bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Impact of Area Deprivation Index on COVID-19 Prevalence: A Contrast Between Rural and Urban U.S. Jurisdictions
Authors: Kitchen Christopher , Hatef Elham , Chang Hsien-Yen , Weiner Jonathan , Kharrazi Hadi
Published: October 2020

Background

The COVID-19 pandemic has impacted communities differentially, with poorer and minority populations being more adversely affected. Prior rural health research suggests such disparities may be exacerbated during the pandemic and in remote parts of the U.S.

Objectives

To understand the spread and impact of COVID-19 across the U.S., county level data for confirmed cases of COVID-19 were examined by Area Deprivation Index (ADI) scores and Metropolitan vs. Nonmetropolitan designations from the National Center for Health Statistics (NCHS). These designations were the basis for making comparisons between Urban and Rural jurisdictions.

Methods

Kendall’s Tau-B was used to compare effect sizes between jurisdictions on select ADI composites and well researched social determinants of health (SDH). Spearman coefficients and a moderation analysis using Poisson modeling was used to explore the relationship between ADI and COVID-19 prevalence in the context of county designation.

Results

Results show that the relationship between area deprivation and COVID-19 prevalence was positive and higher for rural counties, when compared to urban ones and that family income and poverty had a stronger relationship with prevalence than other ADI component measures.

Conclusions

Though most Americans live in Metropolitan Areas, rural communities were found to be associated with a stronger relationship between deprivation and COVID-19 prevalence. Models for predicting COVID-19 prevalence by ADI and county type reinforced this observation but revealed no moderating effect of county type on ADI.
Diagnostic Accuracy of FDA Authorized Serology Tests to Detect SARS-CoV-2 Antibodies: A Systematic Review and Meta-analysis
Authors: Pandey Rajesh , Gourishankar Anand
Published: October 2020

Importance

Serology tests are diagnostic and complementary to molecular tests during the COVID-19 pandemic.

Objective

To evaluate the diagnostic accuracy of FDA authorized serology tests for the detection of SARS-CoV-2 infection.

Data sources

A search of MEDLINE, SCOPUS, CINAHL Plus, and EMBASE up to April 4, 2020, was performed to identify studies using the “COVID 19 testing” and “meta-analysis.” FDA website was accessed for the list of tests for emergency use authorization (EUA).

Study Selection

Manufacturer reported serology tests published in the FDA website were selected. Two reviewers independently assessed the eligibility of the selected reports.

Data extraction and synthesis

The meta-analysis was performed in accordance with the PRISMA guidelines. A bivariate analysis using the “random-effects model” was applied for pooled summary estimates of sensitivity, specificity, and the summary receiver operating characteristic curves.

Main outcomes and measures

The primary outcome was the diagnostic accuracy of the serology test for detecting SARS-CoV-2 infection. Subgroup analysis of the diagnostic accuracy with lag time between symptom onset and testing were studied.

Results

Seven manufacturer listed reports were included. The pooled sensitivity was 87% (95% CI, 78% - 93%), the pooled specificity was 100% (95% CI, 97% - 100%), and the area under the hierarchical summary receiver operating characteristic curve was 0.97. At ≤ 7 days, sensitivity was 44% (95% CI, 21% - 70%), and for 8-14 days, sensitivity was 84% (95% CI, 67 % - 94%).For blood draws ≥ 15 days after the onset of symptoms, sensitivity was 96% (95% CI, 93% - 98%). Heterogeneity was substantial, and the risk of bias was low in this analysis.

Conclusions and relevance

FDA authorized serology tests demonstrate high diagnostic accuracy for SARS-CoV-2 infection (certainty of evidence: moderate). There is a wide variation in the test accuracy based on the duration between the onset of symptoms and the tests (certainty of evidence: low).

Key– points

Questions

What is the pooled diagnostic accuracy of FDA authorized serology tests to detect SARS-CoV-2 antibodies?

Findings

In this systematic review and meta-analysis of seven reports from FDA authorized serology tests to detect antibodies against SARS-CoV2 antibodies (3336 patients/ samples) pooled sensitivity was 87%, and pooled specificity was almost 100%. There was a wide variation in test performance based on the duration between the onset of symptoms and the tests.

Meaning

FDA authorized tests are highly accurate to detect antibodies against SARS-CoV-2 antibodies if tests are performed under a similar condition, as presented in the original report. There is a wide variation in the test performance based on the time interval between the onset of symptoms to the tests.
When lockdown policies amplify social inequalities in COVID-19 infections. Evidence from a cross-sectional population-based survey in France
Authors: Bajos Nathalie , Jusot Florence , Pailhé Ariane , Spire Alexis , Martin Claude , Meyer Laurence , Lydié Nathalie , Franck Jeanna-Eve , Zins Marie , Carrat Fabrice ,   for the SAPRIS study group
Published: October 2020

Objectives

To assess social inequalities in the trends in COVID-19 infections following lockdown

Design

A cross-sectional survey conducted among the general population in France in April 2020, during COVID-19 lockdown.

Participants

10 401 participants aged 18-64, from a national cohort who lived in the three metropolitan French regions most affected by the first wave of COVID-19.

Main outcome

The main outcome was occurrence of possible COVID-19 symptoms, defined as the occurrence of sudden onset of cough, fever, dyspnea, ageusia and/or anosmia, that lasted more than three days in the 15 days before the survey. We used multinomial regression models to identify social and health factors related to possible COVID-19 before and during the lockdown.

Results

In all, 1,304 (13.0%; 95% CI: 12.0%-14.0%) reported cases of possible COVID-19. The effect of lockdown on the occurrence of possible COVID-19 was different across social hierarchies. The most privileged class individuals saw a significant decline in possible COVID-19 infections between the period prior to lockdown and during the lockdown (from 8.8% to 4.3%, P=0.0001) while the decline was less pronounced among working class individuals (6.9% before lockdown and 5.5% during lockdown, P=0.03). This differential effect of lockdown remained significant after adjusting for other factors including history of chronic disease. The odds of being contaminated during lockdown as opposed to the prior period increased by 57% among working class individuals (OR=1.57; 95% CI: 1.0-2.48). The same was true for those engaged in in-person professional activities during lockdown (OR=1.53; 95% CI: 1.03-2.29).

Interpretation

Lockdown was associated with social inequalities in the decline in COVID-19 infections, calling for the adoption of preventive policies to account for living and working conditions. Such adoptions are critical to reduce social inequalities related to COVID-19, as working-class individuals also have the highest COVID-19 related mortality, due to higher prevalence of comorbidities.

Section 1: What is already known on this topic

Significant differences in COVID-19 incidence by gender, class and race/ethnicity are recorded in many countries in the world. Lockdown measures implemented throughout the globe have been effective in reducing transmission risks.

Section 2: What this study adds

Our study shows that lockdown’s impact was socially differentiated and has benefited the working classes the least. Such results underline the need to design COVID-19 preventive policies that take into account living and working conditions, as working-class individuals also have the highest COVID-19 related mortality, due to higher prevalence of comorbidities.
Low zinc levels at clinical admission associates with poor outcomes in COVID-19
Authors: Vogel-González Marina , Talló-Parra Marc , Herrera-Fernández Víctor , Pérez-Vilaró Gemma , Chillón Miguel , Nogués Xavier , Gómez-Zorrilla Silvia , López-Montesinos Inmaculada , Villar Judit , Sorli-Redó Maria Luisa , Horcajada Juan Pablo , García-Giralt Natalia , Pascual Julio , Díez Juana , Vicente Rubén , Güerri-Fernández Robert
Published: October 2020

Background

Biomarkers to predict Coronavirus disease-19 (COVID-19) outcome early at infection are urgently needed to improve prognosis and treatment. Zinc balances immune responses and also has a proven direct antiviral action against some viruses. Importantly, zinc deficiency (ZD) is a common condition in elderly and individuals with chronic diseases, two groups with more severe COVID-19 outcomes. We hypothesize that serum zinc content (SZC) influences COVID-19 disease progression and thus might represent a useful biomarker.

Methods

We run a retrospective observational study with 249 COVID-19 patients admitted in Hospital del Mar. We have studied COVID-19 severity and progression attending to SZC at admission. In parallel we have studied SARS-CoV2 replication in the Vero E6 cell line modifying zinc concentrations.

Findings

Our study demonstrates a correlation between serum zinc levels and COVID-19 outcome. Serum zinc levels lower than 50 µg/dl at admission correlated with worse clinical presentation, longer time to reach stability and higher mortality. Our in vitro results indicate that low zinc levels favor viral expansion in SARS-CoV2 infected cells.

Interpretation

SZC is a novel biomarker to predict COVID-19 outcome. We encourage performing randomized clinical trials to study zinc supplementation as potential prophylaxis and treatment with people at risk of zinc deficiency.

Funding

Spanish Ministry of Science and Innovation, “Maria de Maeztu” Programme for Units of Excellence in R&D and Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement of the Generalitat de Catalunya. Instituto Carlos III Fondos de Investigaciones Sanitarias (FIS), CIBER on Frailty and Healthy Ageing and FEDER funds
Long-term COVID-19 symptoms in a large unselected population
Authors: Cirulli Elizabeth T. , Schiabor Barrett Kelly M. , Riffle Stephen , Bolze Alexandre , Neveux Iva , Dabe Shaun , Grzymski Joseph J. , Lu James T. , Washington Nicole L.
Published: October 2020

It is increasingly recognized that SARS-CoV-2 can produce long-term complications after recovery from the acute effects of infection. Here, we report the analysis of 32 self-reported short and long-term symptoms in a general adult population cohort comprised of 357 COVID-19+ cases, 5,497 SARS-CoV-2-negative controls, and 19,095 non-tested individuals. The majority of our COVID-19+ cases are mild, with only 9 of the 357 COVID-19+ cases having been hospitalized. Our results show that 36.1% of COVID-19+ cases have symptoms lasting longer than 30 days, and 14.8% still have at least one symptom after 90 days. These numbers are higher for COVID-19+ cases who were initially more ill, 44.9% at 30 days and 20.8% at 90 days, but even for very mild and initially asymptomatic cases, 21.3% have complications persist for 30 days or longer. In contrast, only 8.4% of participants from the general untested population develop new symptoms lasting longer than 30 days due to any illness during the same study period. The long-term symptoms most enriched in those with COVID-19 are anosmia, ageusia, difficulty concentrating, dyspnea, memory loss, confusion, chest pain, and pain with deep breaths. In addition to individuals who are initially more sick having more long-term symptoms, we additionally observe that individuals who have an initial symptom of dyspnea are significantly more likely to develop long-term symptoms. Importantly, our study finds that the overall level of illness is an important variable to account for when assessing the statistical significance of symptoms that are associated with COVID-19. Our study provides a baseline from which to understand the frequency of COVID-19 long-term symptoms at the population level and demonstrates that, although those most likely to develop long-term COVID-19 complications are those who initially have more severe illness, even those with mild or asymptomatic courses of infection are at increased risk of long-term complications.
Predicting multi-modal symptom trajectories across 7 years in veterans with chronic posttraumatic stress
Authors: Pierce Benjamin , Kirsh Thomas , Kummerfeld Erich , Ferguson Adam R. , Neylan Thomas C. , Cohen Beth E. , Ma Sisi , Nielson Jessica L.
Published: October 2020

Background

Veterans are disproportionately affected by symptoms of post-traumatic stress (PTS) and associated poor health and psychosocial functioning. While most improve over time, others experience severe and persistent concerns. The ability to predict this latter group is critical for early intervention. Characterizing this subgroup has proven difficult, with most studies focusing on PTS and neglect a wider assessment of veterans’ wellbeing. Consequently, little is known about veterans who experience chronic symptoms and far-reaching impairment.

Method

The present study uses dimension reduction, growth mixture modeling, and clustering methods to identify veterans with the worst-faring trajectories of psychiatric symptoms, health, and psychosocial functioning, using data from the seven-year Mind Your Heart study (MYH) of people receiving Veterans Affairs services (n = 747). Random forest classification and feature selection were then used to examine predictors that distinguish the worst-fairing veteran group from others in the cohort.

Results

The combined analyses revealed a subgroup of veterans with severe and diverse symptoms across psychiatric domains, impairment in multiple facets of living, and poor health with deterioration over seven years. This subgroup was distinguished by transdiagnostic symptom severity and greater social isolation, avoidance, anhedonia, cynicism, anger/hostility, and immune response and inflammation.

Conclusions

Veterans whose distress spans multiple domains appear to be more broadly impaired, socially isolated, cynical or angry/hostile to others, and show elevated immunoreactivity and inflammation. Care for this population should be informed by a multidisciplinary approach that is conscious of veterans’ mental and physical health, and interpersonal needs.
Clinical characteristics and outcomes of adult patients admitted with COVID-19 in East London: a retrospective cohort analysis
Authors: Cheng Daryl Oswald , Calderwood Claire Jacqueline , Skyllberg Erik Wilhelm , Ainley Adam Denis Jeremy
Published: October 2020

Background

Descriptions of clinical characteristics of patients hospitalised with coronavirus disease 2019 (COVID-19), their clinical course and short-term in- and outpatient outcomes in deprived urban populations in the United Kingdom are still relatively sparse. We describe the epidemiology, clinical course, experience of non-invasive ventilation and intensive care, mortality and short-term sequalae of patients admitted to two large District General Hospitals across a large East London NHS Trust during the first wave of the pandemic.

Methods

A retrospective analysis was carried out on a cohort of 1,946 patients with a clinical or laboratory diagnosis of COVID-19, including descriptive statistics and survival analysis. A more detailed analysis was undertaken of a subset of patients admitted across three Respiratory Units in the trust.

Results

Increasing age, male sex and Asian ethnicity were associated with worse outcomes. Increasing severity of chest X-ray abnormalities trended with mortality. Radiological changes persisted in over 50% of cases at early follow up (6 weeks). Ongoing symptoms including hair loss, memory impairment, breathlessness, cough and fatigue were reported in 67% of survivors, with 42% of patients unable to return to work due to ongoing symptoms.

Conclusions

Understanding the acute clinical features, course of illness and outcomes of COVID-19 will be vital in preparing for further peaks of the pandemic. Our initial follow up data suggest there are ongoing sequalae of COVID-19 including persistent symptoms and radiological abnormalities. Further data, including longer term follow up data, are necessary to improve our understanding of this novel pathogen and associated disease.

Section 1: What is already known on this topic

Previous studies have reported that increasing age, male sex, Black and Asian ethnicity increased risk of death for patients admitted to hospital with coronavirus disease 2019 (COVID-19). There is little published literature regarding the follow up of patients with COVID-19.

Section 2: What this study adds

Our study is one of the first with follow up data for patients admitted to hospital with COVID-19. We show that radiological abnormality persisted at 6 weeks in over 50% of patients, as well as significantly increased breathlessness in patients without baseline dyspnoea. Our study confirms that increasing age, male sex and Asian ethnicity increased risk of death for patients, but also in an ethnically and socioeconomically diverse population in East London.
Radiomic and clinical predictors of cachexia in non-small cell lung cancer patients treated with immunotherapy
Authors: Mu Wei , Katsoulakis Evangelia , Gage Kenneth L. , Whelan Chris J. , Schabath Matthew B. , Gillies Robert J.
Published: October 2020

Background

Cachexia is present in up to 50% of patients with cancer and may contribute to primary resistance to immunotherapy. Biomarkers to predict cachexia are urgently required for early intervention. Herein, we test the hypothesis that pre-treatment 18F-FDG-PET/CT-based radiomics can be used to predict cachexia and subsequently associated with clinical outcomes among patients with advanced non-small cell lung cancer (NSCLC) who are treated with immunotherapy.

Methods

This retrospective multi-institution study included 210 patients with histologically confirmed stage IIIB-IV NSCLC who were treated with immune checkpoint blockade between June 2011 and August 2019. Baseline (pre-immunotherapy) PET/CT images of 175 patients from Moffitt Cancer Center were used to train (N=123) and test (N=52) a radiomics signature to predict cachexia, which was also used to predict durable clinical benefit (DCB), progression-free survival (PFS) and overall survival (OS) subsequently. An external cohort that enrolled 35 patients from James A. Haley Veterans’ Hospital (VA) was used to further validate the predictive and prognostic value of this signature.

Results

A radiomics signature demonstrated cachexia prediction ability with areas under receiver operating characteristics curves (AUC) of 0.77 (95%CI:0.68-0.85), 0.75 (95%CI:0.60-0.86) and of 0.73 (95%CI:0.53-0.92) in the training, test and external VA cohorts, respectively. For the further investigation of prognostic value, this signature could identify the patients with DCB with AUC of 0.67 (95%CI:0.57-0.77), 0.66 (95%CI:0.51-0.81), and 0.72 (95%CI:0.54-0.89) in these three cohorts. Additionally, the PFS and OS were significantly shorter among patients with higher radiomics signature in all the three cohorts (p<0.05).

Conclusion

Using PET/CT radiomics analysis, cachexia could be predicted before the start of the immunotherapy, making it possible to monitor the patients with a higher risk of cachexia and identify patients most likely to benefit from immunotherapy.
Age-targeted dose allocation can halve COVID-19 vaccine requirements
Authors: Meehan Michael T. , Cocks Daniel G. , Caldwell Jamie M. , Trauer James M. , Adekunle Adeshina I. , Ragonnet Romain R. , McBryde Emma S.
Published: October 2020

In anticipation of COVID-19 vaccine deployment, we use an age-structured mathematical model to investigate the benefits of optimizing age-specific dose allocation to suppress the transmission, morbidity and mortality of SARS-CoV-2 and the associated disease, COVID-19. To minimize transmission, we find that the highest priority individuals across 179 countries are typically those between 30 and 59 years of age because of their high contact rates and higher risk of infection and disease. Conversely, morbidity and mortality are initially most effectively reduced by targeting 60+ year olds who are more likely to experience severe disease. However, when population-level coverage is sufficient — such that herd immunity can be achieved through targeted dose allocation — prioritizing middle-aged individuals becomes the most effective strategy to minimize hospitalizations and deaths. For each metric considered, we show that optimizing the allocation of vaccine doses can more than double their effectiveness.
District level correlates of COVID-19 pandemic in India
Authors: Tamrakar Vandana , Srivastava Ankita , Parmar Mukesh C. , Shukla Sudheer Kumar , Shabnam Shewli , Boro Bandita , Saha Apala , Debbarma Benjamin , Saikia Nandita
Published: October 2020

Background

The number of patients with coronavirus infection (COVID-19) has amplified in India. Understanding the district level correlates of the COVID-19 infection ratio (IR) is therefore essential for formulating policies and intervention.

Objectives

The present study examines the association between socio-economic and demographic characteristics of India’s population and the COVID-19 infection ratio at district level…

Data and Methods

Using crowdsourced data on the COVID-19 prevalence rate, we analyzed state and district level variation in India from March 14 to July 31 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out a regression analysis to highlight the district level demographic, socio-economic, infrastructure, and health-related correlates of the COVID-19 infection ratio.

Results

The results showed that the IR is 42.38 per one hundred thousand population in India. The highest IR was observed in Andhra Pradesh (145.0), followed by Maharashtra (123.6), and was the lowest in Chhattisgarh (10.1). About 80 per cent of infected cases, and 90 per cent of deaths were observed in nine Indian states (Tamil Nadu, Andhra Pradesh, Telangana, Karnataka, Maharashtra, Delhi, Uttar Pradesh, West Bengal, and Gujarat). Moreover, we observed COVID-19 cold-spots in central, northern, western, and north-eastern regions of India. Out of 736 districts, six metropolitan cities (Mumbai, Chennai, Thane, Pune, Bengaluru, and Hyderabad) emerged as the major hotspots in India, containing around 30 per cent of confirmed total COVID-19 cases in the country. Simultaneously, parts of the Konkan coast in Maharashtra, parts of Delhi, the southern part of Tamil Nadu, the northern part of Jammu & Kashmir were identified as hotspots of COVID-19 infection. Moran’s-I value of 0.333showed a positive spatial clusteringlevel in the COVID-19 IR case over neighboring districts. Our regression analysis found that district-level population density (β: 0.05, CI:004-0.06), the percent of urban population (β:3.08, CI: 1.05-5.11), percent of Scheduled Caste Population (β: 3.92, CI: 0.12-7.72),and district-level testing ratio (β: 0.03, CI: 0.01-0.04) are positively associated with the prevalence of COVID-19.

Conclusion

COVID-19 cases were heavily concentrated in 9 states of India. Several demographic, socio-economic, and health-related variables are correlated with COVID-19 prevalence rate. However, after adjusting the role of socio-economic and health-related factors, the COVID-19 infection rate was found to be more rampant in districts with a higher population density, a higher percentage of the urban population, and a higher percentage of deprived castes and with a higher level of testing ratio. The identified hotspots and correlates in this study give crucial information for policy discourse.
SARS-CoV-2 genomic characterization and clinical manifestation of the COVID-19 outbreak in Uruguay
Authors: Elizondo Victoria , Harkins Gordon W. , Mabvakure Batsirai , Smidt Sabine , Zappile Paul , Marier Christian , Maurano Matthew , Perez Victoria , Mazza Natalia , Beloso Carolina , Ifran Silvana , Fernandez Mariana , Santini Andrea , Perez Veronica , Estevez Veronica , Nin Matilde , Manrique Gonzalo , Perez Leticia , Ross Fabiana , Boschi Susana , Zubillaga Maria Noel , Balleste Raquel , Dellicour Simon , Heguy Adriana , Duerr Ralf
Published: October 2020

COVID-19 is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared by the World Health Organization a global public health emergency. Among the severe outbreaks across South America, Uruguay has become known for curtailing SARS-CoV-2 exceptionally well. To understand the SARS-CoV-2 introductions, local transmissions, and associations with genomic and clinical parameters in Uruguay, we sequenced the viral genomes of 44 outpatients and inpatients in a private healthcare system in its capital, Montevideo, from March to May 2020. We performed a phylogeographic analysis using sequences from our cohort and other studies that indicate a minimum of 23 independent introductions into Uruguay, resulting in five major transmission clusters. Our data suggest that most introductions resulting in chains of transmission originate from other South American countries, with the earliest seeding of the virus in late February 2020, weeks before the borders were closed to all non-citizens and a partial lockdown implemented. Genetic analyses suggest a dominance of S and G clades (G, GH, GR) that make up >90% of the viral strains in our study. In our cohort, lethal outcome of SARS-CoV-2 infection significantly correlated with arterial hypertension, kidney failure, and ICU admission (FDR < 0.01), but not with any mutation in a structural or non-structural protein, such as the spike D614G mutation. Our study contributes genetic, phylodynamic, and clinical correlation data about the exceptionally well-curbed SARS-CoV-2 outbreak in Uruguay, which furthers the understanding of disease patterns and regional aspects of the pandemic in Latin America.
Epidemiology of sleep disorders during COVID-19 pandemic: A systematic scoping review
Authors: Tasnim Samia , Rahman Mariya , Pawar Priyanka , Chi Xinli , Yu Qian , Zou Liye , Sultana Abida , McKyer E. Lisako J. , Ma Ping , Hossain Md Mahbub
Published: October 2020

Background

A growing burden of mental health problems has become a global concern amid the coronavirus disease (COVID-19) pandemic. Sleep disorders are major mental health problems associated with increased psychosocial stressors; however, no research synthesis is available on the epidemiology of it. In this systematic scoping review, we aimed to assess the current evidence on the epidemiological burden, associated factors, and interventions from the existing literature.

Method

Seven major health databases and additional sources were searched to identify, evaluate, and synthesize empirical studies on the prevalence and correlates of sleep disorders and available interventions. The Joanna Briggs Institute Methodology for Scoping Review were used, and the findings were reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.

Results

A total of 78 articles were retrieved, the prevalence of sleeping disorders ranged from 2.3% to 76.6%. Age, sex, level of education, physical and mental health, COVID-19 related factors, occupation especially being health care workers (HCW) were the main associated factors. Only two intentions were identified to address the issue.

Conclusion

The finding of this review indicated a high burden of sleep disorder with limited interventions that necessitate informing policymakers and practitioners to facilitate future research and implementations.

Brief summary

Current Knowledge/Study Rationale

Despite the paramount importance of sleep for the physical and mental wellbeing of individuals, sleep hygiene is often neglected which resulted in a high prevalence of sleep disorders across the globe. This condition is likely to worsen amid this pandemic. This is the first systematic scoping review of sleep disorders during the COVID-19 pandemic.

Study Impact

The findings of our study suggest a high prevalence of sleep disorder and highlight a wide range of socio-demographic factors to identify population groups vulnerable to the adverse outcomes of sleep disorder with limited interventions. These pieces of evidence will guide clinicians to make informed choices for better management of patients and aid public health professionals to prevent sleep disorder epidemic concurring with the current pandemic.
Global projections of lives saved from COVID-19 with universal mask use
Authors: Gakidou Emmanuela , Pigott David M. , Troeger Christopher E. , Hulland Erin N. , Reiner Robert C. , Adolph Christopher , Aravkin Aleksandr Y. , Bachmeier Steven D. , Bang-Jensen Bree , Barber Ryan M. , Bisignano Catherine , Bloom Sabina , Carter Austin , Castro Emma , Chakrabarti Suman , Chattopadhyay Jhilik , Chen Christine , Cogen Rebecca M. , Collins James K. , Combs Emily , Cormier Natalie Maria , Dai Xiaochen , Dangel William James , Deen Amanda , Ewald Samuel B. , Flaxman Abraham D. , Fullman Nancy , Guo Gaorui , Hackman Kathryn , He Jiawei , Henry Nathaniel J. , Johanns Casey , Larson Samantha Leigh , Lazzar-Atwood Alice , LeGrand Kate E. , Lescinsky Haley , Lim Stephen S. , Linebarger Emily , Lozano Rafael , Ma Rui , Magistro Beatrice , Månsson Johan C. , Marczak Laurie B. , Miller-Petrie Molly K. , Mokdad Ali H. , Mooney Meghan D. , Odell Christopher M. , O’Halloran James K. , Ostroff Samuel M. , Pasovic Maja , Patel Disha J. , Penberthy Louise , Ramshaw Rebecca E. , Roth Gregory A. , Shaw David H. , Sheena Brittney S. , Sholokhov Aleksei , Sorensen Reed J. D. , Sparks Gianna , Spurlock Emma Elizabeth , Subart Michelle L. , Walker Ally , Watson Alexandrea , Welgan Catherine A. , Wiens Kirsten E. , Woyczynski Lauren , Xu Liming , Hay Simon I. , Brauer Michael , Vos Theo , Murray Christopher J. L. ,   IHME COVID-19 Forecasting Team
Published: October 2020

BACKGROUND

Social distancing mandates (SDM) have reduced health impacts from COVID-19 but also resulted in economic downturns that have led many nations to relax SDM. Until deployment of an efficacious and equitable vaccine, intervention options to reduce COVID-19 mortality and minimize restrictive SDM are sought by society.

METHODS

A susceptible-exposed-infectious-recovered (SEIR) deterministic transmission model was parameterized with data on reported deaths, cases, and select covariates to predict infections and deaths from COVID-19 through March 01, 2021. We explore three scenarios: a “non-adaptive” scenario where neither mask use or SDM adapt to changing conditions, a “reference” where current national levels of mask use are maintained and SDM reintroduced when deaths rise, and an increase in mask use to 95% coverage levels (“universal mask”). We reviewed published studies to set priors on the magnitude of reduction in transmission through increasing mask use.

RESULTS

Mask use was estimated at 59.0% of people globally on October 19, 2020. Universal mask use could avert 733,310 deaths (95% UI 385,981 to 1,107,759) between October 27, 2020 and March 01, 2021, the difference between the predicted 2.95 million deaths (95% UI 2.70 to 3.35) in the reference scenario and 2.22 million deaths (95% UI 2.00 to 2.45) in the universal mask scenario over this time period.

CONCLUSIONS

The cumulative toll of the COVID-19 pandemic could be substantially reduced by the universal adoption of masks before the availability of a vaccine. This low-cost, low-barrier policy, whether customary or mandated, has enormous health benefits with presumed marginal economic costs.
Absence of SARS-CoV-2 neutralizing activity in pre-pandemic sera from individuals with recent seasonal coronavirus infection
Authors: Poston Daniel , Weisblum Yiska , Wise Helen , Templeton Kate , Jenks Sara , Hatziioannou Theodora , Bieniasz Paul
Published: October 2020

Cross-reactive immune responses elicited by seasonal coronaviruses might impact SARS-CoV-2 susceptibility and disease outcomes. We measured neutralizing activity against SARS-CoV-2 in pre-pandemic sera from patients with prior PCR-confirmed seasonal coronavirus infection. While neutralizing activity against seasonal coronaviruses was detected in nearly all sera, cross-reactive neutralizing activity against SARS-CoV-2 was undetectable.
Exclusion of bacterial co-infection in COVID-19 using baseline inflammatory markers and their response to antibiotics
Authors: Mason Claire Y , Kanitkar Tanmay , Richardson Charlotte J , Lanzman Marisa , Stone Zak , Mahungu Tabitha , Mack Damien , Wey Emmanuel Q , Lamb Lucy , Balakrishnan Indran , Pollara Gabriele
Published: October 2020

Background

COVID-19 is infrequently complicated by secondary bacterial infection, but nevertheless antibiotic prescriptions are common. We used community-acquired pneumonia (CAP) as a benchmark to define the processes that occur in a bacterial pulmonary infection, and tested the hypothesis that baseline inflammatory markers and their response to antibiotic therapy could distinguish CAP from COVID-19.

Methods

In patients admitted to Royal Free Hospital (RFH) and Barnet Hospital (BH) we defined CAP by lobar consolidation on chest radiograph, and COVID-19 by SARS-CoV-2 detection by PCR. Data were derived from routine laboratory investigations.

Results

On admission all CAP and >90% COVID-19 patients received antibiotics. We identified 106 CAP and 619 COVID-19 patients at RFH. CAP was characterised by elevated white cell count (WCC) and C-reactive protein (CRP) compared to COVID-19 (median WCC 12.48 (IQR 8.2-15.3) vs 6.78 (IQR 5.2-9.5) x106cells/ml and median CRP CRP 133.5 (IQR 65-221) vs 86 (IQR 42-160) mg/L). Blood samples collected 48-72 hours into admission revealed decreasing CRP in CAP but not COVID-19 (CRP difference −33 (IQR −112 to +3.5) vs +15 (IQR −15 to +70) mg/L respectively). In the independent validation cohort (BH) consisting of 169 CAP and 181 COVID-19 patients, admission WCC >8.2×106cells/ml or falling CRP during admission identified 95% of CAP cases, and predicted the absence of bacterial co-infection in 45% of COVID-19 patients.

Conclusions

We propose that in COVID-19 the absence of both elevated baseline WCC and antibiotic-related decrease in CRP can exclude bacterial co-infection and facilitate antibiotic stewardship efforts.
Prediction of immunotherapy response using deep learning of PET/CT images
Authors: Mu Wei , Jiang Lei , Shi Yu , Tunali Ilke , Gray Jhanelle E. , Katsoulakis Evangelia , Tian Jie , Gillies Robert J. , Schabath Matthew B.
Published: October 2020

Currently only a fraction of patients with non-small cell lung cancer (NSCLC) experience durable clinical benefit (DCB) from immunotherapy, robust biomarkers to predict response prior to initiation of therapy are an emerging clinical need. PD-L1 expression status from immunohistochemistry is the only clinically approved biomarker, but a non-invasive complimentary approach that could be used when tissues are not available or when the IHC fails and can be assessed longitudinally would have important implications for clinical decision support. In this study, 18F-FDG-PET/CT images and clinical data were curated from 697 NSCLC patients from three institutions. Utilizing PET/CT images, a deeply-learned-score (DLS) was developed by training a small-residual-convolutional-network model to predict the PD-L1 expression status, which was further used to predict DCB, progression-free survival (PFS), and overall survival (OS) in both retrospective and prospective test cohorts of immunotherapy-treated patients with advanced stage NSCLC. This PD-L1 DLS significantly discriminated PD-L1 positive and negative patients (AUC≥0.82 in all cohorts). Further, higher PD-L1 DLS was significantly associated with higher probability of DCB, longer PFS, and longer OS. The DLS combined with clinical characteristics achieved C-indices of 0.86, 0.83 and 0.81 for DCB prediction, 0.73, 0.72 and 0.70 for PFS prediction, and 0.78, 0.72 and 0.75 for OS prediction in the retrospective, prospective and external cohorts, respectively. The DLS provides a non-invasive and promising approach to predict PD-L1 expression and to infer clinical outcomes for immunotherapy-treated NSCLC patients. Additionally, the multivariable models have the potential to guide individual pre-therapy decisions pending in larger prospective trials.

Statement of Significance

PD-L1 expression status by immunohistochemistry (IHC) is the only clinically-approved biomarker to trigger immunotherapy treatment decisions, but a non-invasive complimentary approach that could be used when tissues are not available or when the IHC fails and can be assessed longitudinally would have important implications for clinical decision support. Utilizing PET/CT images, we developed and tested a convolutional neural network model to predict PD-L1 expression status with high accuracy in cohorts from different institutions. And the generated signature may serve as a prognostic biomarker for immunotherapy response in patients with NSCLC, and outperforms the clinical characteristics.
Factors associated with progression to critical illness in 28 days among COVID-19 patients: results from a tertiary care hospital in Istanbul, Turkey
Authors: Sili Uluhan , Ay Pinar , Topuzoglu Ahmet , Bilgin Huseyin , Tigen Elif Tukenmez , Sengel Buket Erturk , Caglayik Dilek Yagci , Balcan Baran , Kocakaya Derya , Yildizeli Sehnaz Olgun , Gul Fethi , Bilgili Beliz , Sarinoglu Rabia Can , Yagci Aysegul Karahasan , Durmusoglu Lutfiye Mulazimoglu , Eryuksel Emel , Odabasi Zekaver , Direskeneli Haner , Karakurt Sait , Cinel Ismail , Korten Volkan
Published: October 2020

Background

Coronavirus disease 2019 (COVID-19) quickly spread worldwide to become a pandemic. This study aimed to define the predictors of critical illness development within 28 days postadmission.

Methods

We conducted a prospective cohort study including 477 PCR-positive COVID-19 patients admitted to a tertiary care hospital in Istanbul from March 12 to May 12, 2020. The development of critical illness, e.g., invasive mechanical ventilation and/or death, was followed for a period of 28 days postadmission. Demographic characteristics, number of comorbidities, illness severity at admission defined by the WHO scale, vital signs, laboratory findings and period of admission to the hospital were independent variables. Cox proportional hazards analysis was performed, and the C-index was calculated.

Results

The median (IQR) age of the cohort was 55.0 (44.0-67.0) years, and 50.1% were male. The most common presenting symptoms were cough, dyspnea and fatigue. Overall, 65.2% of the patients had at least one comorbidity. Hydroxychloroquine was given to 99.2% of the patients. Critical illness developed in 45 (9.4%; 95% CI: 7.0%-12.4%) patients. In the multivariable analysis, age (HR: 1.05, p<0.001), number of comorbidities (HR: 1.33, p=0.02), procalcitonin ≥0.25 µg/L (HR: 2.12, p=0.03) and LDH ≥350 U/L (HR: 2.04, p=0.03) were independently associated with critical illness development. The WHO scale on admission was the strongest predictor of critical illness (HR: 4.15, p<0.001). Prognosis improved within the study period (p<0.05). The C-index of the model was 0.92.

Conclusions

Age, comorbidity number, WHO scale, LDH and procalcitonin were independently associated with critical illness development. Mortality from COVID-19 seems to be decreasing as the pandemic advances.

summary

We analyzed 477 confirmed COVID-19 patients for the development of critical illness, e.g., invasive mechanical ventilation and/or death within 28 days postadmission. Age, comorbidity number, WHO scale, LDH and procalcitonin were independently associated with critical illness development.
Development and Validation of a Highly Generalizable Deep Learning Pulmonary Embolism Detection Algorithm
Authors: Schmid Ryan , Johnson Jacob , Ngo Jennifer , Lamoureux Christine , Baker Brian , Ngo Lawrence
Published: October 2020

Several algorithms have been developed for the detection of pulmonary embolism, though generalizability and bias remain potential weaknesses due to small sample size and sample homogeneity. We developed and validated a highly generalizable deep-learning algorithm, Emboleye, for the detection of PE by using a large and diverse dataset, which included 30,574 computed tomography (CT) exams sourced from over 2,000 hospital sites. On angiography exams, Emboleye demonstrates an AUROC of 0.79 with a specificity of 0.99 while maintaining a sensitivity of 0.37 and PPV of 0.77. On non-angiography CT exams, Emboleye demonstrates an AUROC of 0.77 with a specificity of 0.99 while maintaining a sensitivity of 0.18 and PPV of 0.35.
Risk of death among people with rare autoimmune diseases compared to the general population in England during the 2020 COVID-19 pandemic
Authors: Peach Emily , Rutter Megan , Lanyon Peter , Grainge Matthew J , Hubbard Richard , Aston Jeanette , Bythell Mary , Stevens Sarah , Pearce Fiona
Published: October 2020

Objectives

To quantify the risk of death among people with rare autoimmune rheumatic diseases (RAIRD) during the UK 2020 COVID-19 pandemic compared to the general population, and compared to their pre-COVID risk.

Methods

We conducted a cohort study in Hospital Episode Statistics for England 2003 onwards, and linked data from the NHS Personal Demographics Service. We used ONS published data for general population mortality rates.

Results

We included 168,691 people with a recorded diagnosis of RAIRD alive on 01/03/2020. Their median age was 61.7 (IQR 41.5-75.4) years, and 118,379 (70.2%) were female. Our case ascertainment methods had a positive predictive value of 85%. 1,815 (1.1%) participants died during March and April 2020. The age-standardised mortality rate (ASMR) among people with RAIRD (3669.3, 95% CI 3500.4-3838.1 per 100,000 person-years) was 1.44 (95% CI 1.42-1.45) times higher than the average ASMR during the same months of the previous 5 years, whereas in the general population of England it was 1.38 times higher. Age-specific mortality rates in people with RAIRD compared to the pre-COVID rates were higher from the age of 35 upwards, whereas in the general population the increased risk began from age 55 upwards. Women had a greater increase in mortality rates during COVID-19 compared to men.

Conclusion

The risk of all-cause death is more prominently raised during COVID-19 among people with RAIRD than among the general population. We urgently need to quantify how much risk is due to COVID-19 infection and how much is due to disruption to healthcare services.

Key messages

People with RAIRD had an increased risk of dying during COVID-19 from age 35 years onwards, whereas in the general population it increased from the age of 55 onwards.

Women had a greater increase in their risk of death during COVID-19 compared to men.

The risk of working age people with RAIRD dying during COVID-19 was similar to that of someone 20 years older in the general population.
Polyester Nasal Swabs Collected in a Dry Tube are a Robust and Inexpensive, Minimal Self-Collection Kit for SARS-CoV-2 Testing
Authors: Padgett Leah R. , Kennington Lauren A. , Ahls Charlotte L. , Samarasinghe Delini K. , Tu Yuan-Po , Wallander Michelle L. , Elliott James S. , Rains Douglas
Published: October 2020

Background

Polyester nasal swabs stored in saline or in a dry tube were evaluated as an alternative to foam nasal swabs for SARS-CoV-2 testing by reverse transcription quantitative polymerase chain reaction (RT-qPCR) since they may be inexpensively manufactured at high capacity.

Methods

Surrogate clinical specimens were prepared by inoculating foam and polyester nasal swabs with residual SARS-CoV-2 positive clinical specimens diluted in porcine or human matrix. Dry swab elution with phosphate buffered saline (PBS) was evaluated by vortex, swab swirling, and passive methodologies. Surrogate and clinical nasal specimen stability were evaluated at refrigerated (4°C) and elevated temperatures (40°C for 12 hours, 32°C hold) through 72 hours.

Results

Polyester swabs demonstrated equivalent performance to foam swabs for detection of low and high SARS-CoV-2 viral loads. Dry swab elution performed with PBS and mechanical disruption by vortex resulted in nearly complete quantitative recovery of virus. Dry polyester and foam surrogate specimens were stable through 72 hours both when refrigerated and after high temperature excursion, which simulated specimen transport without cold chain. Similarly, clinical specimens collected with polyester swabs and stored dry were stable through 72 hours in the presence and absence of cold chain. Polyester surrogate specimens stored in saline were stable through 72 hours refrigerated but only through 48 hours at elevated temperatures.

Conclusions

Polyester nasal swabs stored in dry collection tubes comprise a robust and inexpensive self-collection method for SARS-CoV-2 viral load testing, which is stable under conditions required for home collection and shipment to the laboratory.
Holocephali From the Irati Formation (Paraná Basin), Brazil: Origin, Paleogographical and Paleoenvironmental Considerations
Authors: Chahud Artur
Published: October 2020

The Permian (Cisuralian) Irati Formation, from the Brazilian southeastern Paraná Basin bears, at some levels, Chondrichthyes, besides other vertebrates. Outcrops of this unit are frequent at the state of São Paulo eastern belt. Two members of the Irati are recognized at this state, Assistência, the upper, and Taquaral. A sandy facies, mostly at the base of the Taquaral, is noteworthy by the richness of the Chondrichthyes, mainly Holocephali. The Petalodontiformes are the Chondrichthyes most abundant, so far referred toItapyrodus punctatus. Recent studies of several specimens revealed that some morphotypes must belong to different species of the genusItapyrodus. Others are akin to this genus, justifying a proposition of an endemic family Itapyrodidae. The presence of this endemic family is an argument, among others, for a proposed isolation of two Brazilan Basins northeast Parnaíba and southeast Paraná, during the time of deposition of the Irati, inasmuch as Itapyrodidae are present in both basins.
Kidney injury molecule-1 is a potential receptor for SARS-CoV-2
Authors: Yang Chen , Zhang Yu , Chen Hong , Chen Yuchen , Yang Dong , Shen Ziwei , Wang Xiaomu , Liu Xinran , Xiong Mingrui , Huang Kun
Published: October 2020

COVID-19 patients present high incidence of kidney abnormalities, which are associated with poor prognosis and high mortality. Identification of SARS-CoV-2 in kidney of COVID-19 patients suggests renal tropism and direct infection. Presently, it is generally recognized that SARS-CoV-2 initiates invasion through binding of receptor-binding domain (RBD) of spike protein to host cell-membrane receptor ACE2, however, whether there is additional target of SARS-CoV-2 in kidney remains unclear. Kidney injury molecule-1 (KIM1) is a transmembrane protein that drastically up-regulated after renal injury. Here, binding between SARS-CoV2-RBD and the extracellular Ig V domain of KIM1 was identified by molecular simulations and co-immunoprecipitation, which was comparable in affinity to that of ACE2 to SARS-CoV-2. Moreover, KIM1 facilitated cell entry of SARS-CoV2-RBD, which was potently blockaded by a rationally designed KIM1-derived polypeptide. Together, the findings suggest KIM1 may mediate and exacerbate SARS-CoV-2 infection in a ‘vicious cycle’, and KIM1 could be further explored as a therapeutic target.