Daily stressors and mental health following disaster: A school-based assessment of adolescent disaster survivors in China and Nepal.

Postdisaster daily stressors, the economic and social challenges caused or exacerbated by disasters, have significant consequences for mental health but are rarely investigated in child and adolescent populations. We assessed posttraumatic stress symptoms (PTSS), depression, and anxiety among adolescents affected by disasters in China and Nepal and examined the specific contributions of disaster-related trauma exposure and daily stressors across mental health outcomes. A school-based, cross-sectional study was conducted with a stratified random sampling design. Adolescents living in disaster-affected areas of southern China and Nepal (N = 4,215, 52.7% female, age range: 15-19 years) completed translated, validated measures. Mixed effects logistic regression analyses were conducted using a priori risk factors. PTSS were reported by 22.7% of participants and were higher among Nepali adolescents but did not differ between genders. Depressive symptoms were reported by 45.2% of the sample and were higher among Nepali adolescents and girls in both countries. Across all settings, disaster-related trauma exposure was a significant risk factor for PTSS, depressive, and anxiety symptoms, China: odds ratios (ORs) = 1.44-2.06, Nepal, ORs = 1.21-2.53. High levels of household and interpersonal daily stressors further improved the models and contributed significantly to all mental health difficulties, China: ORs = 1.77-1.98, Nepal: ORs = 1.49-1.90. Postdisaster economic insecurity and interpersonal stressors are thus, likely to worsen adolescent mental health outcomes. Programs that identify and address structural inequalities for adolescents in disaster-affected settings will have cascading effects for mental health.

A dynamic case-based reasoning system for responding to infectious disease outbreaks

Infectious diseases are a global public health problem, which requires timely and effective responses. This study proposes a novel model that contributes to the development of such responses. First, the problem scenario features of infectious disease emergency scenarios are extracted, and the problem scenario is structurally described. A Markov model is adopted to analyze the scenario evolution of the infectious disease outbreaks. Then, a dynamic case-based reasoning model is built. Different matching algorithms are designed for crisp symbols, crisp numbers, interval numbers, and fuzzy linguistic variables. The similarity between the target scenario and various historical scenarios is calculated. Finally, an optimized dynamic emergency decision guide is provided. An experiment is conducted to test the validity and feasibility of the proposed method. The results suggest that the model can realistically simulate the process of infectious disease outbreaks and quickly match the recorded scenarios to generate effective and real-time responses.

A Game Model for Medical Service Pricing Based on the Diagnosis Related Groups.

Background: Currently there are various issues that exist in the medical institutions in China as a result of the price-setting in DRGs, which include the fact that medical institutions tend to choose patients and that the payment standard for complex cases cannot reasonably compensate the cost. Objective: The main objective is to prevent adverse selection problems in the operations of a diagnosis-related groups (DRGs) system with the game pricing model for scientific and reasonable pricing. Methods: The study proposes an improved bargaining game model over three stages, with the government and patients forming an alliance. The first stage assumes the alliance is the price maker in the Stackelberg game to maximize social welfare. Medical institutions are a price taker and decide the level of quality of medical service to maximize their revenue. A Stackelberg equilibrium solution is obtained. The second stage assumes medical institutions dominate the Stackelberg game and set an optimal service quality for maximizing their revenues. The alliance as the price taker decides the price to maximize the social welfare. Another Stackelberg equilibrium solution is achieved. The final stage establishes a Rubinstein bargaining game model to combine the Stackelberg equilibrium solutions in the first and second stage. A new equilibrium between the alliance and medical institutions is established. Results: The results show that if the price elasticity of demand increases, the ratio of cost compensation on medical institutions will increase, and the equilibrium price will increase. The equilibrium price is associated with the coefficient of patients' quality preference. The absolute risk aversion coefficient of patients affects government compensation and total social welfare. Conclusion: In a DRGs system, considering the demand elasticity and the quality preference of patients, medical service pricing can prevent an adverse selection problem. In the future, we plan to generalize these models to DRGs pricing systems with the effects of competition of medical institutions. In addition, we suggest considering the differential compensation for general hospitals and community hospitals in a DRGs system, in order to promote the goal of hierarchical diagnosis and treatment.

An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic.

The COVID-19 pandemic is a significant public health problem globally, which causes difficulty and trouble for both people's travel and public transport companies' management. Improving the accuracy of bus passenger flow prediction during COVID-19 can help these companies make better decisions on operation scheduling and is of great significance to epidemic prevention and early warnings. This research proposes an improved STL-LSTM model (ISTL-LSTM), which combines seasonal-trend decomposition procedure based on locally weighted regression (STL), multiple features, and three long short-term memory (LSTM) neural networks. Specifically, the proposed ISTL-LSTM method consists of four procedures. Firstly, the original time series is decomposed into trend series, seasonality series, and residual series through implementing STL. Then, each sub-series is concatenated with new features. In addition, each fused sub-series is predicted by different LSTM models separately. Lastly, predicting values generated from LSTM models are combined in a final prediction value. In the case study, the prediction of daily bus passenger flow in Beijing during the pandemic is selected as the research object. The results show that the ISTL-LSTM model could perform well and predict at least 15% more accurately compared with single models and a hybrid model. This research fills the gap of bus passenger flow prediction under the influence of the COVID-19 pandemic and provides helpful references for studies on passenger flow prediction.

Risk Factors and Prognosis in Patients with COVID-19 and Liver Injury: A Retrospective Analysis.

PURPOSE: COVID-19 is a new infectious disease with global spread. The aim of the present study was to explore possible risk factors and evaluate prognosis in COVID-19 with liver injury. METHODS: A retrospective study was conducted on 356 COVID-19 patients in the Third People's Hospital of Yichang, Hubei, China. Clinical characteristics and laboratory tests between patients with and without liver injury were compared, while risk factors of COVID-19-related liver injury were analyzed. Univariate and multivariate Cox regression analyses were conducted to identify risk factors of in-hospital death. RESULTS: Of the patients with liver injury, severe and critical types of COVID-19 comprised 12.43% and 14.69%, respectively, higher than in patients without liver injury (both P<0.05). CRP and male sex were independent risk factors for for patients with liver injury, while decreased lymphocyte count (HR 0.024, 95% CI 0.001-0.821) and elevated monocytes (HR 1.951, 95% CI 1.040-3.662) and CRP (HR 1.028, 95% CI 1.010-1.045) were independent risk factors of prognosis of death in COVID-19 patients with liver injury. CONCLUSION: Liver injury is a common complication in severe COVID-19 patients. Male sex and elevated CRP were independent risk factors in COVID-19 complicated by liver damage. Liver damage with increased CRP and monocyte count and decreased lymphocyte count may imply a poor prognosis.

Novel Case-Based Reasoning System for Public Health Emergencies.

PURPOSE: Several threatening infectious diseases, including influenza, Ebola, SARS, and COVID-19, have affected human society over the past decades. These disease outbreaks naturally inspire a demand for sustained and advanced safety and suppression measures. To protect public health and safety, further research developments on emergency analysis methods and approaches for effective emergency treatment generation are urgently needed to mitigate the severity of the pandemic and save lives. METHODS: To address these issues, a novel case-based reasoning (CBR) system is proposed using three phases. In the first phase, the similarity between the current case and the historical cases is calculated under a variety of heterogeneous information. In the second phase, a filter approach based on grey clustering analysis is created to retrieve relevant cases. In the third phase, the cases retrieved are taken as initial host nests in a cuckoo search (CS) algorithm, and our system searches an optimal solution through iteration of this algorithm. RESULTS: The proposed model is compared with a CBR method improved by particle swarm optimization (PSO) and a CBR method improved by a differential evolution algorithm (DE), to confirm the efficiency of our CS algorithm in adapting solutions for public health emergencies. The results show that the proposed model is better than the existing algorithms. CONCLUSION: The proposed model improves the speed of case retrieval using grey clustering and increases solution accuracy with CS algorithms. The present research can contribute to government, CDC, and infectious disease emergency management fields with regard to the implementation of fast and accurate public biohazard prevention and control measures based on a variety of heterogeneous information.

Fatal outcome in a liver transplant recipient with COVID-19.

Liver injury is common in patients with COVID-19, but little is known about its clinical presentation and severity in the context of liver transplant. We describe a case of COVID-19 in a patient who underwent transplant 3 years ago for hepatocellular carcinoma. The patient came to clinic with symptoms of respiratory disease; pharyngeal swabs for severe acute respiratory syndrome coronavirus 2 were positive. His disease progressed rapidly from mild to critical illness and was complicated by several nosocomial infections and multiorgan failure. Despite multiple invasive procedures and rescue therapies, he died from the disease. The management of COVID-19 in the posttransplant setting presents complex challenges, emphasizing the importance of strict prevention strategies.