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Introduction: Lung cancer is the leading cause of cancer-related death in the US with an estimated 236,740 new cases and 130,180 deaths expected in 2022. While early detection with low-dose computed tomography reduces lung cancer mortality by at least 20%, there has been a low uptake of lung cancer screening (LCS) use in the US. The COVID-19 pandemic caused significant disruption in cancer screening. Yet, little is known about how COVID-19 impacted already low use of LCS. This study aims to estimate LCS use before (2019) and during (2020 and 2021) the COVID-19 pandemic among LCS-eligible population in the US. Method(s): We used population-based, nationally representative, cross-section data from the 2019 (n=4,484), 2020 (n=1,239) and 2021 (n=1,673) Behavioral Risk Factor Surveillance System, Lung Cancer Screening module. The outcome was self-reported LCS use among eligible adults in the past 12 months. For 2019 and 2020, the eligibility was defined based on US Preventive Services Task Force (USPSTF) initial criteria-adults aged 55 to 80 years old, who were current and former smokers (had quit within the past 15 years) with at least 30 pack years of smoking history. For 2021, we used the USPSTF updated criteria- adults aged 50 to 80 years, current and former smokers (who had quit within the past 15 years) with at least 20 pack years of smoking history. We applied sampling weights to account for the complex survey design to generate population estimates and conducted weighted descriptive statistics and logistic regression models. Result(s): Overall, there were an estimated 1,559,137 LCS-eligible respondents from 16 US states in 2019 (AZ, ID, KY, ME, MN, MS, MT, NC, ND, PA, RI, SC, UT, VT, WV, WI), 200,301 LCS-eligible respondents from five states in 2020 (DE, ME, NJ, ND, SD), and 668,359 LCS-eligible respondents from four states in 2021 (ME, MI, NJ, RI). Among 2,427,797 LCS-eligible adults, 254,890;38,875;and 122,240 individuals reported receiving LCS in 2019, 2020 and 2021, respectively. Overall, 16.4% (95% CI 14.4-18.5), 19.4% (95% CI 15.3-24.3), and 18.3% (95% CI 15.6-21.3) received LCS during 2019, 2020, and 2021, respectively. In all years, the proportion of LCS use was higher among adults aged 65-74, insured, those with fair and poor health, lung disease and history of cancer (other than lung cancer). In 2020, a higher proportion of adults living in urban areas reported receiving LCS compared to those living in rural areas (20.36% vs. 12.7%, p=0.01). Compared to non-Hispanic White adults, the odds of receiving LCS was lower among Hispanic adults and higher among Non-Hispanic American Indian/Alaskan Native adults in 2020 and 2021, respectively. Conclusion(s): LCS uptake remains low in the US. An estimated 2,011,792 adults at high-risk for developing lung cancer did not receive LCS during 2019, 2020 and 2021. Efforts should be focused to increase LCS awareness and uptake across the US to reduce lung cancer burden.
ABSTRACT
COVID-19 pandemic is a global crisis resulting in significant mortality and morbidity worldwide. Together with the severe acute respiratory syndrome (SARS) coronavirus which became a pandemic in 2002–2003 and the Middle East respiratory syndrome (MERS) which was surfaced in 2012, the current and novel pathogen-novel coronavirus 2019 is the third highly pathogenic human coronavirus that has emerged in the last two decades with rapid transmissibility. The major concern now is to save lives especially of the vulnerable population and also develop herd immunity which will protect the community as a whole. However, this pandemic has a significant impact on mental health and poses a challenge to an individual's psychological resilience. Patients, health professionals, and the general public are under severe psychological pressure which may lead to numerous psychological problems, such as anxiety, fear, depression, and disturbed sleep. The most prominent psychological symptoms seen are anxiety, depression, and stress-related symptoms apart from drug abuse, domestic violence, and higher rates of suicide. The whole population, i.e., those affected with the infection and those not infected, are equally affected with mental health issues. The health care worker who is at high risk to develop the disease has been reported to have disturbed mental health well-being. Various psychological treatments like awareness talk, demonstrating health coping strategies, dealing effectively with stress along with lifestyle modifications have shown to be helpful in such a situation. Psychological crisis intervention will play a pivotal role in the overall deployment of disease control. A mental health helpline which can provide easy access to mental health professionals and serve as a platform for expert counseling facilities for common people, patients, vulnerable population, and students is the need for this hour. It can help to deal with the fear, anxieties related to the infection and also the anxieties related to the uncertainties in the near future. This chapter will focus on the various medical aspects and mental health challenges during COVID-19 pandemic and the various strategies to overcome them efficiently. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Taylor and Francis Pte Ltd. 2022.
ABSTRACT
The ominous spread of the COVID-19 pandemic is attributed to the droplets respired during coughing, sneezing or speaking. These droplets undergo evaporation to become aerosols, which, along with the larger droplets, are believed to ultimately spread the virus. In this current work, a small, enclosed region like an elevator (containing a COVID infected passenger) is considered where the risk of infection is high as the commonly practiced norm of social distancing is not possible. Numerical simulations are performed using OpenFOAM. Two different types of elevators – one equipped with a sliding door and the other with a collapsible gate, are considered and the change in droplet behavior is examined. Certain parameters pertaining to the risk of virus transmission have been quantified and assessed thoroughly, such as the percentage of droplets floating in the height range from a person's waist height to his mouth height, the radial span of the floating droplets from the infected passenger's mouth. From these parameters, the safety measures to be adopted by other copassengers can be determined. After an extensive study, it has been found that the collapsible gate elevator is safer than the sliding door elevator along with added advantages in the context of disease transmission. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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In a closed-loop supply chain (CLSC), acquiring end-of-life vehicles (ELVs) and their components from both primary and secondary markets has posed a huge uncertainty and risk. Moreover, the constant supply of ELV components with minimization of cost and exploitation of natural resources is another pressing challenge. To address the issues, the present study has developed a risk simulation framework to study market uncertainty/risk in a CLSC. In the first phase of the framework, a total of 12 important variables are identified from the existing studies. The total interpretive structural model (TISM) is used to develop a causal relationship network among the variables. Then, Matriced Impacts Cruoses Multiplication Applique a un Classement is used for determining the nature of relationships (i.e., driving or dependence power). In the second phase, the relationship of TISM is used to derive a Bayesian belief network model for determining the level of risks (i.e., high, medium, and low) associated with the CLSC through the generation of conditional probabilities across 1) multi-, 2) single-, and 3) without-parent nodes. The study findings will help decision-makers in adopting strategic and operational interventions to increase the effectiveness and resiliency of the network. Furthermore, it will help practitioners to make decisions on change management implementation for stakeholders'performance audits on the attributes of the ELV recovery program and developing resilience in the CLSC network. Overall, the present study holistically contributes to a broader investigation of the implications of strategic decisions in automobile manufacturers and resellers. IEEE
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The drug abuse epidemic has been on the rise in the past few years, particularly after the start of COVID-19 pandemic. Our preliminary observations on Reddit alone show that discussions on drugs from 2018 to 2020 increased between a range of 45% to 200%, and so has the number of unique users participating in those discussions. Existing efforts focused on utilizing social media to distinguish potential drug abuse chats from unharmful chats regardless of what drug is being abused. Others focused on understanding the trends and causes of drug abuse from social media. To this end, we introduce PRISTINE (opioid crisis detection on reddit), our work dynamically detects-and extracts evolving misleading drug names from Reddit comments using reinforced Dynamic Query Expansion (DQE) and constructs a textual Graph Convolutional Network with the aid of powerful pre-trained embeddings to detect which type of drug class a Reddit comment corresponds to. Further, we perform extensive experiments to investigate the effectiveness of our model. © 2022 IEEE.
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The infection and life cycle of severe acute respiratory syndrome coronavirus 2 are widely studied, yet multiple gaps exist in the knowledge that affects therapeutic developments against coronavirus disease 2019 (COVID-19). Predominantly caused by a respiratory virus, COVID-19 is not restricted to the respiratory tract but affects multiple organs of the body including the cardiovascular, neurological, immunological, and renal systems. COVID-19 affects all age groups, although the elderly population inherently presenting with multiple comorbidities are disproportionately affected. The majority of the patients experience mild symptoms, although moderate, severe, and critical symptoms occur in a smaller group of patients. Interestingly, the effects of the disease can be acute or chronic and present an ongoing health care challenge. Epigenetic mechanisms of COVID-19 (DNA methylation, histone posttranslational modifications, histone citrullination, etc.) are an emerging field and present enormous potential toward the medical management of COVID-19. Angiotensin converting enzyme 2, an important protein in the cardiovascular system, is a receptor for viral entry into cells, and the epigenetic processes that regulate this protein have been widely studied. Identification of the epitranscriptomic profile has led to the identification of putative biomarkers for disease diagnosis and trials of novel epidrugs for targeted therapy. © 2023 Elsevier Inc. All rights reserved.
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The history of the medical robot is not very far from the first experiment in the 1980s. Nowadays robot in the medical sector plays a vital role in monitoring patient's health condition from distance. This paper aimed at developing an auxiliary medical solution that could provide a wide range of non-invasive diagnoses carried out by an automated robot whose motion can also be controlled manually using either a mobile application or voice command. The authors also incorporate modern features of video conferences and automated patient data management systems using the Internet of Things (IoT) which eventually facilitate medical practitioners in proper investigation from distance. The results of the clinical trial among 6 persons indicated that the robot could measure different health parameters properly using the proposed non-invasive method. The non-invasive results are verified by standard testing equipment and conventional clinical investigation and are also presented in this paper. The developed medical robot having a wide range of functionality could play a significant role in reducing human workload and ensuring timely medical assistance during a challenging crisis pandemic period like COVID-19. © 2023 IEEE.
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Combating fake news on social media is a critical challenge in today's digital age, especially when misinformation is spread regarding vital matters such as the Covid-19 pandemic. Manual verification of all content is infeasible;hence, Artificial Intelligence is used to classify fake news. Our ensemble model uses multiple Natural Language Processing techniques to analyze the truthfulness of the text in tweets. We create custom parameters that analyze the consistency and truthfulness of domains contained in hyperlinked URLs. We then combine these parameters with the results of our deep learning models to achieve classification with greater than 99% accuracy. We have proposed a novel method to calculate a custom coefficient, the Combined Metric of Prediction Uncertainty (CMPU), which is a measure of how uncertain the model is of its classification of a given tweet. Using CMPU, we have proposed the creation of a priority queue following which the tweets classified with the lowest certainty can be manually verified. By manually verifying 3.93% of tweets, we were able to improve the accuracy from 99.02% to 99.77%. © 2022 IEEE.
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The iconic Ganges River dolphin Platanista gangetica gangetica is endemic to the Indian subcontinent and has been classified as the most endangered cetacean due to sharp decline in the population size of this obligatory freshwater animal. The species is vulnerable to multiple anthropogenic activities such as habitat fragmentation caused by construction of structural barriers (dams and barrages) and dredging activities, reduced freshwater flow, huge siltation load, depletion of fish stock, use of vulnerable gears, and lack of public awareness. Geographical expansion of artisanal fishing, poaching for collection of their flesh (used as fish bait) and fat and oil (used as an ointment for joint pain and gout), injuries and mortalities due to entanglement with fishing gear are other threats for the species. In addition, they can bioaccumulate several hazardous and toxic chemical pollutants in their body tissues, which are detrimental for their sustenance. Due to the outbreak of novel coronavirus (2019—COVID) pandemic the positive and negative impacts have also been observed and discussed. The following precautionary measures should be undertaken for their conservation: ban fishing in the dolphin hotspot areas and sanctuaries;multispecies management along with law enforcement and sustainable fishing practices;dolphin-fishery interactions should be solved through fishing gear modifications (e.g., mesh size). © 2022 Elsevier Inc. All rights reserved
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Ever since the COVID-19 outbreak, various works have focused on using multitude of different static and dynamic features to aid the prediction of disease forecasting models. However, in the absence of historical pandemic data these models will not be able to give any meaningful insight about the areas which are most likely to be affected based on preexisting conditions. Furthermore, the black box nature of neural networks often becomes an impediment for the concerned authorities to derive any meaning from. In this paper, we propose a novel explainable Graph Neural Network (GNN) framework called Graph-COVID-19-Explainer (GC-Explainer) that gives explainable prediction for the severity of the spread during initial outbreak. We utilize a comprehensive set of static population characteristics to use as node features of Graph where each node corresponds to a geographical region. Unlike post-hoc methods of GNN explanations, we propose a framework for learning important features during the training of the model. We further apply our model on real-world early pandemic data to show the validity of our approach. Through GC-Explainer, we show that static features along with spatial dependency among regions can be used to explain the varied degree of severity in outbreak during the early part of the pandemic and provide a framework to identify the at-risk areas for any infectious disease outbreak, especially when historical data is not available. © 2022 IEEE.
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Purpose: In 2014, real estate investment trust (REIT) emerged as a new alternative investment option in India. This research aims to give an empirical authentication of the Indian REITs performance from April 2019 to July 2022 across a range of investment variables. Design/methodology/approach: Using monthly total returns in Indian Rupee, risk-adjusted Indian REIT performance and investment portfolio characteristics are examined. Indian REITs' potential in a diversified multi-asset portfolio is analysed using the mean-variance analysis, asset allocation diagram and efficient frontier. Findings: During April 2019–July 2022, Indian REITs provided a lower return than stocks but outperformed bonds despite coronavirus disease 2019 (COVID-19) lockdowns, which hurt the traditional working from office concept. The study also examined REIT allocation to an Indian mixed-asset portfolio and the benefits of a diversified portfolio. Practical implications: Indian REITs provide a liquid, transparent alternative to direct property for investors seeking exposure to Indian real estate markets. Indian REITs gave real estate companies an extra funding source and investors an alternate asset. This paper explores Indian REITs' potential opportunities, given that domestic and foreign investors' demand for transparent property investment in India. The analysis found a positive early performance despite a challenging environment. Originality/value: This paper offers the first empirical performance validation of Indian REITs as a way to obtain exposure to commercial property in India and the REITs' role in a diversified asset portfolio. The authors' study improves investors' decision-making abilities by providing empirically validated, valuable and practicable property investing insights. © 2022, Emerald Publishing Limited.
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As the world moves toward the "New Normal” with borderless innovation and remote work, Multinational Enterprises (MNEs) are increasingly involved in tapping talent that is external to organizational boundaries. This study distills learnings from the use of globally distributed external talent that has been facilitated by innovation intermediaries, a development that holds significant managerial implications for the post-COVID industrial era. Moving beyond the conventional and recognized need for global talent management practices, we provide a perspective on talent management outside organizational boundaries, a topic that that has received limited attention so far. Through the lenses of open innovation and talent management, we define a typology of innovation problems on the basis of latent talent needs. We take a step further, and for each problem type, we identify the competencies that are relevant, the reward mechanisms of the intermediaries, and the extent of collaboration required with internal talent. This typology provides a basis for researchers in the talent management community to study talent acquisition and management strategies of MNEs across various contexts and various innovation needs. © 2022 Wiley Periodicals LLC.
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COVID-19 and the ensuing vaccine capacity constraints have emphasized the importance of proper prioritization during vaccine rollout. This problem is complicated by heterogeneity in risk levels, contact rates, and network topology which can dramatically and unintuitively change the efficacy of vaccination and must be taken into account when allocating resources. This paper proposes a general model to capture a wide array of network heterogeneity while maintaining computational tractability and formulates vaccine prioritization as an optimal control problem. Pontryagin's Maximum Principle is used to derive properties of optimal, potentially highly dynamic, allocation policies, providing significant reductions in the set of candidate policies. Extensive numerical simulations of COVID-19 vaccination are used to corroborate these findings and further illicit optimal policy characteristics and the effects of various system, disease, and population parameters. © 2022 IEEE.
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Purpose of Study: GeauxHealth! is a multi-institutional, multi-disciplinary collaboration designed to create an easy-to-use guide for community health resources based on significant need in the Greater New Orleans area. The 2019 New Orleans Community Health Assessment found that New Orleanians identified mental health, substance use, women's health and chronic medical diagnosis management as areas of concern. Health barriers identified include crime and violence, infrastructure, environmental factors, healthy food, housing and income Additionally, the COVID-19 pandemic has further highlighted health inequity in communities across the United States. In an effort to address these findings and to promote awareness of social determinants of health among providers, Geauxhealth.org was created with a vision to be an updated, user-friendly website created by medical trainees for medical trainees and patients. Methods Used: 139 residents and fellows across multiple specialties within Tulane School of Medicine and LSUHSC School of Medicine were surveyed before and after the introduction of Geauxhealth.org. Summary of Results: When asked about confidence in referring patients to community resources when needed, 27% of trainees answered "Definitely not confident" and 31% answered "Somewhat confident." Nearly all respondents noted that barriers to referring patients to resources included: "Lack of Time" and "Lack of Knowledge." 96% of trainees noted they would use a website or app if available for referring patients to community resources. Post-intervention results are currently being analyzed. Conclusion(s): Awareness of social determinants of health is the first step in addressing health inequity experienced by the patients we serve. GeauxHealth! is designed to bridge the gap between awareness and action. Over time, the hope is for GeauxHealth! to serve as education for [Table presented] incoming residents, to be a utilized tool by providers, hospital employees and patients and to create a framework for the development of health resource guides in other cities. Copyright © 2023 Southern Society for Clinical Investigation.
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Purpose: In 2014, real estate investment trust (REIT) emerged as a new alternative investment option in India. This research aims to give an empirical authentication of the Indian REITs performance from April 2019 to July 2022 across a range of investment variables. Design/methodology/approach: Using monthly total returns in Indian Rupee, risk-adjusted Indian REIT performance and investment portfolio characteristics are examined. Indian REITs' potential in a diversified multi-asset portfolio is analysed using the mean-variance analysis, asset allocation diagram and efficient frontier. Findings: During April 2019–July 2022, Indian REITs provided a lower return than stocks but outperformed bonds despite coronavirus disease 2019 (COVID-19) lockdowns, which hurt the traditional working from office concept. The study also examined REIT allocation to an Indian mixed-asset portfolio and the benefits of a diversified portfolio. Practical implications: Indian REITs provide a liquid, transparent alternative to direct property for investors seeking exposure to Indian real estate markets. Indian REITs gave real estate companies an extra funding source and investors an alternate asset. This paper explores Indian REITs' potential opportunities, given that domestic and foreign investors' demand for transparent property investment in India. The analysis found a positive early performance despite a challenging environment. Originality/value: This paper offers the first empirical performance validation of Indian REITs as a way to obtain exposure to commercial property in India and the REITs' role in a diversified asset portfolio. The authors' study improves investors' decision-making abilities by providing empirically validated, valuable and practicable property investing insights. © 2022, Emerald Publishing Limited.
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The education sector has suffered a catastrophic setback due to the ongoing COVID pandemic, with classrooms being closed indefinitely. The current study aims to solve the existing dilemma by examining COVID transmission inside a classroom and providing long-term sustainable solutions. In this work, a standard 5 × 3 × 5 m3 classroom is considered where 24 students are seated, accompanied by a teacher. A computational fluid dynamics simulation based on OpenFOAM is performed using a Eulerian-Lagrangian framework. Based on the stochastic dose-response framework, we have evaluated the infection risk in the classroom for two distinct cases: (i) certain students are infected and (ii) the teacher is infected. If the teacher is infected, the probability of infection could reach 100% for certain students. When certain students are infected, the maximum infection risk for a susceptible person reaches 30%. The commonly used cloth mask proves to be ineffective in providing protection against infection transmission, reducing the maximum infection probability by approximately 26% only. Another commonly used solution in the form of shields installed on desks has also failed to provide adequate protection against infection, reducing the infection risk only by 50%. Furthermore, the shields serve as a source of fomite mode of infection. Screens suspended from the ceiling, which entrap droplets, have been proposed as a novel solution that reduces the infection risk by 90% and 95% compared to the no screen scenario besides being completely devoid of fomite infection mode. The manifestation of infection risk in the domain was investigated, and it was found out that in the case of screens the maximum infection risk reached the value of only 0.2 (20% infection probability) in 1325 s. © 2023 Author(s).