[Cross-sectional study on the use of masks among occupational groups with high-risk positions for overseas import and pollution transmission].

Objective: To investigate the wearing of masks and the knowledge of masks among high-risk positions for overseas import and pollution transmission. Methods: From May 14 to 17, 2022, a convenient sampling method was used to conduct an online survey among 963 workers in high-risk positions for overseas import and pollution transmission in Beijing. The behaviors of individual use and wearing masks, the distribution and supervision of the unit, the knowledge of personal mask protection and the subjective feelings of wearing masks were analyzed. The χ(2) test and logistic regression model were used to analyze the influencing factors of the correct selection of masks. Results: The majority of the workers in high-risk positions for overseas import and pollution transmission were male (86.0%, 828/963), age concentration in 18-44 years old (68.2%, 657/963), and the majority of them had college or bachelor degrees (49.4%, 476/963). 79.4%(765/963) of the workers chose the right type of masks, female, 45-59 years old and high school education or above were the risk factors for correct selection of masks (P <0.05). Workers had good behaviors such as wearing/removing masks, but only 10.5% (101/963) could correctly rank the protective effect of different masks. 98.4% (948/963) of the workers believed that their work units had provided masks to their employees, and 99.1% (954/963) and 98.2%(946/963) of them had organized training and supervision on the use of masks, respectively. 47.4%(456/963) of the workers were uncomfortable while wearing masks. Conclusion: The overall selection and use of masks among occupational groups in high-risk positions for overseas import and pollution transmission in China need to be further standardized. It is necessary to strengthen supervision and inspection on the use of masks among occupational groups, and take improvement measures to improve the comfort of wearing masks.

MIMIC model of teachers and students attitudes towards online learning during Covid-19: A gender perspective

Academic institutions around the globe have shifted to online learning because of the unpredictable spread of COVID-19. The present study aimed to compare teachers' and students' attitudes towards online learning during the pandemic and to examine the effects of gender differences on their attitudes. In study 1, we adapted the Test of eLearning Related Attitudes for Pakistani students in three steps: expert review, piloting, and validation. The individual and collective expert review was performed to adapt the teacher version into the student version using the Technique for Research of Information by the Animation of a Group of Experts (TRIAGE). We tested three sets of measurement invariance models for participants' status and gender in study 2. Data were collected from 289 university teachers (men = 158, women = 131) and 444 undergraduate students (boys = 156, girls = 287). The results demonstrated that both groups had highly positive yet different attitudes towards online learning. Teachers were more satisfied than students. Model fit was poor, and the overall factor structure, factor loadings, and intercepts varied across groups. Intergroup gender invariance illustrated heterogeneity in attitudes towards online learning favoring men teachers and boy students. Study strengths and implications for the promotion of a positive experience of online learning are discussed.

A feature extraction and deep learning approach for network traffic volume prediction considering detector reliability

Accurate traffic volume prediction plays a crucial role in urban traffic control by relieving congestion through improved regulation of traffic volume. Network‐level traffic volume prediction and detector failure have rarely been considered in the literature. This paper proposes a framework based on long short‐term memory and the multilayer perceptron that can predict network‐level traffic volumes even with detector failure. A profile model learns the profile of the detector's signature (traffic pattern). Detectors with similar profiles are considered to have similar traffic patterns and are grouped into a cluster. Failed detectors can obtain reference information from similar detectors in the same cluster without additional information. A predictive model is developed for each cluster. The proposed method is validated using Japan Road Traffic Information Center data for three cities. The computational results indicate that the proposed method performs well both on typical days and atypical days (the COVID‐19 lockdown period and the 2021 Tokyo Olympics). Further, it considers detector reliability: the increase in mean absolute error is less than 1 veh/5 min when the probability of detector failure increases to 20%. [ FROM AUTHOR] Copyright of Computer-Aided Civil & Infrastructure Engineering is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.

The mediating role of social media fatigue on social media usage behaviour of the ageing society during the COVID-19 pandemic

At the peak of the COVID-19 pandemic, research on social media in the Malaysian context focused on its benefits and overlooked its drawbacks. To investigate this, we looked at an ageing society whose psychological health was severely affected during the pandemic. This study developed a model based on the Stressor-Strain-Outcome (SSO) framework that predicts factors that prompt passive social media use in Malaysia's ageing society during the COVID-19 pandemic. Convenient sampling was utilised to collect responses from 389 Malaysian older adults through an online survey. The direct effects of stressors, including information overload, communication overload, complexity, privacy, and fear of missing out on the strain of social media fatigue, and indirect effects on the outcome of passive social media use were investigated. For the assessment of the study model, partial least squares structural equation modelling (PLS-SEM) was applied. Out of 11 hypotheses, four direct and three indirect hypotheses were accepted. The study findings did not support the direct and indirect effects of privacy and fear of missing out on social media fatigue and passive social media use, respectively. Findings reveal complexity as the more significant factor influencing social media fatigue, and indirectly, contributing towards the passive use of social media. This study contributes to understanding how social media interaction affects an ageing society during the pandemic lockdown. Despite widespread interest in this field, research on ageing populations concerning social media effects and pandemics is still in its early stages in Malaysia. The study's conclusion offers a thorough examination of its limitations and provides valuable recommendations for future research endeavours. © SEARCH Journal 2023.

Change in anti‐COVID‐19 behaviour and anti‐immigrant prejudice during the COVID‐19 pandemic: Longitudinal evidence from five European countries

During a pandemic, it is vital to identify factors that motivate individuals to behave in ways that limit virus transmission (i.e., anti‐COVID‐19 behaviour). Fear has been suggested to motivate health‐oriented behaviour, yet fear of the virus (i.e., fear of COVID‐19) could have unintended consequences, such as an increase in anti‐immigrant prejudice. In a three‐wave longitudinal study (NT1 = 4275) in five European countries from April to October 2020, we investigated how social norms, the impact of the pandemic on individuals, and intergroup contact affected fear of COVID‐19 and—or in turn—anti‐COVID‐19 behaviour and prejudice towards immigrants. A latent change score model—distinguishing between intra‐ and inter‐individual changes in outcomes—indicated that fear of COVID‐19 influenced neither anti‐COVID‐19 behaviour nor prejudice. Anti‐COVID‐19 behaviour was increased by anti‐COVID‐19 norms (i.e., belief that others perform anti‐COVID‐19 behaviours), while prejudice was influenced by positive and negative direct and mass‐mediated intergroup contact.

E-Learning Readiness Assessment Using Machine Learning Methods

Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.

Association studies between COVID-19 and SSc-ILD

Severe COVID-19 patients may develop pulmonary fibrosis, similar to SSc-ILD disease, suggesting a potential link between the two diseases. However, there are limited treatment options for SSc-ILD-type diseases. Therefore, investigating pathological markers of the two diseases can provide valuable insights for treating related conditions. RNA sequencing technology offers high throughput and precision. However, the bimodal nature of RNA-Seq data cannot be accurately captured by commonly used algorithms such as DESeq2. To address this issue, the Beta-Poisson model has been developed to identify differentially expressed genes. Unlike the classical DESeq2 algorithm, the Beta-Poisson model introduces a Beta distribution to construct a new hybrid distribution in place of the Gamma distribution of the Gamma-Poisson distribution, effectively characterizing the bimodal features of RNA-Seq data. The transcriptomes of SARS-CoV infection and SSc-ILD disease in the lung epithelial cell dataset were analyzed to identify common differentially expressed genes of SARS-CoV and SSc-ILD disease. Gene function and signaling pathway enrichment analysis and protein-protein interaction (PPI) network were used to identify common pathways and drug targets for SSc-ILD with COVID-19 infection. The results show that there are 50 differentially expressed genes in common between COVID-19 and SSC-ILD. The functions of these genes are mainly enriched in immune system response, interferon signaling pathway and other related signaling pathways, and enriched in biological processes such as cell defense response to virus and interferon regulation. Based on the detection of hub genes based on PPIs network, it is predicted that STAT1, ISG15, IRF7, MX1, EIF2AK2, DDX58, OAS1, OAS2, IFIT1 and IFIT3 are the key genes involved in the pathological phenotype of the two diseases. Based on the key genes, the interaction of transcription factor (TF) and miRNA with common differentially expressed genes is also identified. The possible pathological markers of the two diseases and related molecular regulatory mechanisms of disease treatment are revealed to provide theoretical basis for the treatment of the two diseases. © 2023 Editorial Office of Journal of Shenzhen University. All rights reserved.

Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions

Large language models have abilities in creating high-volume human-like texts and can be used to generate persuasive misinformation. However, the risks remain under-explored. To address the gap, this work first examined characteristics of AI-generated misinformation (AI-misinfo) compared with human creations, and then evaluated the applicability of existing solutions. We compiled human-created COVID-19 misinformation and ed it into narrative prompts for a language model to output AI-misinfo. We found significant linguistic differences within human-AI pairs, and patterns of AI-misinfo in enhancing details, communicating uncertainties, drawing conclusions, and simulating personal tones. While existing models remained capable of classifying AI-misinfo, a significant performance drop compared to human-misinfo was observed. Results suggested that existing information assessment guidelines had questionable applicability, as AI-misinfo tended to meet criteria in evidence credibility, source transparency, and limitation acknowledgment. We discuss implications for practitioners, researchers, and journalists, as AI can create new challenges to the societal problem of misinformation. © 2023 Owner/Author.

Impact of ICER Assessments on Payer Decision Making in the United States

Objectives: To evaluate how payers utilize Institute for Clinical and Economic Review (ICER) assessments to inform coverage or formulary decisions. Method(s): Double-blinded, web-based survey was fielded through Xcenda's research panel, the Managed Care Network, from June to July 2022. Result(s): A total of 51 payers from health plans (n=27), integrated delivery networks (n=12), and pharmacy benefit managers (n=12) participated in the survey. When assessing the usefulness of ICER's value assessment framework (VAF) to inform formulary decisions within their organizations, 57% of payers indicated it was extremely/very useful, 33% indicated somewhat useful, and 10% indicated not at all/not very useful. Most respondents (73%) agreed that ICER assessments are aligned with their organization's internal assessment. Utilization of ICER's VAF was most prevalent in high-cost drug or disease states (78%), rare/orphan disease states (71%), and oncology/hematology disease states (67%). Payers reported less use in primary care disease states (29%), COVID-19 (8%), and digital therapeutics (4%). In the last 24 months, 20% of payers reported ICER's recommendations often influenced coverage decisions, 59% indicated occasional influence, and 22% indicated no influence. In the last 24 months, payers indicated the top 5 ICER assessments that influenced their coverage decisions included high cholesterol (38%), Alzheimer's disease (36%), atopic dermatitis (33%), multiple myeloma (31%), and chemotherapy-induced neutropenia (28%). ICER assessments that were less impactful included beta thalassemia (3%), digital health technologies (3%), and supervised injection facilities (3%). Payers reported using ICER assessments to inform both expanded and restricted coverage decisions. Conclusion(s): Payers find ICER's VAF useful to inform their organization's formulary decisions. ICER's assessments often align with payers' internal assessments and are most frequently utilized for high-cost drugs or disease states. Payers indicate ICER assessments have affected both expansion and restriction in their coverage policies.Copyright © 2023

Analysis of social and psychological factors influencing the treatment of patients with chronic kidney disease under the regular epidemic prevention and control of coronavirus disease 2019.

Objective To explore the sociodemographic and psychological factors influencing the continuity of treatment of patients with chronic kidney disease under the regular epidemic prevention and control of coronavirus disease 2019 (COVID-19). Methods A total of 277 patients with chronic kidney disease who were admitted to Department of Nephrology, The First Affiliated Hospital of Naval Medical University (Second Military Medical University) from Apr. 2020 to Mar. 2021 were enrolled and divided into 3 groups: non-dialysis group (n=102), hemodialysis (HD) group (n=108), and peritoneal dialysis (PD) group (n=67). All patients were investigated by online and offline questionnaires, including self-designed basic situation questionnaire, self-rating anxiety scale (SAS), and self-rating depression scale (SDS). The general sociodemographic data, anxiety and depression of the 3 groups were compared, and the influence of sociodemographic and psychological factors on the interruption or delay of treatment was analyzed by binary logistic regression model. Results There were significant differences in age distribution, marital status, occupation, medical insurance type, caregiver type, whether there was an urgent need for hospitalization and whether treatment was delayed or interrupted among the 3 groups (all P0.05). The average SAS score of 65 PD patients was 38.15+/-15.83, including 53 (81.5%) patients without anxiety, 7 (10.8%) patients with mild anxiety, and 5 (7.7%) patients with moderate to severe anxiety. The average SAS score of 104 patients in the HD group was 36.86+/-14.03, including 81 (77.9%) patients without anxiety, 18 (17.3%) patients with mild anxiety, and 5 (4.8%) patients with moderate to severe anxiety. There were no significant differences in the mean score of SAS or anxiety severity grading between the 2 groups (both P0.05). The mean SDS scores of 65 PD patients were 53.42+/-13.30, including 22 (33.8%) patients without depression, 21 (32.3%) patients with mild depression, and 22 (33.8%) patients with moderate to severe depression. The mean SDS scores of 104 patients in the HD group were 50.79+/-10.76, including 36 (34.6%) patients without depression, 56 (53.8%) patients with mild depression, and 12 (11.6%) patients with moderate to severe depression. There were no significant differences in mean SDS scores or depression severity grading between the 2 groups (both P0.05). The results of intra-group comparison showed that the incidence and severity of depression were higher than those of anxiety in both groups. Multivariate binary logistic regression analysis showed that high school education level (odds ratio OR=5.618, 95% confidence interval CI) 2.136-14.776, P0.01), and unmarried (OR=6.916, 95% CI 1.441-33.185, P=0.016), divorced (OR= 5.588, 95% CI 1.442-21.664, P=0.013), urgent need for hospitalization (OR=8.655, 95% CI 3.847-19.476, P0.01) could positively promote the continuity of treatment in maintenance dialysis patients under the regular epidemic prevention and control of COVID-19. In the non-dialysis group, no sociodemographic and psychological factors were found to be associated with the interruption or delay of treatment (P0.05). Conclusion Education, marital status, and urgent need for hospitalization are correlated with the continuity of treatment in patients with chronic kidney disease on maintenance dialysis.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Perception of Hurricane and COVID-19 Risks for Household Evacuation and Shelter Intentions

The COVID-19 pandemic presented new challenges for scholars and government officials to predict people's evacuation decisions under a conflicting natural disaster. In this study, we examined households' evacuation and shelter intentions given the potential conflicts between the perceived risks from a hurricane and the coexisting public health crisis. We surveyed households living inside hurricane evacuation zones in Florida during the 2020 hurricane season. Data were first used to examine the evacuation and shelter intentions before and during the pandemic. We then measured respondents' hurricane and COVID-19 risk perception, respectively. The impacts of both risk perceptions on respondents' hurricane evacuation intentions were explored. We found that when people felt unsafe to stay home for a Category 2, 3, or 4 hurricane, their intended evacuation was about the same before and during the pandemic regardless of their COVID-19 risk perception. The COVID-19 risk perception, however, significantly lowered the evacuation intention for a Category 1 hurricane. It also significantly influenced evacuees' preference for nontraditional shelters such as government-contracted hotels. The results of our study have practical implications for emergency management and public health governance. Our study also provides insights into decision-making under the conflict between natural hazards and infectious diseases. (English) [ FROM AUTHOR] La pandemia del COVID-19 planteó nuevos retos a los eruditos y funcionarios gubernamentales para predecir las decisiones de evacuación de la gente sometida a un desastre natural conflictivo. En este estudio, examinamos la evacuación e intenciones de albergue de familias teniendo en cuenta potenciales conflictos entre los riesgos percibidos de un huracán y las crisis coexistentes en la salud pública. Encuestamos a las familias que residían en las zonas de evacuación por huracanes de la Florida durante la temporada de huracanes del 2020. Los datos se usaron primero para examinar las intenciones de evacuación y de refugio antes y durante la pandemia. Después, medimos la percepción del riesgo de afectaciones por huracanes y COVID-19 de los encuestados, respectivamente. Se exploraron los impactos de ambos tipos de percepciones de riesgo en las intenciones de evacuación, por encuestado. Hallamos que cuando la gente se siente insegura de permanecer en casa frente a huracanes de las categorías 2, 3 y 4, su evacuación intencionada era más o menos la misma de antes y durante la pandemia, sin importar la percepción del riesgo de COVID-19. No obstante, la percepción del riesgo de COVID-19 redujo de manera significativa la evaluación de la intención de evacuación para un huracán de categoría 1. Eso también influyó significativamente en la preferencia de los evacuados por refugios no tradicionales, como los hoteles contratados por el gobierno. Los resultados de nuestro estudio tienen implicaciones prácticas en el manejo de las emergencias y la gobernanza de la salud pública. También proporciona nuestro estudio nuevas visiones en lo que concierne a toma de decisiones bajo condiciones de conflicto entre las catástrofes naturales y la enfermedades contagiosas. (Spanish) [ FROM AUTHOR] 新冠肺炎流行病与其它自然灾害相互冲突, 给专家和政府预测人群的疏散决定提出了新的挑战。我们探讨了家庭疏散和避难的意愿, 考虑了飓风的感知风险与公共卫生危机之间的潜在冲突。我们调查了2020年飓风季节美国佛罗里达州飓风疏散区内的家庭。首先, 基于数据探讨了流行病之前和期间的疏散和避难意愿。然后, 我们分别度量了受访者对飓风和新冠肺炎的风险感知。探讨了这两种风险感知对受访者飓风疏散意愿的影响。我们发现, 在2、3或4级飓风中, 当人们认为居家危险时, 不管如何感知新冠肺炎风险, 人们在新冠肺炎流行之前和期间的疏散意愿大致相同。然而, 新冠肺炎风险感知显著降低了1级飓风的撤离意愿。它还显著影响了疏散者对非传统庇护所（如, 政府指定酒店）的偏好。我们的研究结果, 对应急管理和公共卫生治理具有实际意义。我们的研究, 还为自然灾害和传染病相互冲突情况下的决策提供了见解。 (Chinese) [ FROM AUTHOR] Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

PSYCHOLOGICAL DISTRESS OVER 12 MONTHS POST-DIAGNOSIS IN A NATIONAL EARLY INFLAMMATORY ARTHRITIS COHORT

BackgroundIn inflammatory arthritis patients, the concomitant decline of their mental wellbeing is an increasing concern[1,2]. It is important to not only describe the trajectory of psychological distress in early disease stages, but also understand which clinical outcome measures are most associated with these changes.ObjectivesUsing data from the National Early Inflammatory Arthritis Audit (NEIAA), we assessed trends in psychological wellbeing over 12 months after initial diagnosis and mapped these against clinical outcomes to identify significant associations.MethodsNEIAA collects data from patients referred with suspected early inflammatory arthritis in rheumatology services in England and Wales. We used data provided by 20,472 patients eligible for follow-up (diagnosis of inflammatory arthritis) between May 1st, 2018, and April 1st, 2022. Data items included baseline demographics e.g., age and gender, and clinical variables e.g., rheumatic disease comorbidity index (RDCI), DAS28, and patient reported outcomes.Psychological distress was measured by the sum score of Patient Health Questionnaire Anxiety and Depression Screener (PHQ4ADS). Using mixed effects regression models, we analysed the co-variability of PHQ4ADS with demographic factors and clinical outcomes over 12 months. Time was included as a dummy-coded covariant.ResultsThe analysis included 36% of patients (7,378 out of 20,472) who completed the baseline patient outcome survey. In this cohort, PHQ4ADS scores decreased from a baseline average of 4.7 (CI: [4.6, 4.8]) to 2.62 (CI: [2.5, 2.8]) at 12 months post-diagnosis. The proportion of patients screening positive decreased from 50.0% (CI: [48.9, 51.1]) at baseline to 23.8% (CI: [21.8, 25.9]) at 12 months.At baseline, psychological distress correlated significantly with age, gender, ethnicity, RDCI, prior depression diagnosis, and baseline DAS28 (Figure 1). No significant correlations were found between psychological distress and working diagnosis, seropositivity, or the assessment being recorded after the start of the COVID-19 pandemic. Younger ages were nonlinearly associated with higher distress levels (coefficient per decade: -0.006;p<0.001;CI: [-0.009, -0.003]) (Figure 1a). Distress levels in females were higher than that of males (coefficient: 0.5;p<0.001;CI: [0.4, 0.7]) (Figure 1b). White patients reported lower PHQ4ADS scores compared to non-white patients (coefficient: -0.7;p<0.001;CI: [-1.0, -0.4]) (Figure 1c). Higher distress levels were also associated with higher RDCI (coefficient: 0.2;p<0.001;CI: [0.1, 0.3]) and prior diagnosis of depression (coefficient: 1.8;p<0.001;CI: [1.5, 2.2]) (Figure 1d, 1e). Furthermore, higher baseline DAS28 scores correlated with more severe psychological distress (coefficient: 0.8;p<0.001;CI: [0.7, 0.8]) (Figure 1f).By 12-months, psychological distress decreased significantly overall, which correlated significantly with ethnicity (coefficient: 0.8;p=0.005;CI: [0.3, 1.4]) and baseline DAS28 (coefficient: -0.5;p<0.001;CI: [-0.6, -0.4]). Compared to white patients, the reduction was significantly greater for non-white patients, but the level of distress was no longer different at 12 months (Figure 1c). While those with higher baseline DAS28 showed a greater reduction in psychological distress, the distress levels remained higher at 12 months (Figure 1f).Figure 1.Changes in psychological distress correlated with age, gender, ethnicity, RDCI, prior depression diagnosis, and baseline DAS28.[Figure omitted. See PDF]ConclusionIn this early inflammatory arthritis cohort, mental health burden was high. Age, gender, ethnicity, RDCI, prior depression diagnosis and baseline DAS28 significantly correlated with psychological distress at baseline. Supporting mental health should be a focus of clinical care for this population and it may be beneficial to use an approach that is culturally valid for non-white patients and accounts for multimorbidity.References[1]Euesden, J, et al. Psychosomatic medicine 79.6 (2017): 638.[2]Lwin, MN, et al. Rheumatology and therapy 7.3 (2020): 457-471.AcknowledgementsThe authors would like to thank the Healthcare Quality Improvement Partnership (HQIP) as the commisioner of NEIAA, British Society for Rheumatology as the audit providers, Net Solving as the audit platform developers, and the Wellcome Trust (ST12406) for funding to support L.Z..Disclosure of InterestsLucy Zhao: None declared, James Galloway Speakers bureau: Has received honoraria from AbbVie Celgene, Chugai, Gillead, Janssen, Eli Lilly, Pfizer, Roche, and UCB, Jo Ledingham: None declared, Sarah Gallagher: None declared, Neena Garnavos: None declared, Paul Amlani-Hatcher: None declared, Nicky Wilson: None declared, Lewis Carpenter Consultant of: Statistical consultancy for Pfizer, Kirsty Bannister: None declared, Sam Norton Speakers bureau: Has received honoraria from Janssen and Pfizer.

A Dynamic-Static Dual Input Deep Neural Network Algorithm for Diagnosing COVID-19 by Cough

The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.

The impact of COVID-19 prevention and control measures on residential burglary hotspots: A case study of the core urban areas of Beijing

In order to analyze the impact of COVID-19 prevention and control measures on the hotspots of residential burglary, the data of crimes that occurred during the First Level Response period of Major Public Health Emergencies in Beijing in 2020 and the same period in 2019 were collected, and the changes of hotspots during the two periods were compared by using kernel density estimation and predictive accuracy index. Consequently, the environmental features such as street network, point of interest (POI) diversity, crime locations, and repeat victimization in significantly varied hotspot areas were investigated. The results show that: 1) After the outbreak of the pandemic, the occurrence of residential burglary in the core urban areas of Beijing dropped significantly, and daily occurrence of crimes during the First Level Response period in 2020 decreased by 66.8% compared with the same days in 2019. 2) The eight major hotspots that existed in 2019 apparently declined during the corresponding days in 2020, five of them basically disappeared, and three hotspots weakened. 3) The declined hotspots were generally clustered around traffic hubs, areas with high diversity of POIs, clustered crimes, and repeat victimizations. 4) Home isolation and social restriction strategies implemented during the First Level Response period reduced the opportunities of offenders, and the real-name inspection adopted in public places increased the exposure risk of offenders, which are the main reasons for the hotspots decline during the pandemic. This work has some implications for crime prevention and police resources optimization during the pandemic. © 2023, Editorial office of PROGRESS IN GEOGRAPHY. All rights reserved.

Malnutrition Prevalence and Economic Burden Among US Hospitalized Older Adult Patients with and without COVID-19

Objectives: Malnutrition is a prevalent condition affecting 30-50% of hospitalized patients. Malnutrition is linked to impairments in health outcomes and increased economic burden on healthcare systems. We assessed the prevalence and burden of malnutrition by examining demographic characteristics, Disease Related Group (DRG) payments and associated claims among Medicare inpatients (65+ years) with and without COVID-19. Method(s): Hospital inpatient COVID-19 claims from the Centers for Medicare & Medicaid Services (CMS) Inpatient Prospective Payment System (IPPS) between October 2020 - September 2021 were analyzed. The International Classification of Diseases, Tenth Revision, and Clinical Modification (ICD-10-CM) were used for malnutrition diagnoses. Demographic variables were compared based on the COVID-19 status;economic burden was analyzed by DRG payment of malnutrition cases with and without COVID-19. Result(s): Among 7,394,657 Medicare inpatient claims, only 12% had a documented malnutrition diagnosis. Of these patients, 1.2% had COVID-19. Regardless of COVID-19 status, malnourished patients averaged 75 years of age, and were predominantly female (54%) and White (78%) followed by Black (14%), and Hispanic (2%). Sepsis, kidney failure, and urinary tract infection (UTI) were the most common primary diagnoses in malnourished patients, regardless of COVID-19 status. Malnourished patients with COVID-19 had significantly higher DRG payments ($27,407 vs. $18,327) and increased cost of outlier payment ($3,208 vs. $2,049) compared to those without COVID-19, regardless of other diagnoses. Conclusion(s): Malnutrition diagnosis was confirmed in only 12% of the Medicare inpatients, thus suggesting that malnutrition continues to be underdiagnosed and undertreated - evidenced by high rates of hospitalizations/claims and payments in both COVID-19 and non-COVID-19 cases. It is imperative for hospitals to implement nutrition-focused protocols to identify, diagnose and address malnutrition among all Medicare inpatients regardless of COVID-19 status (and especially among patients with sepsis, kidney failure, and UTI). Nutrition-focused protocols can effectively improve patient health outcomes and reduce healthcare costs.Copyright © 2023

A retrospective study of knowledge management for integrated resorts (IRs) crisis preparedness

PurposeThis study aims to propose a systematic knowledge management model to explore the causal links leading to the organizational crisis preparedness (OCP) level of integrated resorts (IRs) during the COVID-19 pandemic based on the intangible capital of organizational climate, dynamic capability, substantive capability and commitment.Design/methodology/approachThe authors use data obtained from IRs in Macau. The Wuli–Shili–Renli (WSR) approach underpins the study. Structural equation modeling following fuzzy-set qualitative comparative analysis (fsQCA) was used for data processing.FindingsThe results showed that organizational climate has an essential role in IRs preparedness for crises and affects their dynamic capacity, substantive capacity and commitment. The fsQCA results revealed that the relationships between conditions with a higher level of dynamic and substantive capability lead to higher OCP scores.Practical implicationsExecutives should develop systemic thinking regarding organization preparedness in IRs for crisis management. A comprehensive understanding of the IRs' business environment and crises is necessary, as they will require different factor constellations to allow the organization to perform well in a crisis. Financial support for employees could ensure their assistance when dealing with such situations. Rapid response teams should be set up for daily operations and marketing implementation of each level of the IRs management systems.Originality/valueThis study contributes to the extant literature on IRs crisis management in the OCP aspect. The authors constructed a systematic composite picture of organization executives' knowledge management through the three layers of intangible capitals in WSR. Moreover, the authors explored causal links of WSR from symmetric and asymmetric perspectives.

The Impact of COVID-19 Lockdown on Ambient Air Quality in Shanghai, 2022

The COVID-19 lockdown contributes to the improvement of air quality. Most previous studies have attributed this to the reduction of human activity while ignoring the meteorological changes, this may lead to an overestimation or underestimation of the impact of COVID-19 lockdown measures on air pollution levels. To investigate this issue, we propose an XGBoost-based model to predict the concentrations of PM2.5 and PM10 during the COVID-19 lockdown period in 2022, Shanghai, and thus explore the limits of anthropogenic emission on air pollution levels by comprehensively employing the meteorological factors and the concentrations of other air pollutants. Results demonstrate that actual observations of PM2.5 and PM10 during the COVID-19 lockdown period were reduced by 60.81% and 43.12% compared with the predicted values (regarded as the period without the lockdown measures). In addition, by comparing with the time series prediction results without considering meteorological factors, the actual observations of PM2.5 and PM10 during the lockdown period were reduced by 50.20% and 19.06%, respectively, against the predicted values during the non-lockdown period. The analysis results indicate that ignoring meteorological factors will underestimate the positive impact of COVID-19 lockdown measures on air quality. © 2023 by the authors.

The influence of interdisciplinary literacy on learning effects among college students: the mediating effects of online physical education on learning behavior

IntroductionThe development of high-quality physical education curriculums is required in the information age. Interdisciplinary literacy and student learning behavior are two significant factors that affect the quality of teaching and learning. This study explores the relationship between interdisciplinary literacy (IDL) and learning effects (LE) among Chinese college students during the COVID-19 pandemic, as well as the mediating effects of online physical education learning behaviors (OPELB). This research aims to provide a reference for the development of high-quality online physical education. MethodsThe study involved 691 college students from 10 general universities in Shaanxi Province as research subjects. Descriptive statistics, Pearson correlation analysis, multiple regression analysis and Bootstrap testing were used to evaluate the mediating effects. ResultsThere was a significant positive relationship between the three variables of IDL, OPELB, and LE (p < 0.001). Multiple regression analysis found that IDL significantly and positively predicted LE and OPELB (p < 0.001), and OPELB predicted LE (p < 0.001). IDL among college students had a total effect of 0.816 on LE, with OPELB accounting for 22.67% of the mediated effect. DiscussionThis study demonstrates that OPELB has a partial mediating effect on IL and LE, and stable IDL and OPELB improve LE. Therefore, teachers should pay attention to improving students' IDL while encouraging them to develop better OPELB to achieve satisfactory learning outcomes.

Travel behavior adjustment based epidemic spreading model and prediction for COVID-19

Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people' s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took "ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word "ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word "ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers. © 2023 Chinese Physical Society.