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INTRODUCTION: Understanding the changing epidemiology of adults hospitalized with coronavirus disease 2019 (COVID-19) informs research priorities and public health policies. METHODS: Among adults (≥18 years) hospitalized with laboratory-confirmed, acute COVID-19 between 11 March 2021, and 31 August 2022 at 21 hospitals in 18 states, those hospitalized during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron-predominant period (BA.1, BA.2, BA.4/BA.5) were compared to those from earlier Alpha- and Delta-predominant periods. Demographic characteristics, biomarkers within 24â hours of admission, and outcomes, including oxygen support and death, were assessed. RESULTS: Among 9825 patients, median (interquartile range [IQR]) age was 60 years (47-72), 47% were women, and 21% non-Hispanic Black. From the Alpha-predominant period (Mar-Jul 2021; N = 1312) to the Omicron BA.4/BA.5 sublineage-predominant period (Jun-Aug 2022; N = 1307): the percentage of patients who had ≥4 categories of underlying medical conditions increased from 11% to 21%; those vaccinated with at least a primary COVID-19 vaccine series increased from 7% to 67%; those ≥75 years old increased from 11% to 33%; those who did not receive any supplemental oxygen increased from 18% to 42%. Median (IQR) highest C-reactive protein and D-dimer concentration decreased from 42.0â mg/L (9.9-122.0) to 11.5â mg/L (2.7-42.8) and 3.1 mcg/mL (0.8-640.0) to 1.0 mcg/mL (0.5-2.2), respectively. In-hospital death peaked at 12% in the Delta-predominant period and declined to 4% during the BA.4/BA.5-predominant period. CONCLUSIONS: Compared to adults hospitalized during early COVID-19 variant periods, those hospitalized during Omicron-variant COVID-19 were older, had multiple co-morbidities, were more likely to be vaccinated, and less likely to experience severe respiratory disease, systemic inflammation, coagulopathy, and death.
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Background: Suicide is a leading cause of death, and rates of attempted suicide have increased during the COVID-19 pandemic. The under-diagnosed psychiatric phenotype of dissociation is associated with elevated suicidal self-injury;however, it has largely been left out of attempts to predict and prevent suicide. Objective: We designed an artificial intelligence approach to identify dissociative patients and predict prior suicide attempts in an unbiased, data-driven manner. Method: Participants were 30 controls and 93 treatment-seeking female patients with posttraumatic stress disorder (PTSD) and various levels of dissociation, including some with the PTSD dissociative subtype and some with dissociative identity disorder (DID). Results: Unsupervised learning models identified patients along a spectrum of dissociation. Moreover, supervised learning models accurately predicted prior suicide attempts with an score up to 0.83. DID had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in PTSD and DID. Conclusions: These findings expand our understanding of the dissociative phenotype and underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury. HIGHLIGHTS Dissociation, feelings of detachment and disruption in one's sense of self and surroundings, is associated with an elevated risk of suicidal self-injury;however, it has largely been left out of attempts to predict and prevent suicide. Using machine learning techniques, we found dissociative identity disorder had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in posttraumatic stress disorder and dissociative identity disorder. These findings underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.
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Background: As of August 2021, every region of the world has been affected by the COVID-19 pandemic, with more than 196,000,000 cases worldwide. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that up to 70% (95% CI: 54% - 80%) of imported cases could remain undetected relative to the sensitivity of surveillance in Singapore. The percentage of undetected imported cases rises to 75% (95% CI 66% - 82%) when comparing to the surveillance sensitivity in multiple countries. Conclusions: Our analysis shows that a large number of COVID-19 cases remain undetected across the world. These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China. © 2021 Bhatia S et al.
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Background: Since the start of the COVID-19 epidemic in late 2019, there have been more than 152 affected regions and countries with over 110,000 confirmed cases outside mainland China.
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Introduction: LTCFs are at risk of COVID-19 outbreaks but evidence regarding SARS-CoV-2 acquisition and transmission routes among their employees remains weak. Objectives: We investigated the relative contribution of occupational (vs. community) exposure for COVID-19 acquisition among employees of a university affiliated LTCF in Switzerland, from March to June 2020. Methods: This is a prospective cohort study with a nested analysis of a COVID-19 seroprevalence study among LTCF staff. We performed Poisson regression to determine risk factors for seropositivity and to measure the influence of community vs. nosocomial exposure to COVID-19 on SARS-CoV-2 seropositivity using adjusted prevalence ratios (aPR). In addition, we conducted a COVID-19 outbreak investigation in a LTCF ward using both epidemiological and genetic sequencing data. We constructed a maximum likelihood phylogenetic tree and evaluated strain relatedness to discriminate between community- vs. hospital-acquired infections among employees. Results: Among 285 LTCF employees, we included 176 participants in the seroprevalence study, of whom 30 (17%) became seropositive for SARS-CoV-2. The majority (141/176, 80%) were healthcare workers and had ≥ 1 symptom compatible with COVID-19 (127/167, 76%). Risk factors for seropositivity included exposure to a COVID- 19 patient in the LTCF (aPR 2.6;95%CI 0.9-8.1) and exposure to a SARS-CoV-2 positive person in the community (aPR 1.7;95%CI 0.8- 3.5). Among 18 employees included in the outbreak investigation, phylogenetic analysis suggests that 8 (44%) acquired their infection in the community. Conclusion: During the first pandemic wave, there was a high burden of COVID-19 among LTCF employees. Both occupational and community exposures contributed to seropositivity and infection risk. These data may allow to better assess occupational health hazards and related legal implications during the COVID-19 pandemic. (Figure Presented).
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BACKGROUND: Nosocomial outbreaks of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are frequent despite implementation of conventional infection control measures. An outbreak investigation was undertaken using advanced genomic and statistical techniques to reconstruct likely transmission chains and assess the role of healthcare workers (HCWs) in SARS-CoV-2 transmission. METHODS: A nosocomial SARS-CoV-2 outbreak in a university-affiliated rehabilitation clinic was investigated, involving patients and HCWs, with high coverage of pathogen whole-genome sequences (WGS). The time-varying reproduction number from epidemiological data (Rt) was estimated, and maximum likelihood phylogeny was used to assess genetic diversity of the pathogen. Genomic and epidemiological data were combined into a Bayesian framework to model the directionality of transmission, and a case-control study was performed to investigate risk factors for nosocomial SARS-CoV-2 acquisition in patients. FINDINGS: The outbreak lasted from 14th March to 12th April 2020, and involved 37 patients (31 with WGS) and 39 employees (31 with WGS), 37 of whom were HCWs. Peak Rt was estimated to be between 2.2 and 3.6. The phylogenetic tree showed very limited genetic diversity, with 60 of 62 (96.7%) isolates forming one large cluster of identical genomes. Despite the resulting uncertainty in reconstructed transmission events, the analyses suggest that HCWs (one of whom was the index case) played an essential role in cross-transmission, with a significantly greater fraction of infections (P<2.2e-16) attributable to HCWs (70.7%) than expected given the number of HCW cases (46.7%). The excess of transmission from HCWs was higher when considering infection of patients [79.0%; 95% confidence interval (CI) 78.5-79.5%] and frail patients (Clinical Frailty Scale score >5; 82.3%; 95% CI 81.8-83.4%). Furthermore, frail patients were found to be at greater risk for nosocomial COVID-19 than other patients (adjusted odds ratio 6.94, 95% CI 2.13-22.57). INTERPRETATION: This outbreak report highlights the essential role of HCWs in SARS-CoV-2 transmission dynamics in healthcare settings. Limited genetic diversity in pathogen genomes hampered the reconstruction of individual transmission events, resulting in substantial uncertainty in who infected whom. However, this study shows that despite such uncertainty, significant transmission patterns can be observed.