Mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors.

Objective To investigate the mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors. Methods A total of 540 military healthcare workers in shelter hospitals in Shanghai were investigated with patient health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Athens insomnia scale (AIS) to explore their mental health status, and logistic regression was used to analyze the influencing factors. Results A total of 536 valid questionnaires were collected, with an effective rate of 99.3% (536/540). The incidence of depression, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai was 45.5% (244/536), 26.1% (140/536) and 59.5% (319/536), respectively. Logistic regression analysis showed that whether people resided in Shanghai, the proportion of negative information in daily browsing information and diet status in shelter hospitals were the influencing factors of depression, anxiety and insomnia (all P<0.05);age and confidence in the future of Shanghai were the influencing factors of depression and insomnia (all P<0.05);and the time spent daily on epidemic-related information was an influencing factor of insomnia (P=0.021). Conclusion The incidence of depressive, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai is high during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant. Psychological consequences of the epidemic should be monitored regularly and continuously to promote the mental health of military healthcare workers.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Traffic Flow Forecasting in the COVID-19: A Deep Spatial-temporal Model Based on DiscreteWavelet Transformation

Traffic flowprediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into highdimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.

Mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors

Objective To investigate the mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors. Methods A total of 540 military healthcare workers in shelter hospitals in Shanghai were investigated with patient health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Athens insomnia scale (AIS) to explore their mental health status, and logistic regression was used to analyze the influencing factors. Results A total of 536 valid questionnaires were collected, with an effective rate of 99.3% (536/540). The incidence of depression, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai was 45.5% (244/536), 26.1% (140/536) and 59.5% (319/536), respectively. Logistic regression analysis showed that whether people resided in Shanghai, the proportion of negative information in daily browsing information and diet status in shelter hospitals were the influencing factors of depression, anxiety and insomnia (all P<0.05);age and confidence in the future of Shanghai were the influencing factors of depression and insomnia (all P<0.05);and the time spent daily on epidemic-related information was an influencing factor of insomnia (P=0.021). Conclusion The incidence of depressive, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai is high during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant. Psychological consequences of the epidemic should be monitored regularly and continuously to promote the mental health of military healthcare workers.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Clinical and laboratory features among severe COVID⁃19 patients with anxiety and depression symptoms

Objective: To investigate the incidence rates of anxiety and depression among the COVID-19 patients and their association with clinical features and laboratory variables. Methods: A total of 371 COVID-19 patients were recruited from Wuhan Leishenshan Hospital from Jan 20 to May 10, 2020. The anxiety and depression were assessed by using the Hosptial Anxiety and Depression Score (HAD), the Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS). The clinical features and laboratory variables were collected through electronic medical record. Statistical analyses were used to investigate the influence factors associated with anxiety and depression. Results: Among the 371 COVID-19 patients, the frequency of anxiety measured by HAD or SAS was 22. 91% and 24. 26%, respectively. The frequency of depression based on HAD or SDS was 16. 17% and 9. 43%, respectively. There were more female unmarried individuals in the anxiety or depression group. Anxiety or depression scores were significantly inversely correlated with the time for nucleic acid test turning negative. D-dimer and interleukin-6 (IL-6) were significantly elevated in the individuals with anxiety and depression. Statistically significant downregulations of lymphocyte counts, hemoglobin, and creatinine were found in anxiety and depression group. There was a negative association between creatinine and anxiety or depression scores. One unit upregulation of IL-6 and downregula-tion of lymphocyte counts could increase the hazard odds ratio of anxiety or depression by 10. 7% and 68. 9%, respectively. Conclusion: The COVID-19 patients with anxiety or depression symptoms had several different clinical features and laboratory findings in comparison with the patients without, which could lead to a poor prognosis of this disease. Clinicians should pay more attention to these indicators for anxiety or depression. Targeted psychological interventions should be implemented to minimize the negative impact of the psychological burden and to improve the quality life and disease outcome. © 2023 Editorial Board of Medical Journal of Wuhan University. All rights reserved.

Characterization and Source Apportionment of PM in Handan—A Case Study during the COVID-19

Handan is a typical city affected by regional particulate pollution. In order to investigate particulate matter (PM) characterization, source contributions and health risks for the general populations, we collected PM samples at two sites affected by a pollution event (12–18 May 2020) during the COVID-19 pandemic and analyzed the major components (SNA, OCEC, WSIIs, and metal elements). A PCA-MLR model was used for source apportionment. The carcinogenic and non-carcinogenic risks caused by metal elements in the PM were assessed. The results show that the renewal of old neighborhoods significantly influences local PM, and primarily the PM10;the average contribution to PM10 was 27 μg/m3. The source apportionment has indicated that all other elements came from dust, except Cd, Pb and Zn, and the contribution of the dust source to PM was 60.4%. As PM2.5 grew to PM10, the PM changed from basic to acidic, resulting in a lower NH4+ concentration in PM10 than PM2.5. The carcinogenic risk of PM10 was more than 1 × 10−6 for both children and adults, and the excess mortality caused by the renewal of the community increased by 23%. Authorities should pay more attention to the impact of renewal on air quality. The backward trajectory and PSCF calculations show that both local sources and short-distance transport contribute to PM—local sources for PM10, and short-distance transport in southern Hebei, northern Henan and northern Anhui for PM2.5, SO2 and NO2. © 2023 by the authors.

Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation

Lupus nephritis (LN) is one of the most common and serious clinical manifestations of systemic lupus erythe-matosus (SLE), which causes serious damage to the kidneys of patients. To effectively assist the pathological diagnosis of LN, many researchers utilize a scheme combining multi-threshold image segmentation (MIS) with metaheuristic algorithms (MAs) to classify LN. However, traditional MAs-based MIS methods tend to fall into local optima in the segmentation process and find it difficult to obtain the optimal threshold set. Aiming at this problem, this paper proposes an improved water cycle algorithm (SCWCA) and applies it to the MIS method to generate an SCWCA-based MIS method. Besides, this MIS method uses a non-local means 2D histogram to represent the image information and utilizes Renyi's entropy as the fitness function. First, SCWCA adds a sine initialization mechanism (SS) in the initial stage of the original WCA to generate the initial solution to improve the population quality. Second, the covariance matrix adaptation evolution strategy (CMA-ES) is applied in the population location update stage of WCA to mine high-quality population information. To validate the excellent performance of the SCWCA-based MIS method, the comparative experiment between some peers and SCWCA was carried out first. The experimental results show that the solution of SCWCA was closer to the global optimal solution and can effectively deal with the local optimal problems. In addition, the segmentation experiments of the SCWCA-based MIS method and other equivalent methods on LN images showed that the former can obtain higher-quality segmented LN images.

Digital entrepreneurial intentions and actions in China during the COVID-19 pandemic with policy implications

Despite the rapid development of digital start-ups and market expansion in China, there have been challenges for developing digital business in recent years. Unique economic, institutional, and social factors, as well as the recent COVID-19 pandemic, influence the digital entrepreneurs and their businesses. Yet, the literature on the changing digital entrepreneurial behaviour during the COVID-19 pandemic remains limited. The intentions and behaviours of these entrepreneurs in relation to their digital start-ups and the impact caused by exogenous changes require deeper investigation. By adopting an intention-based social cognitive perspective, this study examines the factors influencing digital entrepreneurs' intentions and actions in managing their start-ups. We also present a holistic framework with regard to the changing entrepreneurial behaviour and policy implications for the development of digital start-ups.

The development of mental health care in Guangzhou, China

As an upper-middle-income country, China has a vast territory, a large population and substantial variations in the nature of mental health service provision. Guangzhou, one of the five biggest cities in China, is considered as indicative of the current development of mental health services in a more economically developed part of China. In this paper, we first introduce mental health services in Guangzhou and their historical development: from the rising development of mental health care (1949 to the mid-1960s), the stagnant development period of the psychiatric hospitals (1966-1976), the rapid development period of the psychiatric hospitals (1978 to the 1990s), to the integrated development period of community mental health care (2000-2020). This historical trajectory partly reflects the development of mental health services across China. Second, from the public health perspective, we introduce the current mental health care delivery system and resources in Guangzhou, including the psychiatric specialist hospitals, psychiatric units of general hospitals, and community mental health care. Third, based on the current mental health service system, we highlight community mental health care in Guangzhou. We will describe these elements of the mental health system under the following headings: organizational setup, management, and services. We also explore the design of community mental health service models and the challenges in the post-covid-19 era. Finally, we provide considerations and recommendations for mental health service delivery in Guangzhou in the future.Copyright © 2023 The Authors

Dynamic spillovers between clean energy and non-ferrous metals markets in China: A network-based analysis during the COVID-19 pandemic

Due to the close production link between clean energy and non-ferrous metals, their price and market dynamics can easily affect one another through production costs. Furthermore, with the increased financialization of clean energy and non-ferrous metals markets, investment risk can easily spread between them. Therefore, this paper intends to explore the risk contagion between the two markets through the spillover index model and the minimum spanning tree (MST) method. Employing the data collected in China, this paper quantifies the magnitude of risk transfer by the volatility spillovers of eight clean energy stock markets as identified in The Energy Conservation and Environmental Protection Clean Industry Statistical Classification 2021 and the eight corresponding non-ferrous metals futures markets, while fully considering the heterogeneity between sub-markets. First, we find that risk is mainly transmitted from clean energy to non-ferrous metals. Second, this paper identifies not only the most influential market but also the shortest path of risk contagion based on the MST topology analysis. Last, the empirical results show that the COVID-19 has increased the scale of risk transmission between the two markets and their connectivity. During the COVID-19 period, the shortest path between the two markets shifted from "hydropower–gold” to "smart grid–zinc”, and the systematically influential markets correspondingly become smart grid and zinc. The results obtained in this paper might have practical implications for policymakers seeking to achieve effective risk management, which could also facilitate investors for diversification benefits. © 2023 Elsevier Ltd

Integrated wearable smart sensor system for real-time multi-parameter respiration health monitoring

Monitoring respiration is vital for personal diagnosis of chronic diseases. However, the existing respiratory sensors have severe limitations, such as single function, finite detection parameters, and lack of smart signal analysis. Here, we present an integrated wearable and low-cost smart respiratory monitoring sensor (RMS) system with artificial intelligence (AI)-assisted diagnosis of respiratory abnormality by detecting multi-parameters of human respiration. Coupling with intelligent analysis and data mining algorithms embedded in a phone app, the lighter system of 7.3 g can acquire real-time self-calibrated parameters, including breathing frequency, apnea hypopnea index (AHI), vital capacity (VC), peak expiratory flow (PEF), and other respiratory indexes with an accuracy >95.21%. The data can be wirelessly transferred to the user's data cloud terminal. The RMS system enables comprehensive multi-physiological parameters analysis for auxiliary diagnosing and classifying diseases, including sleep apnea, rhinitis, and chronic lung diseases, as well as rehabilitation of COVID-19, and exhibits advantages of portable healthcare. © 2022 The Authors

Three-Way Group Decision Making Based on Evidential Reasoning with Probabilistic Linguistic Term Assessments

In consideration of the different importance degrees that may be assigned to all possible linguistic terms, this paper investigates a novel three-way group decision-making method based on the probabilistic linguistic term set (PLTS) information systems. We first construct PLTS information systems based on multiple attributes. Considering the reliabilities of the experts, we determine the weights of the experts by the similarities of the information provided by the expert with regard to other experts. Subsequently, using the evidential reasoning (ER) method, we aggregate the information provided by all experts and obtain the conditional probability of each object. The introduction of the ER rules and the weights of experts successfully solve the problem of conflict between the evaluation information. Then an approach is presented to calculate loss functions and thresholds, which reduces the subjectivity of the decision-making process. Next, the decision result of each object is deduced based on the minimum-loss principle. Finally, a case study about the selection of mask foundries during the COVID-19 is used to demonstrate the effectiveness of our proposed method. And the superiority of our proposed method are proved by comparative analysis. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

Mortality and prognosis of older patients during COVID-19 pandemic

An ongoing outbreak of pneumonia caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2) started in December 2009 in Wuhan, China, and was gradually spread around the world. This is the third coronavirus infection in two decades, after the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS). It was officially named Corona Virus Disease 2019 (COVID-19) by the World Health Organization (WHO), in February 2020. Since then, the virus has become widespread, turning into a pandemic, in March 2020. Currently, the number of people diagnosed with COVID-19 worldwide has crossed the five million mark, causing more than 300,000 deaths. © Springer Nature Switzerland AG 2020.

Current status of the application of radiodiagnosis and radiotherapy resources in Huai'an City in 2020.

Objective: To investigate the current status of the radiodiagnosis and radiotherapy resources and medical exposure frequencies in Huai'an City. Method(s): According to the monitoring data in the " Radiological Health Information Management Platform of Jiangsu Province" in 2020, a descriptive analysis was conducted on the personnel, equipment, and exposure frequencies in the institutes engaged in radiation-based diagnosis and treatment in Huai'an. Result(s): A total of 181 hospitals were investigated in 2020, including 7 tertiary hospitals(3.9%), 24 secondary hospitals(13.3%), 126 primary hospitals(69.6%), and 24 ungraded hospitals(13.3%). There were 1 397 radiation workers, including 948 males and 449 females, and 459 pieces of radiation equipment, including 420 for radio diagnosis, 13 for radiotherapy, 4 for nuclear medicine, and 22 for interventional radiology. The hospitals of different grades greatly differed in the number of radiation workers and equipment. There were 80.1, 20.9, 2.2, and 2.0 radiation workers and 15.7, 5.5, 1.4, and 1.6 pieces of radiation equipment in the tertiary hospitals, secondary hospitals, primary hospitals, and ungraded hospitals, respectively on average. There were 2 903 246, 27 648, 8 087, and 17 874 receiving radiodiagnosis, radiotherapy, nuclear medicine, and interventional radiology, respectively in 2020. The medical exposure frequencies in the whole city were calculated to be 599.50 per 1 000 population, including 334.59 treated with conventional X-rays, 239.82 receiving CT scan, 14.18 for breast and dentistry, 3.62 receiving interventional radiology, 5.61 treated with radiotherapy, and 1.69 receiving nuclear medicine. Conclusion(s): The radiodiagnosis and radiotherapy resources are unbalanced in the hospitals of different grades in Huai'an City. Therefore, it is necessary to pay more attention to the reasonable allocation of medical resources. Meanwhile, radiation workers in secondary hospitals may be more prone to job burnout. Facing the normalization of the prevention and control of COVID-19 epidemic, the supervision and management of radiological health should be strengthened to ensure the legitimacy of radiological diagnosis and radiotherapy and theoptimization of protection. The frequencies of medical exposure are consistent with the local economic level. To analyze the differences in medical exposure frequencies and predict the developmental trends in different regions, it is necessary to establish a more accurate prediction model.Copyright © 2021 by the Chinese Medical Association.

A Staging Prediction Model for COVID-19 Pandemic under Strong Public Health Interventions

With the extensive implementation of the strong public health interventions in China, many models proposed to predict COVID-19 epidemic are no longer applicable to the current epidemic development. In this paper, a COVID-19 prediction method is proposed based on a staging SEITR model with consideration of strong public health interventions in China. The method simulates preventive and control measures such as mass nucleic acid testing and quarantine of close contacts by introducing the role of Isolates and the transformation of Exposed to Isolated. The experimental evaluation uses real epidemic data from six cities including Nanjing, Yangzhou, and etc. The accuracy of prediction for total number of infections reaches 95.8% with the data of the first 15 days of the outbreak. In addition, the prediction accuracy of the end of the pandemic is 95.07%. These show that the proposed method can effectively predict the course of the epidemic and it is practical for relevant departments to formulate reasonable prevention and control measures. © 2022 IEEE.

Global issues in obstetric anaesthesia: perspectives from South Africa, Japan, China, Latin America and North America.

South Africa is classified as a low- and middle-income country, with a complex mixture of resource-rich and resource-limited settings. In the major referral hospitals, the necessary skill level exists for the management of complex challenges. However, this contrasts with the frequently-inadequate skill levels of anaesthesia practitioners in resource-limited environments. In Japan, obstetricians administer anaesthesia for 40% of caesarean deliveries and 80% of labour analgesia. Centralisation of delivery facilities is now occurring and it is expected that obstetric anaesthesiologists will be available 24 h a day in centralised facilities in the future. In China, improvements in women's reproductive, maternal, neonatal, child, and adolescent health are critical government policies. Obstetric anaesthesia, especially labour analgesia, has received unprecedented attention. Chinese obstetric anaesthesiologists are passionate about clinical research, focusing on efficacy, safety, and topical issues. The Latin-American region has different landscapes, people, languages, and cultures, and is one of the world's regions with the most inequality. There are large gaps in research, knowledge, and health services, and the World Federation of Societies of Anaesthesiologists is committed to working with governmental and non-governmental organisations to improve patient care and access to safe anaesthesia. Anaesthesia workforce challenges, exacerbated by coronavirus disease 2019, beset North American healthcare. Pre-existing struggles by governments and decision-makers to improve health care access remain, partly due to unfamiliarity with the role of the anaesthesiologist. In addition to weaknesses in work environments and dated standards of work culture, the work-life balance demanded by new generations of anaesthesiologists must be acknowledged.

Vulnerability to COVID-19 Infection in Milwaukee: Effects of Individual and Neighborhood Characteristics

This study focuses on variation of the prevalence rate of COVID-19 over time by age and race/ethnicity and how neighborhood social vulnerability affects the COVID-19 prevalence in the whole epidemic as well as its three consecutive sub-waves in Milwaukee County, Wisconsin. It found that the highest prevalence rate was for young adults (18-44). Hispanics and Asians were more likely to be infected than were non-Hispanic whites and African Americans. The high neighborhood social vulnerability was associated with greater risk of infection especially for persons over age 25, for Hispanics and Asians. High prevalence rates were significantly and strongly associated with all major factors of the social vulnerability in early stage of the pandemic, especially with the factor associated with Hispanic and immigrant population. Throughout the epidemic, the Hispanic/immigrant and African American factors had a reduced but still significant effect, but the socioeconomic factor was not significant and the explained variance across neighborhoods was smaller.

A privacy-preserving blockchain-based tracing model for virus-infected people in cloud

The outbreak of COVID-19 has exposed the privacy of positive patients to the public, which will lead to violations of users' rights and even threaten their lives. A privacy-preserving scheme involving virus-infected positive patients is proposed by us. The traditional ciphertext policy attribute-based encryption (CP-ABE) has the features of enhanced plaintext security and fine-grained access control. However, the encryption process requires the high computational performance of the device, which puts a high strain on resource-limited devices. After semi-honest users successfully decrypt the data, they will get the real private data, which will cause serious privacy leakage problems. Traditional cloud-based data management architectures are extremely vulnerable in the face of various cyberattacks. To address the above challenges, a verifiable ABE scheme based on blockchain and local differential privacy is proposed, using LDP to perturb the original data locally to a certain extent to resist collusion attacks, outsourcing encryption and decryption to corresponding service providers to reduce the pressure on mobile terminals, and deploying smart contracts in combination with blockchain for fair execution by all parties to solve the problem of returning wrong search results in a semi-honest cloud server. Detailed security proofs are performed through the defined security goals, which shows that the proposed scheme is indeed privacy-protective. The experimental results show that the scheme is optimized in terms of data accuracy, computational overhead, storage performance, and fairness. In terms of efficiency, it greatly reduces the local load, enhances personal privacy protection, and has high practicality as well as reliability. As far as we know, it is the first case of applying the combination of LDP technology and blockchain to a tracing system, which not only mitigates poisoning attacks on user data, but also improves the accuracy of the data, thus making it easier to identify infected contacts and making a useful contribution to health prevention and control efforts. © 2022 Elsevier Ltd

Urate-lowering therapy adherence and the association with medication beliefs, self-efficacy, depression, anxiety, and COVID-19 pandemic-related concern in Chinese gout patients

Background: Under the current situation of COVID-19 pandemic, more medical resources are assigned to the prevention and control of the pandemic, while relatively less attention is paid to patients with chronic diseases. Previous studies reported that COVID-19 deaths were mainly observed among male patients with multiple comorbidities, and the major comorbidities were hypertension, diabetes, coronary heart disease, cerebral infarction, and chronic bronchitis, all of which are chronic diseases. As one of the most common chronic diseases that occurs in male, no report regarding how COVID-19 impacts gout patients psychologically due to the general susceptibility, their mental state and willingness to adhere to ULT treatment and the actual ULT adherence so far. This study aims to assess urate-lowering therapy adherence and the relationship with medication beliefs, self-efficacy, depression, anxiety, and COVID-19 pandemic-related concern in Chinese gout patients during the COVID-19 outbreak. Method(s): The cross-sectional study employed a total of 101 gout patients receiving urate-lowering therapy to evaluate adherence, medication beliefs, self-efficacy, depression, anxiety, and COVID-19 pandemic-related concern via a mobile app-based questionnaire. Statistical analysis was performed using SPSS 22.0. Result(s): 101 valid responses were included in the statistical analysis. Patients' adherence rate to urate-lowering therapy during the COVID-19 outbreak was 22.8%, higher than that in normal periods (9.6%). Compared with adherent groups, non-adherent gout patients had shorter disease duration, lower self-efficacy, lower Necessity about urate-lowering therapy score, higher Concerns about urate-lowering therapy score, and smaller Necessity-Concerns differential. Depression and anxiety rates (3.0% and 5.0%, respectively) during the COVID-19 break were lower than that in normal periods. Additionally, depression, anxiety, as well as COVID-19 pandemic-related concern (27.7%) were not related to ULT adherence. Conclusion(s): Adherence rate to urate-lowering therapy in Chinese gout patients during the COVID-19 outbreak was 13.2%, higher than normal times, but still very poor. Except for a little concern about being more susceptible to the virus, patients' mental state is relatively good. While the country puts great efforts in COVID-19 prevention and control, attention must also be paid to the medication management of patients with chronic diseases such as gout.

Identifying fire safety in hospitals: Evidence from Changsha, China

Since the outbreak of the COVID-19 pandemic, fires occurred frequently in hospitals managing COVID-19, and caused over 279 deaths. Fire safety in hospitals should be identified clearly and taken seriously. Fire probability and fire service coverage for hospitals from a national perspective in China were analyzed in this study. Calculated with the generalized Barrois model, the annual fire frequency of hospital building exceeds 0.5, when its floor area reaches approximately 180,000 m2. Based on the number of hospital fires in Changsha and that of hospitals in China from 2014 to 2017, the average annual fire probability of a hospital in China was calculated to be 0.017. The total effective coverage rate (TECR) of fire service for hospitals in Changsha, China was esti-mated to be between 11.82 % and 25.74 %, based on real-time travel times extracted from the Baidu Map. The TECR of national fire service for hospitals was estimated to be between 14.18 % and 30.89 %, according to the ratio of the number of hospitals and the number of fire stations in China to that in Changsha. Currently, recruiting medical staff as fire volunteers can quickly improve fire safety in hospitals for a low cost.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).