Potential cost of not vaccinating against COVID19 in the United States

Introduction: As of April 2022, the COVID19 global pandemic has resulted in over six million deaths globally, and over 81 million cases of COVID19 in the United States. Objective(s): The objective of the presentation is to share estimated direct and indirect costs due to COVID19 infection juxtaposed with the costs of COVID19 vaccine administration in the United States. Method(s): A literature review was conducted to identify potential cost savings from being immunized against COVID19. The costs of COVID19 vaccinations, direct costs related to healthcare and types of indirect costs were noted. Result(s): After reviewing over 40 resources, several costs were identified. The cost of COVID19 vaccine series, as defined by the Centers of Medicare and Medicaid Services (CMS), is currently USD40 for single-dose and USD40 per dose in a multiple-dose series. It is estimated that the average hospitalisation stay of an uninsured inpatient was ~USD7000-USD10,000 per day. The average cost of 12 major metropolitan cities in the United States were estimated for primary care facilities, urgent care facilities, and emergency room visits at USD195, USD239, USD1,425, respectively. As of April 2, 2022, 77% of the US have received at least one dose of COVID19 vaccine and 66% are considered to be fully vaccinated against COVID19 primary series. Conclusion(s): According to the data, the cost reduction in healthcare is consequential and cost-effective in vaccinating the population. This analysis contributes to the limited reports of a national cost-benefit analysis.

Weight Gain in Young Adults During the Covid-19 Pandemic

Introduction: During the COVID-19 pandemic, measures taken to prevent the spread of the coronavirus have led to significant changes in the lifestyle and habits of young people. The pandemic led to poor dietary patterns, reduced physical activities, and increased mental stress, which are all risk factors for weight gain. The goal of this study was to investigate the pattern of weight gain among young adults between age 18-50 years during the pandemic, and to identify factors associated with significant weight gain of 20 pounds of more. Method(s): We included young adults between ages 18 to 50 years with at least one documented weight in their electronic health records before the start of the COVID-19 pandemic shelter-in-place orders (3/19/2019 to 3/19/2020) and at least one documented weight after COVID-19 vaccines became available (12/14/2020 to 12/14/2021). Multivariable logistic regression analysis was used to identify factors associated with greater than 20 pounds of weight gain. Odds ratios (OR) and 95% confidence intervals (CI) were calculated. Result(s): The study cohort included 133,750 young adults aged 18-50 years (median age 43 years. 39.7% men). The cohort is racially and ethnically diverse, with 22.6% self-identified as White, 7.2% Black, 48.1% Hispanic, and 17.3% Asian. During the pandemic, 53866 (40.3%) lost weight or had no weight gain, 50662 (37.9%) gained 0-9 pounds (lbs), 19422 (14.5%) gained 10-19 lbs, and 9800 (7.3%) gained 20 lbs or more. Individuals who gained 20 lbs or more were younger and more likely to reside in low-income neighborhoods. Multivariable logistic regression demonstrated the following factors to be associated with significant weight gain: male sex (OR 1.10, 95% CI 1.05-1.15), Black race (OR 1.14, 95% CI 1.05-1.23), low income (OR 1.16, 95% CI 1.16-1.35), and history of depression (OR 1.64, 95% CI 1.56-1.73). Conclusion(s): In this cohort of young adults, 59.7% experienced weight gain during the pandemic, with 7.5% gaining 20 lbs or more. Factors associated with significant weight gain included male sex, black race, low income, and a history of depression. Intervention strategies to promote healthy lifestyle may be particularly important for patients with depression, and young adults from lowincome neighborhoods.

CHRONIC EMPYEMA MANAGED WITH TUBE THORACOSTOMY

SESSION TITLE: Problems in the Pleura Case Posters 2 SESSION TYPE: Case Report Posters PRESENTED ON: 10/17/2022 12:15 pm - 01:15 pm INTRODUCTION: Hematologic malignancies can often be complicated by pleural effusion due to leukemic infiltration of the pleura (1). Long term management of resulting chronic plural effusion can be complicated when there is evidence of trapped lung. Subsequent infection may lead to development of chronic empyema which can be difficult to manage in chronically ill patients (2). CASE PRESENTATION: A 65-year-old male with history of chronic myeloid leukemia status post stem cell transplant was admitted with dyspnea and cough. Computed tomography (CT) chest imaging revealed increased volume loss on the left with new air fluid level in a chronic left pleural effusion. (Image 1) Patient's history was significant for chronic left pleural effusion, which was first identified in 2015 and found to be a malignant effusion with evidence of leukemia involvement. Repeat imaging in 2018 (Image 2) revealed continued chronic pleural effusion. Patient was admitted in August 2021 with COVID-19 pneumonia and CT Chest showed chronic loculated left sided pleural effusion. Patient elected to continue to monitor the chronic effusion, which was completed as outpatient every 4 to 6 weeks (Image 3). He remained clinically stable until the presentation to a hospital in January 2022. The chronic empyema was initially managed with tube thoracostomy, intrapleural fibrinolytics and antibiotics. Cultures were significant for Moraxella catarrhalis and Streptococcus pneumoniae. He was determined to be a poor surgical candidate for decortication and treatment with empyema tube was initiated. The empyema tube was incrementally withdrawn as an outpatient and subsequently removed with good clinical recovery. DISCUSSION: Chronic empyema is characterized by thickened parietal and visceral pleura which limits the ability of the lung to re-expand. Surgical management with decortication is the definitive management, however, in poor surgical candidates, management becomes more complicated. Open pleural drainage with an open pleural window can be considered. An alternative option converts tube thoracostomy to open pleural drainage, as was utilized in this patient (2). While comparison of surgical vs non-surgical management of empyema suggests similar mortality (3), non-surgical management of chronic empyema needs more investigation to determine the optimal treatment modality. CONCLUSIONS: Empyema remains a difficult condition to manage. Treatment modalities of chronic empyema are limited in those patients who remain poor surgical candidates. Reference #1: Faiz SA, Sahay S, Jimenez CA. Pleural effusions in acute and chronic leukemia and myelodysplastic syndrome. Curr Opin Pulm Med. 2014 Jul;20(4):340-6. Reference #2: Biswas A, Jantz MA, Penley AM, Mehta HJ. Management of chronic empyema with unexpandable lung in poor surgical risk patients using an empyema tube. Lung India. 2016;33(3):267-271. Reference #3: Redden MD, Chin TY, van Driel ML. Surgical versus non-surgical management for pleural empyema. Cochrane Database Syst Rev. 2017;3(3):CD010651. Published 2017 Mar 17. DISCLOSURES: No relevant relationships by Shannon Burke No relevant relationships by Abigail Go No relevant relationships by Jen Minoff no disclosure on file for Ravi Nayak;

Estimating the Category of Districts in a State Based on COVID Test Positivity Rate (TPR): A Study Using Supervised Machine Learning Approach

In this paper, the districts of a state are categorized into Category A, Category B, and Category C based on COVID TPR using supervised machine learning approaches. As per the report published by WHO, the TPR should be less than 5% by which we can say that the infection is under control in the locality. TPR is the number of COVID tests found positive to the number of COVID tests performed. Currently, government of all states are taking decisions based on COVID TPR of a district whether it has limited restrictions (Category A-low spread) or partial lockdown (Category B-moderate spread) or complete lockdown (Category C-high spread). In this work, a synthetic dataset is generated by considering the WHO guideline by taking TPR values from 0 TO 5% for Category A, > 5% − ≤10% for Category B, and > 10% for Category C. Then, this input data is fed into the supervised machine learning models such as decision tree (DT), neural network (NN), k-nearest neighbour (k-NN), and support vector machine (SVM) for training to find the best machine learning (ML) model with high classification accuracy for prediction of the Categories (A/B/C). Afterwards, the testing data (TPR value) is generated using random distribution function for 100 districts, and this testing data is fed into the ML models to estimate the category under which the district exist. The analysis of the above methods is performed using Orange 3.29.3 data analytics tool. From the results, it is observed that DT performs better in predicting the category of the districts with high probability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Machine Intelligent Techniques for COVID-19 Detection: A Critical Review and Analysis

Every human being is discussing a highly addressed topic in the current days which is about the COrona VIrus Disease (COVID) in 2019-2020. The outbreak of corona has affected the human race all over the world, the patient count is increasing day by day, and doctors are in a critically need of computer-aided diagnosis with machine learning (ML) algorithms that will discover and diagnose the coronavirus for a large number of patients. Also, it is more complicated to estimate the discharge time and the criticalness of the patient during treatment. Chest computed tomography (CT) scan was the best tool for the corona diagnosis. Also survival analysis methods in ML outperform better in predicting discharge time. In this, we survey on the COVID 19 diagnosis with a chain of CT scan pictures mined from the COVID-19 data set by using ML algorithms like marine predator, simplified suspected infected recovered (SIR), image acquisition, and some more techniques and also survival analysis techniques of ML. The survey clearly explains the models used up to now which are highly defined for the diagnosis of COVID-19 Virus. © 2021 IEEE.

Does preoperative SABR increase the risk of complications from lung cancer resection? A secondary analysis of the MISSILE trial

Background: The standard of care for stage I non-small cell lung cancer (NSCLC) is surgical resection. Stereotactic ablative radiotherapy (SABR) plays an important role in the management of early NSCLC in patients who are poor operative candidates, or more recently during the COVID-19 pandemic, as a bridge to surgery, when operating room access is limited. The impact of preoperative SABR on surgical resection has not been extensively explored in terms of length of hospital stay (LOS) and difficulty of surgical resection (DSR). Our unique published prospective MISSILE study afforded the opportunity to examine this. Methods: LOS and perioperative outcomes were assessed for patients with stage I NSCLC who received preoperative SABR and subsequent surgical resection (RS) within 10 weeks and compared with a similar cohort who underwent surgery alone (S) from 2014 to 2017 using a propensity-score matched analysis. DSR was assessed on the basis of operative time, blood transfusions, conversion rates (CR) and increased sublobar to lobar resection (SL). Results: Forty patients in the RS cohort were compared with 168 patients in the S cohort. Univariable and multivariable logistic regression models were generated as a comparison for all patients (n = 208). LOS was similar between the cohorts (mean 5.2 [standard deviation (SD) 4.7] d v. 4.3 [SD 2.2] d, p = 0.90). There were no differences between cohorts for blood transfusions (0% v. 0%), mean operative time (2.4 [SD 1.0] h v. 2.5 [SD 1.2] h, p = 0.60), conversion rates (21.9% v. 18.8%, p = 0.76) or increased SL (9.4% v. 0%, p = 0.24). Three patients who received radiotherapy did not proceed to surgery, 1 because of concerns of radiation pneumonitis. Conclusion: Preoperative SABR in patients with stage I NSCLC does not have a significant impact on the DSR and LOS.

Nonnegative Matrix Factorization to Understand Spatio-Temporal Traffic Pattern Variations During COVID-19: A Case Study

Due to the rapid developments in Intelligent Transportation System (ITS) and increasing trend in the number of vehicles on road, abundant of road traffic data is generated and available. Understanding spatio-temporal traffic patterns from this data is crucial and has been effectively helping in traffic plannings, road constructions, etc. However, understanding traffic patterns during COVID-19 pandemic is quite challenging and important as there is a huge difference in-terms of people’s and vehicle’s travel behavioural patterns. In this paper, a case study is conducted to understand the variations in spatio-temporal traffic patterns during COVID-19. We apply nonnegative matrix factorization (NMF) to elicit patterns. The NMF model outputs are analysed based on the spatio-temporal pattern behaviours observed during the year 2019 and 2020, which is before pandemic and during pandemic situations respectively, in Great Britain. The outputs of the analysed spatio-temporal traffic pattern variation behaviours will be useful in the fields of traffic management in Intelligent Transportation System and management in various stages of pandemic or unavoidable scenarios in-relation to road traffic. © 2021, Springer Nature Singapore Pte Ltd.

Understanding the Spatio-temporal Topic Dynamics of Covid-19 using Nonnegative Tensor Factorization: A Case Study

Social media platforms facilitate mankind a data-driven world by enabling billions of people to share their thoughts and activities ubiquitously. This huge collection of data, if analysed properly, can provide useful insights into people's behavior. More than ever, now is a crucial time under the Covid-19 pandemic to understand people's online behaviors detailing what topics are being discussed, and where (space) and when (time) they are discussed. Given the high complexity and poor quality of the huge social media data, an effective spatio-temporal topic detection method is needed. This paper proposes a tensor-based representation of social media data and Non-negative Tensor Factorization (NTF) to identify the topics discussed in social media data along with the spatio-temporal topic dynamics. A case study on Covid-19 related tweets from the Australia Twittersphere is presented to identify and visualize spatio-temporal topic dynamics on Covid-19.

Do market and herding effect really impact on investment decision making in the Indian share market?

“Mad March – 2020, witnessed dramatic down-slide in the world‟s top stock exchanges due to COVID-19 pandemic with worrying volatility which resulted in traders panic sold off their holdings out of fear”.2020‟s first quarter witnessed substantial losses in the several well-recognized stock indices, especially between March 6 to 18, more than 20% that were triggered downward by the outbreak of COVID-19. Dow Jones Industrial Average and S&P 500 experienced the worst first quarter ever in the history during the year 2020 reducing its value by 23.2%. The year 2020 witnessed several historical landmark changes in the Indian share market movements along with other prominent stock exchanges of the globe. On March 23rd, 2020, Benchmark index SENSEX touched intraday lowest value of 25880 and NIFTY fell to the lowest value of 7583. Throughout the globe, including Indian investors, started to rush for clearing their holdings ahead of dark lines created by the pandemic in spite of most of the financial analysts‟ suggestion for fresh buy and/or to hold previous purchase for long. Supporting financial experts‟ views, within the next nine months SENSEX has gained around 100% and stood at 48834.34 on 8th Jan 2021. There are many studies both in India and outside the country that have provided evidence for the role of behavioral factors on investment decision-making at respective stock markets. Here authors attempted to verify, „weather market factor and herding effect of behavioral variables do influences on investment decision making of Indian share market investors?‟ © 2021 Karadeniz Technical University. All rights reserved.