Multiple pathways of SARS-CoV-2 nosocomial transmission uncovered by integrated genomic and epidemiological analyses during the second wave of the COVID-19 pandemic in the UK

Care in the Community: A COVID-19 initiative to reduce hospital re-presentations among community-dwelling people

OBJECTIVE

The COVID-19 pandemic has had a substantial impact on the utilisation of hospital and emergency department (ED) services. We examined the effect of a rapid response service on hospital re-presentations among people discharged from the ED and short-stay wards at a tertiary referral hospital.

METHODS

This retrospective cohort study compared 112 patients who completed the Care in the Community program with 112 randomly selected controls. Both cases and controls were discharged from hospital between September 2020 and June 2021. Intervention patients were evaluated by a multidisciplinary team, who implemented a goal-directed program of up to 4-weeks duration. Logistic regression, negative binomial regression and Cox proportional hazards regression were used to evaluate outcomes at 28 days and at 6 months.

RESULTS

The median time between referral and the first home visit was 3.9 days. In adjusted analyses, the intervention reduced hospital re-presentations at 28 days (odds ratio: .40, 95% confidence interval (CI): .17-.94) and lengthened the time to the first hospital re-presentation (hazard ratio: .59, 95% CI: .38-.92). Although the intervention did not reduce the total number of hospital re-presentations at 6 months (adjusted incidence rate ratio: .73, 95% CI: .49-1.08), it reduced total time spent in hospital by 303 days (582 vs. 885).

CONCLUSIONS

This study is among the first to investigate the effect of a community-based intervention on hospital re-presentations during the COVID-19 pandemic. It provides evidence that a sustainable 4-week intervention is associated with reduced hospital re-presentations and time spent in hospital.

The clinical and epidemiological impacts of whole genomic sequencing on bacterial and virological agents

Whole Genome Sequencing (WGS) is a molecular biology tool consisting in the sequencing of the entire genome of a given organism. Due to its ability to provide the finest available resolution of bacterial and virological genetics, it is used at several levels in the field of infectiology. On an individual scale and through application of a single technique, it enables the typological identification and characterization of strains, the characterization of plasmids, and enhanced search for resistance genes and virulence factors. On a collective scale, it enables the characterization of strains and the determination of phylogenetic links between different microorganisms during community outbreaks and healthcare-associated epidemics. The information provided by WGS enables real-time monitoring of strain-level epidemiology on a worldwide scale, and facilitates surveillance of the resistance dissemination and the introduction or emergence of pathogenic variants in humans or their environment. There are several possible approaches to completion of an entire genome. The choice of one method rather than another is essentially dictated by the matrix, either a clinical sample or a culture isolate, and the clinical objective. WGS is an advanced technology that remains costly despite a gradual decrease in its expenses, potentially hindering its implementation in certain laboratories and thus its use in routine microbiology. Even though WGS is making steady inroads as a reference method, efforts remain needed in view of so harmonizing its interpretations and decreasing the time to generation of conclusive results.

Evaluating pooled testing for asymptomatic screening of healthcare workers in hospitals

BACKGROUND

There is evidence that during the COVID pandemic, a number of patient and HCW infections were nosocomial. Various measures were put in place to try to reduce these infections including developing asymptomatic PCR (polymerase chain reaction) testing schemes for healthcare workers. Regularly testing all healthcare workers requires many tests while reducing this number by only testing some healthcare workers can result in undetected cases. An efficient way to test as many individuals as possible with a limited testing capacity is to consider pooling multiple samples to be analysed with a single test (known as pooled testing).

METHODS

Two different pooled testing schemes for the asymptomatic testing are evaluated using an individual-based model representing the transmission of SARS-CoV-2 in a 'typical' English hospital. We adapt the modelling to reflect two scenarios: a) a retrospective look at earlier SARS-CoV-2 variants under lockdown or social restrictions, and b) transitioning back to 'normal life' without lockdown and with the omicron variant. The two pooled testing schemes analysed differ in the population that is eligible for testing. In the 'ward' testing scheme only healthcare workers who work on a single ward are eligible and in the 'full' testing scheme all healthcare workers are eligible including those that move across wards. Both pooled schemes are compared against the baseline scheme which tests only symptomatic healthcare workers.

RESULTS

Including a pooled asymptomatic testing scheme is found to have a modest (albeit statistically significant) effect, reducing the total number of nosocomial healthcare worker infections by about 2[Formula: see text] in both the lockdown and non-lockdown setting. However, this reduction must be balanced with the increase in cost and healthcare worker isolations. Both ward and full testing reduce HCW infections similarly but the cost for ward testing is much less. We also consider the use of lateral flow devices (LFDs) for follow-up testing. Considering LFDs reduces cost and time but LFDs have a different error profile to PCR tests.

CONCLUSIONS

Whether a PCR-only or PCR and LFD ward testing scheme is chosen depends on the metrics of most interest to policy makers, the virus prevalence and whether there is a lockdown.

Combining viral genomics and clinical data to assess risk factors for severe COVID-19 (mortality, ICU admission, or intubation) amongst hospital patients in a large acute UK NHS hospital Trust

Throughout the COVID-19 pandemic, valuable datasets have been collected on the effects of the virus SARS-CoV-2. In this study, we combined whole genome sequencing data with clinical data (including clinical outcomes, demographics, comorbidity, treatment information) for 929 patient cases seen at a large UK hospital Trust between March 2020 and May 2021. We identified associations between acute physiological status and three measures of disease severity; admission to the intensive care unit (ICU), requirement for intubation, and mortality. Whilst the maximum National Early Warning Score (NEWS2) was moderately associated with severe COVID-19 (A = 0.48), the admission NEWS2 was only weakly associated (A = 0.17), suggesting it is ineffective as an early predictor of severity. Patient outcome was weakly associated with myriad factors linked to acute physiological status and human genetics, including age, sex and pre-existing conditions. Overall, we found no significant links between viral genomics and severe outcomes, but saw evidence that variant subtype may impact relative risk for certain sub-populations. Specific mutations of SARS-CoV-2 appear to have little impact on overall severity risk in these data, suggesting that emerging SARS-CoV-2 variants do not result in more severe patient outcomes. However, our results show that determining a causal relationship between mutations and severe COVID-19 in the viral genome is challenging. Whilst improved understanding of the evolution of SARS-CoV-2 has been achieved through genomics, few studies on how these evolutionary changes impact on clinical outcomes have been seen due to complexities associated with data linkage. By combining viral genomics with patient records in a large acute UK hospital, this study represents a significant resource for understanding risk factors associated with COVID-19 severity. However, further understanding will likely arise from studies of the role of host genetics on disease progression.