Symptom-based case definitions for COVID-19: time and geographical variations for detection at hospital admission among 260,000 patients (preprint)

Introduction: Case definitions are used to guide clinical practice, surveillance, and research protocols. However, how they identify COVID-19-hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory-confirmed COVID-19, in the ISARIC prospective cohort study database, meeting widely used case definitions. Methods: Patients were assessed using the CDC, ECDC, WHO, and UKHSA case definitions by age, region, and time. Case fatality ratios (CFR) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non-laboratory-confirmed test-result were excluded. Results: 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe and Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC), and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years; geographical and time variations were also observed. Estimated CFRs were similar for the patients that met the case definitions. However, when more patients did not meet the case definition, the CFR increased. Conclusions: The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients (preprint)

BackgroundWhilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. MethodsHere, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. ResultsOur analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61 - 0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. ConclusionsAlthough clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome.

The burden and dynamics of hospital-acquired SARS-CoV-2 in England (preprint)

Hospital-based transmission played a dominant role in MERS-CoV and SARS-CoV epidemics but large-scale studies of its role in the SARS-CoV-2 pandemic are lacking. Such transmission risks spreading the virus to the most vulnerable individuals and can have wider-scale impacts through hospital-community interactions. Using data from acute hospitals in England we quantify within-hospital transmission, evaluate likely pathways of spread and factors associated with heightened transmission risk, and explore the wider dynamical consequences. We show that hospital transmission is likely to have been a major contributor to the burden of COVID-19 in England. We estimate that between June 2020 and March 2021 between 95,000 and 167,000 patients acquired SARS-CoV-2 in hospitals with nosocomially-infected patients likely to have been the main sources of transmission to other patients. Increased transmission to patients was associated with hospitals having fewer single rooms and lower heated volume per bed. Moreover, we show that reducing hospital transmission could substantially enhance the efficiency of punctuated lockdown measures in suppressing community transmission. These findings reveal the previously unrecognised scale of hospital transmission, have direct implications for targeting of hospital control measures, and highlight the need to design hospitals better-equipped to limit the transmission of future high consequence pathogens.

ISARIC COVID-19 Clinical Data Report: 8 June 2020 (preprint)

ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global uptake of this resource has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report is a part of a series and includes the results of data analysis on 8 June 2020. We thank all of the data contributors for their ongoing support. As of 8JUN20, data have been entered for 67,130 patients from 488 sites across 37 countries. For this report, we show data for 42,656 patients with confirmed disease who were enrolled >14 days prior. This update includes about 2,400 new cases from France, and we thank these collaborators for this significant addition to the dataset. Some highlights from this report The median time from onset of symptoms to hospital admission is 5 days, but a proportion of patients take longer to get to the hospital (average 14.6 days, standard deviation 8.1). COVID-19 patients tend to require prolonged hospitalisation; of the 88% with a known outcome, the median length of admission to death or discharge is 8 days and the mean 11.5. 17% of patients were admitted to ICU/HDU, about 40% of these on the very day of hospital admission. Antibiotics were given to 83% of patients, antivirals to 9%, steroids to 15%, which becomes 93%, 50% and 27%, respectively for those admitted to ICU/HDU. Attention has been called on overuse of antibiotics and need to adhere to antibiotic stewardship principles. 67% of patients received some degree of oxygen supplementation: of these 23.4% received NIV and 15% IMV. This relatively high proportion of oxygen use will have implications for oxygen surge planning in healthcare facilities. Some centres may need to plan to boost capacity to deliver oxygen therapy if this is not readily available. WHO provides operational advice on surge strategy here https://apps.who.int/iris/bitstream/handle/10665/331746/WHO-2019-nCoV-Oxygen_sources-2020.1-eng.pdf