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1.
Neuroepidemiology ; 56(SUPPL 1):86, 2022.
Article in English | EMBASE | ID: covidwho-1813059

ABSTRACT

Objective: The rising burden of stroke in Malaysia has prompted attention on the primary caregivers of stroke survivors. Caregiving burden following stroke is a significant health care concern, however, the assessment of caregiving burden is understudied in Malaysia. This study aims to measure the informal stroke caregivers' burden one month after the patients were discharged from the stroke care. Methods: Acute stroke survivors and their informal caregivers were recruited from three major hospitals in the northeast of Peninsula Malaysia. To minimize the risk of direct contact during the COVID-19 pandemic, the caregivers who fulfilled study criteria were interviewed by phone. Information obtained included caregiver's demographic, self-reported caregiver burden measured using two commonly used tools, namely the Malay version Zarit Burden Interview (ZBI) and The Malay version Caregiver Assessment of Function and Upset (CAFU). ZBI consists of 22 questions and CAFU has a 15-item multidimensional measure of dependence. We measured ZBI and CAFU scores twice, one at baseline (post-discharge) and another one month later. The scores were presented as mean and standard deviation (SD). Results: A total of 93 informal caregivers were recruited with 66% of them being female. Generally, the caregiving burden was reduced from discharge (baseline) to one-month post-discharge. The overall mean (SD) scores for ZBI reduced from 23.7 (SD=14.45) to 19.9 (SD=12.67). The overall mean (SD) for CAFU dependency scores reduced from 43.5 (SD=12.65) to 39.4 (SD=15.19), for CAFU upset scores reduced from 5.3 (SD=7.69) to 3.9 (SD=5.47). Between male and female caregiver, the mean ZBI reduced for 13.2% and 23.1% while the CAFU dependency score reduced from 41.7 to 39.4 and 46.4 to 39.4 respectively. Conclusion: The burden felt by the caregivers was initially high but reduced significantly even after 1- month. Any psychosocial support or intervention aimed to ease caregiver burden should be started early.

2.
Front Psychol ; 13:766036, 2022.
Article in English | PubMed | ID: covidwho-1809574

ABSTRACT

INTRODUCTION: The 2019-2020 pandemic COVID-19 has become a global health crisis. While many recent studies on COVID-19 pandemic have focused on disease epidemiology and psychological status of patients, few have explored the multi-facet influential factors or combined perspectives from both the patients and healthcare workers. The purposes of this study were to: analyze the influencing factors of psychological distress of COVID-19 patients;and describe the experience of healthcare workers relieving psychological distress. MATERIALS AND METHODS: This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting two populations: COVID-19 patient and health workers, respectively. In the quantitative part, we recruited a convenience sample of patients with COVID-19 from five hospitals in Wuhan, Hubei Province from 10 to 15 April, 2020. Besides, we collected data by using participants' socio-demographic information sheet, the Connor-Davidson Resilience Scale-10, the Herth Hope Index, the Distress Thermometer, the Revised Chinese Version of Mishel Uncertainty in Illness Scale, and the Chinese Version of Wake Forest Physician Trust Scale. In the qualitative part, the participants were healthcare workers involved in medical aid missions in Hubei Province, China. Meanwhile, we used sampling with convenient and purposive, data collection with a semi-structured online video interview, and text transcription with Colaizzi's phenomenological method. RESULTS: The results reveal that 25.7% of patients reported higher level of psychological distress (n = 31, scoring ≥4). After controlling the sociodemographic variables, only severity of COVID-19 (β = 0.282, P = 0.025) and uncertainty in illness (β = 0.345, P = 0.007) shown significant effect on psychological distress in the regression model (F = 10.862, R (2) = 0.222, P < 0.001). The experience of healthcare workers emerged five themes: Particularly needed psychological care, Manifestation of negative emotion, Manifestation of proactive adaptation, Strategies relieving psychological distress, and gains of healthcare workers after delivering effective psychological care. CONCLUSION: The 25.7% of patients with COVID-19 still suffered from psychological distress, which should receive timely attention from healthcare workers. And the severity of the disease and disease uncertainty have a significant impact on distress. It is critical to train the healthcare workers on detecting the different manifestation of psychological distress, offering timely disease related information, and applying communication strategies.

3.
IEEE Trans Pattern Anal Mach Intell ; Pp, 2022.
Article in English | PubMed | ID: covidwho-1806969

ABSTRACT

In this paper, we contribute a new million-scale recognition benchmark, containing uncurated 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. Firstly, we collect 4M name lists and download 260M faces from the Internet. Then, a Cleaning Automatically utilizing Self-Training pipeline is devised to purify the tremendous WebFace260M, which is efficient and scalable. To our best knowledge, the cleaned WebFace42M is the largest public face recognition training set in the community. Referring to practical deployments, Face Recognition under Inference Time conStraint (FRUITS) protocol and a new test set with rich attributes are constructed. Moreover, we gather a large-scale masked face sub-set for biometrics assessment under COVID-19. For a comprehensive evaluation of face matchers, three recognition tasks are performed under standard, masked and unbiased settings, respectively. Equipped with this benchmark, we delve into million-scale face recognition problems. Enabled by WebFace42M, we reduce 40% failure rate on the challenging IJB-C set and rank the 3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior performance compared with the public training set. The proposed benchmark shows enormous potential on standard, masked and unbiased face recognition scenarios.

4.
Proc Math Phys Eng Sci ; 478(2260):20220040, 2022.
Article in English | PubMed | ID: covidwho-1806777

ABSTRACT

COVID-19, the disease caused by the novel coronavirus 2019, has caused grave woes across the globe since it was first reported in the epicentre of Wuhan, Hubei, China, in December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that rendered more than 900 million people housebound for more than two months since the lockdown of Wuhan, and elsewhere, on 23 January 2020. Here, we assess the impact of China's massive lockdowns and travel restrictions reflected by the changes in mobility patterns across and within provinces, before and during the lockdown period. We calibrate movement flow between provinces with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicentre Hubei. Moreover, we show that synchronous lockdowns and consequent reduced mobility lag a certain time to elicit an actual impact on suppressing the spread. Such highly coordinated nationwide lockdowns, applied via a top-down approach along with high levels of compliance from the bottom up, are central to mitigating and controlling early-stage outbreaks and averting a massive health crisis.

5.
ACS Nano ; 2022.
Article in English | PubMed | ID: covidwho-1805554

ABSTRACT

The key to controlling the spread of the coronavirus disease 2019 (COVID-19) and reducing mortality is highly dependent on the safe and effective use of vaccines for the general population. Current COVID-19 vaccination practices (intramuscular injection of solution-based vaccines) are limited by heavy reliance on medical professionals, poor compliance, and laborious vaccination recording procedures, resulting in a waste of health resources and low vaccination coverage, etc. In this study, we developed a smart mushroom-inspired imprintable and lightly detachable (MILD) microneedle platform for the effective and convenient delivery of multidose COVID-19 vaccines and decentralized vaccine information storage. The mushroom-like structure allows the MILD system to be easily pressed into the skin and detached from the patch base, acting as a "tattoo" to record the vaccine counts in situ without any storage equipment, offering quick accessibility and effortless readout, saving a great deal of valuable time and energy for both patients and health professionals. After loading inactivated SARS-CoV-2 virus-based vaccines, MILD system induced a high level of antibodies against the SARS-CoV-2 receptor-binding domain (RBD) in vivo without eliciting systemic toxicity and local damage. Collectively, this smart delivery platform serves as a promising carrier to improve COVID-19 vaccination efficacy through its dual capabilities of vaccine delivery and in situ data storage, thus exhibiting great potential for helping to contain the COVID-19 pandemic or a resurgence.

6.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333495

ABSTRACT

BACKGROUND: The COVID-19 epidemic originated in Wuhan City of Hubei Province in December 2019 and has spread throughout China. Understanding the fast evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy. METHODS: We collected individual information on 8,579 laboratory-confirmed cases from official publically sources reported outside Hubei in mainland China, as of February 17, 2020. We estimated the temporal variation of the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level. RESULTS: The median age of the cases was 44 years, with an increasing of cases in younger age groups and the elderly as the epidemic progressed. The delay from symptom onset to hospital admission decreased from 4.4 days (95%CI: 0.0-14.0) until January 27 to 2.6 days (0.0-9.0) from January 28 to February 17. The mean incubation period was estimated at 5.2 days (1.8-12.4) and the mean serial interval at 5.1 days (1.3-11.6). The epidemic dynamics in provinces outside Hubei was highly variable, but consistently included a mix of case importations and local transmission. We estimate that the epidemic was self-sustained for less than three weeks with Rt reaching peaks between 1.40 (1.04-1.85) in Shenzhen City of Guangdong Province and 2.17 (1.69-2.76) in Shandong Province. In all the analyzed locations (n=10) Rt was estimated to be below the epidemic threshold since the end of January. CONCLUSION: Our findings suggest that the strict containment measures and movement restrictions in place may contribute to the interruption of local COVID-19 transmission outside Hubei Province. The shorter serial interval estimated here implies that transmissibility is not as high as initial estimates suggested.

7.
Chinese Pharmacological Bulletin ; 37(7):911-916, 2021.
Article in Chinese | Scopus | ID: covidwho-1792324

ABSTRACT

Studies have shown that COVID-19 patients infected with SARS-CoV-2 have severe pulmonary inflammation and cytokine storm, so the treatment of cytokine storm is an important part of rescuing critically ill patients with COVID-19. As an important cause of death, the preclinical study of cytokine storm is essential, and related experiments in vivo and in vitro are also the only way to develop new drugs for COVID-19 in the future. This paper reviews the in vitro and in vivo experimental methods of cytokine storm research articles at home and abroad in recent years, including the establishment of animal models, cell evaluation methods, pharmacodynamic evaluation indicators, etc., in order to provide reference and guidance for the experimental design methods of cytokine storm. © 2021 Publication Centre of Anhui Medical University. All rights reserved.

8.
IEEE Transactions on Evolutionary Computation ; 2022.
Article in English | Scopus | ID: covidwho-1788787

ABSTRACT

Vaccination uptake has become the key factor that will determine our success in containing the COVID-19 pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel coronavirus pneumonia (COVID-19) pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely-scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can pre-distribute the forecasted number of vaccines to the satellites in advance;after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions;when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and hence accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts, and hence contribute to accelerating the achievement of herd immunity. IEEE

9.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-332928

ABSTRACT

During COVID-19 pandemic, mutations of SARS-CoV-2 produce new strains that can be more infectious or evade vaccines. Viral RNA mutations can arise from misincorporation by RNA-polymerases and modification by host factors. Analysis of SARS-CoV-2 sequence from patients showed a strong bias toward C-to-U mutation, suggesting a potential mutational role by host APOBEC cytosine deaminases that possess broad anti-viral activity. We report the first experimental evidence demonstrating that APOBEC3A, APOBEC1, and APOBEC3G can edit on specific sites of SARS-CoV-2 RNA to produce C-to-U mutations. However, SARS-CoV-2 replication and viral progeny production in Caco-2 cells are not inhibited by the expression of these APOBECs. Instead, expression of wild-type APOBEC3 greatly promotes viral replication/propagation, suggesting that SARS-CoV-2 utilizes the APOBEC-mediated mutations for fitness and evolution. Unlike the random mutations, this study suggests the predictability of all possible viral genome mutations by these APOBECs based on the UC/AC motifs and the viral genomic RNA structure. One-sentence summary: Efficient Editing of SARS-CoV-2 genomic RNA by Host APOBEC deaminases and Its Potential Impacts on the Viral Replication and Emergence of New Strains in COVID-19 Pandemic.

10.
Yaoxue Xuebao ; 57(2):446-452, 2022.
Article in Chinese | EMBASE | ID: covidwho-1780346

ABSTRACT

As one of the "Three Drugs Three Prescriptions" anti-COVID-19 traditional Chinese medicine, Jinhua Qinggan granules (JHQG) has been proved to have clear clinical effects. With complex medicinal flavors and ingredients, there is no systematic research report on chemical composition in vivo or in vitro. An ultrahigh pressure liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-QTOF/MS) method was developed in this study to identify the components of the anti-COVID-19 traditional Chinese medicine JHQG granules. Analyze the collected rat plasma samples after administration and explore the exposed components in rats within 8 hours after intragastric administration. Preliminary pharmacokinetic analysis was then performed on this basis. Through UPLC-QTOF/MS analysis and verification by standard products, a total of 77 chemical components in JHQG formula have been identified, among which 22 compounds were highly exposed in vivo, mainly derived from three medicinal materials of honeysuckle, scutellaria and forsythia. Through the assessment of the blood drug concentration by the compartment model, 6 PK parameters of 4 high-exposure chemical components have been obtained, clarifying the metabolic characteristics of the main exposed components in JHQG briefly. The method is simple, efficient, sensitive and accurate and provides research basis to the clarification of the pharmacodynamics material basis and mechanism of JHQG, which has certain reference significance for the basics and applications research of the traditional Chinese medicine prescriptions in fighting the SARS-CoV-2.

11.
Environmental Science-Nano ; : 11, 2022.
Article in English | Web of Science | ID: covidwho-1778647

ABSTRACT

Hydrogen peroxide (H2O2) solution and its aerosols are common disinfectants, especially for urgent reuse of personal protective equipment during the COVID-19 pandemic. Highly sensitive and selective evaluation of the H2O2 concentration is key to customizing the sufficient disinfection process and avoiding disinfection overuse. Amperometric electrochemical detection is an effective means but poses challenges originated from the precarious state of H2O2. Here, an atomic Co-N-x-C site anchored neuronal-like carbon modified amperometric sensor (denoted as the CoSA-N/C@rGO sensor) is designed, which exhibits a broad detection range (from 250 nM to 50 mM), superior sensitivity (743.3 mu A mM(-1) cm(-2), the best among carbon-based amperometric sensors), strong selectivity (no response to interferents), powerful reliability (only 2.86% decay for one week) and fast response (just 5 s) for residual H2O2 detection. We validated the accuracy and practicability of the CoSA-N/C@rGO sensor in the actual H2O2 disinfection process of personal protective equipment. Further characterization verifies that the electrocatalytic activity and selective reduction of H2O2 is determined by the atomically dispersed Co-N-x-C sites and the high oxygen content of CoSA-N/C@rGO, where the response time and reliability of H2O2 detection is determined by the neuronal-like structure with high nitrogen content. Our findings pave the way for developing a sensor with superior sensitivity, selectivity and stability, rendering promising applications such as medical care and environmental treatment.

12.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-331917

ABSTRACT

Background: Drug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects. Methods: Using a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect. Results: We identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients. Conclusions: Together, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification.

13.
Global Mental Health ; 2022.
Article in English | EMBASE | ID: covidwho-1768726

ABSTRACT

The COVID-19 pandemic caused significant psychological consequences among the public, especially for people in the epicenter. This study examined the "bull's eye" model by comparing the level of psychological distress and the effect of different stressors in Wuhan (the original epicenter) with that in the surrounding areas in Hubei Province during the pandemic. Data were obtained from a cross-national survey of 10,478 respondents between the ages of 18 and 80 years in Hubei Province during the peak of the pandemic. Results of the ordinary least squares regression models showed that Wuhan residents experienced more psychological distress than those in the surrounding areas. Social and economic problems caused by the pandemic, risk exposure, perceived discrimination, and information-seeking behaviors were positively associated with distress. Social assistance was negatively associated with distress. Findings were consistent with the bull's eye model by revealing both a higher level of psychological distress and a stronger effect of stressors among the Wuhan residents than with those in low-risk areas. Thus, policymakers and psychological workers should provide adequate psychological services in high-risk areas. Lowering risk exposure, reducing discrimination against people in the epicenter, and improving information quality are essential to alleviate their psychological distress.

14.
Embase; 2021.
Preprint in English | EMBASE | ID: ppcovidwho-331185

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of novel corona virus disease (COVID-19). The neutralizing monoclonal antibodies (mAbs) targeting the receptor binding domain (RBD) of SARS-CoV-2 are among the most promising strategies to prevent and treat COVID-19. However, SARS-CoV-2 variants of concern (VOCs) profoundly reduced the efficacies of most of mAbs and vaccines approved for clinical use. Herein, we demonstrated mAb 35B5 efficiently neutralizes both wild-type (WT) SARS-CoV-2 and VOCs, including B.1.617.2 (delta) variant, in vitro and in vivo. Cryo-electron microscopy (cryo-EM) revealed that 35B5 neutralizes SARS-CoV-2 by targeting a unique epitope that avoids the prevailing mutation sites on RBD identified in circulating VOCs, providing the molecular basis for its pan-neutralizing efficacy. The 35B5-binding epitope could also be exploited for the rational design of a universal SARS-CoV-2 vaccine.

15.
Emerg Microbes Infect ; : 1-29, 2022.
Article in English | PubMed | ID: covidwho-1764463

ABSTRACT

Diabetes mellitus (DM) is one of the most common underlying diseases that may aggravates COVID-19. In the present study, we explored islet function, the presence of SARS-CoV-2 and pathological changes in the pancreas of patients with COVID-19. Oral glucose tolerance tests (OGTTs) and the C-peptide release test demonstrated a decrease in glucose-stimulated C-peptide secretory capacity and an increase in HbA1c levels in patients with COVID-19. The prediabetic conditions appeared to be more significant in the severe group than in the moderate group. SARS-CoV-2 receptors (ACE2, CD147, TMPRSS2 and neuropilin-1) were expressed in pancreatic tissue. In addition to SARS-CoV-2 virus spike protein and virus RNA, coronavirus-like particles were present in the autophagolysosomes of pancreatic acinar cells of a patient with COVID-19. Furthermore, the expression and distribution of various proteins in pancreatic islets of patients with COVID-19 were altered. These data suggest that SARS-CoV-2 in the pancreas may directly or indirectly impair islet function.

16.
Respirology ; 27:123-123, 2022.
Article in English | Web of Science | ID: covidwho-1762228
17.
Industrial Management and Data Systems ; 2022.
Article in English | Scopus | ID: covidwho-1752274

ABSTRACT

Purpose: The purpose of this paper is to explore the impact of WeChat public platforms (abbreviated as WPP) on blood donation behavior using data from the platforms’ backend and information system. Design/methodology/approach: First, this paper established a time-varying difference-in-difference (DID) model to evaluate the change before and after following the WPP under normal scenarios. The difference-in-difference-in-difference (DDD) method was further used to analyze the heterogeneous effects of gender, age, occupation and education. Second, a logit model was used to examine the impact of WPP on blood donation behavior under emergency scenarios (i.e. COVID-19). Findings: The research shows that following WPP has a positive impact on donation volume. For each donor, the average blood donation volume after following WPP increased by 12.94% compared to before following. The WPP has a greater impact on groups with males, medical staff, middle-aged individuals and those with primary school education. Following WPP also enhanced blood donation behavior in emergency scenarios. During the COVID-19 pandemic, the probability of fans donating blood was 2.6% higher than non-fans, and the average blood donation volume of fans was 7.04% higher than non-fans, which was 5.9% lower than in normal scenarios. Originality/value: For theory, this paper quantified the impact of WPP on blood donation behavior in normal and emergency scenarios and addressed the research gap surrounding the impact exerted by social media on blood donation behavior. For methodology, the time-varying DID model, DDD model and logit model were applied to the field of blood donation, which expanded the application scenarios. For practice, the findings are of great significance for recruiting blood donors and providing evidence for promotion on WPP. © 2022, Emerald Publishing Limited.

18.
Non-conventional in English | National Technical Information Service, Grey literature | ID: grc-753686

ABSTRACT

Rett Syndrome is caused by mutations in Mecp2, which result in a constellation of language, cognitive, motor, and autonomic deficits later in life. Although changes in long-range neuronal connectivity likely underlie the behavioral defects in Rett syndrome, it is unclear how long-range axonal projections are disrupted. Here we develop and apply high-throughput single-cell techniques to identify cell type-specific changes in projections in Mecp2 animals. We identified two subtypes of cortical projection neurons with potential changes in long-range projections, including the corticothalamic neurons and L6b neurons. Our results provide candidate cell types for future in depth studies on the long-range circuitry changes associated with Mecp2 mutation. Furthermore, our approach is generally applicable to other brain areas and disease models to reveal cell type-specific changes in projections that are difficult to detect using conventional methods.

19.
Open Forum Infectious Diseases ; 8(SUPPL 1):S246, 2021.
Article in English | EMBASE | ID: covidwho-1746712

ABSTRACT

Background. Over 29 million people have been infected with COVID-19 in the U.S. alone. While COVID-19 carries serious morbidity and mortality, potential for co-infection with other respiratory infections remains unclear. We aimed to: (1) estimate co-infection prevalence of COVID-19 and influenza, and (2) compare demographics and clinical outcomes of co-infected patients to those of COVID-19 singly-infected patients using U.S. electronic health records (EHR). Methods. Patients in the Optum De-identified COVID-19 EHR database diagnosed with COVID-19 (lab-confirmed or ICD code) between February 2020 and January 2021 were eligible. Influenza co-infection was defined as an influenza diagnosis (lab-confirmed or ICD code) within ±10 days of COVID-19 diagnosis. We report co-infection prevalence for all COVID-19 patients and for a subset of hospitalized COVID-19 patients. Results. Among all COVID-19 patients (N = 549,532), 1,794 (0.3%) were co-infected with influenza. Among the hospitalized subset (N = 80,192), 242 (0.3%) were co-infected with influenza. In sensitivity analyses restricting to lab-confirmed influenza, co-infection prevalence was 0.1% overall and 0.2% among hospitalized patients. No meaningful differences were observed in baseline demographics between co-infected and singly-infected patients. Among hospitalized patients, univariate analysis suggested higher likelihood of invasive ventilation (12.8% vs. 9.8%;p=0.14), respiratory failure (56.2% vs. 46.6%, p< 0.01), and ICU stay (27.3% vs. 23.1%, p=0.13), but no meaningful difference in mortality (13.3% vs. 13.0%, p=0.97), for co-infected as compared to singly-infected COVID-19 patients. Conclusion. In a real-world cohort, we observed a low proportion (0.3%) of COVID-19 patients co-infected with influenza. Co-infected patients had similar baseline characteristics but higher likelihood of hospitalization severity as compared to singly-infected COVID-19 patients. Limitations include low prevalence of circulating influenza and potential missing data bias.

20.
Open Forum Infectious Diseases ; 8(SUPPL 1):S266-S267, 2021.
Article in English | EMBASE | ID: covidwho-1746671

ABSTRACT

Background. Over 32 million cases of COVID-19 have been reported in the US. Outcomes range from mild upper respiratory infection to hospitalization, acute respiratory failure, and death. We assessed risk factors associated with severe disease, defined as hospitalization within 21 days of diagnosis or death, using US electronic health records (EHR). Methods. Patients in the Optum de-identified COVID-19 EHR database who were diagnosed with COVID-19 in 2020 were included in the analysis. Regularized multivariable logistic regression was used to identify risk factors for severe disease. Covariates included demographics, comorbidities, history of influenza vaccination, and calendar time. Results. Of the 193,454 eligible patients, 36,043 (18.6%) were hospitalized within 21 days of COVID-19 diagnosis, and 6,397 (3.3%) died. Calendar time followed an inverse J-shaped relationship where severe disease rates rapidly declined in the first 25 weeks of the pandemic. BMI followed an asymmetric V-shaped relationship with highest rates of disease severity observed at the extremes. In the multivariable model, older age had the strongest association with disease severity (odds ratios and 95% confidence intervals of significant associations in Figure). Other risk factors were male sex, uninsured status, underweight and obese BMI, higher Charlson Comorbidity Index, and individual comorbidities including hypertension. Asthma and overweight BMI were not associated with disease severity. Blacks, Hispanics, and Asians experienced higher odds of disease severity compared to Whites. Conclusion. Odds of hospitalization or death have decreased since the start of the pandemic, with the steepest decline observed up to mid-August, possibly reflecting changes in both testing and treatment. Older age is the most important predictor of severe COVID-19. Obese and underweight, but not overweight, BMI were associated with increased odds of disease severity when compared to normal weight. Hypertension, despite not being included in many guidelines for vaccine prioritization, is a significant risk factor. Pronounced health disparities remain across race and ethnicity after accounting for comorbidities, with minorities experiencing higher disease severity.

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