Comprehensive evaluation of otorhinolaryngological symptoms in COVID-19 patients

Introduction: The aim of this study is to comprehensively evaluate the incidence and natural course of otorhinolaryngological symptoms of COVID-19 infection and its relations to each other and patient's demographics. Method(s): This is a prospective study conducted on symptomatic adult patients proven to be infected with COVID-19. Detailed history was taken from each patient including onset of symptoms. Symptoms were followed up tightly. We focus on otorhinolaryngological (ORL) symptoms and their duration and onset in relation to other symptoms. Data were collected and analyzed in detail. Result(s): Six-hundred eighty-six patients were included in the study, their age ranged from 19-75 years old, and of them 55.1% were males. Cough was found in 53.1% of cases followed by sore throat in 45.8%, anosmia/ hyposmia in 42.3%, headache in 42%, rhinorrhea in 19.5%, dry mouth in 7.6%, globus in 6.1%, epistaxis in 4.4%, and hearing loss in 0.6%. In non-ORL symptoms, fever was found in 54.2%, malaise in 55.1%, dyspnea in 49.3%, and diarrhea in 27.2%. The first symptom was anosmia in 15.7% of cases, sore throat in 6.1 %, cough in 7.9%, and headache in 13.4% of cases. Fever was the first symptom in 22.7%, malaise in 25.1%, and diarrhea in 6.4%. Headache occurred for 5.5 +/- 2 days, anosmia/hyposmia 3 to > 30 days, sore throat 4.1 +/- 1.2 days, rhinorrhea 4.3 +/- 1.1, cough 7.4 +/- 2.5 days, fever 4.7 +/- 2 days, and malaise 6.5 +/- 2.4 days. The cluster of COVID-19-related symptoms showed nine principal components. Conclusion(s): Otorhinolaryngological symptoms are main symptoms in COVID-19 infection, and they should be frequently evaluated to detect suspected cases especially in pauci-symptomatic patients and to properly manage infected patients.Copyright © 2022, The Author(s).

Death Predictors in Patients with COVID-19 on Venovenous ECMO

Background: Patients with coronavirus disease 2019 (COVID-19) and severe acute respiratory distress syndrome (ARDS) need in 10.5 to 15% veno-venous ECMO (V-V ECMO) therapy. The worldwide mortality in COVID-19 patients on ECMO has been described as extremely high with a mortality rate of 40 to 70%. Method(s): We collected data from 56 patients with severe ARDS who received V-V ECMO in 2020 to January 2022 at the University Hospital Magdeburg due to COVID-19 infection. We recorded demographic, pre-, intra-, and posttreatment data retrospectively. We divided the patients into two groups (survivors and nonsurvivors) to build the final prediction model based on our statistic and to detect relevant mortality risk factors. Result(s): Only 39.3% of patients survived the intensive care unit. Compared groups didn't differ in associated diseases. Most of the non-survivors were male (14 [63.6%] vs. 28 [82.4%], p = 0.114). Nonsurvivors showed a higher incidence of bleeding complications (10 [45.5%] vs. 23 [67.6%], p = 0,099), especially hemothorax (1 [4.5%] vs. 7 [20.6%], p = 0.094) and endobronchial bleeding (0 vs. 5 [14.7%], p = 0.059) as well as a higher incidence of bacterial superinfection (9 [40.1%] vs. 22 [64.7%], p = 0.080). Moreover, groups differed concerning the incidence of acute kidney injury without dialysis (1 [4.5% vs. 9 [26.5%], p = 0.036), and acute liver failure (1 [4.5%] vs. 7 [20.6%], p = 0.094). According to the results of bivariate regression analysis, male sex (odd ratio [OR]: 2.66;95% confidence interval [CI]: 0.773-9.194;p = 0.120), major bleeding events (OR: 2.50;95% CI: 0.831-7.574;p = 0.103), bacterial superinfection (OR: 2.65;95% CI: 0.879-7.981;p = 0.084), acute kidney injury without dialysis (OR: 7.56;95% CI: 0.884-64.636;p = 0.065), and acute liver failure (OR: 5.44;95% CI: 0.621-47.756, p = 0.126) were tendentious significant predictors of death. Subsequently, according to the results of multivariate analysis, the most significant factors of mortality were major bleeding events (OR: 3.27;95% CI: 0.888-12.047, p = 0.075) and the bacterial superinfection (OR: 2.81;95% CI: 0.800-9.888, p = 0.107). The mortality prediction model explained 31.8% (Nagelkerke R2) of the variance in-hospital mortality and correctly classified 71.4% of the cases. Conclusion(s): Major bleeding events and bacterial superinfection might be relevant mortality factors in COVID-19 patients on V-V ECMO therapy. Especially prevention of superinfection and strictly anticoagulation management might result in lower mortality rates.

Explaining the Varying Patterns of COVID-19 Deaths Across the United States: 2-Stage Time Series Clustering Framework.

BACKGROUND: Socially vulnerable communities are at increased risk for adverse health outcomes during a pandemic. Although this association has been established for H1N1, Middle East respiratory syndrome (MERS), and COVID-19 outbreaks, understanding the factors influencing the outbreak pattern for different communities remains limited. OBJECTIVE: Our 3 objectives are to determine how many distinct clusters of time series there are for COVID-19 deaths in 3108 contiguous counties in the United States, how the clusters are geographically distributed, and what factors influence the probability of cluster membership. METHODS: We proposed a 2-stage data analytic framework that can account for different levels of temporal aggregation for the pandemic outcomes and community-level predictors. Specifically, we used time-series clustering to identify clusters with similar outcome patterns for the 3108 contiguous US counties. Multinomial logistic regression was used to explain the relationship between community-level predictors and cluster assignment. We analyzed county-level confirmed COVID-19 deaths from Sunday, March 1, 2020, to Saturday, February 27, 2021. RESULTS: Four distinct patterns of deaths were observed across the contiguous US counties. The multinomial regression model correctly classified 1904 (61.25%) of the counties' outbreak patterns/clusters. CONCLUSIONS: Our results provide evidence that county-level patterns of COVID-19 deaths are different and can be explained in part by social and political predictors.

“Mat-O-Covid”: a SARS-CoV-2 (COVID-19) Job Exposure Matrix

While exposure assessment is complex for the occupational risk researcher, the objective of our work is to develop and validate a job-exposure matrix (JEM) for SARS-CoV-2 exposure called “Mat-O-Covid” project (“COVID-Mate” in French). A group of French experts, the JEM was developed for all workers using the 2003 Occupation and Socioprofessional Categories (with a transcoding gateway to the 2008 International Standard Classification of Occupations) and a focus on the health and care sector. The average of the experts' coding was used as estimates for both estimates, exposure "subjects” (colleagues and/or public) and "patients” for the focus on the health and care sector, as well as the probability of prevention for each. Intraclass correlations were considered good to excellent except for health prevention. Compared to the United States O*Net JEM, the evaluation was considered as fair. In conclusion, a "Mat-O-Covid” JEM providing a probability of occupational exposure to SARS-CoV-2 will have implications for research and public health, taking into account that its limitations are known, and its validation is still in progress. NB: Mat-O-Covid is available at

Modeling the differences in the time-series profiles of new COVID-19 daily confirmed cases in 3,108 contiguous U.S. counties: A retrospective analysis.

OBJECTIVE: The COVID-19 pandemic in the U.S. has exhibited a distinct multiwave pattern beginning in March 2020. Paradoxically, most counties do not exhibit this same multiwave pattern. We aim to answer three research questions: (1) How many distinct clusters of counties exhibit similar COVID-19 patterns in the time-series of daily confirmed cases? (2) What is the geographic distribution of the counties within each cluster? and (3) Are county-level demographic, socioeconomic and political variables associated with the COVID-19 case patterns? MATERIALS AND METHODS: We analyzed data from counties in the U.S. from March 1, 2020 to January 2, 2021. Time series clustering identified clusters in the daily confirmed cases of COVID-19. An explanatory model was used to identify demographic, socioeconomic and political variables associated with the outbreak patterns. RESULTS: Three patterns were identified from the cluster solution including counties in which cases are still increasing, those that peaked in the late fall, and those with low case counts to date. Several county-level demographic, socioeconomic, and political variables showed significant associations with the identified clusters. DISCUSSION: The pattern of the outbreak is related both to the geographic location within the U.S. and several variables including population density and government response. CONCLUSION: The reported pattern of cases in the U.S. is observed through aggregation of the daily confirmed COVID-19 cases, suggesting that local trends may be more informative. The pattern of the outbreak varies by county, and is associated with important demographic, socioeconomic, political and geographic factors.

COVID-19 impact on operative management of pediatric neck abscesses

Introduction: For multiple reasons, elective pediatric otolaryngology surgical procedures have declined during the COVID-19 pandemic. However, it is less clear how COVID- 19 has affect acute care surgeries. The purpose of this study was to assess whether the prevalence of pediatric head or neck abscesses managed with operative drainage decreased compared with previous years. Method: In a retrospective study, we evaluated medical records of 203 pediatric cases diagnosed with abscess of the head or neck and treated with incision and drainage at a large tertiary care children's hospital between the dates of April 1 and November 30 from 2015 to 2020. We compared outcomes for each year from 2015 with 2019 with the same date range in 2020, which included location of the infection, abscess size, symptoms, duration of antibiotic treatment before and after operative intervention, microbiology, and the number of cases per year. Results: The mean ± SD presenting age in the 2015-2019 group was 5.10 ± 5.10 years and 2.24 ± 1.91 years in the 2020 group. Neck abscesses were more common than head abscesses from 2015 to 2019 (81.7% vs 18.3%), whereas only neck abscesses presented in 2020 (P = .030). The mean number of cases between 2015 and 2019 was 36 ± 3.81, whereas the number of cases in 2020 was 23 (P = .036). The most common symptom was neck stiffness or swelling in both groups (2015-2019, 74.4% vs 2020, 100%, P = .003). Of those patients who underwent intravenous antibiotic therapy prior to presentation (n = 43), the mean number of days before admission was 1.98 ± 2.95 (n = 40) from 2015 to 2019 and 12.00 ± 10.39 (n = 3) in 2020 (P = .008). The percentage of cases with multiple strains cultured between 2015 and 2019 was 13.8% vs 18.2% in (P = .007). Conclusion: There was a decrease in the number of operative head or neck abscesses in 2020 during the COVID-19 pandemic compared with the mean number per year from 2015 to 2019 within the same date range. COVID-19 mitigation strategies leading to reduced transmission of other viral and bacterial illnesses and a tendency toward prolonged medical management to avoid surgery during the pandemic are 2 possible reasons for this decrease.

Caregiver experience throughout head and neck cancer survivorship: Longitudinal study

Introduction: Caregiver burden (CGB) is a multidimensional concept comprising social, emotional, and financial dimensions. While head and neck cancer (HNC) and its treatment may use functional impairment necessitating extensive regiving, little has been examined regarding HNC-related CGB. Method: Prospective longitudinal survey of treatment-naïve adults with HNC (care receivers) and their primary informal caregivers was undertaken from October 2019 to December 2020. Participants were surveyed at 3 time points: diagnosis, mid-treatment, and end of treatment (EOT). Caregivers were surveyed with the Caregiver Reaction Assessment (CRA) tool, divided into 5 subscales on a scale of 1 to 5. Higher CRA scores indicate stronger negative impact, in all subscales except for the self-esteem domain. Results: Of 108 newly diagnosed HNC survivors, 64 (59%) were eligible for the study cohort, 42 of whom were accompanied by a primary informal caregiver who was subsequently enrolled. Caregivers ranged from spouses/partners (91%), daughters (7%), and parents (2%). Among 10 caregivers (24%) who completed all 3 consecutive time points, CRA scores (median [range]) by subscale at diagnosis were: disrupted schedule, 3 (2-3.6);financial problems, 3 (2-3.6);health problems, 3 (2.5-3.5);family support, 2.2 (1-3.6);and self-esteem, 4.1 (3.4-4.4). These values remained consistent throughout the survivorship trajectory. At EOT, health problems (3.6 [2.5-4]), and self-esteem subscales (3.7 [3.1-4.3]) were most negatively affected. A significant decrease in selfesteem scores from diagnosis to EOT (P = .014) indicates worsening self-esteem. Two caregivers cited changes in employment from diagnosis to EOT, with 1 having to quit their job completely. Some 50% reported an effect on employment status and monthly income due to the COVID-19 pandemic. Conclusion: HNC caregivers appear to have consistently elevated CGB regarding disrupted schedules and financial and health problems throughout survivorship. At EOT, caregiver self-esteem appears to suffer significantly while negative health-related impact worsens.

SARS-CoV-2 (COVID-19) job-exposure matrix: “Mat-O-Covid” Creation (COVID-Mate in French), accuracy study, and perspectives

While exposure assessment is complex for the occupational risk researcher, the objective of our work is to develop and validate a job-exposure matrix (JEM) for SARS-CoV-2 exposure called “Mat-O-Covid” project (“COVID-Mate” in French). A group of French experts, the JEM was developed for all workers using the 2003 Occupation and Socioprofessional Categories (with a transcoding gateway to the 2008 International Standard Classification of Occupations) and a focus on the health and care sector. The average of the experts’ coding was used as estimates for both estimates, exposure “subjects” (colleagues and/or public) and “patients” for the focus on the health and care sector, as well as the probability of prevention for each. Intraclass correlations were considered good to excellent except for health prevention. Compared to the United States O*Net JEM, the evaluation was considered as fair. In conclusion, a “Mat-O-Covid” JEM providing a probability of occupational exposure to SARS-CoV-2 will have implications for research and public health, taking into account that its limitations are known, and its validation is still in progress. © 2021 Elsevier Masson SAS L’évaluation de l'exposition est difficile pour le chercheur du domaine des risques professionnels. L'objectif du travail est de développer et de valider une matrice emplois-exposition (MEE) pour l'exposition professionnelle au SARS-CoV-2 appelée « Mat-O-Covid ». Grâce à un groupe d'expert français, la matrice a été développée pour tous les travailleurs sur la profession et les catégories socioprofessionnelles de 2003 (avec une passerelle de transcodage vers la Classification internationale type des professions de 2008) et un focus sur le secteur de soin et médicosocial. La moyenne des codages des experts a été utilisée comme estimation pour l'exposition « sujets » (collègues et/ou public) et « patients » pour le focus secteurs sanitaires, ainsi que la probabilité de prévention pour chaque. Les corrélations intraclasses ont été considérés comme bonnes à excellentes, sauf pour la prévention en santé. Comparées à la matrice américaine O*Net, l’évaluation a été considérée comme satisfaisante. En conclusion, une MEE « Mat-O-Covid » fournissant une probabilité d'exposition professionnelle au SARS-CoV-2 aura des implications pour la recherche et en santé publique, sous réserve de connaître ses limites et de poursuivre sa validation. © 2021 Elsevier Masson SAS

Seat Assignments With Physical Distancing in Single-Destination Public Transit Settings.

While the importance of physical (social) distancing in reducing the spread of COVID-19 has been well-documented, implementing similar controls in public transit remains an open question. For instance, in the United States, guidance for maximum seating capacity in single-destination public transit settings, such as school buses, is only dependent on the physical distance between passengers. In our estimation, the available models/guidance are suboptimal/inefficient since they do not account for the possibility of passengers being from the same household. This paper discusses and addresses the aforementioned limitation through two types of physical distancing models. First, a mixed-integer programming model is used to assign passengers to seats based on the reported configuration of the vehicle and desired physical distancing requirement. In the second model, we present a heuristic that allows for household grouping. Through several illustrative scenarios, we show that seating assignments can be generated in near real-time, and the household grouping heuristic increases the capacity of the transit vehicles (e.g., airplanes, school buses, and trains) without increasing the risk of infection. A running application and its source code are available to the public to facilitate adoption and to encourage enhancements.

A retrospective cluster analysis of COVID-19 cases by county (preprint)

The COVID-19 pandemic in the U.S. has exhibited distinct waves, the first beginning in March 2020, the second beginning in early June, and additional waves currently emerging. Paradoxically, almost no county has exhibited this multi-wave pattern. We aim to answer three research questions: (1) How many distinct clusters of counties exhibit similar COVID-19 patterns in the time-series of daily confirmed cases?; (2) What is the geographic distribution of the counties within each cluster? and (3) Are county-level demographic, socioeconomic and political variables associated with the COVID-19 case patterns? We analyzed data from counties in the U.S. from March 1 to October 24, 2020. Time series clustering identified clusters in the daily confirmed cases of COVID-19. An explanatory model was used to identify demographic, socioeconomic and political variables associated the cluster patterns. Four patterns were identified from the timing of the outbreaks including counties experiencing a spring, an early summer, a late summer, and a fall outbreak. Several county-level demographic, socioeconomic, and political variables showed significant associations with the identified clusters. The timing of the outbreak is related both to the geographic location within the U.S. and several variables including age, poverty distribution, and political association. These results show that the reported pattern of cases in the U.S. is observed through aggregation of the COVID-19 cases, suggesting that local trends may be more informative. The timing of the outbreak varies by county, and is associated with important demographic, socioeconomic and geographic factors.

Detection of influenza virus in air samples of patient rooms.

BACKGROUND: Understanding the transmission and dispersal of influenza virus and respiratory syncytial virus (RSV) via aerosols is essential for the development of preventative measures in hospital environments and healthcare facilities. METHODS: During the 2017-2018 influenza season, patients with confirmed influenza or RSV infections were enrolled. Room air samples were collected close (0.30 m) to and distant (2.20 m) from patients' heads. Real-time polymerase chain reaction was used to detect and quantify viral particles in the air samples. The plaque assay was used to determine the infectiousness of the detected viruses. FINDINGS: Fifty-one air samples were collected from the rooms of 29 patients with laboratory-confirmed influenza; 51% of the samples tested positive for influenza A virus (IAV). Among the IAV-positive patients, 65% were emitters (had at least one positive air sample), reflecting a higher risk of nosocomial transmission compared with non-emitters. The majority (61.5%) of the IAV-positive air samples were collected 0.3 m from a patient's head, while the remaining IAV-positive air samples were collected 2.2 m from a patient's head. The positivity rate of IAV in air samples was influenced by distance from the patient's head and day of sample collection after hospital admission. Only three patients with RSV infection were recruited and none of them were emitters. CONCLUSION: Influenza virus can be aerosolized beyond 1 m in patient rooms, which is the distance considered to be safe by infection control practices. Further investigations are needed to determine the extent of infectivity of aerosolized virus particles.