Face Mask Acceptability for Communal Religious Worship During the COVID-19 Pandemic in the United Kingdom: Results from the CONFESS Study.

The COVID-19 pandemic has led to restrictions such as social distancing and mandatory wearing of face masks. Singing and religious gatherings have been linked to infection clusters, and between 2020 and 2021 indoor congregational singing and chanting were prohibited in the United Kingdom. We evaluated attitudes to face mask use and their acceptability as well as changes within places of worship since their reopening in July up to autumn 2020. In this cross-sectional study, participants were recruited using convenience sampling through selective targeting of religious organisations and social media. Participants self-enrolled and completed an online questionnaire, which included open and closed questions. We used multivariable logistic regression to identify factors associated with face mask acceptability. We performed thematic analysis to evaluate responses to open questions. A total of 939 participants were included in the analysis. Median age was 52.7 years and 66.1% were female, while 80.7% identified as Christian. A majority (672/861; 78.0%) of participants would find it acceptable to wear a face mask and reduce their singing or chanting volume if required, even though 428/681 (49.1%) found face masks to be uncomfortable. Multivariable regression found that younger age was associated with a higher acceptability of face masks (adjusted OR (aOR): 0.98 (95% confidence interval (95% CI) 0.96-1.00), p = 0.0218). The majority of respondents stated that religious services had become shorter, attended by fewer people and with reduced singing or chanting. Most (869/893, 97.3%) stated their place of worship complied with government guidelines, with 803/887 (90.5%) reported that their place of worship enforced face mask wearing and 793/887 (89.4%) at least moderately happy with precaution measures. Our study demonstrates the significant impact of COVID-19 in places of worship but a high degree of compliance with guidelines. Face masks, despite practical difficulties, appeared to be more acceptable if there was an incentive of being able to sing and chant.

Use of portable air purifiers to reduce aerosols in hospital settings and cut down the clinical backlog.

SARS-CoV-2 has severely affected capacity in the National Health Service (NHS), and waiting lists are markedly increasing due to downtime of up to 50 min between patient consultations/procedures, to reduce the risk of infection. Ventilation accelerates this air cleaning, but retroactively installing built-in mechanical ventilation is often cost-prohibitive. We investigated the effect of using portable air cleaners (PAC), a low-energy and low-cost alternative, to reduce the concentration of aerosols in typical patient consultation/procedure environments. The experimental setup consisted of an aerosol generator, which mimicked the subject affected by SARS-CoV-19, and an aerosol detector, representing a subject who could potentially contract SARS-CoV-19. Experiments of aerosol dispersion and clearing were undertaken in situ in a variety of rooms with two different types of PAC in various combinations and positions. Correct use of PAC can reduce the clearance half-life of aerosols by 82% compared to the same indoor-environment without any ventilation, and at a broadly equivalent rate to built-in mechanical ventilation. In addition, the highest level of aerosol concentration measured when using PAC remains at least 46% lower than that when no mitigation is used, even if the PAC's operation is impeded due to placement under a table. The use of PAC leads to significant reductions in the level of aerosol concentration, associated with transmission of droplet-based airborne diseases. This could enable NHS departments to reduce the downtime between consultations/procedures.

Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients.

INTRODUCTION: Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses. METHODS: We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset. RESULTS: 807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer; 9/12 (75%) of patients were correctly classified. CONCLUSIONS: We have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic.

Spatiotemporal droplet dispersion measurements demonstrate face masks reduce risks from singing.

COVID-19 has restricted singing in communal worship. We sought to understand variations in droplet transmission and the impact of wearing face masks. Using rapid laser planar imaging, we measured droplets while participants exhaled, said 'hello' or 'snake', sang a note or 'Happy Birthday', with and without surgical face masks. We measured mean velocity magnitude (MVM), time averaged droplet number (TADN) and maximum droplet number (MDN). Multilevel regression models were used. In 20 participants, sound intensity was 71 dB for speaking and 85 dB for singing (p < 0.001). MVM was similar for all tasks with no clear hierarchy between vocal tasks or people and > 85% reduction wearing face masks. Droplet transmission varied widely, particularly for singing. Masks decreased TADN by 99% (p < 0.001) and MDN by 98% (p < 0.001) for singing and 86-97% for other tasks. Masks reduced variance by up to 48%. When wearing a mask, neither singing task transmitted more droplets than exhaling. In conclusion, wide variation exists for droplet production. This significantly reduced when wearing face masks. Singing during religious worship wearing a face mask appears as safe as exhaling or talking. This has implications for UK public health guidance during the COVID-19 pandemic.

Predicting endoscopic activity recovery in England after COVID-19: a national analysis.

BACKGROUND: The COVID-19 pandemic has led to a substantial reduction in gastrointestinal endoscopies, creating a backlog of procedures. We aimed to quantify this backlog nationally for England and assess how various interventions might mitigate the backlog. METHODS: We did a national analysis of data for colonoscopies, flexible sigmoidoscopies, and gastroscopies from National Health Service (NHS) trusts in NHS England's Monthly Diagnostic Waiting Times and Activity dataset. Trusts were excluded if monthly data were incomplete. To estimate the potential backlog, we used linear logistic regression to project the cumulative deficit between actual procedures performed and expected procedures, based on historical pre-pandemic trends. We then made further estimations of the change to the backlog under three scenarios: recovery to a set level of capacity, ranging from 90% to 130%; further disruption to activity (eg, second pandemic wave); or introduction of faecal immunochemical testing (FIT) triaging. FINDINGS: We included data from Jan 1, 2018, to Oct 31, 2020, from 125 NHS trusts. 10 476 endoscopy procedures were done in April, 2020, representing 9·5% of those done in April, 2019 (n=110 584), before recovering to 105 716 by October, 2020 (84·5% of those done in October, 2019 [n=125 072]). Recovering to 100% capacity on the current trajectory would lead to a projected backlog of 162 735 (95% CI 143 775-181 695) colonoscopies, 119 025 (107 398-130 651) flexible sigmoidoscopies, and 194 087 (172 564-215 611) gastroscopies in January, 2021, attributable to the pandemic. Increasing capacity to 130% would still take up to June, 2022, to eliminate the backlog. A further 2-month interruption would add an extra 15·4%, a 4-month interruption would add an extra 43·8%, and a 6-month interruption would add an extra 82·5% to the potential backlog. FIT triaging of cases that are found to have greater than 10 µg haemoglobin per g would reduce colonoscopy referrals to around 75% of usual levels, with the backlog cleared in early 2022. INTERPRETATION: Our work highlights the impact of the pandemic on endoscopy services nationally. Even with mitigation measures, it could take much longer than a year to eliminate the pandemic-related backlog. Urgent action is required by key stakeholders (ie, individual NHS trusts, Clinical Commissioning Groups, British Society of Gastroenterology, and NHS England) to tackle the backlog and prevent delays to patient management. FUNDING: Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) at University College London, National Institute for Health Research University College London Hospitals Biomedical Research Centre, and DATA-CAN, Health Data Research UK.

How COVID-19 has changed the unselected medical take: an observational study.

INTRODUCTION: COVID-19 has had a profound effect on the NHS. Little information has been published as to how the unselected medical take has been affected. METHODS: We retrospectively reviewed patients who were referred to general medicine during March 2020. We compared clinical outcomes of patients with and without COVID-19. RESULTS: 814 patients were included, comprising 777 unique patients. On average, 26 patients were admitted per day. 38% of admitted patients were suspected of COVID-19, with greater numbers of COVID-19 patients in the second half compared to the first half of the month (p<0.001). Logistic regression analyses showed suspected COVID-19 was an independent predictor for inpatient mortality (odds ratio [OR] = 6.09, p<0.001) and 30-day mortality (OR = 4.66, p<0.001). CONCLUSIONS: COVID-19 patients had worse clinical outcomes and increased healthcare use compared to non-COVID-19 patients. Our study highlights some of the challenges in healthcare provision faced during this pandemic.