Following its emergence in Wuhan, China, in late November or early December 2019, the SARS-CoV-2 virus has rapidly spread globally. Genome sequencing of SARS-CoV-2 allows reconstruction of its transmission history, although this is contingent on sampling. We have analyzed 453 SARS-CoV-2 genomes collected between 20 February and 15 March 2020 from infected patients in Washington State, USA. We find that most SARS-CoV-2 infections sampled during this time derive from a single introduction in late January or early February 2020 which subsequently spread locally before active community surveillance was implemented.
IntroductionInfluenza epidemics and pandemics cause significant morbidity and mortality. An effective response to a potential pandemic requires the infrastructure to rapidly detect, characterise, and potentially contain new and emerging influenza strains at both an individual and population level. The objective of this study is to use data gathered simultaneously from community and hospital sites to develop a model of how influenza enters and spreads in a population.Methods and analysisStarting in the 2018–2019 season, we have been enrolling individuals with acute respiratory illness from community sites throughout the Seattle metropolitan area, including clinics, childcare facilities, Seattle-Tacoma International Airport, workplaces, college campuses and homeless shelters. At these sites, we collect clinical data and mid-nasal swabs from individuals with at least two acute respiratory symptoms. Additionally, we collect residual nasal swabs and data from individuals who seek care for respiratory symptoms at four regional hospitals. Samples are tested using a multiplex molecular assay, and influenza whole genome sequencing is performed for samples with influenza detected. Geospatial mapping and computational modelling platforms are in development to characterise the regional spread of influenza and other respiratory pathogens.Ethics and disseminationThe study was approved by the University of Washington’s Institutional Review Board (STUDY00006181). Results will be disseminated through talks at conferences, peer-reviewed publications and on the study website (www.seattleflu.org).
Background: Imaging tests are one of the most sophisticated types of diagnostic tools used in health care, yet there are concerns that imaging is overused. Currently, tests are typically evaluated and implemented based on their accuracy, and there is limited knowledge about the range of patient-centered outcomes (PCOs) that imaging tests may lead to. This study explores patients' experiences and subsequent outcomes of imaging tests most notable to patients. Methods: Adult patients from four primary care clinics who had an x-ray, CT, MRI, or ultrasound in the 12 months before recruitment participated in a single semistructured interview to recount their imaging experience. Interview transcripts were analyzed thematically. Results: Four themes related to PCOs were identified from 45 interviews. Participants' mean age was 53 years (25-83 years), 30 had undergone a diagnostic imaging test, and 15 underwent imaging for screening or monitoring. Themes included knowledge gained from the imaging test, its contribution to their overall health care journey, physical experiences during the test procedure, and impacts of the testing process on emotions. Conclusions: Patients identified various imaging test outcomes that were important to them. Measurement and reporting these outcomes should be considered more often in diagnostic research. Tools for providers and patients to discuss and utilize these outcomes may help promote shared decision making around the use and impact of imaging tests.
BackgroundInfluenza epidemics and pandemics cause significant morbidity and mortality. An effective response to a potential pandemic requires the infrastructure to rapidly detect and contain new and emerging flu strains at a population level. The objective of this study was to use data gathered simultaneously from community and hospital sites to develop a model of how flu enters and spreads in a population.MethodsIn the 2018–2019 season, we enrolled individuals with respiratory illness from community sites throughout the Seattle area, including homeless shelters, childcare facilities, Seattle-Tacoma International Airport, workplaces, college campuses, clinics, and at home (Figure 1). We collected data and nasal swabs from individuals with at least two respiratory symptoms. Additionally, we collected residual nasal swabs and data from individuals who sought care at four regional hospitals. Home-based self-testing for influenza and prediction models for influenza were piloted. Swabs were tested with a multiplex molecular assay, and influenza whole-genome sequencing was performed. Geospatial mapping and computational modeling platforms were developed to characterize regional spread of respiratory pathogens.ResultsA total of 18,847 samples were collected in the 2018–2019 season. Of those tested to date, 291/3,653 (8%) community and 2,393/11,273 (21%) hospital samples have influenza detected. Of the community enrollments, 39% had influenza-like illness. Community enrollees were in age groups not well-represented from hospitals. Influenza A/H3N2 activity peaked on college campuses and homeless shelters 2 weeks before the peak in hospitals. We observed multiple independent introductions of influenza strains into the city and evidence of sustained transmission chains within the city (Figures 2 and 3).ConclusionUtilizing the city-wide infrastructure we developed, we observed the introduction of influenza A/H3N2 into the community before the hospital and evidence of transmissions of unique strains into and within the Seattle area. These data provide the blueprint for implementing city-wide, community-based surveillance systems for rapid detection, real-time assessment of transmission patterns, and interruption of spread of seasonal or pandemic strains. Disclosures Helen Y. Chu, MD MPH, Merck (Advisor or Review Panel member), Michael Boeckh, MD PhD, Ablynx (Consultant, Grant/Research Support), Ansun Biopharma (Consultant, Grant/Research Support), Bavarian Nordic (Consultant), Gilead (Consultant, Grant/Research Support), GlaxoSmithKline (Consultant), Vir Bio (Consultant, Grant/Research Support), Janet A. Englund, MD, Chimerix (Grant/Research Support), GlaxoSmithKline (Grant/Research Support), MedImmune/Astrazeneca (Grant/Research Support), Meissa Vaccines (Consultant), Merck (Grant/Research Support),Novavax (Grant/Research Support), Sanofi Pastuer (Consultant), Matthew Thompson, MD, Alere Inc. (Research Grant or Support), Roche Molecular Diagnostics (Consultant, Research Grant or Support, Speaker’s Bureau), . Oth...
Background: Sickle cell trait (SCT) has been associated with an increased risk of sudden death in athletes during strenuous exercise. In August 2010, the National Collegiate Athletic Association (NCAA) began requiring athletes to be screened for SCT, provide proof of SCT status, or sign a waiver and launched an educational campaign for athletes, coaches, and medical staff. The impact of this program is unknown. The purpose of this study was to determine the incidence of death associated with sickle cell trait (daSCT) in NCAA athletes before and after legislation. Hypothesis: NCAA SCT legislation will decrease the incidence of daSCT. Study Design: Observational study. Level of Evidence: Level 2. Methods: A database of NCAA athlete deaths from 2000 to 2019 was reviewed for daSCT. A total of 8,309,050 athlete-years (AY) were included. Incidence of death was calculated before and after legislation. Results: The incidence of daSCT in Division I (DI) football athletes before legislation (n = 9) was 1:28,145 AY and after legislation (n = 1) was 1:250,468 AY (relative risk [RR], 0.112; 95% CI, 0.003-0.811; P = 0.022), an 89% reduction in risk after legislation was enacted. The incidence of daSCT in African American DI football athletes before legislation (n = 9) was 1:12,519 AY and after legislation (n = 1) was 1:118,464 AY (RR, 0.106; 95% CI, 0.002-0.763; P = 0.017), also an 89% risk reduction after legislation was enacted. For all NCAA athletes, the incidence of daSCT was 1:489,749 AY before legislation (n = 10) and 1:1,705,780 AY after legislation (n = 2) (RR, 0.288; 95% CI, 0.031-1.347; P = 0.146). Conclusion: The incidence of daSCT in DI football athletes has decreased significantly since legislation was enacted. Cases of daSCT outside of football are rare. It is unclear whether the decrease is related to screening for SCT, education, or both. Clinical Relevance: This is the first evidence that NCAA SCT legislation may save lives.
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