Early Detection and Surveillance of the SARS-CoV-2 Variant BA.2.86 - Worldwide, July-October 2023.

Early detection of emerging SARS-CoV-2 variants is critical to guiding rapid risk assessments, providing clear and timely communication messages, and coordinating public health action. CDC identifies and monitors novel SARS-CoV-2 variants through diverse surveillance approaches, including genomic, wastewater, traveler-based, and digital public health surveillance (e.g., global data repositories, news, and social media). The SARS-CoV-2 variant BA.2.86 was first sequenced in Israel and reported on August 13, 2023. The first U.S. COVID-19 case caused by this variant was reported on August 17, 2023, after a patient received testing for SARS-CoV-2 at a health care facility on August 3. In the following month, eight additional U.S. states detected BA.2.86 across various surveillance systems, including specimens from health care settings, wastewater surveillance, and traveler-based genomic surveillance. As of October 23, 2023, sequences have been reported from at least 32 countries. Continued variant tracking and further evidence are needed to evaluate the full public health impact of BA.2.86. Timely genomic sequence submissions to global public databases aided early detection of BA.2.86 despite the decline in the number of specimens being sequenced during the past year. This report describes how multicomponent surveillance and genomic sequencing were used in real time to track the emergence and transmission of the BA.2.86 variant. This surveillance approach provides valuable information regarding implementing and sustaining comprehensive surveillance not only for novel SARS-CoV-2 variants but also for future pathogen threats.

Predictors at Admission of Mechanical Ventilation and Death in an Observational Cohort of Adults Hospitalized With Coronavirus Disease 2019.

BACKGROUND: Coronavirus disease (COVID-19) can cause severe illness and death. Predictors of poor outcome collected on hospital admission may inform clinical and public health decisions. METHODS: We conducted a retrospective observational cohort investigation of 297 adults admitted to 8 academic and community hospitals in Georgia, United States, during March 2020. Using standardized medical record abstraction, we collected data on predictors including admission demographics, underlying medical conditions, outpatient antihypertensive medications, recorded symptoms, vital signs, radiographic findings, and laboratory values. We used random forest models to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for predictors of invasive mechanical ventilation (IMV) and death. RESULTS: Compared with age <45 years, ages 65-74 years and ≥75 years were predictors of IMV (aORs, 3.12 [95% CI, 1.47-6.60] and 2.79 [95% CI, 1.23-6.33], respectively) and the strongest predictors for death (aORs, 12.92 [95% CI, 3.26-51.25] and 18.06 [95% CI, 4.43-73.63], respectively). Comorbidities associated with death (aORs, 2.4-3.8; P < .05) included end-stage renal disease, coronary artery disease, and neurologic disorders, but not pulmonary disease, immunocompromise, or hypertension. Prehospital use vs nonuse of angiotensin receptor blockers (aOR, 2.02 [95% CI, 1.03-3.96]) and dihydropyridine calcium channel blockers (aOR, 1.91 [95% CI, 1.03-3.55]) were associated with death. CONCLUSIONS: After adjustment for patient and clinical characteristics, older age was the strongest predictor of death, exceeding comorbidities, abnormal vital signs, and laboratory test abnormalities. That coronary artery disease, but not chronic lung disease, was associated with death among hospitalized patients warrants further investigation, as do associations between certain antihypertensive medications and death.

Characteristics and Risk Factors of Hospitalized and Nonhospitalized COVID-19 Patients, Atlanta, Georgia, USA, March-April 2020.

We compared the characteristics of hospitalized and nonhospitalized patients who had coronavirus disease in Atlanta, Georgia, USA. We found that risk for hospitalization increased with a patient's age and number of concurrent conditions. We also found a potential association between hospitalization and high hemoglobin A1c levels in persons with diabetes.

Characteristics and Clinical Outcomes of Adult Patients Hospitalized with COVID-19 - Georgia, March 2020.

SARS-CoV-2, the novel coronavirus that causes coronavirus disease 2019 (COVID-19), was first detected in the United States during January 2020 (1). Since then, >980,000 cases have been reported in the United States, including >55,000 associated deaths as of April 28, 2020 (2). Detailed data on demographic characteristics, underlying medical conditions, and clinical outcomes for persons hospitalized with COVID-19 are needed to inform prevention strategies and community-specific intervention messages. For this report, CDC, the Georgia Department of Public Health, and eight Georgia hospitals (seven in metropolitan Atlanta and one in southern Georgia) summarized medical record-abstracted data for hospitalized adult patients with laboratory-confirmed* COVID-19 who were admitted during March 2020. Among 305 hospitalized patients with COVID-19, 61.6% were aged <65 years, 50.5% were female, and 83.2% with known race/ethnicity were non-Hispanic black (black). Over a quarter of patients (26.2%) did not have conditions thought to put them at higher risk for severe disease, including being aged ≥65 years. The proportion of hospitalized patients who were black was higher than expected based on overall hospital admissions. In an adjusted time-to-event analysis, black patients were not more likely than were nonblack patients to receive invasive mechanical ventilation† (IMV) or to die during hospitalization (hazard ratio [HR] = 0.63; 95% confidence interval [CI] = 0.35-1.13). Given the overrepresentation of black patients within this hospitalized cohort, it is important for public health officials to ensure that prevention activities prioritize communities and racial/ethnic groups most affected by COVID-19. Clinicians and public officials should be aware that all adults, regardless of underlying conditions or age, are at risk for serious illness from COVID-19.

Framing Later Life Vulnerability during the COVID-19 Pandemic: A Content Analysis of Newspaper Coverage in Canada and the United States.

This study explores vulnerability narratives used in relation to older adults and others during the COVID-19 pandemic. A mixed-method content analysis was conducted of 391 articles published in two major newspapers in Canada and the USA during the first wave of the pandemic. The findings indicated that during the early months of the pandemic, limited attention was directed towards its impact on older adults or other 'vulnerable' subpopulations in both countries. Where evident, intrinsic (individual-level) risk factors were most consistently used to frame the vulnerability of older adults. In contrast, vulnerability was more likely to be framed as structural with regard to other subpopulations (e.g., ethno-racial minorities). These narratives also differed somewhat in Canadian and US newspapers. The framing of older adults as intrinsically vulnerable reflects ageist stereotypes and promotes downstream policy interventions. Greater attention is needed to the role of structural factors in influencing pandemic-related outcomes among older adults.

COVID-19 Clinical Phenotypes: Presentation and Temporal Progression of Disease in a Cohort of Hospitalized Adults in Georgia, United States.

BACKGROUND: The epidemiological features and outcomes of hospitalized adults with coronavirus disease 2019 (COVID-19) have been described; however, the temporal progression and medical complications of disease among hospitalized patients require further study. Detailed descriptions of the natural history of COVID-19 among hospitalized patients are paramount to optimize health care resource utilization, and the detection of different clinical phenotypes may allow tailored clinical management strategies. METHODS: This was a retrospective cohort study of 305 adult patients hospitalized with COVID-19 in 8 academic and community hospitals. Patient characteristics included demographics, comorbidities, medication use, medical complications, intensive care utilization, and longitudinal vital sign and laboratory test values. We examined laboratory and vital sign trends by mortality status and length of stay. To identify clinical phenotypes, we calculated Gower's dissimilarity matrix between each patient's clinical characteristics and clustered similar patients using the partitioning around medoids algorithm. RESULTS: One phenotype of 6 identified was characterized by high mortality (49%), older age, male sex, elevated inflammatory markers, high prevalence of cardiovascular disease, and shock. Patients with this severe phenotype had significantly elevated peak C-reactive protein creatinine, D-dimer, and white blood cell count and lower minimum lymphocyte count compared with other phenotypes (P < .01, all comparisons). CONCLUSIONS: Among a cohort of hospitalized adults, we identified a severe phenotype of COVID-19 based on the characteristics of its clinical course and poor prognosis. These findings need to be validated in other cohorts, as improved understanding of clinical phenotypes and risk factors for their development could help inform prognosis and tailored clinical management for COVID-19.