Analysis of the Solution of a Model of SARS-CoV-2 Variants and Its Approximation Using Two-Step Lagrange Polynomial and Euler Techniques

In this paper, a vaccination model for SARS-CoV-2 variants is proposed and is studied using fractional differential operators involving a non-singular kernel. It is worth mentioning that variability in transmission rates occurs because of the particular population that is vaccinated, and hence, the asymptomatic infected classes are classified on the basis of their vaccination history. Using the Banach contraction principle and the Arzela–Ascoli theorem, existence and uniqueness results for the proposed model are presented. Two different numerical approaches, the fractional Euler and Lagrange polynomial methods, are employed to approximate the model's solution. The model is then fitted to data associated with COVID-19 deaths in Pakistan between 1 January 2022 and 10 April 2022. It is concluded that our model is much aligned with the data when the order of the fractional derivative (Formula presented.). The two different approaches are then compared with different step sizes. It is observed that they behave alike for small step sizes and exhibit different behaviour for larger step sizes. Based on the numerical assessment of the model presented herein, the impact of vaccination and the fractional order are highlighted. It is also noted that vaccination could remarkably decrease the spikes of different emerging variants of SARS-CoV-2 within the population. © 2023 by the authors.

Impact of Different Exercise Techniques on Menstrual Pain Severity in Postacute Covid-19 Women

Background: The purpose of this investigation was to determine the impact of different exercise techniques on menstrual pain severity in post-acute covid-19 women. Method(s): Thirty post-acute COVID-19 women suffering from primary menstrual pain. Their body mass index ranged from 25 to 35 kg/m2, and their age was between 18 and 25 years old. They were split up into two groups randomly. The control group (n=15) were instructed to avoid regular physical exercise during the study period, and the exercise group (n=15) were requested to perform set of home-based exercise protocol, from the fourth day of the menstrual cycle till the onset of the second menstrual cycle. Both groups were examined for pain intensity Pre-treatment and post-treatment using visual analogue scale (VAS). Result(s): Menstrual pain intensity was significantly reduced in the exercise group (p=0.000) but not statistically different in the control group (p=0.089). Conclusion(s): Different exercise techniques has a positive impact on menstrual pain severity in post-acute covid-19 women. Therefore, it can be recommended for menstrual pain relief in post-acute covid-19 women to promote their daily activities, independent living and quality of life.Copyright © 2021 Muslim OT et al.

Prognostic value of the SII Systemic Immune-Inflammation Index a novel biomarker in coronavirus disease 2019 patient's disease progression to fatality

Objective: To identify a novel biomarker with high prognostic value SII Systemic Immune-Inflammation Index in the disease progression of COVID-19 patients with its cost effectiveness and less time consuming. Methodology: This cross-sectional study was carried out from November 2021 to February 2022 at the Mardan Medical Complex, Khyber Pakhtunkhwah, Pakistan. The receiver operating characteristic (ROC) curve analysis was used to discover the ideal cut-off values for predictors for disease severity stage, i.e. asymptomatic, mild, moderate, severe, critical, based on their greatest Youden's index. The SII (platelet X neutrophil count/lymphocyte counts) formula was used to compute the systemic immune inflammation index. Result(s): Of the 311 cases studied, 233 were included;155 (66.52%) were male and 78 (33.47%) females. Median age was 38 years (IQR: 18 - 79). Patients had a significant increase in various blood parameters, with an increase in SII index between admission and hospitalization. Normal patients had a SII median 398 (IQR: 312 - 567) upon admission, while abnormal patients had a SII median 659 (IQR: 475 - 1540). Throughout hospitalization, SII index of asymptomatic patients median was 684 (IQR: 470 - 933);cut-off value >= 358, mild patients median 909 (IQR: 183 - 1930);cut-off value >= 501, moderate patients median >= 992 (IQR: 248 - 6099);cut-off value >= 903, severe patients median 1063 (IQR: 104 - 5014);cut-off value >= 1147, critical patients median 1230 (IQR: 100 - 8438);cut-off value >= 1481. Conclusion(s): SII was found to be a significant predictor of COVID-19 patients' severity progression to fatality as an independent prognostic factor. SII is being recommended as a low-cost and less time-consuming blood test for COVID-19 patients.Copyright © 2023, Pakistan Medical Association. All rights reserved.

Immunological inflammatory factors in patients diagnosed with COVID-19

Introduction and Aim: Due to the Coronavirus outbreaks, the SARS-CoV-2, also known as COVID-19, has claimed several lives around the world, with the majority of them being elderly, suffering from underlying chronic illnesses, or living in vulnerable conditions. This study aimed to find the immunological factors CD-79, CD-4, IL-2, and TNF-B in COVID-19 patients utilizing nucleocapsid-(N), a protein structure that interacts with genomic RNA to create complexes. Material(s) and Method(s): SARS-COV-2 infection was detected using RT-PCR. The serum levels of IL-2 and TNF-B, as well as the concentrations of CD4 and CD79, were measured. This study included 100 COVID-19 patients. Result(s): The results showed that the serum concentration of TNF-beta and IL-2 in COVID19 patients was significantly higher than that in the general population (with acute and moderate illness) when compared to normal control groups (p<0.05). COVID-19 patients reported higher levels of CD79 as well as CD4 expression than healthy control groups in a study of activated markers. Conclusion(s): Infection with SARS-COV-2 has a high impact on various immunological and inflammatory markers in patients.Copyright © 2023, Indian Association of Biomedical Scientists. All rights reserved.

Global asymptotic stability, extinction and ergodic stationary distribution in a stochastic model for dual variants of SARS-CoV-2

Several mathematical models have been developed to investigate the dynamics SARS-CoV-2 and its different variants. Most of the multi-strain SARS-CoV-2 models do not capture an important and more realistic feature of such models known as randomness. As the dynamical behavior of most epidemics, especially SARS-CoV-2, is unarguably influenced by several random factors, it is appropriate to consider a stochastic vaccination co-infection model for two strains of SARS-CoV-2. In this work, a new stochastic model for two variants of SARS-CoV-2 is presented. The conditions of existence and the uniqueness of a unique global solution of the stochastic model are derived. Constructing an appropriate Lyapunov function, the conditions for the stochastic system to fluctuate around endemic equilibrium of the deterministic system are derived. Stationary distribution and ergodicity for the new co-infection model are also studied. Numerical simulations are carried out to validate theoretical results. It is observed that when the white noise intensities are larger than certain thresholds and the associated stochastic reproduction numbers are less than unity, both strains die out and go into extinction with unit probability. More-over, it is observed that, for weak white noise intensities, the solution of the stochastic system fluctuates around the endemic equilibrium (EE) of the deterministic model. Frequency distributions are also studied to show random fluctuations due to stochastic white noise intensities. The results presented herein also reveal the impact of vaccination in reducing the co-circulation of SARS-CoV-2 variants within a given population. © 2022 International Association for Mathematics and Computers in Simulation (IMACS)

A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world. Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease. No doubt, X-ray is considered as a quick screening method, but due to variations in features of images which are of X-rays category with Corona confirmed cases, the domain expert is needed. To address this issue, we proposed to utilize deep learning approaches. In this study, the dataset of COVID-19, lung opacity, viral pneumonia, and lastly healthy patients' images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently. The performance of the Swin transformer is compared with the other seven deep learning models, including ResNet50, DenseNet121, InceptionV3, Efficient-NetB2, VGG19, ViT, CaIT, Swim transformer provides 98% recall and 96% accuracy on corona affected images of the X-ray category. The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis, and proposed technique is found better in terms of accuracy. Our system could support clin-icians in screening patients for COVID-19, thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients. Also, this paper can contri-bute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients. © 2023, Tech Science Press. All rights reserved.

The Asnaf Zakat Entrepreneurship Research Trend: A Systematic Literature Review

Entrepreneurship is proven as a medium in lifting an economic state of an individual or a community. It is also can be considered as an alternative area to be explored especially in the current situation where the world is affected by the pandemic of COVID-19 globally. It justifies the need to research this topic to leverage its benefits to various perspectives including the asnaf community. Unfortunately, despite its importance, limited studies have been found were conducted on entrepreneurship among asnaf community. Therefore, this research is initiating research on this topic by conducting a systematic literature review on the published works on entrepreneurship among asnaf community to explore the trend of the existing research works. A PRISMA approach is adapted to collect the relevant literature that is scoped only to the Scopus and Web of Science (WoS) databases. The result obtained from this research is expected will provide an overview of the trend of previously conducted research works on the entrepreneurship among asnaf community. Finally, it can be referred by a potential researcher in this topic to plan their future research. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Impact of COVID-19 on endoscopic follow-up of gastroesophageal varices.

Background: Portal hypertension is considered as a major complication of liver cirrhosis. Endoscopy plays a main role in managing of gastrointestinal complications of portal hypertension. Endoscopists are at increased risk for COVID-19 infection because upper gastrointestinal (GI) endoscopy is a high-risk aerosol-generating procedure and may be a potential route for COVID-19. Objectives: To compare the outcome between cirrhotic patients who underwent classic regular endoscopic variceal ligation after primary bleeding episode every 2-4 weeks, and those presented during the era of COVID-19 and their follow-up were postponed 2 months later. Methods: This retrospective study included cross-matched 238 cirrhotic patients with portal hypertension presented with upper GI bleeding, 112 cirrhotic patients presented during the era of COVID19 (group A) underwent endoscopic variceal ligation, another session after 2 weeks and their subsequent follow-up was postponed 2 months later, and 126 cirrhotic patients as control (group B) underwent regular endoscopic variceal band ligation after primary bleeding episode every 2-4 weeks. Results: Eradication of varices was achieved in 32% of cases in group A, and 46% in group was not any statistically significant (p > 0.05); also, there was no any statistical significant difference between both groups regarding occurrence of rebleeding, post endoscopic symptoms, and mortality rate (p > 0.05). Conclusion: Band ligation and injection of esophageal and gastric vary every 2 months were as effective and safe as doing it every 2 to 4 weeks after primary bleeding episode for further studies and validation.

Unlock: Boon or bane for mental health?

Unlocking after lockdown in the current COVID-19 pandemic has apparently resulted in a new normal way of living, but its impact on mental health is still unexplored. The present study tried to explore the mental health status of the general population in India during the current unlocking phase of the COVID-19 outbreak. Furthermore, it aimed at examining the mental health burden in different age groups and finally to find out the association between psychological distress and education loss, financial loss, exposure and acquaintance of COVID 19. The study was conducted from Unlock phase 3.0 to Unlock phase 8.0 on 200 participants. A Google form was created and circulated on different online and social media platforms. The measures used were: the Impact of Event Scale (IES), Depression, Anxiety, and Stress Scale (DASS-21). The data was analyzed with the help of SPSS v21. Results clearly show that the mental health burden of the majority of the population fell into the normal category and a few into the mild category. As far as stress, anxiety, depression, and IES in the unlock phase are concerned, they still exist but in lower figures as compared to lockdown phases. Without a doubt, the burden on mental health has been greatly reduced, but it still exists among the general population. © 2022 Nova Science Publishers, Inc..

COVID-19 pneumonia level detection using deep learning algorithm and transfer learning.

The first COVID-19 confirmed case was reported in Wuhan, China, and spread across the globe with an unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, developing smart, fast, and efficient detection techniques is significant. To this end, we have developed an Artificial Intelligence engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases; in the first phase, we calculate the volume and density of lesions and opacities of the CT scan images of the confirmed COVID-19 patient using Morphological approaches. The second phase classifies the pneumonia level of the confirmed COVID-19 patient. We use a modified Convolution Neural Network (CNN) and k-Nearest Neighbor; we also compared the results of both models to the other classification algorithms to precisely classify lung inflammation. The experiments show that the CNN model can provide testing accuracy up to 95.65% compared with exiting classification techniques. The proposed system in this work can be applied efficiently to CT scan and X-ray image datasets. Also, in this work, the Transfer Learning technique has been used to train the pre-trained modified CNN model on a smaller dataset than the original dataset; the modified CNN achieved 92.80% of testing accuracy for detecting pneumonia on chest X-ray images for the relatively extensive dataset.

Analysis and Simulation of the Spread of COVID-19 in Indonesia Using SIR-FV Modeling with Optimization

The COVID-19 virus pandemic in Indonesia has been going on since March 2020 and is still ongoing with conditions that need to be watched out for. This can be seen from the distribution of the daily active cases addition in Indonesia which is still changing dynamically. An alternative solution that can help to analyze countermeasures for the virus spread is modeling and simulating the spread of cases to estimate pandemic conditions that may occur in Indonesia. A common and widely used epidemiological-based model is the SIR model, which groups individuals affected by a pandemic into several compartments. Using this modeling and utilizing the concept of optimization technology, the modeling process can be carried out more efficiently and accurately. A model is developed, one of the derivatives of SIR modeling, namely SIR-FV, based on the concept of optimization to estimate and simulate various virus spread scenarios. There are 2 scenarios developed for analysis, namely the vaccination program scenario and the contact rate scenario. Based on the scenario simulation, it was found that the vaccination program could have a positive impact on efforts to deal with the COVID-19 pandemic more effectively when compared to the scenario without vaccination. The contact rate scenario also has a significant impact. However, the simulation also shows that if the vaccination program is not supported by adequate health protocols, it will not have any impact on the prevention effort. These results apply to the results of the SIR-FV model. Overall, it can be concluded that the developed model can carry out all of its functions as needed, with the level of accuracy through the MAPE metric reaching 0.012 for the SIRFV model. © 2022 IEEE.

Study of IgG and IgM antibody response afterPfizer mRNA Vaccine first dose in Previously infected with COVID-19and uninfected individuals

The current research aimedto demonstrate the extent such as increase in the rate of immune response to antibodies (IgG, IgM) for people who received the first dose of the Pfizer mRNA vaccine at (1-3) weeks times period and to compare them with people who were not taken for the first dose of the same vaccine and none infected with COVID-19.Also the results appearedsignificant variations in immunoglobulin (IgG) levels (P≤ 0.05) between case (Recipients mRNA vaccination) and control patients, there were. In terms of age and gender, however, there were no significant changes (P≥ 0.05) in immunoglobulin (IgM) levels between case (Recipients mRNA vaccination) and control patients.

Critical Illness Myopathy in COVID-19 Patients: A Case Series

Aim and background: Management in COVID-19 includes the use of steroids, prolonged hospital stay, and long-term ventilatory care using muscle relaxants for lung-protective ventilation. These patients are subjected to fluctuating hemodynamics, blood sugar levels, secondary sepsis, systemic inflammatory response syndrome, and multi-organ dysfunction. This causes an increased risk for developing critical illness polyneuropathy and myopathy. Objectives: The literature assessing the effect of these risk factors on mortality in patients with COVID-19 is scarce. Hence, we assessed the effect of various risk factors and interventions on the long-term outcome in these patients. Materials and methods: We collected retrospective data of critically ill COVID-19 patients who developed from critical illness myopathy. The demographic details, clinical parameters, laboratory values, effect of protocol-based physiotherapy intervention, and long-term outcome of patients in term of residual weakness, dependency, and mortality was collected. Results: Out of the total 324 critically ill COVID-19 patients, 11 patients were diagnosed with critical illness myopathy and were included for data collection. Among the patients who developed critical illness myopathy, in-hospital mortality was around 36.4%. The use of protocol-based physiotherapy interventions like long sitting (P = 0.007) and, chair mobilization (p = 0.001) led to a significant reduction in mortality in COVID-19 patients. Conclusion: In patients with COVID-19 related critical illness myopathy, the use of protocol-based physiotherapy interventions leads to improved survival. Key messages: In patients with COVID-19 related critical illness myopathy, the use of protocol-based physiotherapy interventions has survival benefits.

EFFECTIVENESS OF ZINC AND ROS ON TESTOSTERONE HORMONE LEVELS FOR RECOVERING COVID-19 PATIENTS

COVID-19 (coronavirus disease 2019), cause severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) across all age groups, it’s a positive-sense single-stranded RNA virus, and a member of the Betacoronavirus genus taxonomically (Jiang et al, 2020). Given the importance roles of zinc in combating oxidative damage and viral infections, Zinc also has confirmed roles in both male and female reproduction. The possible depletion of zinc with the oxidative events of COVID-19 is especially relevant to the fertility of affected couples (Sethuram et al, 2021). The aim of study is to determine the relation between zinc value and oxidative stress level represented by ROS (Reactive Oxygen Species) and testosterone level among the recovered COVID-19 patients in reproductive age. 120 men chosen from Center of Medical City, Health Center of 9 Nisan, Poisoning Consultation Center and Kamal AL-Samarrai Hospital, 70 recovered males from COVID-19 within a period of 6 months after the last negative PCR nasopharyngeal swab and 50 as control group (uninfected COVID-19) from the Medical staff and the relatives, during the period from December/ 2020 to February / 2021. Testosterone hormone level were measured for each male, level of COVID-19 anti-nucleocapsid IgG was estimated and designed as selection criteria for recovery from COVID-19. Pearson’s correlation coefficient and A stepwise method in linear regression statistic test was applied to detect the association of testosterone hormone level with zinc and ROS. The mean and standard deviation level of studied parameters are differ between cases of current studying;recovering COVID-19 males and control group then compared with normal value of each test. The levels of COVID-19 anti-nucleocapsid IgG increase among recovering males compared with control group, statistically highly-significant (P-value = 0.00), as well oxidative stress among cases recovered from Covid-19 compared with level of control are statistically highly-significant (P-value= 0.00), while levels of zinc are decreased among cases studied compared with control group, this differences was highly-significant (P-value = 0.00). In conclusion, the most factors affecting Testosterone hormone level identified in the study are Zinc, ROS

Impact of the COVID-19 pandemic on religious tourism amongst Muslims in Iraq

Tourism, as an industry, has become one of the most dynamic sectors of the world economy these days and has specific features that are different from other industries. In the tourism industry, production and consumption points occur spatially at the same time. In addition, the tourism industry contributes to the economic growth of developed regions and can simultaneously distribute the wealth created geographically. It is notable that the coronavirus disease 2019 (COVID-19) pandemic has caused many challenges in the tourism industry regarding the presence of tourists in tourism centres and the closing of all tourism service chains, including food, entertainment, transportation and travel services worldwide. Tourism-related businesses, which are considered as invisible export and one of the engines of development and occupation, have been rendered obsolete. In other words, the businesses, as well as multiple units and activities in the related chain, have been damaged and employees of this industry have lost their jobs. This has led to the recession and regressive course of the developing and large industry of tourism in the world. It is worth noting that the tourism industry includes various sections, the most important of which is religious tourism. All religions in the world have different religious places, works, traditions and customs, which have become amongst the most important tourist attractions. Meanwhile, Muslims and the religion of Islam play a significant role in this branch of tourism. The Hajj, pilgrimage to holy places and the existence of mourning ceremonies or religious celebrations of Muslims are amongst the largest religious tourism events in the world. Given the importance of this issue, the present study aimed to evaluate the impact of the COVID-19 pandemic on religious tourism in Iraq in 2021. This field study was conducted on 4500 Muslim managers and staff of restaurants, hotels, grocery stores, clothing stores and souvenir shops around the holy shrines of imams and religious places in Karbala, Najaf, Kufa, Samarra and Kazemi. According to the results, the tourism of Iraq, which is mainly limited to Muslim religious sites in several major Iraqi cities, has also seen a decline in the number of religious tourists. The negative effects of COVID-19 on religious tourism have also been proved statistically by the Statistical Package for the Social Sciences (SPSS), as µ ≥ 3 has been counted in all indices. Contribution: Our findings offered new insights into the impact of COVID-19 on tourism, based on statistical analysis. In this study, the authors showed how COVID-19 affects various aspects of religious tourism, which has not been addressed in previous researches. © 2022. The Authors.

A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world. Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease. No doubt, X-ray is considered as a quick screening method, but due to variations in features of images which are of X-rays category with Corona confirmed cases, the domain expert is needed. To address this issue, we proposed to utilize deep learning approaches. In this study, the dataset of COVID-19, lung opacity, viral pneumonia, and lastly healthy patients' images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently. The performance of the Swin transformer is compared with the other seven deep learning models, including ResNet50, DenseNet121, InceptionV3, EfficientNetB2, VGG19, ViT, CaIT, Swim transformer provides 98% recall and 96% accuracy on corona affected images of the X-ray category. The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis, and proposed technique is found better in terms of accuracy. Our system could support clinicians in screening patients for COVID-19, thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients. Also, this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.

Automatic Detection and classification of Correct placement of tubes on chest X-rays using deep learning with EfficientNet

In recent years, the rapid growth of data in healthcare has prompted a lot of interest in artificial intelligence (AI). Powerful AI algorithms are essential for extracting information from medical data and assisting clinicians in establishing quick and accurate diagnoses of a variety of ailments. In the current COVID-19 outbreak, critically ill patients were intubated and various medical tubes, including an endotracheal tube (ETT), were implanted to protect the airways. The Nasogastric tube (NGT) is used for feeding, whereas the Central Venous Catheter (CVC) is utilized for a variety of medical operations. The adoption of medical protocols by doctors to ensure proper tube installation is a major issue. Manual examination of CXR pictures takes time and frequently leads to misinterpretation. This research aims to create an Automated Medical Tube Detection System that can detect misplaced tubes from chest x-rays (CXR) using deep learning. As a result, using chest x-rays to detect poorly positioned tubes can save lives. On CXR the proposed CNN-based EfficientNet architecture efficiently detects and classifies incorrectly positioned tubes. After detailed experimentation, we were able to achieve 0.95 average area under the ROC curve (AUC). © 2022 IEEE.

AWARE: An IoT powered Smart Band with Multi-Tenancy Cardinality

The Pandemic caused due to COVID-19 has overweighed the current healthcare system and made us realize that how unaware we were of our health. Although lots of rational and harsh measured were taken to curb the spread of COVID-19 but still we lost millions of lives. During this pandemic technology played a vital role ranging from the invention of vaccine to remotely monitoring the usage with the help of IoT. Among several emerging technologies wearable smart devices were burgeoning as they are also powered by IoT now. Wearable devices are being used in different scenarios ranging from tracking and monitory infected patients to utilize the data for policy making. Proposed is a framework of an ecosystem "AWARE"which comprises of a smart band with advanced PPG and EEG sensors to detect Heart rate, Heart rate Variability, Respiration rate, SpO2, Step and Sleep data. The sensor data will be transferred to the AWARE application on host mobile through BLE and from mobile the user data it will be transferred to AWARE cloud for pre and post processing using Machine Learning algorithms. AWARE can used for monitoring and detection of health anomalies and diseases such as COVID-19 or chronic lifestyle diseases. AWARE works on a multi-Tenancy cardinality framework were the group (i.e., kids, elders, domestic worker) users can share the cloud storage and receive customized notifications. Also, the group manager (i.e., father, employer) will be notified in case of emergencies. Cloud data can be accessed by the users through dashboards. Government authorities can also access user data through APIs. Wearables are widely accepted these days due to its less intrusiveness. Although some professionals are skeptical of these devices, but the advantages are far beyond the minor pitfalls. In fact, several countries have already implemented wearables devices as the primary medium of COVID-19 detection, monitoring. © 2021 IEEE.

Determinants of telemedicine adoption among financially distressed patients with cancer during the COVID-19 pandemic: insights from a nationwide study.

PURPOSE: Telemedicine use during the COVID-19 pandemic among financially distressed patients with cancer, with respect to the determinants of adoption and patterns of utilization, has yet to be delineated. We sought to systematically characterize telemedicine utilization in financially distressed patients with cancer during the COVID-19 pandemic. METHODS: We conducted a cross-sectional analysis of nationwide survey data assessing telemedicine use in patients with cancer during the COVID-19 pandemic collected by Patient Advocate Foundation (PAF) in December 2020. Patients were characterized as financially distressed by self-reporting limited financial resources to manage out-of-pocket costs, psychological distress, and/or adaptive coping behaviors. Primary study outcome was telemedicine utilization during the pandemic. Secondary outcomes were telemedicine utilization volume and modality preferences. Multivariable and Poisson regression analyses were used to identify factors associated with telemedicine use. RESULTS: A convenience sample of 627 patients with cancer responded to the PAF survey. Telemedicine adoption during the pandemic was reported by 67% of patients, with most (63%) preferring video visits. Younger age (19-35 age compared to ≥ 75 age) (OR, 6.07; 95% CI, 1.47-25.1) and more comorbidities (≥ 3 comorbidities compared to cancer only) (OR, 1.79; 95% CI, 1.13-2.65) were factors associated with telemedicine adoption. Younger age (19-35 years) (incidence rate ratios [IRR], 1.78; 95% CI, 24-115%) and higher comorbidities (≥ 3) (IRR; 1.36; 95% CI, 20-55%) were factors associated with higher utilization volume. As area deprivation index increased by 10 units, the number of visits decreased by 3% (IRR 1.03, 95% CI, 1.03-1.05). CONCLUSIONS: The rapid adoption of telemedicine may exacerbate existing inequities, particularly among vulnerable financially distressed patients with cancer. Policy-level interventions are needed for the equitable and efficient provision of this service.