A two-stage Data Envelopment Analysis Approach Incorporating Global Bounded Adjustment Measure to evaluate the efficiency of medical waste recycling systems with undesirable inputs and outputs (preprint)

With the ever-increasing focus on sustainable development, recycling waste and renewable use of waste products has earned immense consideration from academics and policy-makers. The serious pollution, complex types, and strong infectivity of medical waste (MW) have brought serious challenges to management. Although several researchers have addressed the issue of the MW by optimizing MW management networks and systems, there is still a significant gap in systematically evaluating the efficiency of MW recycling systems. Therefore, this paper proposes a two-stage data envelopment analysis (DEA) approach that combines the virtual frontier and the global bounded adjustment measure (BAM-VF-G), considering both undesirable inputs and outputs. In the first stage, the BAM-G model is used to evaluate the efficiency of MW recycling systems, and the BAM-VF-G model is used to further rank super-efficient MW recycling systems. In the second stage, two types of efficiency decomposition models are proposed. The first type of models decomposes unified efficiency into production efficiency (PE) and environment efficiency (EE). Depending upon the systems structure, the second type of models decomposes unified efficiency into the efficiency of the MW collection and transport subsystem (MWCS) and the efficiency of the MW treatment subsystem (MWTS). The novel approach is used to measure the efficiency of the MW recycling systems in China's new first-tier cities (CNFCs), and we find that: (1) Foshan ranks the highest in efficiency, followed by Qingdao and Dongguan, with efficiency values of 0.3593, 0.1765, and 0.1530, respectively. (2) EE has always been lower than PE and is a critical factor influencing the overall efficiency of MW recycling systems in CNFCs. (3) The MWCS lacks resilience, with an efficiency 0.042 lower than that of the MWTC. Following the outbreak of COVID-19, the efficiency of the MWCS has been decreasing year by year, reaching only 0.762 in 2021, which is a decline of 0.111 compared to 2017.

Incidence of thromboembolism in children with primary nephrotic syndrome: a systematic review and metaanalysis.

OBJECTIVE: To estimate the incidence of thromboembolism in children with primary nephrotic syndrome with Meta-analysis. METHODS: Relevant studies published from January 1, 1980 to December 31, 2021 were retrieved from Pubmed, Web of science, Cochrane library, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database(VIP) and Wangfang Database. Quality evaluation of the literatures included was conducted according to Agency for Healthcare Research and Quality(AHRQ) assessment tool, followed by data extraction and Meta-analysis with software RevMan 5.3. RESULTS: A total of seven studies involving 3675 subjects were included. The overall prevalence was 4.9% with 95% CI of 2.83 to 7.05.However, a significant heterogeneity (P < 0.001) was observed with I2 = 89%. The prevalence of venous thromboembolism was 3.3% with 95% CI of 1.7 to 4.9. The prevalence of arterial thromboembolism was 0.5% with 95% CI of 0.2 to 1.4. CONCLUSION: Children with nephrotic syndrome are prone to thromboembolism, and it may lead to disability or death, therefore prevention measures is critical to decreasing the prevalence of thromboembolism.

Effects of Wearing Face Masks on Exercise Capacity and Ventilatory Anaerobic Threshold in Healthy Subjects During the COVID-19 Epidemic.

BACKGROUND Face masks have become an important part of the COVID-19 prevention approach. This study aimed to explore the effect of wearing masks on exercise ability and ventilatory anaerobic threshold (VAT). MATERIAL AND METHODS Thirty-four young, healthy volunteers were included in this study, consisting of 18 men and 16 women. The subjects were randomized to perform 2 cardiopulmonary exercise tests (CPET) on a cycle ergometer with gas exchange analysis, one with and another without wearing a face mask (cross-over design). The general data for all subjects and indicators from the 2 exercise tests performed with and without wearing a face mask were collected. RESULTS In cardiopulmonary exercise tests, wearing a mask significantly (P<0.05) decreased peak indexes (eg, work rate (WR), oxygen consumption per kg body weight (VO2/kg), heart rate (HR), ventilation per minute (VE) and carbon dioxide ventilation equivalent (VE/VCO2)) and anaerobic threshold indexes (eg, WR, HR, VE, breath frequency (BF), dead space ratio (VD/VT), and VE/VCO2). However, the PETCO2 at peak was significantly higher. There was a positive linear correlation between WR difference and VO2 difference at VAT (abbreviated as deltaWR@VAT and deltaVO2@VAT, respectively) (r=0.495, P=0.003). Subgroup analysis of the VAT indexes showed that WR, VO2/kg, and VE were significantly decreased in the advanced VAT group with mask compared with the stable VAT group with mask (P<0.05). Logistic regression showed that deltaVE, deltaBF, and deltaVE/VCO2 had independent influences on VAT. CONCLUSIONS Wearing masks advances VAT in healthy young subjects during CPET. The advanced VAT was associated with changes in VE, BF, and VE/VCO2 while wearing masks.

Comparison of clinical characteristics in patients with coronavirus disease and influenza A in Guangzhou, China.

BACKGROUND: This study aims to compare the epidemiological, clinical and laboratory characteristics between patients with coronavirus disease (COVID-19) and influenza A (H1N1), and to develop a differentiating model and a simple scoring system. METHODS: We retrospectively analyzed the data from patients with COVID-19 and H1N1. The logistic regression model based on clinical and laboratory characteristics was constructed to distinguish COVID-19 from H1N1. Scores were assigned to each of independent discrimination factors based on their odds ratios. The performance of the prediction model and scoring system was assessed. RESULTS: A total of 236 patients were recruited, including 20 COVID-19 patients and 216 H1N1 patients. Logistic regression revealed that age >34 years, temperature ≤37.5 °C, no sputum or myalgia, lymphocyte ratio ≥20% and creatine kinase-myocardial band isoenzyme (CK-MB) >9.7 U/L were independent differentiating factors for COVID-19. The area under curves (AUCs) of the prediction model and scoring system in differentiating COVID-19 from H1N1 were 0.988 and 0.962, respectively. CONCLUSIONS: There are certain differences in clinical and laboratory features between patients with COVID-19 and H1N1. The simple scoring system may be a useful tool for the early identification of COVID-19 patients from H1N1 patients.