Pairwise and Network Meta-Analysis of Antiviral and other Treatment Efficacy in Covid-19 Patients

A total of 77 literatures till November 2020 were screened regarding various interventions to treat COVID-19 patients, among which 16 and 15 studies fulfilling predefined exclusion and inclusion criteria were subjected to Pairwise and Network meta-analysis respectively. In Pairwise meta-analysis, the recovery rate of treatment with Lopinavir/Ritonavir versus other antiviral (OR= 0. 0381, CI= 0.0021-0.6870), placebo (OR= 0.6592, CI= 0.4207-1. 0329), Remdesivir (OR= 0.5286, CI= 0.3915-0.7137) and standard care (OR= 0.9787, CI= 0.8523-1.1238) in fixed and random effect model with 95% confidence limit found statistically significant protection than those of all other treatment. In Network meta-analysis, recovery estimates sizes of treatment, in reference with other antivirals 1.0000 (0.9917, 1.0000) shows less risk with treatment Standard care 0.7811 (0.6696, 0.8417), Remdesivir 0.7717 (0.6491, 0.8144), Lopinavir/ Ritonavir 0.7801 (0.6701, 0.8473), Placebo 0.7219 (0.6178, 0.7836).Copyright © 2023, Institute of Medico-legal Publication. All rights reserved.

QUALITY OF LIFE (QOL) DURING COVID-19 PANDEMIC AMONG DAILY WAGES WORKERSIN URBAN CHENNAI-A DESCRIPTIVE CROSS SECTIONAL STUDY

Introduction: A new era started at the end of February 2020 with the novel coronavirus pandemic, which was found to be sufficiently divergent from the extreme acute respiratory syndromeand which has changed our lives entirely.COVID-19 patients are the topic of many epidemiological studies.However, the goal of this study is to evaluate the quality of life of individuals working for daily wages who are not affected by COVID-19 following the quarantine in Chennai, a city in Southern India. Materials and methods: Descriptive cross-sectional study involving 300 daily wage workers in Chennai was conductedfollowing the announcement of quarantine due to the COVID-19 outbreak. We did the Survey to determine the quality of life, which takes into account the mental health status, financial management and other health issues.The Institutional Review Board gave their approval to the study. All participants agreed to sign the written informed consent form. The data were gatheredby face to face interview with the help of apre-tested and pre-validated questionnaire where theparticipants indicated their socio-demographic details, Physical and mental health status andthe problems faced during lockdown. Data was entered in SPSS version 21 and the results were given in frequencies and percentages. Results: Over all, the quality of life among all the study participants was very poor. Male population had very poor score on physical, psychological and environmental health while comparing to female. Also 46-55 age groups were affected more than the other age groups. Illiterateswere affected more than the population who are having education upto higher secondary. Those who had hypertension and diabetes also had poor score on all three domains. Conclusion: The study findings showed that most of the daily wages population had poor mental health, physical health and environmental status. We must pay attention to the health of the individuals who are considered poor, since their quality of life is low.

Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques.

Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy.

Analysis of COVID-19 vaccination progress using machine learning approach

The past two decades were marked with the outbreaks of many viral diseases such as Chikungunya, Ebola, Zika, Nipah, H7N9 Bird flu, H1N1, SARS and MERS. With a new disease outbreak, the world awoke to this decade. A new Coronavirus outbreak occurred in Wuhan, Hubei Province, China, in December 2019. The majority of the patients who were initially discovered were connected to the 'wet market', a location a location where live animals are treated and sold. The market could have served as a source of amplification from which the virus began to spread to many other areas of China and, eventually, to 213 nations and territories in a very short period of time. On 11 February 2020, The WHO is the World Health Organization dubbed this disease 'COVID-19,' which is an abbreviation for Coronavirus Disease 2019. © 2022 Author(s).

Epidemiological determinants of acute respiratory syndrome Coronavirus-2 disease pandemic and the role of the Bacille-Calmette-Guerin vaccine in reducing morbidity and mortality. (Special Issue: Coronaviruses and COVID-19- Past, Present, and Future.)

This study analyzed the determinants of morbidity, mortality, and case fatality rate (CFR) of the ongoing pandemic of severe acute respiratory syndrome coronavirus-2 disease 2019 (COVID-19). Data for 210 countries and territories available in public domains were analyzed in relation to mandatory vaccination with Bacille-Calmette-Guerin (BCG), population density, median age of the country population, health care expenditure per capita, life expectancy at birth, healthy life expectancy, literacy rate, per capita gross domestic production adjusted to purchasing power (PPP), burden of tuberculosis (TB), acquired immunodeficiency disease caused by human immunodeficiency virus (HI V-AIDS), malaria, cardiovascular disease (CVD), neoplasm, diabetes, deaths due to energy-protein (food) deficiency (EPD), and per capita government spending on safe water and sanitation. Mandatory BCG vaccination showed a highly significant (p < 0.0001) negative correlation with COVID-19 morbidity (r = -0.62) and mortality (r = -0.58) rates, but no significant correlation with CFR. The median age of the nation showed a significant (p < 0.0001) positive correlation with COVID-19 morbidity (r= 0.40) and mortality (r = 0.34) rates, but no significant correlation with CFR. The pandemic resulted in higher morbidity (r= 0.47, p < 0.0001) and mortality (r= 0.25, p = 0.01) rates in countries with a higher PPP than in those with a lower PPP. COVID-19 CFR and morbidity and mortality rates showed no significant correlation with population density, the burden of malaria or diabetes, or the level of spending on safe water and sanitation. Only the burden of TB showed a positive correlation with CFR (r = 0.17, p = 0.05). However, COVID-19 morbidity showed a significant (p 0.05) negative correlation with the burden of TB, HI V-AIDS, CVD, and EPD. Mortality and morbidity in COVID-19 patients showed a positive correlation with per capita health expenditure, life expectancy, the burden of neoplasia, and PPP.

Risk assessment approaches during the novel coronavirus (COVID-19) pandemic

The coronavirus disease (COVID-19) has rapidly spread all over the world affecting more than 20 million people. Early planning and preparedness are vital in mitigation of the impact of the ongoing pandemic. The lessons from previous public health emergencies of the 21st century such as Influenza A (H1N1), Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and Ebola elucidate that the risks assessment associated with disease outbreaks remains integral for the successful response curbing health disasters. Every pandemic or public health emergency poses new challenges. Alike, the SARS-CoV-2 which causes COVID-19 will continue to challenge public health systems and their ability to effectively communicate with the public. Till date, no vaccines and specific antiviral drugs are available moreover there is little evidence on the effectiveness of potential therapeutic agents. Many countries have practiced 'stay-at-home', social distancing, avoided mass gathering, closure of education in institutions, strict public health measures like compulsory wearing of masks, entertainment and public spaces. For effective prevention and control of any infectious diseases the risk assessment and communication are of prime importance. Hence, there is an urgent need to commence SARS-CoV-2 risk assessment for assisting decision-making bodies working on SARS-CoV-2 pandemic. This paper discusses in brief about the rapid risk assessment and diagnostic approaches for COVID-19 pandemic.

Depression and anxiety among students community during COVID-19 pandemic lockdown in Tamil nadu- A web based descriptive cross sectional study

Background: The COVID-19 pandemic has had an appreciable impact on public mental health. Hence continuous monitoring of the population's mental health especially during a pandemic demands immediate attention. The COVID-19 pandemic contributing to a rise in depression and anxiety among college students, with more than one third of them experiencing detrimental mental health challenges. Objective: The objective of this study was to investigate the prevalence of depression and anxiety during the COVID-19 pandemic among medical college students. Methods: This study was conducted among 500 medical college students, after getting approval from the Institutional Ethical Committee. Pre-structured questionnaire-based DASS21 scale was used for this study through Google form after obtaining written consent from the students. Using Google Form, a structured e-questionnaire was created and the link was shared via email. All the data were analysed using SPSS ver24. Results are given in frequency and percentage Results: In our study, we found that prevalence of depression and anxiety, among college students was 16%, and 18%, respectively. Conclusion: In our study, we found that anxiety, depression, and stress were present among medical students. In COVID-19 phase, students were not attending medical colleges and all the teaching process was online. Even at home, medical students were suffering from anxiety, depression, and stress. Early evaluation and intervention should be prioritized to reduce these morbidities among the medical students.,Depression, anxiety, college students.

Epidemiological Determinants of Acute Respiratory Syndrome Coronavirus-2 Disease Pandemic and The Role of the Bacille-Calmette-Guerin Vaccine in Reducing Morbidity and Mortality

This study analyzed the determinants of morbidity, mortality, and case fatality rate (CFR) of the ongoing pandemic of severe acute respiratory syndrome coronavirus-2 disease 2019 (COVID-19). Data for 210 countries and territories available in public domains were analyzed in relation to mandatory vaccination with Bacille-Calmette-Guerin (BCG), population density, median age of the country population, health care expenditure per capita, life expectancy at birth, healthy life expectancy, literacy rate, per capita gross domestic production adjusted to purchasing power (PPP), burden of tuberculosis (TB), acquired immunodeficiency disease caused by human immunodeficiency virus (HI V-AIDS), malaria, cardiovascular disease (CVD), neoplasm, diabetes, deaths due to energy-protein (food) deficiency (EPD), and per capita government spending on safe water and sanitation. Mandatory BCG vaccination showed a highly significant (p<0.0001) negative correlation with COVID-19 morbidity (r = -0.62) and mortality (r = -0.58) rates, but no significant correlation with CFR. The median age of the nation showed a significant (p<0.0001) positive correlation with COVID-19 morbidity (r= 0.40) and mortality (r = 0.34) rates, but no significant correlation with CFR. The pandemic resulted in higher morbidity (r= 0.47, p<0.0001) and mortality (r= 0.25, p = 0.01) rates in countries with a higher PPP than in those with a lower PPP. COVID-19 CFR and morbidity and mortality rates showed no significant correlation with population density, the burden of malaria or diabetes, or the level of spending on safe water and sanitation. Only the burden of TB showed a positive correlation with CFR (r = 0.17, p = 0.05). However, COVID-19 morbidity showed a significant (p =0.05) negative correlation with the burden of TB, HI V-AIDS, CVD, and EPD. Mortality and morbidity in COVID-19 patients showed a positive correlation with per capita health expenditure, life expectancy, the burden of neoplasia, and PPP.