ABSTRACT
BackgroundMortality rates of UK patients hospitalised with COVID-19 appeared to fall during the first wave. We quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital. MethodsThe International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) WHO Clinical Characterisation Protocol UK recruited a prospective cohort admitted to 247 acute UK hospitals with COVID-19 in the first wave (March to August 2020). Outcome was hospital mortality within 28 days of admission. We performed a three-way decomposition mediation analysis using natural effects models to explore associations between week of admission and hospital mortality adjusting for confounders (demographics, comorbidity, illness severity) and quantifying potential mediators (respiratory support and steroids). FindingsUnadjusted hospital mortality fell from 32.3% (95%CI 31.8, 32.7) in March/April to 16.4% (95%CI 15.0, 17.8) in June/July 2020. Reductions were seen in all ages, ethnicities, both sexes, and in comorbid and non-comorbid patients. After adjustment, there was a 19% reduction in the odds of mortality per 4 week period (OR 0.81, 95%CI 0.79, 0.83). 15.2% of this reduction was explained by greater disease severity and comorbidity earlier in the epidemic. The use of respiratory support changed with greater use of non-invasive ventilation (NIV). 22.2% (OR 0.94, 95%CI 0.94, 0.96) of the reduction in mortality was mediated by changes in respiratory support. InterpretationThe fall in hospital mortality in COVID-19 patients during the first wave in the UK was partly accounted for by changes in case mix and illness severity. A significant reduction was associated with differences in respiratory support and critical care use, which may partly reflect improved clinical decision making. The remaining improvement in mortality is not explained by these factors, and may relate to community behaviour on inoculum dose and hospital capacity strain. FundingNIHR & MRC Key points / Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSRisk factors for mortality in patients hospitalised with COVID-19 have been established. However there is little literature regarding how mortality is changing over time, and potential explanations for why this might be. Understanding changes in mortality rates over time will help policy makers identify evolving risk, strategies to manage this and broader decisions about public health interventions. Added value of this studyMortality in hospitalised patients at the beginning of the first wave was extremely high. Patients who were admitted to hospital in March and early April were significantly more unwell at presentation than patients who were admitted in later months. Mortality fell in all ages, ethnic groups, both sexes and in patients with and without comorbidity, over and above contributions from falling illness severity. After adjustment for these variables, a fifth of the fall in mortality was explained by changes in the use of respiratory support and steroid treatment, along with associated changes in clinical decision-making relating to supportive interventions. However, mortality was persistently high in patients who required invasive mechanical ventilation, and in those patients who received non-invasive ventilation outside of critical care. Implications of all the available evidenceThe observed reduction in hospital mortality was greater than expected based on the changes seen in both case mix and illness severity. Some of this fall can be explained by changes in respiratory care, including clinical learning. In addition, introduction of community policies including wearing of masks, social distancing, shielding of vulnerable patients and the UK lockdown potentially resulted in people being exposed to less virus. The decrease in mortality varied depending on the level of respiratory support received. Patients receiving invasive mechanical ventilation have persistently high mortality rates, albeit with a changing case-mix, and further research should target this group. Severe COVID-19 disease has primarily affected older people in the UK. Many of these people, but not all have significant frailty. It is essential to ensure that patients and their families remain at the centre of decision-making, and we continue with an individualised approach to their treatment and care.
ABSTRACT
BackgroundShort-term forecasts of infectious disease can aid situational awareness and planning for outbreak response. Here, we report on multi-model forecasts of Covid-19 in the UK that were generated at regular intervals starting at the end of March 2020, in order to monitor expected healthcare utilisation and population impacts in real time. MethodsWe evaluated the performance of individual model forecasts generated between 24 March and 14 July 2020, using a variety of metrics including the weighted interval score as well as metrics that assess the calibration, sharpness, bias and absolute error of forecasts separately. We further combined the predictions from individual models into ensemble forecasts using a simple mean as well as a quantile regression average that aimed to maximise performance. We compared model performance to a null model of no change. ResultsIn most cases, individual models performed better than the null model, and ensembles models were well calibrated and performed comparatively to the best individual models. The quantile regression average did not noticeably outperform the mean ensemble. ConclusionsEnsembles of multi-model forecasts can inform the policy response to the Covid-19 pandemic by assessing future resource needs and expected population impact of morbidity and mortality.
ABSTRACT
BackgroundThe COVID-19 pandemic continues to grow at an unprecedented rate. Healthcare workers (HCWs) are at higher risk of SARS-CoV-2 infection than the general population but risk factors for HCW infection are not well described. MethodsWe conducted a prospective sero-epidemiological study of HCWs at a UK teaching hospital using a SARS-CoV-2 immunoassay. Risk factors for seropositivity were analysed using multivariate logistic regression. Findings410/5,698 (7{middle dot}2%) staff tested positive for SARS-CoV-2 antibodies. Seroprevalence was higher in those working in designated COVID-19 areas compared with other areas (9{middle dot}47% versus 6{middle dot}16%) Healthcare assistants (aOR 2{middle dot}06 [95%CI 1{middle dot}14-3{middle dot}71]; p=0{middle dot}016) and domestic and portering staff (aOR 3{middle dot}45 [95% CI 1{middle dot}07-11{middle dot}42]; p=0{middle dot}039) had significantly higher seroprevalence than other staff groups after adjusting for age, sex, ethnicity and COVID-19 working location. Staff working in acute medicine and medical sub-specialities were also at higher risk (aOR 2{middle dot}07 [95% CI 1{middle dot}31-3{middle dot}25]; p<0{middle dot}002). Staff from Black, Asian and minority ethnic (BAME) backgrounds had an aOR of 1{middle dot}65 (95% CI 1{middle dot}32 - 2{middle dot}07; p<0{middle dot}001) compared to white staff; this increased risk was independent of COVID-19 area working. The only symptoms significantly associated with seropositivity in a multivariable model were loss of sense of taste or smell, fever and myalgia; 31% of staff testing positive reported no prior symptoms. InterpretationRisk of SARS-CoV-2 infection amongst HCWs is heterogeneous and influenced by COVID-19 working location, role, age and ethnicity. Increased risk amongst BAME staff cannot be accounted for solely by occupational factors. FundingWellcome Trust, Addenbrookes Charitable Trust, National Institute for Health Research, Academy of Medical Sciences, the Health Foundation and the NIHR Cambridge Biomedical Research Centre. Research in context Evidence before this studySpecific risk factors for SARS-CoV-2 infection in healthcare workers (HCWs) are not well defined. Additionally, it is not clear how population level risk factors influence occupational risk in defined demographic groups. Only by identifying these factors can we mitigate and reduce the risk of occupational SARS-CoV-2 infection. We performed a review of the evidence for HCW-specific risk factors for SARS-CoV-2 infection. We searched PubMed with the terms "SARS-CoV-2" OR "COVID-19" AND "Healthcare worker" OR "Healthcare Personnel" AND "Risk factor" to identify any studies published in any language between December 2019 and September 2020. The search identified 266 studies and included a meta-analysis and two observational studies assessing HCW cohort seroprevalence data. Seroprevalence and risk factors for HCW infections varied between studies, with contradictory findings. In the two serological studies, one identified a significant increased risk of seroprevalence in those working with COVID-19 patients (Eyre et al 2020), as well as associations with job role and department. The other study (Dimcheff et al 2020) found no significant association between seropositivity and any identified demographic or occupational factor. A meta-analysis of HCW (Gomez-Ochoa et al 2020) assessed >230,000 participants as a pooled analysis, including diagnoses by both RT-PCR and seropositivity for SARS-CoV-2 antibodies and found great heterogeneity in study design and reported contradictory findings. Of note, they report a seropositivity rate of 7% across all studies reporting SARS-CoV-2 antibodies in HCWs. Nurses were the most frequently affected healthcare personnel and staff working in non-emergency inpatient settings were the most frequently affected group. Our search found no prospective studies systematically evaluating HCW specific risk factors based entirely on seroprevalence data. Added value of this studyOur prospective cohort study of almost 6,000 HCWs at a large UK teaching hospital strengthens previous findings from UK-based cohorts in identifying an increased risk of SARS-CoV-2 exposure amongst HCWs. Specifically, factors associated with SARS-CoV-2 exposure include caring for confirmed COVID-19 cases and identifying as being within specific ethnic groups (BAME staff). We further delineated the risk amongst BAME staff and demonstrate that occupational factors alone do not account for all of the increased risk amongst this group. We demonstrate for the first time that healthcare assistants represent a key at-risk occupational group, and challenge previous findings of significantly higher risk amongst nursing staff. Seroprevalence in staff not working in areas with confirmed COVID-19 patients was only marginally higher than that of the general population within the same geographical region. This observation could suggest the increased risk amongst HCWs arises through occupational exposure to confirmed cases and could account for the overall higher seroprevalence in HCWs, rather than purely the presence of staff in healthcare facilities. Over 30% of seropositive staff had not reported symptoms consistent with COVID-19, and in those who did report symptoms, differentiating COVID-19 from other causes based on symptom data alone was unreliable. Implications of all the available evidenceInternational efforts to reduce the risk of SARS-CoV-2 infection amongst HCWs need to be prioritised. The risk of SARS-CoV-2 infection amongst HCWs is heterogenous but also follows demonstrable patterns. Potential mechanisms to reduce the risk for staff working in areas with confirmed COVID-19 patients include improved training in hand hygiene and personal protective equipment (PPE), better access to high quality PPE, and frequent asymptomatic testing. Wider asymptomatic testing in healthcare facilities has the potential to reduce spread of SARS-CoV-2 within hospitals, thereby reducing patient and staff risk and limiting spread between hospitals and into the wider community. The increased risk of COVID-19 amongst BAME staff cannot be explained by purely occupational factors; however, the increased risk amongst minority ethnic groups identified here was stark and necessitates further evaluation.
ABSTRACT
Identifying linked cases of infection is a key part of the public health response to viral infectious disease. Viral genome sequence data is of great value in this task, but requires careful analysis, and may need to be complemented by additional types of data. The Covid-19 pandemic has highlighted the urgent need for analytical methods which bring together sources of data to inform epidemiological investigations. We here describe A2B-COVID, an approach for the rapid identification of linked cases of coronavirus infection. Our method combines knowledge about infection dynamics, data describing the movements of individuals, and novel approaches to genome sequence data to assess whether or not cases of infection are consistent or inconsistent with linkage via transmission. We apply our method to analyse and compare data collected from two wards at Cambridge University Hospitals, showing qualitatively different patterns of linkage between cases on designated Covid-19 and non-Covid-19 wards. Our method is suitable for the rapid analysis of data from clinical or other potential outbreak settings.
ABSTRACT
Understanding the trajectory of the daily numbers of deaths in people with CoVID-19 is essential to decisions on the response to the CoVID-19 pandemic. Estimating this trajectory from data on numbers of deaths is complicated by the delay between deaths occurring and their being reported to the authorities. In England, Public Health England receives death reports from a number of sources and the reporting delay is typically several days, but can be several weeks. Delayed reporting results in considerable uncertainty about the number of deaths that occurred on the most recent days. In this article, we estimate the number of deaths per day in each of five age strata within seven English regions. We use a Bayesian hierarchical model that involves a submodel for the number of deaths per day and a submodel for the reporting delay distribution. This model accounts for reporting-day effects and longer-term changes over time in the delay distribution. We show how the model can be fitted in a computationally efficient way when the delay distribution is same in multiple strata, e.g. over a wide range of ages.
ABSTRACT
England has been heavily affected by the SARS-CoV-2 pandemic, with severe lock-down mitigation measures now gradually being lifted. The real-time pandemic monitoring presented here has contributed to the evidence informing this pandemic management. Estimates on the 10th May showed lock-down had reduced transmission by 75%, the reproduction number falling from 2.6 to 0.61. This regionally-varying impact was largest in London of 81% (95% CrI: 77%-84%). Reproduction numbers have since slowly increased, and on 19th June the probability that the epidemic is growing was greater than 5% in two regions, South West and London. An estimated 8% of the population had been infected, with a higher proportion in London (17%). The infection-to-fatality ratio is 1.1% (0.9%-1.4%) overall but 17% (14%-22%) among the over-75s. This ongoing work will be key to quantifying any widespread resurgence should accrued immunity and effective contact tracing be insufficient to preclude a second wave.
ABSTRACT
COVID-19 is reported to have been effectively brought under control in China at its initial start place. To understand the COVID-19 outbreak in China and provide potential lessons for other parts of the world, in this study we combine a mathematical modelling with multiple datasets to estimate its transmissibility and severity and how it was affected by the unprecedented control measures. Our analyses show that before 29th January 2020, the ascertainment rate is 6.9%(95%CI: 3.5 - 14.6%); then it increased to 41.5%(95%CI: 30.6 - 65.1%). The basic reproduction number (R0) was 2.23(95%CI: 1.86 - 3.22) before 8th February 2020; then it dropped to 0.04(95%CI: 0.01 - 0.10). This estimation also indicates that the effect on transmissibility of control measures taken since 23rd January 2020 emerged about two weeks late. The confirmed case fatality rate is estimated at 4.41%(95%CI: 3.65 - 5.30%). This shows that SARS-CoV-2 virus is highly transmissible but less severe than SARS-CoV-1 and MERS-CoV. We found that at the early stage, the majority of R0 comes from the undetected infected people. This implies that the successful control in China was achieved through decreasing the contact rates among people in general populations and increasing the rate of detection and quarantine of the infected cases.
