Changing genomic epidemiology of COVID-19 in long-term care facilities during the 2020-2022 pandemic, Washington State.

BACKGROUND: Long-term care facilities (LTCFs) are vulnerable to disease outbreaks. Here, we jointly analyze SARS-CoV-2 genomic and paired epidemiologic data from LTCFs and surrounding communities in Washington state (WA) to assess transmission patterns during 2020-2022, in a setting of changing policy. We describe sequencing efforts and genomic epidemiologic findings across LTCFs and perform in-depth analysis in a single county. METHODS: We assessed genomic data representativeness, built phylogenetic trees, and conducted discrete trait analysis to estimate introduction sizes over time, and explored selected outbreaks to further characterize transmission events. RESULTS: We found that transmission dynamics among cases associated with LTCFs in WA changed over the course of the COVID-19 pandemic, with variable introduction rates into LTCFs, but decreasing amplification within LTCFs. SARS-CoV-2 lineages circulating in LTCFs were similar to those circulating in communities at the same time. Transmission between staff and residents was bi-directional. CONCLUSIONS: Understanding transmission dynamics within and between LTCFs using genomic epidemiology on a broad scale can assist in targeting policies and prevention efforts. Tracking facility-level outbreaks can help differentiate intra-facility outbreaks from high community transmission with repeated introduction events. Based on our study findings, methods for routine tree building and overlay of epidemiologic data for hypothesis generation by public health practitioners are recommended. Discrete trait analysis added valuable insight and can be considered when representative sequencing is performed. Cluster detection tools, especially those that rely on distance thresholds, may be of more limited use given current data capture and timeliness. Importantly, we noted a decrease in data capture from LTCFs over time. Depending on goals for use of genomic data, sentinel surveillance should be increased or targeted surveillance implemented to ensure available data for analysis.

Comparison of Echocardiography and Multi-Planar Gated Acquisition Scans for Predicting Cancer-Treatment-Related Cardiovascular Dysfunction.

BACKGROUND: Current guidelines recommend using sequential cardiac imaging to monitor for cancer treatment-related cardiac dysfunction (CTRCD) in patients undergoing potentially cardiotoxic chemotherapy. Multiple different imaging cardiac modalities are available and there are few prospective head-to-head comparative studies to help guide treatment. OBJECTIVES: To perform an exploratory prospective cohort study of "real-world" CTRCD comparing multigated acquisition nuclear ventriculography (MUGA) at the referring cancer specialist's discretion with a novel echocardiographic strategy at an Australian tertiary hospital. METHOD: Patients were recruited from haematology and oncology outpatient clinics if they were scheduled for treatment with anthracyclines and/or trastuzumab. Patients underwent simultaneous MUGA-based cardiac imaging (conventional strategy) at a frequency according to evidenced-based guidelines in addition to researcher-conducted echocardiographic imaging. The echocardiographic imaging was performed in all patients at time points recommended by international society guidelines. Outcomes included adherence to guideline recommendations, concordance between MUGA and echocardiographic left ventricular ejection fraction (LVEF) measurements, and detection of cardiac dysfunction (defined as >5% LVEF decrement from baseline by three-dimensional [3D]-LVEF). A secondary end point was accuracy of global longitudinal strain in predicting cardiac dysfunction. RESULTS: In total, 35 patients were recruited, including 15 with breast cancer, 19 with haematological malignancy, and one with gastric cancer. MUGA and echocardiographic LVEF measurements correlated poorly with limits of agreement of 30% between 3D-LVEF and MUGA-LVEF and 37% for 3D-LVEF and MUGA-LVEF. Only one case (2.9%) of CTRCD was diagnosed by MUGA, compared with 12 (34.2%) cases by echocardiography. Four (4) patients had >10% decrement in 3D-LVEF that was not detected by MUGA. Global longitudinal strain at 2 months displayed significant ability to predict CTRCD (area under the curve, 0.75, 95% confidence interval, 0.55-0.94). CONCLUSIONS: The MUGA correlates poorly with echocardiographic assessment with substantial discrepancy between MUGA and echocardiography in CTRCD diagnosis. Echocardiographic and MUGA imaging strategies should not be considered equivalent for imaging cancer patients, and a single imaging modality should ideally be used per patient to prevent misdiagnosis by inter-modality variation These findings should be considered hypothesis-generating and require confirmation with larger studies.

Fine-scale spatial and social patterns of SARS-CoV-2 transmission from identical pathogen sequences.

Pathogen genomics can provide insights into disease transmission patterns, but new methods are needed to handle modern large-scale pathogen genome datasets. Genetically proximal viruses indicate epidemiological linkage and are informative about transmission events. Here, we leverage pairs of identical sequences using 114,298 SARS-CoV-2 genomes collected via sentinel surveillance from March 2021 to December 2022 in Washington State, USA, with linked age and residence information to characterize fine-scale transmission. The location of pairs of identical sequences is highly consistent with expectations from mobility and social contact data. Outliers in the relationship between genetic and mobility data can be explained by SARS-CoV-2 transmission between postal codes with male prisons, consistent with transmission between prison facilities. Transmission patterns between age groups vary across spatial scales. Finally, we use the timing of sequence collection to understand the age groups driving transmission. This work improves our ability to characterize transmission from large pathogen genome datasets.