Application and utility of boosting machine learning model based on laboratory test in the differential diagnosis of non-COVID-19 pneumonia and COVID-19.

BACKGROUND: Non-Coronavirus disease 2019 (COVID-19) pneumonia and COVID-19 have similar clinical features but last for different periods, and consequently, require different treatment protocols. Therefore, they must be differentially diagnosed. This study uses artificial intelligence (AI) to classify the two forms of pneumonia using mainly laboratory test data. METHODS: Various AI models are applied, including boosting models known for deftly solving classification problems. In addition, important features that affect the classification prediction performance are identified using the feature importance technique and SHapley Additive exPlanations method. Despite the data imbalance, the developed model exhibits robust performance. RESULTS: eXtreme gradient boosting, category boosting, and light gradient boosted machine yield an area under the receiver operating characteristic of 0.99 or more, accuracy of 0.96-0.97, and F1-score of 0.96-0.97. In addition, D-dimer, eosinophil, glucose, aspartate aminotransferase, and basophil, which are rather nonspecific laboratory test results, are demonstrated to be important features in differentiating the two disease groups. CONCLUSIONS: The boosting model, which excels in producing classification models using categorical data, excels in developing classification models using linear numerical data, such as laboratory tests. Finally, the proposed model can be applied in various fields to solve classification problems.

COVID-19 and the burden of ill-health: a double crisis of disruptions and inequalities.

In this paper, we attempt to show how the novel coronavirus disease (COVID-19) has disrupted routine health services in India and has created further inequalities in the society. By taking a few examples of non-COVID diseases and conditions like immunization, maternal health services, tuberculosis and non-communicable diseases, this paper shows how these services have been disrupted by the pandemic. The paper argues that these disruptions have not emerged only as a result of the current crisis, but because of the paradigm shifts in the healthcare delivery in the country towards privatization which have disproportionately marginalized particular sections of the society. The paper concludes by stating that if adequate measures are not taken now to transform the health system and strengthen the public healthcare system, it might lead to catastrophic consequences in the future, especially for the marginalized sections.

Mortality and Health Spending during the First Year of the COVID-19 Pandemic. Comparing Central, Eastern and Western Europe

The article shows the relationships between the COVID and non-COVID deaths during the first year of the pandemic, compared with the stringency of restrictions imposed and the compul- sory spending on healthcare. We compare these relationships among European countries, analysing weekly data and applying cointegration models. Regarding the pandemic's inten- sity, we split the period into two: March - August 2020 and September 2020 - February 2021. We find that, most often, if there was a relationship between the stringency index and COVID or non-COVID mortality, it was usually positive and mortality driven. That sug- gests that although the governments tailored the restrictions to the growing mortality rate, they were unable to control the pandemic. No relationships, or negative ones, were most of- ten found in these countries where the spending on healthcare was the highest (i.e., Northern and Western European countries). The biggest weekly changes in non-COVID deaths during the second sub-period were observed in the Central and Eastern European countries, where government healthcare expenditures per capita are the lowest.

Temporary suspension of visiting as experienced by non-covid-19 patients undergoing orthopaedic surgery, their relatives, and health professionals, a multimethod study

Background: Hospitals worldwide implemented visitor restrictions policies due to the SARS-CoV-2 pandemic. Aim: To investigate the impact of visitor restrictions experienced by non-Covid-19 orthopaedic patients, their relatives, and health professionals from a person-centred practice (PCP) perspective. Methods: Seventy-eight patients/relatives completed a self-reported survey. Template Analysis was conducted on twenty-five telephone interviews and one focus group interview. Findings: Despite accepting the restrictions for the country's good, we found a profound need for visits among the relatives. However, one-fifth of the patients were content, and one-third got more rest than earlier admissions. We uncovered a current readiness to develop an organisational PCP culture focused on the good of the patient. All stakeholders were attentive to the importance of the relatives' role, and the need for the involvement of relatives as team players became evident. Conclusion: The study emphasised the need for PCP strategies to involve relatives when visitor restrictions are necessary.

Crowded Out: The Pandemic's Toll on Non-COVID Patients in India

During the COVID-19 pandemic, the focus on treating the affected patients led to a decline in the quantity and quality of care for non-COVID patients. Globally, there are several accounts of cancellations of non-essential procedures (e.g. knee replacements), difficulties in access to cancer care and general apprehension of people to visit hospitals. The primary consequence was an increase in out-of-hospital mortality driven by endocrine, cardiovascular and neoplasms which were largely unaccounted for. In India, studies estimate that in the first four months of lockdown (April–July 2020) there was a 22–25% increase in excess mortality across non-COVID patients who required dialysis. Across all subsequent waves, access to healthcare was difficult for non-COVID patients. Both medical and surgical admissions for cancer and heart disease declined. Similar experiences were recorded across Italy, Slovenia, Belgium and other parts of the world. Some of the possible drivers include reduced ability to seek health care due to lockdowns, apprehension on being infected and lack of clear information on infection control measures. In addition, new diseases and risk factors such as mucormycosis were seen especially in India, where the estimated prevalence was 70 times that of global data. Those affected most were women and the disadvantaged populations. Further, experience from previous epidemics, such as that seen in Zambia, hold important lessons. In 2003, the outbreaks of AIDS resulted in the Govt. of Zambia diverting the total health budget towards AIDS care. Whereas pandemics may require a reallocation of health resources, it is critical to find the balance between managing an underlying disease burden and those affected by the pandemic. This requires better preparedness through strengthening health infrastructure, early warning systems and emergency response plans. © TheEditor(s) (ifapplicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021, 2022.

On-scene time delays for epileptic seizures in emergencies during a social pandemic: A population-based study.

OBJECTIVES: The on-scene time of Emergency Medical Services (EMS), including time for hospital selection, is critical for people in an emergency. However, the outbreak of the novel coronavirus disease 2019 (COVID-19) led to longer delays in providing immediate care for individuals with non-COVID-19-related emergencies, such as epileptic seizures. This study aimed to examine factors associated with on-scene time delays for people with epilepsy (PWE) with seizures needing immediate amelioration. MATERIALS & METHODS: We conducted a population-based retrospective cohort study for PWE transported by EMS between 2016 and 2021. We used data from the Hiroshima City Fire Service Bureau database, divided into three study periods: "Pre period", the period before the COVID pandemic (2016-2019); "Early period", the early period of the COVID pandemic (2020); and "Middle period", the middle period of the COVID pandemic (2021). We performed linear regression modeling to identify factors associated with changes in EMS on-scene time for PWE during each period. In addition, we estimated the rate of total EMS call volume required to maintain the same on-scene time for PWE transported by EMS during the pandemic expansion. RESULTS: Among 2,205 PWE transported by EMS, significant differences in mean age and prevalence of impaired consciousness were found between pandemic periods. Total EMS call volume per month for all causes during the same month <5,000 (-0.55 min, 95% confidence interval [CI] -1.02 - -0.08, p = 0.022) and transport during the Early period (-1.88 min, 95%CI -2.75 - -1.00, p < 0.001) decreased on-scene time, whereas transport during the Middle period (1.58 min, 95%CI 0.70 - 2.46, p < 0.001) increased on-scene time for PWE transported by EMS. The rate of total EMS call volume was estimated as 0.81 (95%CI -0.04 - 1.07) during the expansion phase of the pandemic to maintain the same degree of on-scene time for PWE transported by EMS before the pandemic. CONCLUSIONS: On-scene time delays on PWE in critical care settings were observed during the Middle period. When the pandemic expanded, the EMS system required resource allocation to maintain EMS for time-sensitive illnesses such as epileptic seizures. Timely system changes are critical to meet dramatic social changes.

Impact of conducting hand hygiene audit in COVID-19 care locations of India-A large scale national multicentric study - HHAC study.

PURPOSE: Hand hygiene (HH), the core element in infection prevention in healthcare, especially for multidrug resistant organism's transmission. The role of HH audits and HH adherence rates in the COVID-19 pandemic, especially in resource limited settings, are yet to be established. METHODS: A nationwide multicenter study was conducted in India, involving public, private, teaching and non-teaching COVID healthcare facilities (COVID-HCFs) using the IBhar mobile application based on WHO's hand hygiene audit tool. The HH adherence rates (HHAR) such as complete HHAR (HHCAR), total HHAR (HHTAR), profession specific HHAR, WHO's 5 HH moment specific HHAR and associated variables were measured over 6 month duration (June-December 2021). RESULTS: A total of 2,01,829 HH opportunities were available and the HHCAR and HHTAR were 27.3% and 59.7%. The HHTAR was significantly higher in the west zone (72.2%), private institutes (65.6%), non-teaching institutes (67.7%), nurses (61.6%), HH moments 2 (71.8%) and 3 (72.1%), and morning shift (61.4%). The HHTAR was better in non-COVID HCFs (65.4%) than COVID-HCFs (57.8%) as well as non-COVID ICUs (68.1%) than COVID ICUs (58.7%). The HHTAR was increased from month 1 to month 6 except a small decrease in the month of December. CONCLUSIONS: The hand hygiene adherence is comparable with adherence rate during COVID-19 pandemic in western countries as well as the resource limited settings. The use of gloves during the pandemic and simplified HH techniques and their influence over the HH adherence to be studied further. The sustainable adherence rate over long duration needs to be ensured by continuing the HH audit using multimodal interventions.

Examination of Gender Difference in Heart Disease-Related Excess Deaths during COVID-19 Pandemic Era: Findings from the United States

Background/Objective: Heart disease is the leading cause of death among women in the United States, and women are experiencing more strokes at younger ages than men. Despite accumulating evidence of increased burden of heart disease among women, there is little data on gender difference in heart disease-related mortality during the COVID-19 pandemic. Method(s): This study extracted the data of weekly number of deaths between January 2017 and December 2020 from the United States Center for Disease and Control and Prevention (CDC) mortality and morbidity data, modified to a monthly scale. Stratified by gender, the study applied the Farrington method on monthly data to calculate excess number of deaths. Excess heart disease-related deaths were observed in March and July 2020 for both males and females. Result(s): While the overall number of heart disease-related deaths was higher in men than women among US population <75 years old, a greater rate increase of heart disease-related deaths in 2020 from 2019 was observed among women than men. This increased burden was more pronounced among young women <25 years old. A similar pattern of excess deaths caused by underlying heart disease condition was observed for both genders during COVID-19 pandemic. On the other hand, increase in heart disease-related death burden in 2020 from 2019 was greater amongst females than males. This may be partially accounted for by deferred cardiovascular care and prevention amongst women during the pandemic. Conclusion(s): While no gender difference was observed in excess deaths caused by underlying heart disease condition, females faced a greater increase in heart disease-related death burden during the pandemic compared to pre-pandemic than males. Copyright: Copyright © 2022 The Author(s).

Forging clinical collaborations and cooperation in overcoming challenges for non-COVID-19 patients during COVID-19 times: Surgical case studies requiring a multidisciplinary and inter-agency approach in Malaysia

The COVID-19 pandemic has brought global health services to a standstill. National healthcare systems and medical staffing in many countries have reached crisis levels due to the phenomenal outbreak. Healthcare resources have been strained to meet the unprecedented numbers of patient admissions with a significant amount of funding and manpower being channelled towards tackling this global pandemic. Despite the rollout of vaccinations, the development of new viral strains has now presented a new challenge. With the inevitable conversion of tertiary public hospitals to specialized COVID-19 centres with 'Full Covid Status' and the mobilization of its doctors from all specialities to care for these patients, the non-COVID-19 patients are becoming more neglected. The lack of elective surgeries performed and non-emergent admissions due to the unavailability of beds and personnel to care for this group of patients are concerning. As most of the focus and resources are now aimed at COVID-19 patients, the need to forge collaborations and cooperation between hospitals, agencies and healthcare systems are pertinent to ensure the provision of quality treatment for those suffering from non-COVID-19 diseases. To highlight this effort in Malaysia, we would like to present 2 case studies of non-COVID-19 patients undergoing elective surgeries through intergovernmental ministerial collaborations and a public-private partnership.Copyright © The Author(s) 2022.

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19.

The SARS-CoV-2 virus causes a respiratory disease in humans, known as COVID-19. The confirmatory diagnostic of this disease occurs through the real-time reverse transcription and polymerase chain reaction test (RT-qPCR). However, the period of obtaining the results limits the application of the mass test. Thus, chest X-ray computed tomography (CT) images are analyzed to help diagnose the disease. However, during an outbreak of a disease that causes respiratory problems, radiologists may be overwhelmed with analyzing medical images. In the literature, some studies used feature extraction techniques based on CNNs, with classification models to identify COVID-19 and non-COVID-19. This work compare the performance of applying pre-trained CNNs in conjunction with classification methods based on machine learning algorithms. The main objective is to analyze the impact of the features extracted by CNNs, in the construction of models to classify COVID-19 and non-COVID-19. A SARS-CoV-2 CT data-set is used in experimental tests. The CNNs implemented are visual geometry group (VGG-16 and VGG-19), inception V3 (IV3), and EfficientNet-B0 (EB0). The classification methods were k-nearest neighbor (KNN), support vector machine (SVM), and explainable deep neural networks (xDNN). In the experiments, the best results were obtained by the EfficientNet model used to extract data and the SVM with an RBF kernel. This approach achieved an average performance of 0.9856 in the precision macro, 0.9853 in the sensitivity macro, 0.9853 in the specificity macro, and 0.9853 in the F1 score macro.

A Markov Chain Monte Carlo (MCMC) Multivariate Analysis of the Association of Vital Parameter Variation With the Lunar Cycle in Patients Hospitalized With COVID-19.

INTRODUCTION: Over the last three years, the world has been battling a long-drawn pandemic resulting from the coronavirus outbreak. Despite the safety measures, there have been multiple pandemic waves happening throughout the world. Therefore, it is necessary to understand the fundamental characteristics of COVID-19 transmission and pathogenesis to overcome the threat of the pandemic. This study focused on hospitalized COVID-19 patients because of their high mortality rate, which indicates the need to improve inpatient management. METHODS: Based on the cyclic nature of the pandemic, observations were made to examine the influence of lunar phases on six vital parameters of COVID-19 patients. A multivariate analysis was carried out to study the interactions of lunar phase pairwise on COVID-19 statuses and COVID-19 status pairwise on lunar phases by treating six vital parameters as independent entities. RESULTS: The results of multivariate analysis on the data of 215,220 vital values showed that lunar phases are associated with trends in variations in the vital parameters of COVID-19-infected patients. CONCLUSION: In summary, our results show that patients infected with COVID-19 appear to be more susceptible to lunar influence compared to non-COVID-19 patients. Furthermore, this study shows a vital parameter destabilization window (DSW) that can help identify which hospitalized COVID-19 patients can recover. Our pilot study forms the basis for future studies to eventually establish the incorporation of variation of vital signs with the lunar cycle into the standard of care for COVID-19 patients.

Burden of Influenza and Respiratory Syncytial Viruses in Suspected COVID-19 Patients: A Cross-Sectional and Meta-Analysis Study.

Non-SARS-CoV-2 respiratory viral infections, such as influenza virus (FluV) and human respiratory syncytial virus (RSV), have contributed considerably to the burden of infectious diseases in the non-COVID-19 era. While the rates of co-infection in SARS-CoV-2-positive group (SCPG) patients have been determined, the burden of other respiratory viruses in the SARS-CoV-2-negative group (SCNG) remains unclear. Here, we conducted a cross-sectional study (São José do Rio Preto county, Brazil), and we collected our data using a meta-analysis to evaluate the pooled prevalence of FluV and RSV among SCNG patients. Out of the 901 patients suspected of COVID-19, our molecular results showed positivity of FluV and RSV in the SCNG was 2% (15/733) and 0.27% (2/733), respectively. Co-infection with SARS-CoV-2 and FluV, or RSV, was identified in 1.7% of the patients (3/168). Following our meta-analysis, 28 studies were selected (n = 114,318 suspected COVID-19 patients), with a pooled prevalence of 4% (95% CI: 3-6) for FluV and 2% (95% CI: 1-3) for RSV among SCNG patients were observed. Interestingly, FluV positivity in the SCNG was four times higher (OR = 4, 95% CI: 3.6-5.4, p < 0.01) than in the SCPG. Similarly, RSV positivity was significantly associated with SCNG patients (OR = 2.9, 95% CI: 2-4, p < 0.01). For subgroup analysis, cold-like symptoms, including fever, cough, sore throat, headache, myalgia, diarrhea, and nausea/vomiting, were positively associated (p < 0.05) with the SCPG. In conclusion, these results show that the pooled prevalence of FluV and RSV were significantly higher in the SCNG than in the SCPG during the early phase of the COVID-19 pandemic.

THE IMPACT OF COVID-19 PANDEMIC ON DIAGNOSIS AND TREATMENT IN OTORHINOLARYNGOLOGY PATIENTS IN CROATIA.

COVID-19 pandemic resulted in a decrease in the number of diagnostic and therapeutic procedures in most ENT departments. We performed a survey among ENT specialists in Croatia aiming to assess how the pandemic influenced their practice, and consequently the patient diagnosis and treatment. The majority of the 123 participants who completed the survey stated that there was a delay in diagnosis and treatment of ENT diseases, which they expected to have negative effects on patient outcomes. Since the pandemic is still ongoing, there is the need for improvement at different levels of the healthcare system to minimize the consequences of the pandemic in non-COVID patients.

The impact of the COVID-19 pandemic and related policy responses on non-COVID-19 healthcare utilization in China.

We investigate the impact of the COVID-19 pandemic and related policy responses on non-COVID-19 healthcare utilization (i.e., different types of outpatient care) up to October 2020 in China. Using an administrative database from a large prefecture-level city, we find that both direct exposure to the COVID-19 pandemic and the strict containment policy responses led to reductions in outpatient care utilization. The largest decline during the lockdown was observed in preventive care visits, which nevertheless recovered to pre-pandemic levels 2 months after the lockdown. The disruptions in prenatal care visits could not be offset by the recovery later on. Chronic care and emergency department visits had not returned to pre-pandemic levels as of October 2020, which may be driven by extended days' supply of prescription medication, increased use of telemedicine, and improved health-protective behaviors. In the reopening period, there were increases in visits for mental and sleep disorders, especially among children, and for medical abortion. Among health facilities at all levels, primary care facilities saw the least reductions in total outpatient visits. Our results emphasize the need for strategies to ensure access to urgent or essential care services when managing the current epidemiologic transition and future crises.

T-cell dysfunction as a potential contributing factor in post-COVID-19 mucormycosis.

BACKGROUND: The second wave of COVID-19 in India was followed by large number of mucormycosis cases. Indiscriminate use of immunosuppressive drugs, underlying diseases such as diabetes, cancers, or autoimmune diseases was thought to be the cause. However, the mortality was not as high as that seen in non-COVID mucormycosis. OBJECTIVE: To study the detailed characteristics of T-cells for evaluating the underlying differences in the T-cell immune dysfunction in post-COVID and non-COVID mucor patients. MATERIAL AND METHOD: The study included histopathologically confirmed cases of mucor (13 post-COVID, 13 non-COVID) and 15 healthy individuals (HI). Expression of T-cell activation (CD44, HLADR, CD69, CD38) and exhaustion (CTLA, PD-1, LAG-3 and TIM-3) markers was evaluated by flow cytometry. RESULTS: All cases showed significant depletion of T-cells compared to HI. Both post-COVID and non-COVID groups showed increased activation and exhaustion as compared to HI. Non-COVID mucor group showed significant activation of CD4+ T cells for HLADR and CD38 (p = .025, p = .054) and marked T-cell exhaustion in form of expression of LAG-3 on both CD4+ T and CD8+ T cells in comparison with post-COVID patients (p = .011, p = .036). Additionally, co-expression of PD-1 & LAG-3 and LAG-3 & TIM-3 on CD8+ T cells was statistically significant in non-COVID mucor patients (p = .016, p = .027). CONCLUSION: Immunosuppression in non-COVID mucor showed pronounced exhaustion of T-cells in comparison to post-COVID mucor cases implicating T-cell immune dysfunction is much more severe in non-COVID mucor which are in a state of continuous activation followed by extreme exhaustion leading to poorer outcome.

Interpretable Differential Diagnosis of Non-COVID Viral Pneumonia, Lung Opacity and COVID-19 Using Tuned Transfer Learning and Explainable AI.

The coronavirus epidemic has spread to virtually every country on the globe, inflicting enormous health, financial, and emotional devastation, as well as the collapse of healthcare systems in some countries. Any automated COVID detection system that allows for fast detection of the COVID-19 infection might be highly beneficial to the healthcare service and people around the world. Molecular or antigen testing along with radiology X-ray imaging is now utilized in clinics to diagnose COVID-19. Nonetheless, due to a spike in coronavirus and hospital doctors' overwhelming workload, developing an AI-based auto-COVID detection system with high accuracy has become imperative. On X-ray images, the diagnosis of COVID-19, non-COVID-19 non-COVID viral pneumonia, and other lung opacity can be challenging. This research utilized artificial intelligence (AI) to deliver high-accuracy automated COVID-19 detection from normal chest X-ray images. Further, this study extended to differentiate COVID-19 from normal, lung opacity and non-COVID viral pneumonia images. We have employed three distinct pre-trained models that are Xception, VGG19, and ResNet50 on a benchmark dataset of 21,165 X-ray images. Initially, we formulated the COVID-19 detection problem as a binary classification problem to classify COVID-19 from normal X-ray images and gained 97.5%, 97.5%, and 93.3% accuracy for Xception, VGG19, and ResNet50 respectively. Later we focused on developing an efficient model for multi-class classification and gained an accuracy of 75% for ResNet50, 92% for VGG19, and finally 93% for Xception. Although Xception and VGG19's performances were identical, Xception proved to be more efficient with its higher precision, recall, and f-1 scores. Finally, we have employed Explainable AI on each of our utilized model which adds interpretability to our study. Furthermore, we have conducted a comprehensive comparison of the model's explanations and the study revealed that Xception is more precise in indicating the actual features that are responsible for a model's predictions.This addition of explainable AI will benefit the medical professionals greatly as they will get to visualize how a model makes its prediction and won't have to trust our developed machine-learning models blindly.

The Pattern of Non-Covid-19 Paediatric Admissions in Malaysia Teaching Hospitals: Before and during the Covid-19 Pandemic with Movement Control Order

To study the characteristics of paediatric patients admitted to two non-COVID-19 teaching hospitals before and during the implementation of the Movement Control Order (MCO) in Malaysia. The retrospective study was performed in two phases (before MCO between February to March 2020, and during MCO lockdown between March to April 2020) in two teaching hospitals on the East Coast of Peninsular Malaysia. Hospitalized children <18 years were included and those coming for elective procedures or oncology treatment were excluded. The clinical data were retrieved from both hospitals' admission records. There was a total of 496 and 191 admissions to two teaching hospitals on the East Coast of Peninsular Malaysia, respectively. A significant reduction in the number of non-COVID-19 hospital admissions was seen in both hospitals. For Hospital Universiti Sains Malaysia, the daily hospital admissions were reduced with a mean of 10 (before MCO) to 7 (during MCO) admissions/day (95% CI 1.54,7.54, p=0.001). In Sultan Ahmad Shah Medical Centre, a reduction in hospital admission was seen from 5 (before MCO) to 3 (during MCO) admissions/day (95% CI 0.61, 3.15, p=0.005). Our study observed a general drop in non-COVID-related respiratory illnesses and infectious disease cases during the MCO period. There were significant differences in neurological (p=0.029) and accident (p = 0.001) cases admissions observed between the two periods. Copyright © 2022, Faculty of Medicine, University of Malaya. All rights reserved.

Assessment of exposure risk among healthcare workers exposed to confirmed cases of COVID-19 working in non-COVID zones of a teaching hospital in Northeast India: A cross-sectional study.

Introduction: Healthcare workers (HCWs) are at increased risk of acquiring the COVID-19 disease, if there is a breach in the personal protection while managing patients. Objectives: 1. To estimate the pattern of risk exposure among healthcare workers exposed to confirmed cases of COVID-19 working in non-COVID zones of a Teaching Hospital, North-East India. 2. To determine the association between pattern of exposure with their COVID-19 status. Materials and Method: This was a hospital-based cross-sectional study conducted among all HCWs who had occupational exposure to laboratory confirmed COVID-19 cases between July and September 2020 in-non COVID zones of a major tertiary care hospital in Tripura. Results: The present study showed that 215 HCWs were exposed to confirmed cases of COVID-19 while working in non-COVID zones of the hospital. Among the exposed, 39.5% of HCWs had high-risk exposure and 8.8% of HCWs were detected as COVID-19 positive. A majority of the study subjects experienced exposure in the hospital wards (66.0%), in surgical departments (19.5%), had close contact (less than one meter distance) with positive COVID-19 cases (73.5%), and had an exposure of more than 15 minutes (51.2%). The COVID status of the exposed HCWs was significantly associated with no source control (P = 0.016), close contact with COVID-19 positive cases (P = 0.026), more duration of exposure (P < 0.05), use of any PPE (P = 0.000). COVID status was also significantly associated with the high-risk exposure of the participants (P = 0.000). Conclusion: Strict enforcement of the infection control measures like universal precautions should be practiced by HCWs to prevent hospital-acquired infections.

COVID-19: Salt in the Wound of Health Care Inequality and the Cause of a New Health Care Disparity.

The year 2020 will forever be associated with a new chapter in the history of global health, COVID-19. However, this new chapter would have a similar message because many other chapters were written before it. The COVID-19 pandemic would disproportionately affect minorities, those of low socioeconomic class, and those with limited access to health care. However, COVID-19 would also bring about a new health care disparity, limiting access to medical care for those with non COVID-19 related medical needs.

Coronavirus disease 2019 (COVID-19) excess mortality outcomes associated with pandemic effects study (COPES): A systematic review and meta-analysis.

Background and aim: With the Coronavirus Disease 2019 (COVID-19) pandemic continuing to impact healthcare systems around the world, healthcare providers are attempting to balance resources devoted to COVID-19 patients while minimizing excess mortality overall (both COVID-19 and non-COVID-19 patients). To this end, we conducted a systematic review (SR) to describe the effect of the COVID-19 pandemic on all-cause excess mortality (COVID-19 and non-COVID-19) during the pandemic timeframe compared to non-pandemic times. Methods: We searched EMBASE, Cochrane Database of SRs, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Cochrane Controlled Trials Register (CENTRAL), from inception (1948) to December 31, 2020. We used a two-stage review process to screen/extract data. We assessed risk of bias using Newcastle-Ottawa Scale (NOS). We used Critical Appraisal and Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Results: Of 11,581 citations, 194 studies met eligibility. Of these studies, 31 had mortality comparisons (n = 433,196,345 participants). Compared to pre-pandemic times, during the COVID-19 pandemic, our meta-analysis demonstrated that COVID-19 mortality had an increased risk difference (RD) of 0.06% (95% CI: 0.06-0.06% p < 0.00001). All-cause mortality also increased [relative risk (RR): 1.53, 95% confidence interval (CI): 1.38-1.70, p < 0.00001] alongside non-COVID-19 mortality (RR: 1.18, 1.07-1.30, p < 0.00001). There was "very low" certainty of evidence through GRADE assessment for all outcomes studied, demonstrating the evidence as uncertain. Interpretation: The COVID-19 pandemic may have caused significant increases in all-cause excess mortality, greater than those accounted for by increases due to COVID-19 mortality alone, although the evidence is uncertain. Systematic review registration: [https://www.crd.york.ac.uk/prospero/#recordDetails], identifier [CRD42020201256].