Socioeconomic and Demographic Risk Factors for SARS-CoV-2 Seropositivity Among Healthcare Workers in a UK Hospital: A Prospective Cohort Study.

BACKGROUND: To protect healthcare workers (HCWs) from the consequences of disease due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is necessary to understand the risk factors that drive exposure and infection within hospitals. Insufficient consideration of key socioeconomic variables is a limitation of existing studies that can lead to bias and residual confounding of proposed risk factors for infection. METHODS: The Co-STARs study prospectively enrolled 3679 HCWs between April 2020 and September 2020. We used multivariate logistic regression to comprehensively characterize the demographic, occupational, socioeconomic, and environmental risk factors for SARS-CoV-2 seropositivity. RESULTS: After adjusting for key confounders, relative household overcrowding (odds ratio [OR], 1.4 [95% confidence interval {CI}, 1.1-1.9]; P = .006), Black, Black British, Caribbean, or African ethnicity (OR, 1.7 [95% CI, 1.2-2.3]; P = .003), increasing age (ages 50-60 years: OR, 1.8 [95% CI, 1.3-2.4]; P < .001), lack of access to sick pay (OR, 1.8 [95% CI, 1.3-2.4]; P < .001). CONCLUSIONS: Socioeconomic and demographic factors outside the hospital were the main drivers of infection and exposure to SARS-CoV-2 during the first wave of the pandemic in an urban pediatric referral hospital. Overcrowding and out-of-hospital SARS-CoV-2 contact are less amenable to intervention. However, lack of access to sick pay among externally contracted staff is more easily rectifiable. Our findings suggest that providing easier access to sick pay would lead to a decrease in SARS-CoV-2 transmission and potentially that of other infectious diseases in hospital settings. CLINICAL TRIALS REGISTRATION: NCT04380896.

Long-Term Persistence of Spike Protein Antibody and Predictive Modeling of Antibody Dynamics After Infection With Severe Acute Respiratory Syndrome Coronavirus 2.

BACKGROUND: Antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been shown to neutralize the virus in vitro and prevent disease in animal challenge models on reexposure. However, the current understanding of SARS-CoV-2 humoral dynamics and longevity is conflicting. METHODS: The COVID-19 Staff Testing of Antibody Responses Study (Co-Stars) prospectively enrolled 3679 healthcare workers to comprehensively characterize the kinetics of SARS-CoV-2 spike protein (S), receptor-binding domain, and nucleoprotein (N) antibodies in parallel. Participants screening seropositive had serial monthly serological testing for a maximum of 7 months with the Meso Scale Discovery Assay. Survival analysis determined the proportion of seroreversion, while 2 hierarchical gamma models predicted the upper and lower bounds of long-term antibody trajectory. RESULTS: A total of 1163 monthly samples were provided from 349 seropositive participants. At 200 days after symptoms, >95% of participants had detectable S antibodies, compared with 75% with detectable N antibodies. S antibody was predicted to remain detectable in 95% of participants until 465 days (95% confidence interval, 370-575 days) using a "continuous-decay" model and indefinitely using a "decay-to-plateau" model to account for antibody secretion by long-lived plasma cells. S-antibody titers were correlated strongly with surrogate neutralization in vitro (R2 = 0.72). N antibodies, however, decayed rapidly with a half-life of 60 days (95% confidence interval, 52-68 days). CONCLUSIONS: The Co-Stars data presented here provide evidence for long-term persistence of neutralizing S antibodies. This has important implications for the duration of functional immunity after SARS-CoV-2 infection. In contrast, the rapid decay of N antibodies must be considered in future seroprevalence studies and public health decision-making. This is the first study to establish a mathematical framework capable of predicting long-term humoral dynamics after SARS-CoV-2 infection. CLINICAL TRIALS REGISTRATION: NCT04380896.

Phylogenetic Survival Analysis.

Going back in time through a phylogenetic tree makes it possible to evaluate ancestral genomes and assess their potential to acquire key polymorphisms of interest over evolutionary time. Knowledge of this kind may allow for the emergence of key traits to be predicted and pre-empted from currently circulating strains in the future. Here, we present a novel genome-wide survival analysis and use the emergence of drug resistance in Mycobacterium tuberculosis as an example to demonstrate the potential and utility of the technique.

Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks.

Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low-frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.

During an infectious disease outbreak, tracing who infected whom allows public health scientists to see how a pathogen is spreading and to establish effective control measures. Traditionally, this involves identifying the individuals an infected person comes into contact with and monitoring whether they also become unwell. However, this information is not always available and can be inaccurate. One alternative is to track the genetic data of pathogens as they spread. Over time, pathogens accumulate mutations in their genes that can be used to distinguish them from one another. Genetically similar pathogens are more likely to have spread during the same outbreak, while genetically dissimilar pathogens may have come from different outbreaks. However, there are limitations to this approach. For example, some pathogens accumulate genetic mutations very slowly and may not change enough during an outbreak to be distinguishable from one another. Additionally, some pathogens can spread rapidly, leaving less time for mutations to occur between transmission events. To overcome these challenges, Torres Ortiz et al. developed a more sensitive approach to pathogen genetic testing that took advantage of the multiple pathogen populations that often coexist in an infected patient. Rather than tracking only the most dominant genetic version of the pathogen, this method also looked at the less dominant ones. Torres Ortiz et al. performed genome sequencing of SARS-CoV-2 (the virus that causes COVID-19) samples from 451 healthcare workers, patients, and patient contacts at participating London hospitals. Analysis showed that it was possible to detect multiple genetic populations of the virus within individual patients. These subpopulations were often more similar in patients that had been in contact with one another than in those that had not. Tracking the genetic data of all viral populations enabled Torres Ortiz et al. to trace transmission more accurately than if only the dominant population was used. More accurate genetic tracing could help public health scientists better track pathogen transmission and control outbreaks. This may be especially beneficial in hospital settings where outbreaks can be smaller, and it is important to understand if transmission is occurring within the hospital or if the pathogen is imported from the community. Further research will help scientists understand how pathogen population genetics evolve during outbreaks and may improve the detection of subpopulations present at very low frequencies.

A systematic review of tuberculosis detection and prevention studies in prisons.

Many studies have demonstrated that prisons are hotspots of tuberculosis disease and transmission. Despite this, it remains unclear which interventions are most effective at controlling tuberculosis in prisons. The objective was to determine the study designs used to investigate tuberculosis control in prisons, and the efficacy of interventions undertaken. This systematic review included published studies which had the aim of reducing TB incidence or prevalence, or increasing the number of people screened for active pulmonary tuberculosis in incarcerated populations. 2,429 records were identified, 178 full-text articles were screened, and 17 studies included. The majority of reports were before/after or prospective non-comparative studies. The median study duration was 23 months (range 5-144). The most common intervention was the introduction of active case finding (10/17 studies) but the timing and methods varied. Comparable pre- and post intervention outcome values were infrequently reported; therefore, it was not possible to quantify the efficacy of interventions. Data from studies of tuberculosis control in prisons is limited by a lack of: controlled interventions, reporting of pre-intervention methods, and comparable pre- and post-intervention outcomes. Prospective comparative trials of adequate duration to determine trends in incidence are necessary to understand which interventions are effective in prisons.

The influence of time on the sensitivity of SARS-CoV-2 serological testing.

Sensitive serological testing is essential to estimate the proportion of the population exposed or infected with SARS-CoV-2, to guide booster vaccination and to select patients for treatment with anti-SARS-CoV-2 antibodies. The performance of serological tests is usually evaluated at 14-21 days post infection. This approach fails to take account of the important effect of time on test performance after infection or exposure has occurred. We performed parallel serological testing using 4 widely used assays (a multiplexed SARS-CoV-2 Nucleoprotein (N), Spike (S) and Receptor Binding Domain assay from Meso Scale Discovery (MSD), the Roche Elecsys-Nucleoprotein (Roche-N) and Spike (Roche-S) assays and the Abbott Nucleoprotein assay (Abbott-N) on serial positive monthly samples collected as part of the Co-STARs study ( www.clinicaltrials.gov , NCT04380896) up to 200 days following infection. Our findings demonstrate the considerable effect of time since symptom onset on the diagnostic sensitivity of different assays. Using a time-to-event analysis, we demonstrated that 50% of the Abbott nucleoprotein assays will give a negative result after 175 days (median survival time 95% CI 168-185 days), compared to the better performance over time of the Roche Elecsys nucleoprotein assay (93% survival probability at 200 days, 95% CI 88-97%). Assays targeting the spike protein showed a lower decline over the follow-up period, both for the MSD spike assay (97% survival probability at 200 days, 95% CI 95-99%) and the Roche Elecsys spike assay (95% survival probability at 200 days, 95% CI 93-97%). The best performing quantitative Roche Elecsys Spike assay showed no evidence of waning Spike antibody titers over the 200-day time course of the study. We have shown that compared to other assays evaluated, the Abbott-N assay fails to detect SARS-CoV-2 antibodies as time passes since infection. In contrast the Roche Elecsys Spike Assay and the MSD assay maintained a high sensitivity for the 200-day duration of the study. These limitations of the Abbott assay should be considered when quantifying the immune correlates of protection or the need for SARS-CoV-2 antibody therapy. The high levels of maintained detectable neutralizing spike antibody titers identified by the quantitative Roche Elecsys assay is encouraging and provides further evidence in support of long-lasting SARS-CoV-2 protection following natural infection.

Genomic signatures of pre-resistance in Mycobacterium tuberculosis.

Recent advances in bacterial whole-genome sequencing have resulted in a comprehensive catalog of antibiotic resistance genomic signatures in Mycobacterium tuberculosis. With a view to pre-empt the emergence of resistance, we hypothesized that pre-existing polymorphisms in susceptible genotypes (pre-resistance mutations) could increase the risk of becoming resistant in the future. We sequenced whole genomes from 3135 isolates sampled over a 17-year period. After reconstructing ancestral genomes on time-calibrated phylogenetic trees, we developed and applied a genome-wide survival analysis to determine the hazard of resistance acquisition. We demonstrate that M. tuberculosis lineage 2 has a higher risk of acquiring resistance than lineage 4, and estimate a higher hazard of rifampicin resistance evolution following isoniazid mono-resistance. Furthermore, we describe loci and genomic polymorphisms associated with a higher risk of resistance acquisition. Identifying markers of future antibiotic resistance could enable targeted therapy to prevent resistance emergence in M. tuberculosis and other pathogens.