BackgroundSARS-CoV-2 spreads in hospitals, but the contribution of these settings to the overall COVID-19 burden at a national level is unknown. MethodsWe used comprehensive national English datasets and simulation modelling to determine the total burden (identified and unidentified) of symptomatic hospital-acquired infections. Those unidentified would either be 1) discharged before symptom onset ("missed"), or 2) have symptom onset 7 days or fewer from admission ("misclassified"). We estimated the contribution of "misclassified" cases and transmission from "missed" symptomatic infections to the English epidemic before 31st July 2020. FindingsIn our dataset of hospitalised COVID-19 patients in acute English Trusts with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired (with symptom onset 8 or more days after admission and before discharge). We estimated that only 30% (range across weeks and 200 simulations: 20-41%) of symptomatic hospital-acquired infections would be identified. Misclassified cases and onward transmission from missed infections could account for 15% (mean, 95% range over 200 simulations: 14{middle dot}1%-15{middle dot}8%) of cases currently classified as community-acquired COVID-19. From this, we estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200-16,400) or 20.1% (19.2%-20.7%) of all identified hospitalised COVID-19 cases. ConclusionsTransmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the "first wave", but fewer than 1% of all SARS-CoV-2 infections in England. Using symptom onset as a detection method for hospital-acquired SARS-CoV-2 likely misses a substantial proportion (>60%) of hospital-acquired infections. FundingNational Institute for Health Research, UK Medical Research Council, Society for Laboratory Automation and Screening, UKRI, Wellcome Trust, Singapore National Medical Research Council. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed with the terms "((national OR country) AND (contribution OR burden OR estimates) AND ("hospital-acquired" OR "hospital-associated" OR "nosocomial")) AND Covid-19" for articles published in English up to July 1st 2021. This identified 42 studies, with no studies that had aimed to produce comprehensive national estimates of the contribution of hospital settings to the COVID-19 pandemic. Most studies focused on estimating seroprevalence or levels of infection in healthcare workers only, which were not our focus. Removing the initial national/country terms identified 120 studies, with no country level estimates. Several single hospital setting estimates exist for England and other countries, but the percentage of hospital-associated infections reported relies on identified cases in the absence of universal testing. Added value of this studyThis study provides the first national-level estimates of all symptomatic hospital-acquired infections with SARS-CoV-2 in England up to the 31st July 2020. Using comprehensive data, we calculate how many infections would be unidentified and hence can generate a total burden, impossible from just notification data. Moreover, our burden estimates for onward transmission suggest the contribution of hospitals to the overall infection burden. Implications of all the available evidenceLarge numbers of patients may become infected with SARS-CoV-2 in hospitals though only a small proportion of such infections are identified. Further work is needed to better understand how interventions can reduce such transmission and to better understand the contributions of hospital transmission to mortality.
BackgroundCOVID-19 outbreaks are still occurring in English care homes despite the non-pharmaceutical interventions (NPIs) in place. MethodsWe developed a stochastic compartmental model to simulate the spread of SARS-CoV-2 within an English care home. We quantified the outbreak risk under the NPIs already in place, the role of community prevalence in driving outbreaks, and the relative contribution of all importation routes into the care home. We also considered the potential impact of additional control measures, namely: increasing staff and resident testing frequency, using lateral flow antigen testing (LFD) tests instead of PCR, enhancing infection prevention and control (IPC), increasing the proportion of residents isolated, shortening the delay to isolation, improving the effectiveness of isolation, restricting visitors and limiting staff to working in one care home. FindingsThe model suggests that importation of SARS-CoV-2 by staff, from the community, is the main driver of outbreaks, that importation by visitors or from hospitals is rare, and that the past testing strategy (monthly testing of residents and daily testing of staff by PCR) likely provides negligible benefit in preventing outbreaks. Daily staff testing by LFD was 39% (95% 18-55%) effective in preventing outbreaks at 30 days compared to no testing. InterpretationIncreasing the frequency of testing in staff and enhancing IPC are important to preventing importations to the care home. Further work is needed to understand the impact of vaccination in this population, which is likely to be very effective in preventing outbreaks. FundingThe National Institute for Health Research, European Union Horizon 2020, Canadian Institutes of Health Research, French National Research Agency, UK Medical Research Council. The World Health Organisation funded the development of the COS-LTCF Shiny application. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSCare homes have been identified as being at increased risk of COVID-19 outbreaks, and a number of modelling studies have considered the transmission dynamics of SARS-CoV-2 in this setting. We searched the PubMed database and bioRxiv and medRxivs COVID-19 SARS-CoV-2 preprints for English-language articles on the 11th May 2021, with the search terms ("COVID-19" OR "SARS-CoV-2" OR "coronavirus") AND ("care home" OR "LTCF" OR "long term care facility" OR "nursing home") AND ("model"). In addition to these searches, we identified articles relevant to this work through informal networks. These searches returned 87 studies, of which 12 explicitly modelled SARS-CoV-2 transmission within care homes and explored the effectiveness of non-pharmaceutical interventions in these settings. These studies employed a number of modelling approaches (agent-based and compartmental models) and considered various strategies for mitigating epidemic spread within care homes. Only one of these studies modelled care homes in England, but didnt consider individual care homes as separate entities (transmission between residents in separate facilities was equally likely as within one facility) and only modelled one intervention within the care home: the effect of restricting visitors. Another study modelled a different type of long-term care facility, a rehabilitation facility in France. Other studies modelled care homes in Canada, Scotland, and the US. These modelled care homes were larger than the average English care home. Only one study included importation of SARS-CoV-2 to care homes from hospitals through resident hospitalisation. Added value of this studyWe developed a stochastic compartmental model describing the transmission dynamics of SARS-CoV-2 within English care homes. This study is the first to assess the relative importance of all SARS-CoV-2 importation routes to care homes (including resident hospitalisation) and to quantify the impact of a range of non-pharmaceutical interventions against SARS-CoV-2 particularly for English care homes. We found that community prevalence, through staff importations, was the main driver of outbreaks in care homes at 30 days, not importation from hospital visits nor by visitors. In line with this, we found daily testing of staff to be the most effective testing strategy in preventing outbreaks. We show the previous testing strategy (PCR testing residents once every 28 days and staff once a week) to be ineffective in preventing outbreaks and suggest that more frequent testing of staff is required. Restricting visitors bore little effect on the probability of an outbreak occurring by day 30. Interventions focusing on decreasing the transmission of SARS-CoV-2 in the care home were the most effective in reducing the frequency of outbreaks. We provide a Shiny application for users to explore alternative care home characteristics, outbreak characteristics and interventions. Implications of all the available evidencePreventing the importation of SARS-CoV-2 to care homes from the community through staff is key to preventing outbreaks. Infection prevention and control (IPC) measures targeting transmission within the care home and frequent testing of staff, ideally daily, are the most effective strategies considered. Many care homes in England are currently unable to meet the additional workload daily testing would entail, therefore additional support should be considered to enable these measures. Allowing visitors should be considered given their general positive contribution to residents physical and mental health and likely negligible contribution to outbreaks.
Nosocomial transmission of SARS-CoV-2 is a key concern and evaluating the effect of testing and infection prevention control strategies is essential for guiding policy in this area. Using a within-hospital SEIR transition model of SARS-CoV-2 in a typical UK hospital, we predict that approximately 20% of infections in inpatients, and 89% of infections in HCWs were due to nosocomial transmission. Placing suspected COVID-19 patients in single rooms or bays has the potential to reduce hospital-acquired infections in patients by up to 80%. Periodic testing of HCWs has a smaller effect on the patient-burden of COVID-19 but would considerably reduce infection in HCWs by as much as 64% and result in only a small proportion of staff absences (approximately 1% per day). This is considerably fewer than currently observed due to suspected COVID-19 and self-isolation.
