healthcare efficiency in corrections may decline as the prison population continues to age.

Dynamic efficiency assessment of the Chinese hotel industry

The paper introduces for the first time a totally dynamic two-stage approach to analyzing the hotel industry's technical efficiency at the sub-national level. The first stage uses data envelopment window analysis (DEWA) to assess regional hotel sectors' technical efficiency over time. Unlike previous studies, the second stage uses a dynamic Tobit model to investigate the impact of macro contextual factors on the hotel sector efficiency. The study chooses the Chinese hotel industry during the period 2001-2006 as its application setting. The findings of the investigation indicate that the Chinese hotel industry is approaching an efficient operation in general, recovering from a major dip in 2003 resulting from the Severe Acute Respiratory Syndrome (SARS) outbreak. In addition, the study introduces a novel two-dimensional efficiency-based matrix to assess the competitive advantage of different regions of the Chinese hotel sector. The paper presents strategic market implications for hoteliers, government decision-makers, and destination management organizations. The proposed methods are applicable for situations in which an exogenous event of a destabilizing impact (e.g., SARS) does occur.