Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections.
Ontology highlight
ABSTRACT:
SUBMITTER: Illingworth CJ
PROVIDER: S-EPMC8384420 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
Publications
Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections.
Illingworth Christopher Jr CJ Hamilton William L WL Warne Ben B Routledge Matthew M Popay Ashley A Jackson Chris C Fieldman Tom T Meredith Luke W LW Houldcroft Charlotte J CJ Hosmillo Myra M Jahun Aminu S AS Caller Laura G LG Caddy Sarah L SL Yakovleva Anna A Hall Grant G Khokhar Fahad A FA Feltwell Theresa T Pinckert Malte L ML Georgana Iliana I Chaudhry Yasmin Y Curran Martin D MD Parmar Surendra S Sparkes Dominic D Rivett Lucy L Jones Nick K NK Sridhar Sushmita S Forrest Sally S Dymond Tom T Grainger Kayleigh K Workman Chris C Ferris Mark M Gkrania-Klotsas Effrossyni E Brown Nicholas M NM Weekes Michael P MP Baker Stephen S Peacock Sharon J SJ Goodfellow Ian G IG Gouliouris Theodore T de Angelis Daniela D Török M Estée ME
eLife 20210824
SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates ...[more]
Similar Datasets
Project description:BackgroundThe mortality risk of co-infections/secondary infections (CoI/ScI) is under-reported in patients with non-critical COVID-19, leading to the under-management of CoI/ScI and publication bias in the medical literature. We aimed to investigate the association between CoI/ScI and mortality in patients hospitalised with mild-to-severe COVID-19.MethodsWe conducted a retrospective cohort study at a COVID-19 treatment hospital in Vietnam and collected all eligible medical records, with CoI/ScI status as the exposure (non-CoI/ScI and CoI/ScI, with the latter including nature of pathogen [bacterial, fungal, or bacterial + fungal] and multidrug-resistance pathogen [no MDRp or ≥ 1 MDRp]). The outcome was all-cause mortality, defined as in-hospital death by all causes or being discharged under critical illness. We used time-dependent analysis to report rates of mortality with 95% confidence intervals (95% CI, Poisson regression) and hazard ratios (HR) with 95% CI (Cox proportional hazards regression with Holm's method for multiplicity control).ResultsWe followed 1466 patients (median age 61, 56.4% being female) for a median of 9 days. We recorded 387 (26.4%) deaths (95/144 [66.0%] in the CoI/ScI group and 292/1322 [22.1%] in the non-CoI/ScI group). Adjusted mortality rates (per 100 person-days) of the CoI/ScI (6.4, 95% CI 5.3 to 7.8), including bacterial (8.0, 95% CI 7.2 to 8.9), no MDRp (5.9, 95% CI 4.8 to 7.4), and ≥ 1 MDRp (9.0, 95% CI 8.2 to 10.0) groups were higher than that of the non-CoI/ScI group (2.0, 95% CI 1.8 to 2.2). These corresponded to higher risks of mortality in the overall CoI/ScI (HR 3.27, 95% CI 2.58 to 4.13, adjusted p < 0.001), bacterial CoI/ScI (HR 3.79, 95% CI 2.97 to 4.83, adjusted p < 0.001), no MDRp CoI/ScI (HR 3.13, 95% CI 2.42 to 4.05, adjusted p < 0.001), and ≥ 1 MDRp CoI/ScI group (HR 3.89, 95% CI 2.44 to 6.21, adjusted p < 0.001). We could not attain reliable estimates for fungal and bacterial + fungal CoI/ScI.ConclusionCompared with the non-CoI/ScI group, patients with CoI/ScI had a significantly higher risk of all-cause mortality, regardless of resistance status. More evidence is needed to confirm the mortality risks in patients with fungal or bacterial + fungal CoI/ScI.
| S-EPMC11385502 | biostudies-literature
Project description:Across the world, the COVID-19 pandemic has disproportionately affected economically disadvantaged groups. This differential impact has numerous possible explanations, each with significantly different policy implications. We examine, for the first time in a low- or middle-income country, which mechanisms best explain the disproportionate impact of the virus on the poor. Combining an epidemiological model with rich data from Bogotá, Colombia, we show that total infections and inequalities in infections are largely driven by inequalities in the ability to work remotely and in within-home secondary attack rates. Inequalities in isolation behavior are less important but non-negligible, while access to testing and contract-tracing plays practically no role because it is too slow to contain the virus. Interventions that mitigate transmission are often more effective when targeted on socioeconomically disadvantaged groups.
| S-EPMC9117199 | biostudies-literature
Project description:BackgroundReal-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.MethodsWe report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.FindingsThe framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]).InterpretationDynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections.FundingMedical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.
| S-EPMC9296105 | biostudies-literature
Project description:In the wake of community coronavirus disease 2019 (COVID-19) transmission in the United States, there is a growing public health concern regarding the adequacy of resources to treat infected cases. Hospital beds, intensive care units (ICUs), and ventilators are vital for the treatment of patients with severe illness. To project the timing of the outbreak peak and the number of ICU beds required at peak, we simulated a COVID-19 outbreak parameterized with the US population demographics. In scenario analyses, we varied the delay from symptom onset to self-isolation, the proportion of symptomatic individuals practicing self-isolation, and the basic reproduction number R 0 Without self-isolation, when R 0 = 2.5, treatment of critically ill individuals at the outbreak peak would require 3.8 times more ICU beds than exist in the United States. Self-isolation by 20% of cases 24 h after symptom onset would delay and flatten the outbreak trajectory, reducing the number of ICU beds needed at the peak by 48.4% (interquartile range 46.4-50.3%), although still exceeding existing capacity. When R 0 = 2, twice as many ICU beds would be required at the peak of outbreak in the absence of self-isolation. In this scenario, the proportional impact of self-isolation within 24 h on reducing the peak number of ICU beds is substantially higher at 73.5% (interquartile range 71.4-75.3%). Our estimates underscore the inadequacy of critical care capacity to handle the burgeoning outbreak. Policies that encourage self-isolation, such as paid sick leave, may delay the epidemic peak, giving a window of time that could facilitate emergency mobilization to expand hospital capacity.
| S-EPMC7183199 | biostudies-literature
Project description: Not available
| S-EPMC10242507 | biostudies-literature
Project description:Purpose of review:Non-tuberculous mycobacterial [NTM] infections in the hospital setting are a complex and often challenging entity. The goal of this review is to discuss diagnostic and treatment strategies for established as well as emerging nosocomial NTM infections. Recent findings:NTM outbreaks have been documented in a variety of hospital settings. Contamination of medical devices or aqueous solutions is often implicated in the spread of infection. More recently, the slow grower M. chimaera has been reported in the setting of contaminated heater-cooler devices used for cardiopulmonary bypass and extracorporeal membrane oxygenation. In addition, increases in medical tourism for cosmetic surgery have led to outbreaks of rapidly growing mycobacteria. Summary:Diagnosis and treatment of nosocomial NTM pose many challenges for the clinician. Surgical resection or debridement as well as combination antimycobacterial therapy are a mainstay in therapeutic management. Strict infection control and prevention practices are critical to the identification and cessation of outbreaks.
| S-EPMC6334766 | biostudies-literature
Project description:Hospital-onset COVID-19 infections (HOCIs) are associated with excess morbidity and mortality in patients and healthcare workers. The aim of this review was to explore and describe the current literature in HOCI surveillance. Medline, EMBASE, the Cochrane Database of Systematic Reviews, the Cochrane Register of Controlled Trials, and MedRxiv were searched up to 30 November 2020 using broad search criteria. Articles of HOCI surveillance systems were included. Data describing HOCI definitions, HOCI incidence, types of HOCI identification surveillance systems, and level of system implementation were extracted. A total of 292 citations were identified. Nine studies on HOCI surveillance were included. Six studies reported on the proportion of HOCI among hospitalized COVID-19 patients, which ranged from 0 to 15.2%. Six studies provided HOCI case definitions. Standardized national definitions provided by the UK and US governments were identified. Four studies included healthcare workers in the surveillance. One study articulated a multimodal strategy of infection prevention and control practices including HOCI surveillance. All identified HOCI surveillance systems were implemented at institutional level, with eight studies focusing on all hospital inpatients and one study focusing on patients in the emergency department. Multiple types of surveillance were identified. Four studies reported automated surveillance, of which one included real-time analysis, and one included genomic data. Overall, the study quality was limited by the observational nature with short follow-up periods. In conclusion, HOCI case definitions and surveillance methods were developed pragmatically. Whilst standardized case definitions and surveillance systems are ideal for integration with existing routine surveillance activities and adoption in different settings, we acknowledged the difficulties in establishing such standards in the short-term.
| S-EPMC8278304 | biostudies-literature
Project description:BackgroundFew small studies have described hospital-acquired infections (HAIs) occurring in patients with COVID-19.Research questionWhat characteristics in critically ill patients with COVID-19 are associated with HAIs and how are HAIs associated with outcomes in these patients?Study design and methodsMulticenter retrospective analysis of prospectively collected data including adult patients with severe COVID-19 admitted to eight Italian hub hospitals from February 20, 2020, through May 20, 2020. Descriptive statistics and univariate and multivariate Weibull regression models were used to assess incidence, microbial cause, resistance patterns, risk factors (ie, demographics, comorbidities, exposure to medication), and impact on outcomes (ie, ICU discharge, length of ICU and hospital stays, and duration of mechanical ventilation) of microbiologically confirmed HAIs.ResultsOf the 774 included patients, 359 patients (46%) demonstrated 759 HAIs (44.7 infections/1,000 ICU patient-days; 35% multidrug-resistant [MDR] bacteria). Ventilator-associated pneumonia (VAP; n = 389 [50%]), bloodstream infections (BSIs; n = 183 [34%]), and catheter-related BSIs (n = 74 [10%]) were the most frequent HAIs, with 26.0 (95% CI, 23.6-28.8) VAPs per 1,000 intubation-days, 11.7 (95% CI, 10.1-13.5) BSIs per 1,000 ICU patient-days, and 4.7 (95% CI, 3.8-5.9) catheter-related BSIs per 1,000 ICU patient-days. Gram-negative bacteria (especially Enterobacterales) and Staphylococcus aureus caused 64% and 28% of cases of VAP, respectively. Variables independently associated with infection were age, positive end expiratory pressure, and treatment with broad-spectrum antibiotics at admission. Two hundred thirty-four patients (30%) died in the ICU (15.3 deaths/1,000 ICU patient-days). Patients with HAIs complicated by septic shock showed an almost doubled mortality rate (52% vs 29%), whereas noncomplicated infections did not affect mortality. HAIs prolonged mechanical ventilation (median, 24 days [interquartile range (IQR), 14-39 days] vs 9 days [IQR, 5-13 days]; P < .001), ICU stay (24 days [IQR, 16-41 days] vs 9 days [IQR, 6-14 days]; P = .003), and hospital stay (42 days [IQR, 25-59 days] vs 23 days [IQR, 13-34 days]; P < .001).InterpretationCritically ill patients with COVID-19 are at high risk for HAIs, especially VAPs and BSIs resulting from MDR organisms. HAIs prolong mechanical ventilation and hospitalization, and HAIs complicated by septic shock almost double mortality.Trial registryClinicalTrials.gov; No.: NCT04388670; URL: www.clinicaltrials.gov.
| S-EPMC8056844 | biostudies-literature
Project description:ObjectivesCoronavirus disease 2019 (COVID-19) survivors are reporting residual abnormalities after discharge from hospital. Limited information is available about this stage of recovery or the lingering effects of the virus on pulmonary function and inflammation. This study aimed to describe lung function in patients recovering from COVID-19 hospitalization and to identify biomarkers in serum and induced sputum samples from these patients.MethodsPatients admitted to Spanish hospitals with laboratory-confirmed COVID-19 infection by a real-time PCR (RT-PCR) assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were recruited for this study. Each hospital screened their lists of discharged patients at least 45 days after symptom onset. SARS-CoV-2-infected patients were divided into mild/moderate and severe disease groups according to the severity of their symptoms during hospitalization. Patients' epidemiological and medical histories, comorbidities, chronic treatments, and laboratory parameters were evaluated. Pulmonary function tests, the standardized 6-minute walk test (6MWT) and chest computed tomography (CT) were also performed. The levels of proteases, their inhibitors, and shed receptors were measured in serum and induced sputum samples.ResultsA total of 100 patients with respiratory function tests were included in this study. The median number of days after the onset of symptoms was 104 (IQR 89.25, 126.75). COVID-19 was severe in 47% of patients (47/100). CT was normal in 48% of patients (48/100). Lung function was normal forced expiratory volume in one second (FEV1) ≥80%, forced vital capacity (FVC) ≥80%, FEV1/FVC ≥0.7, and diffusing capacity for carbon monoxide (DLCO) ≥80% in 92% (92/100), 94% (94/100), 100% (100/100) and 48% (48/100) of patients, respectively. Multivariate analysis showed that a DLCO <80% (OR 5.92; 95%CI 2.28-15.37; p < 0.0001) and a lower serum lactate dehydrogenase level (OR 0.98; 95%CI 0.97-0.99) were associated with the severe disease group of SARS-CoV-2 cases during hospital stay.ConclusionsA diffusion deficit (DLCO <80%) was still present after hospital discharge and was associated with the most severe SARS-CoV-2 cases.
| S-EPMC7920814 | biostudies-literature
Project description:Background The ongoing global SARS-CoV-2 pandemic has caused over 4.7 million infections greatly challenging healthcare workers (HCW) and medical institutions worldwide. The SARS-CoV-2 pandemic has shown to significantly impact mental and physical health of HCW. Thus, implementation of testing facilities supporting HCW are urgently needed. Methods A low-threshold SARS-CoV-2 testing facility was introduced at the University Hospital Bonn, Germany, in March 2020. Irrespective of clinical symptoms employees were offered a voluntary and free SARS-CoV-2 test. Furthermore, employees returning from SARS-CoV-2 risk regions and employees after risk contact with SARS-CoV-2 infected patients or employees were tested for SARS-CoV-2 infection. Pharyngeal swabs were taken and reverse transcription polymerase chain reaction for detection of SARS-CoV-2 was performed, test results being available within 24 h. Profession, symptoms and reason for SARS-CoV-2 testing of employees were recorded. Results Between 9th March and April 30, 2020, a total of 1510 employees were tested for SARS-CoV-2 infection. 1185 employees took advantage of the low-threshold testing facility. One percent (n = 11) were tested positive for SARS-CoV-2 infection, 18% being asymptomatic, 36% showing mild and 36% moderate/severe symptoms (missing 10%). Furthermore, of 56 employees returning from SARS-CoV-2 risk regions, 18% (10/56) were tested SARS-CoV-2 positive. After risk contact tracking by the hospital hygiene 6 patient-to-employee transmissions were identified in 163 employees with contact to 55 SARS-CoV-2 positive patients. Conclusion In the absence of easily accessible public SARS-CoV-2 testing facilities low-threshold SARS-CoV-2 testing facilities in hospitals with rapid testing resources help to identify SARS-CoV-2 infected employees with absent or mild symptoms, thus stopping the spread of infection in vulnerable hospital environments. High levels of professional infection prevention training and implementation of specialized wards as well as a perfectly working hospital hygiene network identifying and tracking risk contacts are of great importance in a pandemic setting. Highlights • Incidence of SARS-CoV-2 infections are higher in employees of a hospital environment compared to the general population.• Low-threshold testing facility helps to identify SARS-CoV-2 positive employees with absent or mild clinical symptoms.• Tracking of risk contacts by the hospital hygiene prevent distribution of SARS-CoV-2 infection.
| S-EPMC7580697 | biostudies-literature