Early Introduction and Community Transmission of SARS-CoV-2 Omicron Variant, New York, New York, USA

Applying prospective tree-temporal scan statistics to genomic surveillance data to detect emerging SARS-CoV-2 variants and salmonellosis clusters in New York City

BACKGROUND

The detection of communicable disease clusters in genomic surveillance data typically involves the application of rule-based signaling criteria, which can be arbitrary. In contrast, scan statistics that are used for spatiotemporal cluster detection can flexibly scan in calendar time, and scan statistics that are used for pharmacovigilance can flexibly scan along hierarchical tree structures that are based on diagnosis codes.

METHODS

New York City (NYC) Health Department staff applied tree-temporal scan statistics prospectively to genomic surveillance data with a hierarchical nomenclature for COVID-19 and salmonellosis cases that were diagnosed among NYC residents. We searched weekly for recent case increases at any granularity, from large phylogenetic branches to small groups of indistinguishable isolates. Using free and open-source TreeScan software, we looked for emerging SARS-CoV-2 variants based on Pango lineages during August 2021-November 2023 and emerging clusters of Salmonella isolates based on allele codes during November 2022-November 2023.

RESULTS

The SARS-CoV-2 Omicron subvariant EG.5.1 first signaled as locally emerging on 22 June 2023, 7 weeks before the World Health Organization designated it as a variant of interest. During 1 year of salmonellosis analyses, TreeScan detected 15 credible clusters that were worth investigating for common exposures and two data-quality issues for correction.

CONCLUSION

A challenge was the maintenance of timely and specific lineage assignments, and a limitation was that genetic distances between tree nodes were not considered. By automatically sifting through genomic data and generating ranked shortlists of nodes with statistically unusual recent case increases, TreeScan assisted in detecting emerging variants and clusters of communicable diseases and in prioritizing them for investigation.

Formal wellness training of academic radiology leaders improves work-life conflict

OBJECTIVE

To investigate the effect of formal leadership training of academic radiology leaders within an academic center on their own burnout and professional fulfillment.

METHODS

The study cohort was academic radiology leaders within one of the largest academic organizations of academic radiologists within the United States. All academic radiology leaders within the organization were electronically mailed a weblink to a confidential IRB-approved survey in April 2021. The survey included validated questions from the Stanford Professional Fulfillment Index (PFI), values alignment, teamwork, overload, and work-family conflict. Academic leaders were invited in May 2021 to participate in instructor-led formal training on leading wellness focusing on 5 core leadership skills - emotional intelligence, self-care, resilience support, demonstrating care, and managing burnout. An identical follow-up survey was electronically mailed 6 months after initial training in November 2021.

RESULTS

The overall response rate of academic radiology leaders was 59% (19/32). For both measures, there was acceptable internal consistency (Cronbach's α = 0.63 for work exhaustion and α = 0.90 for fulfillment). There was a statistically significant improvement in work-family conflict (3.32 vs 2.86; p = 0.04). No statistically significant differences were identified for fulfillment, work exhaustion, alignment, work overload, and teamwork scores after training.

CONCLUSION

Formal instruction in leading wellness improved work-life conflict for academic radiology leaders. There was no significant change in burnout, fulfillment nor organizational alignment of the leaders.

CLINICAL RELEVANCE STATEMENT

Formal instruction in leading wellness raised awareness and improved work-life conflict in academic radiology leaders.

Comparative Analysis of Viral Load and Cytokines during SARS-CoV-2 Infection between Pregnant and Non-Pregnant Women

To better understand the vulnerabilities of pregnant women during the COVID-19 pandemic, we conducted a comprehensive, retrospective cohort study to assess differences in immune responses to SARS-CoV-2 infection between pregnant and non-pregnant women. Nasopharyngeal swabs and serum specimens from 90 pregnant and 278 age-matched non-pregnant women were collected from 15 March 2020 to 23 July 2021 at NewYork-Presbyterian Queens Hospital in New York City. Multiplex reverse transcription polymerase chain reaction, neutralizing antibody, and cytokine array assays were used to assess the incidence, viral load, antibody titers and profiles, and examine cytokine expression patterns. Our results show a lower incidence of SARS-CoV-2 infection in pregnant women compared with non-pregnant women. Pregnant women infected with SARS-CoV-2 exhibited a substantially lower viral load. In addition, the levels of both anti-spike protein receptor-binding domain IgG neutralizing antibodies and anti-N Protein IgG were elevated in pregnant women. Finally, cytokine profiling revealed differential expression of leptin across cohorts. These findings suggest that pregnancy is associated with distinct immune and virological responses to SARS-CoV-2 infection, characterized by lower infection rates, substantially lower viral loads, and enhanced antibody production. Differential cytokine expression indicates unique immune modulation in pregnant women.

Genomic Insights Into the Evolution and Demographic History of the SARS-CoV-2 Omicron Variant: Population Genomics Approach

Background

A thorough understanding of the patterns of genetic subdivision in a pathogen can provide crucial information that is necessary to prevent disease spread. For SARS-CoV-2, the availability of millions of genomes makes this task analytically challenging, and traditional methods for understanding genetic subdivision often fail.

Objective

The aim of our study was to use population genomics methods to identify the subtle subdivisions and demographic history of the Omicron variant, in addition to those captured by the Pango lineage.

Methods

We used a combination of an evolutionary network approach and multivariate statistical protocols to understand the subdivision and spread of the Omicron variant. We identified subdivisions within the BA.1 and BA.2 lineages and further identified the mutations associated with each cluster. We further characterized the overall genomic diversity of the Omicron variant and assessed the selection pressure for each of the genetic clusters identified.

Results

We observed concordant results, using two different methods to understand genetic subdivision. The overall pattern of subdivision in the Omicron variant was in broad agreement with the Pango lineage definition. Further, 1 cluster of the BA.1 lineage and 3 clusters of the BA.2 lineage revealed statistically significant signatures of selection or demographic expansion (Tajima's D<-2), suggesting the role of microevolutionary processes in the spread of the virus.

Conclusions

We provide an easy framework for assessing the genetic structure and demographic history of SARS-CoV-2, which can be particularly useful for understanding the local history of the virus. We identified important mutations that are advantageous to some lineages of Omicron and aid in the transmission of the virus. This is crucial information for policy makers, as preventive measures can be designed to mitigate further spread based on a holistic understanding of the variability of the virus and the evolutionary processes aiding its spread.