ABSTRACT
ObjectivesPatients requiring haemodialysis are at increased risk of serious illness with SARS-CoV-2 infection. To improve the understanding of transmission risks in six Scottish renal dialysis units, we utilised the rapid whole-genome sequencing data generated by the COG-UK consortium. MethodsWe combined geographical, temporal and genomic sequence data from the community and hospital to estimate the probability of infection originating from within the dialysis unit, the hospital or the community using Bayesian statistical modelling and compared these results to the details of epidemiological investigations. ResultsOf 671 patients, 60 (8.9%) became infected with SARS-CoV-2, of whom 16 (27%) died. Within-unit and community transmission were both evident and an instance of transmission from the wider hospital setting was also demonstrated. ConclusionsNear-real-time SARS-CoV-2 sequencing data can facilitate tailored infection prevention and control measures, which can be targeted at reducing risk in these settings.
ABSTRACT
Many western countries used shielding (extended self-isolation) of people presumed to be at high-risk from COVID-19 to protect them and reduce healthcare demand. To investigate the effectiveness of this strategy, we linked family practitioner, prescribing, laboratory, hospital and death records and compared COVID-19 outcomes among shielded and non-shielded individuals in the West of Scotland. Of the 1.3 million population, 27,747 (2.03%) were advised to shield, and 353,085 (26.85%) were classified a priori as moderate risk. COVID-19 testing was more common in the shielded (7.01%) and moderate risk (2.03%) groups, than low risk (0.73%). Referent to low-risk, the shielded group had higher confirmed infections (RR 8.45, 95% 7.44-9.59), case-fatality (RR 5.62, 95% CI 4.47-7.07) and population mortality (RR 57.56, 95% 44.06-75.19). The moderate-risk had intermediate confirmed infections (RR 4.11, 95% CI 3.82-4.42) and population mortality (RR 25.41, 95% CI 20.36-31.71) but, due to their higher prevalence, made the largest contribution to deaths (PAF 75.30%). Age [≥]70 years accounted for 49.55% of deaths. In conclusion, shielding has not been effective at preventing deaths in individuals at high risk. Also, to be effective as a population strategy, shielding criteria would need to be widely expanded to include other criteria, such as the elderly.
ABSTRACT
BACKGROUNDIt is now well recognised that the risk of severe COVID-19 increases with some long-term conditions (LTCs). However, prior research primarily focuses on individual LTCs and there is a lack of data on the influence of multimorbidity ([≥]2 LTCs) on the risk of COVID-19. Given the high prevalence of multimorbidity, more detailed understanding of the associations with multimorbidity and COVID-19 would improve risk stratification and help protect those most vulnerable to severe COVID-19. Here we examine the relationships between multimorbidity, polypharmacy (a proxy of multimorbidity), and COVID-19; and how these differ by sociodemographic, lifestyle, and physiological prognostic factors. METHODS AND FINDINGSWe studied data from UK Biobank (428,199 participants; aged 37-73; recruited 2006-2010) on self-reported LTCs, medications, sociodemographic, lifestyle, and physiological measures which were linked to COVID-19 test data. Poisson regression models examined risk of COVID-19 by multimorbidity/polypharmacy and effect modification by COVID-19 prognostic factors (age/sex/ethnicity/socioeconomic status/smoking/physical activity/BMI/systolic blood pressure/renal function). 4,498 (1.05%) participants were tested; 1,324 (0.31%) tested positive for COVID-19. Compared with no LTCs, relative risk (RR) of COVID-19 in those with 1 LTC was no higher (RR 1.12 (CI 0.96-1.30)), whereas those with [≥]2 LTCs had 48% higher risk; RR 1.48 (1.28-1.71). Compared with no cardiometabolic LTCs, having 1 and [≥]2 cardiometabolic LTCs had a higher risk of COVID-19; RR 1.28 (1.12-1.46) and 1.77 (1.46-2.15), respectively. Polypharmacy was associated with a dose response increased risk of COVID-19. All prognostic factors were associated with a higher risk of COVID-19 infection in multimorbidity; being non-white, most socioeconomically deprived, BMI [≥]40 kg/m2, and reduced renal function were associated with the highest risk of COVID-19 infection: RR 2.81 (2.09-3.78); 2.79 (2.00-3.90); 2.66 (1.88-3.76); 2.13 (1.46-3.12), respectively. No multiplicative interaction between multimorbidity and prognostic factors was identified. Important limitations include the low proportion of UK Biobank participants with COVID-19 test data (1.05%) and UK Biobank participants being more affluent, healthier and less ethnically diverse than the general population. CONCLUSIONSIncreasing multimorbidity, especially cardiometabolic multimorbidity, and polypharmacy are associated with a higher risk of developing COVID-19. Those with multimorbidity and additional factors, such as non-white ethnicity, are at heightened risk of COVID-19. Author summaryO_ST_ABSWhy was this study done?C_ST_ABSO_LIMultimorbidity is a growing global challenge, but thus far LTC prognostic factors for severe COVID-19 primarily involve single conditions and there is a lack of data on the influence of multimorbidity on the risk of COVID-19. C_LIO_LIAs countries move from the lockdown phase of COVID-19, clinicians need more information about risk stratification to appropriately advise patients with multimorbidity about risk prevention steps. C_LI What did the researchers do and find?O_LIParticipants with multimorbidity ([≥]2 LTCs) had a 48% higher risk of a positive COVID-19 test, those with cardiometabolic multimorbidity had a 77% higher risk, than those without that type of multimorbidity. C_LIO_LIThose from non-white ethnicities with multimorbidity had nearly three times the risk of having COVID-19 infection compared to those of white ethnicity C_LIO_LIPeople with multimorbidity with the highest risk of COVID-19 infection were the most socioeconomically deprived, those with BMI [≥]40 kg/m2, and those with reduced renal function. C_LI What do these findings mean?O_LIIndividuals with [≥]2 LTCs, especially if these are cardiometabolic in nature, should be particularly stringent in adhering to preventive measures, such as physical distancing and hand hygiene. C_LIO_LIOur findings have implications for clinicians, occupational health and employers when considering work-place environments, appropriate advice for patients, and adaptations that might be required to protect such staff, identified here, as higher risk. C_LI
